(mellow music) >> Hello everyone, welcome to theCUBE's coverage of AWS Startup Showcase. This is the closing panel session on AI machine learning, the top startups generating generative AI on AWS. It's a great panel. This is going to be the experts talking about riding the wave in generative AI. We got Ankur Mehrotra, who's the director and general manager of AI and machine learning at AWS, and Clem Delangue, co-founder and CEO of Hugging Face, and Ori Goshen, who's the co-founder and CEO of AI21 Labs. Ori from Tel Aviv dialing in, and rest coming in here on theCUBE. Appreciate you coming on for this closing session for the Startup Showcase. >> Thanks for having us. >> Thank you for having us. >> Thank you. >> I'm super excited to have you all on. Hugging Face was recently in the news with the AWS relationship, so congratulations. Open source, open science, really driving the machine learning. And we got the AI21 Labs access to the LLMs, generating huge scale live applications, commercial applications, coming to the market, all powered by AWS. So everyone, congratulations on all your success, and thank you for headlining this panel. Let's get right into it. AWS is powering this wave here. We're seeing a lot of push here from applications. Ankur, set the table for us on the AI machine learning. It's not new, it's been goin' on for a while. Past three years have been significant advancements, but there's been a lot of work done in AI machine learning. Now it's released to the public. Everybody's super excited and now says, "Oh, the future's here!" It's kind of been going on for a while and baking. Now it's kind of coming out. What's your view here? Let's get it started. >> Yes, thank you. So, yeah, as you may be aware, Amazon has been in investing in machine learning research and development since quite some time now. And we've used machine learning to innovate and improve user experiences across different Amazon products, whether it's Alexa or Amazon.com. But we've also brought in our expertise to extend what we are doing in the space and add more generative AI technology to our AWS products and services, starting with CodeWhisperer, which is an AWS service that we announced a few months ago, which is, you can think of it as a coding companion as a service, which uses generative AI models underneath. And so this is a service that customers who have no machine learning expertise can just use. And we also are talking to customers, and we see a lot of excitement about generative AI, and customers who want to build these models themselves, who have the talent and the expertise and resources. For them, AWS has a number of different options and capabilities they can leverage, such as our custom silicon, such as Trainium and Inferentia, as well as distributed machine learning capabilities that we offer as part of SageMaker, which is an end-to-end machine learning development service. At the same time, many of our customers tell us that they're interested in not training and building these generative AI models from scratch, given they can be expensive and can require specialized talent and skills to build. And so for those customers, we are also making it super easy to bring in existing generative AI models into their machine learning development environment within SageMaker for them to use. So we recently announced our partnership with Hugging Face, where we are making it super easy for customers to bring in those models into their SageMaker development environment for fine tuning and deployment. And then we are also partnering with other proprietary model providers such as AI21 and others, where we making these generative AI models available within SageMaker for our customers to use. So our approach here is to really provide customers options and choices and help them accelerate their generative AI journey. >> Ankur, thank you for setting the table there. Clem and Ori, I want to get your take, because the riding the waves, the theme of this session, and to me being in California, I imagine the big surf, the big waves, the big talent out there. This is like alpha geeks, alpha coders, developers are really leaning into this. You're seeing massive uptake from the smartest people. Whether they're young or around, they're coming in with their kind of surfboards, (chuckles) if you will. These early adopters, they've been on this for a while; Now the waves are hitting. This is a big wave, everyone sees it. What are some of those early adopter devs doing? What are some of the use cases you're seeing right out of the gate? And what does this mean for the folks that are going to come in and get on this wave? Can you guys share your perspective on this? Because you're seeing the best talent now leaning into this. >> Yeah, absolutely. I mean, from Hugging Face vantage points, it's not even a a wave, it's a tidal wave, or maybe even the tide itself. Because actually what we are seeing is that AI and machine learning is not something that you add to your products. It's very much a new paradigm to do all technology. It's this idea that we had in the past 15, 20 years, one way to build software and to build technology, which was writing a million lines of code, very rule-based, and then you get your product. Now what we are seeing is that every single product, every single feature, every single company is starting to adopt AI to build the next generation of technology. And that works both to make the existing use cases better, if you think of search, if you think of social network, if you think of SaaS, but also it's creating completely new capabilities that weren't possible with the previous paradigm. Now AI can generate text, it can generate image, it can describe your image, it can do so many new things that weren't possible before. >> It's going to really make the developers really productive, right? I mean, you're seeing the developer uptake strong, right? >> Yes, we have over 15,000 companies using Hugging Face now, and it keeps accelerating. I really think that maybe in like three, five years, there's not going to be any company not using AI. It's going to be really kind of the default to build all technology. >> Ori, weigh in on this. APIs, the cloud. Now I'm a developer, I want to have live applications, I want the commercial applications on this. What's your take? Weigh in here. >> Yeah, first, I absolutely agree. I mean, we're in the midst of a technology shift here. I think not a lot of people realize how big this is going to be. Just the number of possibilities is endless, and I think hard to imagine. And I don't think it's just the use cases. I think we can think of it as two separate categories. We'll see companies and products enhancing their offerings with these new AI capabilities, but we'll also see new companies that are AI first, that kind of reimagine certain experiences. They build something that wasn't possible before. And that's why I think it's actually extremely exciting times. And maybe more philosophically, I think now these large language models and large transformer based models are helping us people to express our thoughts and kind of making the bridge from our thinking to a creative digital asset in a speed we've never imagined before. I can write something down and get a piece of text, or an image, or a code. So I'll start by saying it's hard to imagine all the possibilities right now, but it's certainly big. And if I had to bet, I would say it's probably at least as big as the mobile revolution we've seen in the last 20 years. >> Yeah, this is the biggest. I mean, it's been compared to the Enlightenment Age. I saw the Wall Street Journal had a recent story on this. We've been saying that this is probably going to be bigger than all inflection points combined in the tech industry, given what transformation is coming. I guess I want to ask you guys, on the early adopters, we've been hearing on these interviews and throughout the industry that there's already a set of big companies, a set of companies out there that have a lot of data and they're already there, they're kind of tinkering. Kind of reminds me of the old hyper scaler days where they were building their own scale, and they're eatin' glass, spittin' nails out, you know, they're hardcore. Then you got everybody else kind of saying board level, "Hey team, how do I leverage this?" How do you see those two things coming together? You got the fast followers coming in behind the early adopters. What's it like for the second wave coming in? What are those conversations for those developers like? >> I mean, I think for me, the important switch for companies is to change their mindset from being kind of like a traditional software company to being an AI or machine learning company. And that means investing, hiring machine learning engineers, machine learning scientists, infrastructure in members who are working on how to put these models in production, team members who are able to optimize models, specialized models, customized models for the company's specific use cases. So it's really changing this mindset of how you build technology and optimize your company building around that. Things are moving so fast that I think now it's kind of like too late for low hanging fruits or small, small adjustments. I think it's important to realize that if you want to be good at that, and if you really want to surf this wave, you need massive investments. If there are like some surfers listening with this analogy of the wave, right, when there are waves, it's not enough just to stand and make a little bit of adjustments. You need to position yourself aggressively, paddle like crazy, and that's how you get into the waves. So that's what companies, in my opinion, need to do right now. >> Ori, what's your take on the generative models out there? We hear a lot about foundation models. What's your experience running end-to-end applications for large foundation models? Any insights you can share with the app developers out there who are looking to get in? >> Yeah, I think first of all, it's start create an economy, where it probably doesn't make sense for every company to create their own foundation models. You can basically start by using an existing foundation model, either open source or a proprietary one, and start deploying it for your needs. And then comes the second round when you are starting the optimization process. You bootstrap, whether it's a demo, or a small feature, or introducing new capability within your product, and then start collecting data. That data, and particularly the human feedback data, helps you to constantly improve the model, so you create this data flywheel. And I think we're now entering an era where customers have a lot of different choice of how they want to start their generative AI endeavor. And it's a good thing that there's a variety of choices. And the really amazing thing here is that every industry, any company you speak with, it could be something very traditional like industrial or financial, medical, really any company. I think peoples now start to imagine what are the possibilities, and seriously think what's their strategy for adopting this generative AI technology. And I think in that sense, the foundation model actually enabled this to become scalable. So the barrier to entry became lower; Now the adoption could actually accelerate. >> There's a lot of integration aspects here in this new wave that's a little bit different. Before it was like very monolithic, hardcore, very brittle. A lot more integration, you see a lot more data coming together. I have to ask you guys, as developers come in and grow, I mean, when I went to college and you were a software engineer, I mean, I got a degree in computer science, and software engineering, that's all you did was code, (chuckles) you coded. Now, isn't it like everyone's a machine learning engineer at this point? Because that will be ultimately the science. So, (chuckles) you got open source, you got open software, you got the communities. Swami called you guys the GitHub of machine learning, Hugging Face is the GitHub of machine learning, mainly because that's where people are going to code. So this is essentially, machine learning is computer science. What's your reaction to that? >> Yes, my co-founder Julien at Hugging Face have been having this thing for quite a while now, for over three years, which was saying that actually software engineering as we know it today is a subset of machine learning, instead of the other way around. People would call us crazy a few years ago when we're seeing that. But now we are realizing that you can actually code with machine learning. So machine learning is generating code. And we are starting to see that every software engineer can leverage machine learning through open models, through APIs, through different technology stack. So yeah, it's not crazy anymore to think that maybe in a few years, there's going to be more people doing AI and machine learning. However you call it, right? Maybe you'll still call them software engineers, maybe you'll call them machine learning engineers. But there might be more of these people in a couple of years than there is software engineers today. >> I bring this up as more tongue in cheek as well, because Ankur, infrastructure's co is what made Cloud great, right? That's kind of the DevOps movement. But here the shift is so massive, there will be a game-changing philosophy around coding. Machine learning as code, you're starting to see CodeWhisperer, you guys have had coding companions for a while on AWS. So this is a paradigm shift. How is the cloud playing into this for you guys? Because to me, I've been riffing on some interviews where it's like, okay, you got the cloud going next level. This is an example of that, where there is a DevOps-like moment happening with machine learning, whether you call it coding or whatever. It's writing code on its own. Can you guys comment on what this means on top of the cloud? What comes out of the scale? What comes out of the benefit here? >> Absolutely, so- >> Well first- >> Oh, go ahead. >> Yeah, so I think as far as scale is concerned, I think customers are really relying on cloud to make sure that the applications that they build can scale along with the needs of their business. But there's another aspect to it, which is that until a few years ago, John, what we saw was that machine learning was a data scientist heavy activity. They were data scientists who were taking the data and training models. And then as machine learning found its way more and more into production and actual usage, we saw the MLOps become a thing, and MLOps engineers become more involved into the process. And then we now are seeing, as machine learning is being used to solve more business critical problems, we're seeing even legal and compliance teams get involved. We are seeing business stakeholders more engaged. So, more and more machine learning is becoming an activity that's not just performed by data scientists, but is performed by a team and a group of people with different skills. And for them, we as AWS are focused on providing the best tools and services for these different personas to be able to do their job and really complete that end-to-end machine learning story. So that's where, whether it's tools related to MLOps or even for folks who cannot code or don't know any machine learning. For example, we launched SageMaker Canvas as a tool last year, which is a UI-based tool which data analysts and business analysts can use to build machine learning models. So overall, the spectrum in terms of persona and who can get involved in the machine learning process is expanding, and the cloud is playing a big role in that process. >> Ori, Clem, can you guys weigh in too? 'Cause this is just another abstraction layer of scale. What's it mean for you guys as you look forward to your customers and the use cases that you're enabling? >> Yes, I think what's important is that the AI companies and providers and the cloud kind of work together. That's how you make a seamless experience and you actually reduce the barrier to entry for this technology. So that's what we've been super happy to do with AWS for the past few years. We actually announced not too long ago that we are doubling down on our partnership with AWS. We're excited to have many, many customers on our shared product, the Hugging Face deep learning container on SageMaker. And we are working really closely with the Inferentia team and the Trainium team to release some more exciting stuff in the coming weeks and coming months. So I think when you have an ecosystem and a system where the AWS and the AI providers, AI startups can work hand in hand, it's to the benefit of the customers and the companies, because it makes it orders of magnitude easier for them to adopt this new paradigm to build technology AI. >> Ori, this is a scale on reasoning too. The data's out there and making sense out of it, making it reason, getting comprehension, having it make decisions is next, isn't it? And you need scale for that. >> Yes. Just a comment about the infrastructure side. So I think really the purpose is to streamline and make these technologies much more accessible. And I think we'll see, I predict that we'll see in the next few years more and more tooling that make this technology much more simple to consume. And I think it plays a very important role. There's so many aspects, like the monitoring the models and their kind of outputs they produce, and kind of containing and running them in a production environment. There's so much there to build on, the infrastructure side will play a very significant role. >> All right, that's awesome stuff. I'd love to change gears a little bit and get a little philosophy here around AI and how it's going to transform, if you guys don't mind. There's been a lot of conversations around, on theCUBE here as well as in some industry areas, where it's like, okay, all the heavy lifting is automated away with machine learning and AI, the complexity, there's some efficiencies, it's horizontal and scalable across all industries. Ankur, good point there. Everyone's going to use it for something. And a lot of stuff gets brought to the table with large language models and other things. But the key ingredient will be proprietary data or human input, or some sort of AI whisperer kind of role, or prompt engineering, people are saying. So with that being said, some are saying it's automating intelligence. And that creativity will be unleashed from this. If the heavy lifting goes away and AI can fill the void, that shifts the value to the intellect or the input. And so that means data's got to come together, interact, fuse, and understand each other. This is kind of new. I mean, old school AI was, okay, got a big model, I provisioned it long time, very expensive. Now it's all free flowing. Can you guys comment on where you see this going with this freeform, data flowing everywhere, heavy lifting, and then specialization? >> Yeah, I think- >> Go ahead. >> Yeah, I think, so what we are seeing with these large language models or generative models is that they're really good at creating stuff. But I think it's also important to recognize their limitations. They're not as good at reasoning and logic. And I think now we're seeing great enthusiasm, I think, which is justified. And the next phase would be how to make these systems more reliable. How to inject more reasoning capabilities into these models, or augment with other mechanisms that actually perform more reasoning so we can achieve more reliable results. And we can count on these models to perform for critical tasks, whether it's medical tasks, legal tasks. We really want to kind of offload a lot of the intelligence to these systems. And then we'll have to get back, we'll have to make sure these are reliable, we'll have to make sure we get some sort of explainability that we can understand the process behind the generated results that we received. So I think this is kind of the next phase of systems that are based on these generated models. >> Clem, what's your view on this? Obviously you're at open community, open source has been around, it's been a great track record, proven model. I'm assuming creativity's going to come out of the woodwork, and if we can automate open source contribution, and relationships, and onboarding more developers, there's going to be unleashing of creativity. >> Yes, it's been so exciting on the open source front. We all know Bert, Bloom, GPT-J, T5, Stable Diffusion, that work up. The previous or the current generation of open source models that are on Hugging Face. It has been accelerating in the past few months. So I'm super excited about ControlNet right now that is really having a lot of impact, which is kind of like a way to control the generation of images. Super excited about Flan UL2, which is like a new model that has been recently released and is open source. So yeah, it's really fun to see the ecosystem coming together. Open source has been the basis for traditional software, with like open source programming languages, of course, but also all the great open source that we've gotten over the years. So we're happy to see that the same thing is happening for machine learning and AI, and hopefully can help a lot of companies reduce a little bit the barrier to entry. So yeah, it's going to be exciting to see how it evolves in the next few years in that respect. >> I think the developer productivity angle that's been talked about a lot in the industry will be accelerated significantly. I think security will be enhanced by this. I think in general, applications are going to transform at a radical rate, accelerated, incredible rate. So I think it's not a big wave, it's the water, right? I mean, (chuckles) it's the new thing. My final question for you guys, if you don't mind, I'd love to get each of you to answer the question I'm going to ask you, which is, a lot of conversations around data. Data infrastructure's obviously involved in this. And the common thread that I'm hearing is that every company that looks at this is asking themselves, if we don't rebuild our company, start thinking about rebuilding our business model around AI, we might be dinosaurs, we might be extinct. And it reminds me that scene in Moneyball when, at the end, it's like, if we're not building the model around your model, every company will be out of business. What's your advice to companies out there that are having those kind of moments where it's like, okay, this is real, this is next gen, this is happening. I better start thinking and putting into motion plans to refactor my business, 'cause it's happening, business transformation is happening on the cloud. This kind of puts an exclamation point on, with the AI, as a next step function. Big increase in value. So it's an opportunity for leaders. Ankur, we'll start with you. What's your advice for folks out there thinking about this? Do they put their toe in the water? Do they jump right into the deep end? What's your advice? >> Yeah, John, so we talk to a lot of customers, and customers are excited about what's happening in the space, but they often ask us like, "Hey, where do we start?" So we always advise our customers to do a lot of proof of concepts, understand where they can drive the biggest ROI. And then also leverage existing tools and services to move fast and scale, and try and not reinvent the wheel where it doesn't need to be. That's basically our advice to customers. >> Get it. Ori, what's your advice to folks who are scratching their head going, "I better jump in here. "How do I get started?" What's your advice? >> So I actually think that need to think about it really economically. Both on the opportunity side and the challenges. So there's a lot of opportunities for many companies to actually gain revenue upside by building these new generative features and capabilities. On the other hand, of course, this would probably affect the cogs, and incorporating these capabilities could probably affect the cogs. So I think we really need to think carefully about both of these sides, and also understand clearly if this is a project or an F word towards cost reduction, then the ROI is pretty clear, or revenue amplifier, where there's, again, a lot of different opportunities. So I think once you think about this in a structured way, I think, and map the different initiatives, then it's probably a good way to start and a good way to start thinking about these endeavors. >> Awesome. Clem, what's your take on this? What's your advice, folks out there? >> Yes, all of these are very good advice already. Something that you said before, John, that I disagreed a little bit, a lot of people are talking about the data mode and proprietary data. Actually, when you look at some of the organizations that have been building the best models, they don't have specialized or unique access to data. So I'm not sure that's so important today. I think what's important for companies, and it's been the same for the previous generation of technology, is their ability to build better technology faster than others. And in this new paradigm, that means being able to build machine learning faster than others, and better. So that's how, in my opinion, you should approach this. And kind of like how can you evolve your company, your teams, your products, so that you are able in the long run to build machine learning better and faster than your competitors. And if you manage to put yourself in that situation, then that's when you'll be able to differentiate yourself to really kind of be impactful and get results. That's really hard to do. It's something really different, because machine learning and AI is a different paradigm than traditional software. So this is going to be challenging, but I think if you manage to nail that, then the future is going to be very interesting for your company. >> That's a great point. Thanks for calling that out. I think this all reminds me of the cloud days early on. If you went to the cloud early, you took advantage of it when the pandemic hit. If you weren't native in the cloud, you got hamstrung by that, you were flatfooted. So just get in there. (laughs) Get in the cloud, get into AI, you're going to be good. Thanks for for calling that. Final parting comments, what's your most exciting thing going on right now for you guys? Ori, Clem, what's the most exciting thing on your plate right now that you'd like to share with folks? >> I mean, for me it's just the diversity of use cases and really creative ways of companies leveraging this technology. Every day I speak with about two, three customers, and I'm continuously being surprised by the creative ideas. And the future is really exciting of what can be achieved here. And also I'm amazed by the pace that things move in this industry. It's just, there's not at dull moment. So, definitely exciting times. >> Clem, what are you most excited about right now? >> For me, it's all the new open source models that have been released in the past few weeks, and that they'll keep being released in the next few weeks. I'm also super excited about more and more companies getting into this capability of chaining different models and different APIs. I think that's a very, very interesting development, because it creates new capabilities, new possibilities, new functionalities that weren't possible before. You can plug an API with an open source embedding model, with like a no-geo transcription model. So that's also very exciting. This capability of having more interoperable machine learning will also, I think, open a lot of interesting things in the future. >> Clem, congratulations on your success at Hugging Face. Please pass that on to your team. Ori, congratulations on your success, and continue to, just day one. I mean, it's just the beginning. It's not even scratching the service. Ankur, I'll give you the last word. What are you excited for at AWS? More cloud goodness coming here with AI. Give you the final word. >> Yeah, so as both Clem and Ori said, I think the research in the space is moving really, really fast, so we are excited about that. But we are also excited to see the speed at which enterprises and other AWS customers are applying machine learning to solve real business problems, and the kind of results they're seeing. So when they come back to us and tell us the kind of improvement in their business metrics and overall customer experience that they're driving and they're seeing real business results, that's what keeps us going and inspires us to continue inventing on their behalf. >> Gentlemen, thank you so much for this awesome high impact panel. Ankur, Clem, Ori, congratulations on all your success. We'll see you around. Thanks for coming on. Generative AI, riding the wave, it's a tidal wave, it's the water, it's all happening. All great stuff. This is season three, episode one of AWS Startup Showcase closing panel. This is the AI ML episode, the top startups building generative AI on AWS. I'm John Furrier, your host. Thanks for watching. (mellow music)

(upbeat music) >> Hello, everyone, and welcome to theCUBE's presentation of the "AWS Startup Showcase." This is season three, episode one. The focus of this episode is AI/ML: Top Startups Building Foundational Models, Infrastructure, and AI. It's great topics, super-relevant, and it's part of our ongoing coverage of startups in the AWS ecosystem. I'm your host, John Furrier, with theCUBE. Today, we're excited to be joined by Jay Marshall, VP of Business Development at Neural Magic. Jay, thanks for coming on theCUBE. >> Hey, John, thanks so much. Thanks for having us. >> We had a great CUBE conversation with you guys. This is very much about the company focuses. It's a feature presentation for the "Startup Showcase," and the machine learning at scale is the topic, but in general, it's more, (laughs) and we should call it "Machine Learning and AI: How to Get Started," because everybody is retooling their business. Companies that aren't retooling their business right now with AI first will be out of business, in my opinion. You're seeing massive shift. This is really truly the beginning of the next-gen machine learning AI trend. It's really seeing ChatGPT. Everyone sees that. That went mainstream. But this is just the beginning. This is scratching the surface of this next-generation AI with machine learning powering it, and with all the goodness of cloud, cloud scale, and how horizontally scalable it is. The resources are there. You got the Edge. Everything's perfect for AI 'cause data infrastructure's exploding in value. AI is just the applications. This is a super topic, so what do you guys see in this general area of opportunities right now in the headlines? And I'm sure you guys' phone must be ringing off the hook, metaphorically speaking, or emails and meetings and Zooms. What's going on over there at Neural Magic? >> No, absolutely, and you pretty much nailed most of it. I think that, you know, my background, we've seen for the last 20-plus years. Even just getting enterprise applications kind of built and delivered at scale, obviously, amazing things with AWS and the cloud to help accelerate that. And we just kind of figured out in the last five or so years how to do that productively and efficiently, kind of from an operations perspective. Got development and operations teams. We even came up with DevOps, right? But now, we kind of have this new kind of persona and new workload that developers have to talk to, and then it has to be deployed on those ITOps solutions. And so you pretty much nailed it. Folks are saying, "Well, how do I do this?" These big, generational models or foundational models, as we're calling them, they're great, but enterprises want to do that with their data, on their infrastructure, at scale, at the edge. So for us, yeah, we're helping enterprises accelerate that through optimizing models and then delivering them at scale in a more cost-effective fashion. >> Yeah, and I think one of the things, the benefits of OpenAI we saw, was not only is it open source, then you got also other models that are more proprietary, is that it shows the world that this is really happening, right? It's a whole nother level, and there's also new landscape kind of maps coming out. You got the generative AI, and you got the foundational models, large LLMs. Where do you guys fit into the landscape? Because you guys are in the middle of this. How do you talk to customers when they say, "I'm going down this road. I need help. I'm going to stand this up." This new AI infrastructure and applications, where do you guys fit in the landscape? >> Right, and really, the answer is both. I think today, when it comes to a lot of what for some folks would still be considered kind of cutting edge around computer vision and natural language processing, a lot of our optimization tools and our runtime are based around most of the common computer vision and natural language processing models. So your YOLOs, your BERTs, you know, your DistilBERTs and what have you, so we work to help optimize those, again, who've gotten great performance and great value for customers trying to get those into production. But when you get into the LLMs, and you mentioned some of the open source components there, our research teams have kind of been right in the trenches with those. So kind of the GPT open source equivalent being OPT, being able to actually take, you know, a multi-$100 billion parameter model and sparsify that or optimize that down, shaving away a ton of parameters, and being able to run it on smaller infrastructure. So I think the evolution here, you know, all this stuff came out in the last six months in terms of being turned loose into the wild, but we're staying in the trenches with folks so that we can help optimize those as well and not require, again, the heavy compute, the heavy cost, the heavy power consumption as those models evolve as well. So we're staying right in with everybody while they're being built, but trying to get folks into production today with things that help with business value today. >> Jay, I really appreciate you coming on theCUBE, and before we came on camera, you said you just were on a customer call. I know you got a lot of activity. What specific things are you helping enterprises solve? What kind of problems? Take us through the spectrum from the beginning, people jumping in the deep end of the pool, some people kind of coming in, starting out slow. What are the scale? Can you scope the kind of use cases and problems that are emerging that people are calling you for? >> Absolutely, so I think if I break it down to kind of, like, your startup, or I maybe call 'em AI native to kind of steal from cloud native years ago, that group, it's pretty much, you know, part and parcel for how that group already runs. So if you have a data science team and an ML engineering team, you're building models, you're training models, you're deploying models. You're seeing firsthand the expense of starting to try to do that at scale. So it's really just a pure operational efficiency play. They kind of speak natively to our tools, which we're doing in the open source. So it's really helping, again, with the optimization of the models they've built, and then, again, giving them an alternative to expensive proprietary hardware accelerators to have to run them. Now, on the enterprise side, it varies, right? You have some kind of AI native folks there that already have these teams, but you also have kind of, like, AI curious, right? Like, they want to do it, but they don't really know where to start, and so for there, we actually have an open source toolkit that can help you get into this optimization, and then again, that runtime, that inferencing runtime, purpose-built for CPUs. It allows you to not have to worry, again, about do I have a hardware accelerator available? How do I integrate that into my application stack? If I don't already know how to build this into my infrastructure, does my ITOps teams, do they know how to do this, and what does that runway look like? How do I cost for this? How do I plan for this? When it's just x86 compute, we've been doing that for a while, right? So it obviously still requires more, but at least it's a little bit more predictable. >> It's funny you mentioned AI native. You know, born in the cloud was a phrase that was out there. Now, you have startups that are born in AI companies. So I think you have this kind of cloud kind of vibe going on. You have lift and shift was a big discussion. Then you had cloud native, kind of in the cloud, kind of making it all work. Is there a existing set of things? People will throw on this hat, and then what's the difference between AI native and kind of providing it to existing stuff? 'Cause we're a lot of people take some of these tools and apply it to either existing stuff almost, and it's not really a lift and shift, but it's kind of like bolting on AI to something else, and then starting with AI first or native AI. >> Absolutely. It's a- >> How would you- >> It's a great question. I think that probably, where I'd probably pull back to kind of allow kind of retail-type scenarios where, you know, for five, seven, nine years or more even, a lot of these folks already have data science teams, you know? I mean, they've been doing this for quite some time. The difference is the introduction of these neural networks and deep learning, right? Those kinds of models are just a little bit of a paradigm shift. So, you know, I obviously was trying to be fun with the term AI native, but I think it's more folks that kind of came up in that neural network world, so it's a little bit more second nature, whereas I think for maybe some traditional data scientists starting to get into neural networks, you have the complexity there and the training overhead, and a lot of the aspects of getting a model finely tuned and hyperparameterization and all of these aspects of it. It just adds a layer of complexity that they're just not as used to dealing with. And so our goal is to help make that easy, and then of course, make it easier to run anywhere that you have just kind of standard infrastructure. >> Well, the other point I'd bring out, and I'd love to get your reaction to, is not only is that a neural network team, people who have been focused on that, but also, if you look at some of the DataOps lately, AIOps markets, a lot of data engineering, a lot of scale, folks who have been kind of, like, in that data tsunami cloud world are seeing, they kind of been in this, right? They're, like, been experiencing that. >> No doubt. I think it's funny the data lake concept, right? And you got data oceans now. Like, the metaphors just keep growing on us, but where it is valuable in terms of trying to shift the mindset, I've always kind of been a fan of some of the naming shift. I know with AWS, they always talk about purpose-built databases. And I always liked that because, you know, you don't have one database that can do everything. Even ones that say they can, like, you still have to do implementation detail differences. So sitting back and saying, "What is my use case, and then which database will I use it for?" I think it's kind of similar here. And when you're building those data teams, if you don't have folks that are doing data engineering, kind of that data harvesting, free processing, you got to do all that before a model's even going to care about it. So yeah, it's definitely a central piece of this as well, and again, whether or not you're going to be AI negative as you're making your way to kind of, you know, on that journey, you know, data's definitely a huge component of it. >> Yeah, you would have loved our Supercloud event we had. Talk about naming and, you know, around data meshes was talked about a lot. You're starting to see the control plane layers of data. I think that was the beginning of what I saw as that data infrastructure shift, to be horizontally scalable. So I have to ask you, with Neural Magic, when your customers and the people that are prospects for you guys, they're probably asking a lot of questions because I think the general thing that we see is, "How do I get started? Which GPU do I use?" I mean, there's a lot of things that are kind of, I won't say technical or targeted towards people who are living in that world, but, like, as the mainstream enterprises come in, they're going to need a playbook. What do you guys see, what do you guys offer your clients when they come in, and what do you recommend? >> Absolutely, and I think where we hook in specifically tends to be on the training side. So again, I've built a model. Now, I want to really optimize that model. And then on the runtime side when you want to deploy it, you know, we run that optimized model. And so that's where we're able to provide. We even have a labs offering in terms of being able to pair up our engineering teams with a customer's engineering teams, and we can actually help with most of that pipeline. So even if it is something where you have a dataset and you want some help in picking a model, you want some help training it, you want some help deploying that, we can actually help there as well. You know, there's also a great partner ecosystem out there, like a lot of folks even in the "Startup Showcase" here, that extend beyond into kind of your earlier comment around data engineering or downstream ITOps or the all-up MLOps umbrella. So we can absolutely engage with our labs, and then, of course, you know, again, partners, which are always kind of key to this. So you are spot on. I think what's happened with the kind of this, they talk about a hockey stick. This is almost like a flat wall now with the rate of innovation right now in this space. And so we do have a lot of folks wanting to go straight from curious to native. And so that's definitely where the partner ecosystem comes in so hard 'cause there just isn't anybody or any teams out there that, I literally do from, "Here's my blank database, and I want an API that does all the stuff," right? Like, that's a big chunk, but we can definitely help with the model to delivery piece. >> Well, you guys are obviously a featured company in this space. Talk about the expertise. A lot of companies are like, I won't say faking it till they make it. You can't really fake security. You can't really fake AI, right? So there's going to be a learning curve. They'll be a few startups who'll come out of the gate early. You guys are one of 'em. Talk about what you guys have as expertise as a company, why you're successful, and what problems do you solve for customers? >> No, appreciate that. Yeah, we actually, we love to tell the story of our founder, Nir Shavit. So he's a 20-year professor at MIT. Actually, he was doing a lot of work on kind of multicore processing before there were even physical multicores, and actually even did a stint in computational neurobiology in the 2010s, and the impetus for this whole technology, has a great talk on YouTube about it, where he talks about the fact that his work there, he kind of realized that the way neural networks encode and how they're executed by kind of ramming data layer by layer through these kind of HPC-style platforms, actually was not analogous to how the human brain actually works. So we're on one side, we're building neural networks, and we're trying to emulate neurons. We're not really executing them that way. So our team, which one of the co-founders, also an ex-MIT, that was kind of the birth of why can't we leverage this super-performance CPU platform, which has those really fat, fast caches attached to each core, and actually start to find a way to break that model down in a way that I can execute things in parallel, not having to do them sequentially? So it is a lot of amazing, like, talks and stuff that show kind of the magic, if you will, a part of the pun of Neural Magic, but that's kind of the foundational layer of all the engineering that we do here. And in terms of how we're able to bring it to reality for customers, I'll give one customer quote where it's a large retailer, and it's a people-counting application. So a very common application. And that customer's actually been able to show literally double the amount of cameras being run with the same amount of compute. So for a one-to-one perspective, two-to-one, business leaders usually like that math, right? So we're able to show pure cost savings, but even performance-wise, you know, we have some of the common models like your ResNets and your YOLOs, where we can actually even perform better than hardware-accelerated solutions. So we're trying to do, I need to just dumb it down to better, faster, cheaper, but from a commodity perspective, that's where we're accelerating. >> That's not a bad business model. Make things easier to use, faster, and reduce the steps it takes to do stuff. So, you know, that's always going to be a good market. Now, you guys have DeepSparse, which we've talked about on our CUBE conversation prior to this interview, delivers ML models through the software so the hardware allows for a decoupling, right? >> Yep. >> Which is going to drive probably a cost advantage. Also, it's also probably from a deployment standpoint it must be easier. Can you share the benefits? Is it a cost side? Is it more of a deployment? What are the benefits of the DeepSparse when you guys decouple the software from the hardware on the ML models? >> No you actually, you hit 'em both 'cause that really is primarily the value. Because ultimately, again, we're so early. And I came from this world in a prior life where I'm doing Java development, WebSphere, WebLogic, Tomcat open source, right? When we were trying to do innovation, we had innovation buckets, 'cause everybody wanted to be on the web and have their app and a browser, right? We got all the money we needed to build something and show, hey, look at the thing on the web, right? But when you had to get in production, that was the challenge. So to what you're speaking to here, in this situation, we're able to show we're just a Python package. So whether you just install it on the operating system itself, or we also have a containerized version you can drop on any container orchestration platform, so ECS or EKS on AWS. And so you get all the auto-scaling features. So when you think about that kind of a world where you have everything from real-time inferencing to kind of after hours batch processing inferencing, the fact that you can auto scale that hardware up and down and it's CPU based, so you're paying by the minute instead of maybe paying by the hour at a lower cost shelf, it does everything from pure cost to, again, I can have my standard IT team say, "Hey, here's the Kubernetes in the container," and it just runs on the infrastructure we're already managing. So yeah, operational, cost and again, and many times even performance. (audio warbles) CPUs if I want to. >> Yeah, so that's easier on the deployment too. And you don't have this kind of, you know, blank check kind of situation where you don't know what's on the backend on the cost side. >> Exactly. >> And you control the actual hardware and you can manage that supply chain. >> And keep in mind, exactly. Because the other thing that sometimes gets lost in the conversation, depending on where a customer is, some of these workloads, like, you know, you and I remember a world where even like the roundtrip to the cloud and back was a problem for folks, right? We're used to extremely low latency. And some of these workloads absolutely also adhere to that. But there's some workloads where the latency isn't as important. And we actually even provide the tuning. Now, if we're giving you five milliseconds of latency and you don't need that, you can tune that back. So less CPU, lower cost. Now, throughput and other things come into play. But that's the kind of configurability and flexibility we give for operations. >> All right, so why should I call you if I'm a customer or prospect Neural Magic, what problem do I have or when do I know I need you guys? When do I call you in and what does my environment look like? When do I know? What are some of the signals that would tell me that I need Neural Magic? >> No, absolutely. So I think in general, any neural network, you know, the process I mentioned before called sparcification, it's, you know, an optimization process that we specialize in. Any neural network, you know, can be sparcified. So I think if it's a deep-learning neural network type model. If you're trying to get AI into production, you have cost concerns even performance-wise. I certainly hate to be too generic and say, "Hey, we'll talk to everybody." But really in this world right now, if it's a neural network, it's something where you're trying to get into production, you know, we are definitely offering, you know, kind of an at-scale performant deployable solution for deep learning models. >> So neural network you would define as what? Just devices that are connected that need to know about each other? What's the state-of-the-art current definition of neural network for customers that may think they have a neural network or might not know they have a neural network architecture? What is that definition for neural network? >> That's a great question. So basically, machine learning models that fall under this kind of category, you hear about transformers a lot, or I mentioned about YOLO, the YOLO family of computer vision models, or natural language processing models like BERT. If you have a data science team or even developers, some even regular, I used to call myself a nine to five developer 'cause I worked in the enterprise, right? So like, hey, we found a new open source framework, you know, I used to use Spring back in the day and I had to go figure it out. There's developers that are pulling these models down and they're figuring out how to get 'em into production, okay? So I think all of those kinds of situations, you know, if it's a machine learning model of the deep learning variety that's, you know, really specifically where we shine. >> Okay, so let me pretend I'm a customer for a minute. I have all these videos, like all these transcripts, I have all these people that we've interviewed, CUBE alumnis, and I say to my team, "Let's AI-ify, sparcify theCUBE." >> Yep. >> What do I do? I mean, do I just like, my developers got to get involved and they're going to be like, "Well, how do I upload it to the cloud? Do I use a GPU?" So there's a thought process. And I think a lot of companies are going through that example of let's get on this AI, how can it help our business? >> Absolutely. >> What does that progression look like? Take me through that example. I mean, I made up theCUBE example up, but we do have a lot of data. We have large data models and we have people and connect to the internet and so we kind of seem like there's a neural network. I think every company might have a neural network in place. >> Well, and I was going to say, I think in general, you all probably do represent even the standard enterprise more than most. 'Cause even the enterprise is going to have a ton of video content, a ton of text content. So I think it's a great example. So I think that that kind of sea or I'll even go ahead and use that term data lake again, of data that you have, you're probably going to want to be setting up kind of machine learning pipelines that are going to be doing all of the pre-processing from kind of the raw data to kind of prepare it into the format that say a YOLO would actually use or let's say BERT for natural language processing. So you have all these transcripts, right? So we would do a pre-processing path where we would create that into the file format that BERT, the machine learning model would know how to train off of. So that's kind of all the pre-processing steps. And then for training itself, we actually enable what's called sparse transfer learning. So that's transfer learning is a very popular method of doing training with existing models. So we would be able to retrain that BERT model with your transcript data that we have now done the pre-processing with to get it into the proper format. And now we have a BERT natural language processing model that's been trained on your data. And now we can deploy that onto DeepSparse runtime so that now you can ask that model whatever questions, or I should say pass, you're not going to ask it those kinds of questions ChatGPT, although we can do that too. But you're going to pass text through the BERT model and it's going to give you answers back. It could be things like sentiment analysis or text classification. You just call the model, and now when you pass text through it, you get the answers better, faster or cheaper. I'll use that reference again. >> Okay, we can create a CUBE bot to give us questions on the fly from the the AI bot, you know, from our previous guests. >> Well, and I will tell you using that as an example. So I had mentioned OPT before, kind of the open source version of ChatGPT. So, you know, typically that requires multiple GPUs to run. So our research team, I may have mentioned earlier, we've been able to sparcify that over 50% already and run it on only a single GPU. And so in that situation, you could train OPT with that corpus of data and do exactly what you say. Actually we could use Alexa, we could use Alexa to actually respond back with voice. How about that? We'll do an API call and we'll actually have an interactive Alexa-enabled bot. >> Okay, we're going to be a customer, let's put it on the list. But this is a great example of what you guys call software delivered AI, a topic we chatted about on theCUBE conversation. This really means this is a developer opportunity. This really is the convergence of the data growth, the restructuring, how data is going to be horizontally scalable, meets developers. So this is an AI developer model going on right now, which is kind of unique. >> It is, John, I will tell you what's interesting. And again, folks don't always think of it this way, you know, the AI magical goodness is now getting pushed in the middle where the developers and IT are operating. And so it again, that paradigm, although for some folks seem obvious, again, if you've been around for 20 years, that whole all that plumbing is a thing, right? And so what we basically help with is when you deploy the DeepSparse runtime, we have a very rich API footprint. And so the developers can call the API, ITOps can run it, or to your point, it's developer friendly enough that you could actually deploy our off-the-shelf models. We have something called the SparseZoo where we actually publish pre-optimized or pre-sparcified models. And so developers could literally grab those right off the shelf with the training they've already had and just put 'em right into their applications and deploy them as containers. So yeah, we enable that for sure as well. >> It's interesting, DevOps was infrastructure as code and we had a last season, a series on data as code, which we kind of coined. This is data as code. This is a whole nother level of opportunity where developers just want to have programmable data and apps with AI. This is a whole new- >> Absolutely. >> Well, absolutely great, great stuff. Our news team at SiliconANGLE and theCUBE said you guys had a little bit of a launch announcement you wanted to make here on the "AWS Startup Showcase." So Jay, you have something that you want to launch here? >> Yes, and thank you John for teeing me up. So I'm going to try to put this in like, you know, the vein of like an AWS, like main stage keynote launch, okay? So we're going to try this out. So, you know, a lot of our product has obviously been built on top of x86. I've been sharing that the past 15 minutes or so. And with that, you know, we're seeing a lot of acceleration for folks wanting to run on commodity infrastructure. But we've had customers and prospects and partners tell us that, you know, ARM and all of its kind of variance are very compelling, both cost performance-wise and also obviously with Edge. And wanted to know if there was anything we could do from a runtime perspective with ARM. And so we got the work and, you know, it's a hard problem to solve 'cause the instructions set for ARM is very different than the instruction set for x86, and our deep tensor column technology has to be able to work with that lower level instruction spec. But working really hard, the engineering team's been at it and we are happy to announce here at the "AWS Startup Showcase," that DeepSparse inference now has, or inference runtime now has support for AWS Graviton instances. So it's no longer just x86, it is also ARM and that obviously also opens up the door to Edge and further out the stack so that optimize once run anywhere, we're not going to open up. So it is an early access. So if you go to neuralmagic.com/graviton, you can sign up for early access, but we're excited to now get into the ARM side of the fence as well on top of Graviton. >> That's awesome. Our news team is going to jump on that news. We'll get it right up. We get a little scoop here on the "Startup Showcase." Jay Marshall, great job. That really highlights the flexibility that you guys have when you decouple the software from the hardware. And again, we're seeing open source driving a lot more in AI ops now with with machine learning and AI. So to me, that makes a lot of sense. And congratulations on that announcement. Final minute or so we have left, give a summary of what you guys are all about. Put a plug in for the company, what you guys are looking to do. I'm sure you're probably hiring like crazy. Take the last few minutes to give a plug for the company and give a summary. >> No, I appreciate that so much. So yeah, joining us out neuralmagic.com, you know, part of what we didn't spend a lot of time here, our optimization tools, we are doing all of that in the open source. It's called SparseML and I mentioned SparseZoo briefly. So we really want the data scientists community and ML engineering community to join us out there. And again, the DeepSparse runtime, it's actually free to use for trial purposes and for personal use. So you can actually run all this on your own laptop or on an AWS instance of your choice. We are now live in the AWS marketplace. So push button, deploy, come try us out and reach out to us on neuralmagic.com. And again, sign up for the Graviton early access. >> All right, Jay Marshall, Vice President of Business Development Neural Magic here, talking about performant, cost effective machine learning at scale. This is season three, episode one, focusing on foundational models as far as building data infrastructure and AI, AI native. I'm John Furrier with theCUBE. Thanks for watching. (bright upbeat music)

(light music) >> Announcer: TheCUBE's live coverage is made possible by funding from Dell Technologies. Creating technologies that drive human progress. (upbeat music) >> Welcome back to the Fira in Barcelona. Dave Vellante with Dave Nicholson. Lisa Martin's been here all week. John Furrier is in our Palo Alto studio, banging out all the news. Don't forget to check out siliconangle.com, thecube.net. This is day four, our last segment, winding down. MWC23, super excited to be here. Wayne Duso, friend of theCUBE, VP of engineering from products at AWS is here with Iyad Tarazi, who's the CEO of Federated Wireless. Gents, welcome. >> Good to be here. >> Nice to see you. >> I'm so stoked, Wayne, that we connected before the show. We texted, I'm like, "You're going to be there. I'm going to be there. You got to come on theCUBE." So thank you so much for making time, and thank you for bringing a customer partner, Federated Wireless. Everybody knows AWS. Iyad, tell us about Federated Wireless. >> We're a software and services company out of Arlington, Virginia, right outside of Washington, DC, and we're really focused on this new technology called Shared Spectrum and private wireless for 5G. Think of it as enterprises consuming 5G, the way they used to consume WiFi. >> Is that unrestricted spectrum, or? >> It is managed, organized, interference free, all through cloud platforms. That's how we got to know AWS. We went and got maybe about 300 products from AWS to make it work. Quite sophisticated, highly available, and pristine spectrum worth billions of dollars, but available for people like you and I, that want to build enterprises, that want to make things work. Also carriers, cable companies everybody else that needs it. It's really a new revolution for everyone. >> And that's how you, it got introduced to AWS. Was that through public sector, or just the coincidence that you're in DC >> No, I, well, yes. The center of gravity in the world for spectrum is literally Arlington. You have the DOD spectrum people, you have spectrum people from National Science Foundation, DARPA, and then you have commercial sector, and you have the FCC just an Uber ride away. So we went and found the scientists that are doing all this work, four or five of them, Virginia Tech has an office there too, for spectrum research for the Navy. Come together, let's have a party and make a new model. >> So I asked this, I'm super excited to have you on theCUBE. I sat through the keynotes on Monday. I saw Satya Nadella was in there, Thomas Kurian there was no AWS. I'm like, where's AWS? AWS is everywhere. I mean, you guys are all over the show. I'm like, "Hey, where's the number one cloud?" So you guys have made a bunch of announcements at the show. Everybody's talking about the cloud. What's going on for you guys? >> So we are everywhere, and you know, we've been coming to this show for years. But this is really a year that we can demonstrate that what we've been doing for the IT enterprise, IT people for 17 years, we're now bringing for telcos, you know? For years, we've been, 17 years to be exact, we've been bringing the cloud value proposition, whether it's, you know, cost efficiencies or innovation or scale, reliability, security and so on, to these enterprise IT folks. Now we're doing the same thing for telcos. And so whether they want to build in region, in a local zone, metro area, on-prem with an outpost, at the edge with Snow Family, or with our IoT devices. And no matter where they want to start, if they start in the cloud and they want to move to the edge, or they start in the edge and they want to bring the cloud value proposition, like, we're demonstrating all of that is happening this week. And, and very much so, we're also demonstrating that we're bringing the same type of ecosystem that we've built for enterprise IT. We're bringing that type of ecosystem to the telco companies, with CSPs, with the ISP vendors. We've seen plenty of announcements this week. You know, so on and so forth. >> So what's different, is it, the names are different? Is it really that simple, that you're just basically taking the cloud model into telco, and saying, "Hey, why do all this undifferentiated heavy lifting when we can do it for you? Don't worry about all the plumbing." Is it really that simple? I mean, that straightforward. >> Well, simple is probably not what I'd say, but we can make it straightforward. >> Conceptually. >> Conceptually, yes. Conceptually it is the same. Because if you think about, firstly, we'll just take 5G for a moment, right? The 5G folks, if you look at the architecture for 5G, it was designed to run on a cloud architecture. It was designed to be a set of services that you could partition, and run in different places, whether it's in the region or at the edge. So in many ways it is sort of that simple. And let me give you an example. Two things, the first one is we announced integrated private wireless on AWS, which allows enterprise customers to come to a portal and look at the industry solutions. They're not worried about their network, they're worried about solving a problem, right? And they can come to that portal, they can find a solution, they can find a service provider that will help them with that solution. And what they end up with is a fully validated offering that AWS telco SAS have actually put to its paces to make sure this is a real thing. And whether they get it from a telco, and, and quite frankly in that space, it's SIs such as Federated that actually help our customers deploy those in private environments. So that's an example. And then added to that, we had a second announcement, which was AWS telco network builder, which allows telcos to plan, deploy, and operate at scale telco network capabilities on the cloud, think about it this way- >> As a managed service? >> As a managed service. So think about it this way. And the same way that enterprise IT has been deploying, you know, infrastructure as code for years. Telco network builder allows the telco folks to deploy telco networks and their capabilities as code. So it's not simple, but it is pretty straightforward. We're making it more straightforward as we go. >> Jump in Dave, by the way. He can geek out if you want. >> Yeah, no, no, no, that's good, that's good, that's good. But actually, I'm going to ask an AWS question, but I'm going to ask Iyad the AWS question. So when we, when I hear the word cloud from Wayne, cloud, AWS, typically in people's minds that denotes off-premises. Out there, AWS data center. In the telecom space, yes, of course, in the private 5G space, we're talking about a little bit of a different dynamic than in the public 5G space, in terms of the physical infrastructure. But regardless at the edge, there are things that need to be physically at the edge. Do you feel that AWS is sufficiently, have they removed the H word, hybrid, from the list of bad words you're not allowed to say? 'Cause there was a point in time- >> Yeah, of course. >> Where AWS felt that their growth- >> They'll even say multicloud today, (indistinct). >> No, no, no, no, no. But there was a period of time where, rightfully so, AWS felt that the growth trajectory would be supported solely by net new things off premises. Now though, in this space, it seems like that hybrid model is critical. Do you see AWS being open to the hybrid nature of things? >> Yeah, they're, absolutely. I mean, just to explain from- we're a services company and a solutions company. So we put together solutions at the edge, a smart campus, smart agriculture, a deployment. One of our biggest deployment is a million square feet warehouse automation project with the Marine Corps. >> That's bigger than the Fira. >> Oh yeah, it's bigger, definitely bigger than, you know, a small section of here. It's actually three massive warehouses. So yes, that is the edge. What the cloud is about is that massive amount of efficiency has happened by concentrating applications in data centers. And that is programmability, that is APIs that is solutions, that is applications that can run on it, where people know how to do it. And so all that efficiency now is being ported in a box called the edge. What AWS is doing for us is bringing all the business and technical solutions they had into the edge. Some of the data may send back and forth, but that's actually a smaller piece of the value for us. By being able to bring an AWS package at the edge, we're bringing IoT applications, we're bringing high speed cameras, we're able to integrate with the 5G public network. We're able to bring in identity and devices, we're able to bring in solutions for students, embedded laptops. All of these things that you can do much much faster and cheaper if you are able to tap in the 4,000, 5,000 partners and all the applications and all the development and all the models that AWS team did. By being able to bring that efficiency to the edge why reinvent that? And then along with that, there are partners that you, that help do integration. There are development done to make it hardened, to make the data more secure, more isolated. All of these things will contribute to an edge that truly is a carbon copy of the data center. >> So Wayne, it's AWS, Regardless of where the compute, networking and storage physically live, it's AWS. Do you think that the term cloud will sort of drift away from usage? Because if, look, it's all IT, in this case it's AWS and federated IT working together. How, what's your, it's sort of a obscure question about cloud, because cloud is so integrated. >> You Got this thing about cloud, it's just IT. >> I got thing about cloud too, because- >> You and Larry Ellison. >> Because it's no, no, no, I'm, yeah, well actually there's- >> There's a lot of IT that's not cloud, just say that okay. >> Now, a lot of IT that isn't cloud, but I would say- >> But I'll (indistinct) cloud is an IT tool, and you see AWS obviously with the Snow fill in the blank line of products and outpost type stuff. Fair to say that you're, doesn't matter where it is, it could be AWS if it's on the edge, right? >> Well, you know, everybody wants to define the cloud as what it may have been when it started. But if you look at what it was when it started and what it is today, it is different. But the ability to bring the experience, the AWS experience, the services, the operational experience and all the things that Iyad had been talking about from the region all to all the way to, you know, the IoT device, if you would, that entire continuum. And it doesn't matter where you start. Like if you start in region and you need to bring your value to other places because your customers are asking you to do so, we're enabling that experience where you need to bring it. If you started at the edge, and- but you want to build cloud value, you know, whether it's again, cost efficiency, scalability, AI, ML or analytics into those capabilities, you can start at the edge with the same APIs, with the same service, the same capabilities, and you can build that value in right from the get go. You don't build this bifurcation or many separations and try to figure out how do I glue them together? There is no gluing together. So if you think of cloud as being elastic, scalable flexible, where you can drive innovation, it's the same exact model on the continuum. And you can start at either end, it's up to you as a customer. >> And I think if, the key to me is the ecosystem. I mean, if you can do for this industry what you've done for the technology- enterprise technology business from an ecosystem standpoint, you know everybody talks about flywheel, but that gives you like the massive flywheel. I don't know what the ratio is, but it used to be for every dollar spent on a VMware license, $15 is spent in the ecosystem. I've never heard similar ratios in the AWS ecosystem, but it's, I go to reinvent and I'm like, there's some dollars being- >> That's a massive ecosystem. >> (indistinct). >> And then, and another thing I'll add is Jose Maria Alvarez, who's the chairman of Telefonica, said there's three pillars of the future-ready telco, low latency, programmable networks, and he said cloud and edge. So they recognizing cloud and edge, you know, low latency means you got to put the compute and the data, the programmable infrastructure was invented by Amazon. So what's the strategy around the telco edge? >> So, you know, at the end, so those are all great points. And in fact, the programmability of the network was a big theme in the show. It was a huge theme. And if you think about the cloud, what is the cloud? It's a set of APIs against a set of resources that you use in whatever way is appropriate for what you're trying to accomplish. The network, the telco network becomes a resource. And it could be described as a resource. We, I talked about, you know, network as in code, right? It's same infrastructure in code, it's telco infrastructure as code. And that code, that infrastructure, is programmable. So this is really, really important. And in how you build the ecosystem around that is no different than how we built the ecosystem around traditional IT abstractions. In fact, we feel that really the ecosystem is the killer app for 5G. You know, the killer app for 4G, data of sorts, right? We started using data beyond simple SMS messages. So what's the killer app for 5G? It's building this ecosystem, which includes the CSPs, the ISVs, all of the partners that we bring to the table that can drive greater value. It's not just about cost efficiency. You know, you can't save your way to success, right? At some point you need to generate greater value for your customers, which gives you better business outcomes, 'cause you can monetize them, right? The ecosystem is going to allow everybody to monetize 5G. >> 5G is like the dot connector of all that. And then developers come in on top and create new capabilities >> And how different is that than, you know, the original smartphones? >> Yeah, you're right. So what do you guys think of ChatGPT? (indistinct) to Amazon? Amazon turned the data center into an API. It's like we're visioning this world, and I want to ask that technologist, like, where it's turning resources into human language interfaces. You know, when you see that, you play with ChatGPT at all, or I know you guys got your own. >> So I won't speak directly to ChatGPT. >> No, don't speak from- >> But if you think about- >> Generative AI. >> Yeah generative AI is important. And, and we are, and we have been for years, in this space. Now you've been talking to AWS for a long time, and we often don't talk about things we don't have yet. We don't talk about things that we haven't brought to market yet. And so, you know, you'll often hear us talk about something, you know, a year from now where others may have been talking about it three years earlier, right? We will be talking about this space when we feel it's appropriate for our customers and our partners. >> You have talked about it a little bit, Adam Selipsky went on an interview with myself and John Furrier in October said you watch, you know, large language models are going to be enormous and I know you guys have some stuff that you're working on there. >> It's, I'll say it's exciting. >> Yeah, I mean- >> Well proof point is, Siri is an idiot compared to Alexa. (group laughs) So I trust one entity to come up with something smart. >> I have conversations with Alexa and Siri, and I won't judge either one. >> You don't need, you could be objective on that one. I definitely have a preference. >> Are the problems you guys solving in this space, you know, what's unique about 'em? What are they, can we, sort of, take some examples here (indistinct). >> Sure, the main theme is that the enterprise is taking control. They want to have their own networks. They want to focus on specific applications, and they want to build them with a skeleton crew. The one IT person in a warehouse want to be able to do it all. So what's unique about them is that they're now are a lot of automation on robotics, especially in warehousing environment agriculture. There simply aren't enough people in these industries, and that required precision. And so you need all that integration to make it work. People also want to build these networks as they want to control it. They want to figure out how do we actually pick this team and migrate it. Maybe just do the front of the house first. Maybe it's a security team that monitor the building, maybe later on upgrade things that use to open doors and close doors and collect maintenance data. So that ability to pick what you want to do from a new processors is really important. And then you're also seeing a lot of public-private network interconnection. That's probably the undercurrent of this show that haven't been talked about. When people say private networks, they're also talking about something called neutral host, which means I'm going to build my own network, but I want it to work, my Verizon (indistinct) need to work. There's been so much progress, it's not done yet. So much progress about this bring my own network concept, and then make sure that I'm now interoperating with the public network, but it's my domain. I can create air gaps, I can create whatever security and policy around it. That is probably the power of 5G. Now take all of these tiny networks, big networks, put them all in one ecosystem. Call it the Amazon marketplace, call it the Amazon ecosystem, that's 5G. It's going to be tremendous future. >> What does the future look like? We're going to, we just determined we're going to be orchestrating the network through human language, okay? (group laughs) But seriously, what's your vision for the future here? You know, both connectivity and cloud are on on a continuum. It's, they've been on a continuum forever. They're going to continue to be on a continuum. That being said, those continuums are coming together, right? They're coming together to bring greater value to a greater set of customers, and frankly all of us. So, you know, the future is now like, you know, this conference is the future, and if you look at what's going on, it's about the acceleration of the future, right? What we announced this week is really the acceleration of listening to customers for the last handful of years. And, we're going to continue to do that. We're going to continue to bring greater value in the form of solutions. And that's what I want to pick up on from the prior question. It's not about the network, it's not about the cloud, it's about the solutions that we can provide the customers where they are, right? And if they're on their mobile phone or they're in their factory floor, you know, they're looking to accelerate their business. They're looking to accelerate their value. They're looking to create greater safety for their employees. That's what we can do with these technologies. So in fact, when we came out with, you know, our announcement for integrated private wireless, right? It really was about industry solutions. It really isn't about, you know, the cloud or the network. It's about how you can leverage those technologies, that continuum, to deliver you value. >> You know, it's interesting you say that, 'cause again, when we were interviewing Adam Selipsky, everybody, you know, all journalists analysts want to know, how's Adam Selipsky going to be different from Andy Jassy, what's the, what's he going to do to Amazon to change? And he said, listen, the real answer is Amazon has changed. If Andy Jassy were here, we'd be doing all, you know, pretty much the same things. Your point about 17 years ago, the cloud was S3, right, and EC2. Now it's got to evolve to be solutions. 'Cause if that's all you're selling, is the bespoke services, then you know, the future is not as bright as the past has been. And so I think it's key to look for what are those outcomes or solutions that customers require and how you're going to meet 'em. And there's a lot of challenges. >> You continue to build value on the value that you've brought, and you don't lose sight of why that value is important. You carry that value proposition up the stack, but the- what you're delivering, as you said, becomes maybe a bigger or or different. >> And you are getting more solution oriented. I mean, you're not hardcore solutions yet, but we're seeing more and more of that. And that seems to be a trend. We've even seen in the database world, making things easier, connecting things. Not really an abstraction layer, which is sort of antithetical to your philosophy, but it creates a similar outcome in terms of simplicity. Yeah, you're smiling 'cause you guys always have a different angle, you know? >> Yeah, we've had this conversation. >> It's right, it's, Jassy used to say it's okay to be misunderstood. >> That's Right. For a long time. >> Yeah, right, guys, thanks so much for coming to theCUBE. I'm so glad we could make this happen. >> It's always good. Thank you. >> Thank you so much. >> All right, Dave Nicholson, for Lisa Martin, Dave Vellante, John Furrier in the Palo Alto studio. We're here at the Fira, wrapping out MWC23. Keep it right there, thanks for watching. (upbeat music)

(upbeat music) >> Hello everyone. Welcome to Silicon Angle news breaking story here. Amazon Web Services, expanding their relationship with Hugging Face, breaking news here on Silicon Angle. I'm John Furrier, Silicon Angle reporter, founder and also co-host of theCUBE. And I have with me Swami from Amazon Web Services, vice president of database analytics machine learning with AWS. Swami, great to have you on for this breaking news segment on AWS's big news. Thanks for coming on, taking the time. >> Hey John, pleasure to be here. >> We've had many conversations on theCUBE over the years. We've watched Amazon really move fast into the large data modeling. You SageMaker became a very smashing success. Obviously you've been on this for a while, now with Chat GPT, open AI, a lot of buzz going mainstream, takes it from behind the curtain, inside the ropes, if you will, in the industry to a mainstream. And so this is a big moment I think in the industry. I want to get your perspective because your news with Hugging Face, I think is a is another tell sign that we're about to tip over into a new accelerated growth around making AI now application aware application centric, more programmable, more API access. What's the big news about with AWS Hugging Face, you know, what's going on with this announcement? >> Yeah, first of all, they're very excited to announce our expanded collaboration with Hugging Face because with this partnership, our goal, as you all know, I mean Hugging Face I consider them like the GitHub for machine learning. And with this partnership, Hugging Face and AWS will be able to democratize AI for a broad range of developers, not just specific deep AI startups. And now with this we can accelerate the training, fine tuning, and deployment of these large language models and vision models from Hugging Face in the cloud. So, and the broader context, when you step back and see what customer problem we are trying to solve with this announcement, essentially if you see these foundational models are used to now create like a huge number of applications, suggest like tech summarization, question answering, or search image generation, creative, other things. And these are all stuff we are seeing in the likes of these Chat GPT style applications. But there is a broad range of enterprise use cases that we don't even talk about. And it's because these kind of transformative generative AI capabilities and models are not available to, I mean, millions of developers. And because either training these elements from scratch can be very expensive or time consuming and need deep expertise, or more importantly, they don't need these generic models. They need them to be fine tuned for the specific use cases. And one of the biggest complaints we hear is that these models, when they try to use it for real production use cases, they are incredibly expensive to train and incredibly expensive to run inference on, to use it at a production scale, so And unlike search, web search style applications where the margins can be really huge, here in production use cases and enterprises, you want efficiency at scale. That's where a Hugging Face and AWS share our mission. And by integrating with Trainium and Inferentia, we're able to handle the cost efficient training and inference at scale. I'll deep dive on it and by training teaming up on the SageMaker front now the time it takes to build these models and fine tune them as also coming down. So that's what makes this partnership very unique as well. So I'm very excited. >> I want to get into the, to the time savings and the cost savings as well on the on the training and inference. It's a huge issue. But before we get into that, just how long have you guys been working with Hugging Face? I know this is a previous relationship. This is an expansion of that relationship. Can you comment on the what's different about what's happened before and then now? >> Yeah, so Hugging Face, we have had an great relationship in the past few years as well where they have actually made their models available to run on AWS in a fashion, even inspect their Bloom project was something many of our customers even used. Bloom Project for context is their open source project, which builds a GPT three style model. And now with this expanded collaboration, now Hugging Face selected AWS for that next generation of this generative AI model, building on their highly successful Bloom project as well. And the nice thing is now by direct integration with Trainium and Inferentia, where you get cost savings in a really significant way. Now for instance, tier 1 can provide up to 50% cost to train savings, and Inferentia can deliver up to 60% better costs and Forex more higher throughput. Now these models, especially as they train that next generation generated AI model, it is going to be not only more accessible to all the developers who use it in open. So it'll be a lot cheaper as well. And that's what makes this moment really exciting because yeah, we can't democratize AI unless we make it broadly accessible and cost efficient, and easy to program and use as well. >> Okay, thanks Swami. We really appreciate. Swami's a Cube alumni, but also vice President, database analyst machine learning web services breaking down the Hugging Face announcement. Obviously the relationship he called it the GitHub of machine learning. This is the beginning of what we will see, a continuing competitive battle with Microsoft. Microsoft launching OpenAI. Amazon's been doing it for years. They got Alexa, they know what they're doing. It's going to be very interesting to see how this all plays out. You're watching Silicon Angle News, breaking here. I'm John Furrier, host of the Cube. Thanks for watching. (ethereal music)

(bright upbeat music) >> Hello, everyone. Welcome to SiliconANGLE News breaking story here. Amazon Web Services expanding their relationship with Hugging Face, breaking news here on SiliconANGLE. I'm John Furrier, SiliconANGLE reporter, founder, and also co-host of theCUBE. And I have with me, Swami, from Amazon Web Services, vice president of database, analytics, machine learning with AWS. Swami, great to have you on for this breaking news segment on AWS's big news. Thanks for coming on and taking the time. >> Hey, John, pleasure to be here. >> You know- >> Looking forward to it. >> We've had many conversations on theCUBE over the years, we've watched Amazon really move fast into the large data modeling, SageMaker became a very smashing success, obviously you've been on this for a while. Now with ChatGPT OpenAI, a lot of buzz going mainstream, takes it from behind the curtain inside the ropes, if you will, in the industry to a mainstream. And so this is a big moment, I think, in the industry, I want to get your perspective, because your news with Hugging Face, I think is another tell sign that we're about to tip over into a new accelerated growth around making AI now application aware, application centric, more programmable, more API access. What's the big news about, with AWS Hugging Face, you know, what's going on with this announcement? >> Yeah. First of all, they're very excited to announce our expanded collaboration with Hugging Face, because with this partnership, our goal, as you all know, I mean, Hugging Face, I consider them like the GitHub for machine learning. And with this partnership, Hugging Face and AWS, we'll be able to democratize AI for a broad range of developers, not just specific deep AI startups. And now with this, we can accelerate the training, fine tuning and deployment of these large language models, and vision models from Hugging Face in the cloud. And the broader context, when you step back and see what customer problem we are trying to solve with this announcement, essentially if you see these foundational models, are used to now create like a huge number of applications, suggest like tech summarization, question answering, or search image generation, creative, other things. And these are all stuff we are seeing in the likes of these ChatGPT style applications. But there is a broad range of enterprise use cases that we don't even talk about. And it's because these kind of transformative, generative AI capabilities and models are not available to, I mean, millions of developers. And because either training these elements from scratch can be very expensive or time consuming and need deep expertise, or more importantly, they don't need these generic models, they need them to be fine tuned for the specific use cases. And one of the biggest complaints we hear is that these models, when they try to use it for real production use cases, they are incredibly expensive to train and incredibly expensive to run inference on, to use it at a production scale. So, and unlike web search style applications, where the margins can be really huge, here in production use cases and enterprises, you want efficiency at scale. That's where Hugging Face and AWS share our mission. And by integrating with Trainium and Inferentia, we're able to handle the cost efficient training and inference at scale, I'll deep dive on it. And by teaming up on the SageMaker front, now the time it takes to build these models and fine tune them is also coming down. So that's what makes this partnership very unique as well. So I'm very excited. >> I want to get into the time savings and the cost savings as well on the training and inference, it's a huge issue, but before we get into that, just how long have you guys been working with Hugging Face? I know there's a previous relationship, this is an expansion of that relationship, can you comment on what's different about what's happened before and then now? >> Yeah. So, Hugging Face, we have had a great relationship in the past few years as well, where they have actually made their models available to run on AWS, you know, fashion. Even in fact, their Bloom Project was something many of our customers even used. Bloom Project, for context, is their open source project which builds a GPT-3 style model. And now with this expanded collaboration, now Hugging Face selected AWS for that next generation office generative AI model, building on their highly successful Bloom Project as well. And the nice thing is, now, by direct integration with Trainium and Inferentia, where you get cost savings in a really significant way, now, for instance, Trn1 can provide up to 50% cost to train savings, and Inferentia can deliver up to 60% better costs, and four x more higher throughput than (indistinct). Now, these models, especially as they train that next generation generative AI models, it is going to be, not only more accessible to all the developers, who use it in open, so it'll be a lot cheaper as well. And that's what makes this moment really exciting, because we can't democratize AI unless we make it broadly accessible and cost efficient and easy to program and use as well. >> Yeah. >> So very exciting. >> I'll get into the SageMaker and CodeWhisperer angle in a second, but you hit on some good points there. One, accessibility, which is, I call the democratization, which is getting this in the hands of developers, and/or AI to develop, we'll get into that in a second. So, access to coding and Git reasoning is a whole nother wave. But the three things I know you've been working on, I want to put in the buckets here and comment, one, I know you've, over the years, been working on saving time to train, that's a big point, you mentioned some of those stats, also cost, 'cause now cost is an equation on, you know, bundling whether you're uncoupling with hardware and software, that's a big issue. Where do I find the GPUs? Where's the horsepower cost? And then also sustainability. You've mentioned that in the past, is there a sustainability angle here? Can you talk about those three things, time, cost, and sustainability? >> Certainly. So if you look at it from the AWS perspective, we have been supporting customers doing machine learning for the past years. Just for broader context, Amazon has been doing ML the past two decades right from the early days of ML powered recommendation to actually also supporting all kinds of generative AI applications. If you look at even generative AI application within Amazon, Amazon search, when you go search for a product and so forth, we have a team called MFi within Amazon search that helps bring these large language models into creating highly accurate search results. And these are created with models, really large models with tens of billions of parameters, scales to thousands of training jobs every month and trained on large model of hardware. And this is an example of a really good large language foundation model application running at production scale, and also, of course, Alexa, which uses a large generator model as well. And they actually even had a research paper that showed that they are more, and do better in accuracy than other systems like GPT-3 and whatnot. So, and we also touched on things like CodeWhisperer, which uses generative AI to improve developer productivity, but in a responsible manner, because 40% of some of the studies show 40% of this generated code had serious security flaws in it. This is where we didn't just do generative AI, we combined with automated reasoning capabilities, which is a very, very useful technique to identify these issues and couple them so that it produces highly secure code as well. Now, all these learnings taught us few things, and which is what you put in these three buckets. And yeah, like more than 100,000 customers using ML and AI services, including leading startups in the generative AI space, like stability AI, AI21 Labs, or Hugging Face, or even Alexa, for that matter. They care about, I put them in three dimension, one is around cost, which we touched on with Trainium and Inferentia, where we actually, the Trainium, you provide to 50% better cost savings, but the other aspect is, Trainium is a lot more power efficient as well compared to traditional one. And Inferentia is also better in terms of throughput, when it comes to what it is capable of. Like it is able to deliver up to three x higher compute performance and four x higher throughput, compared to it's previous generation, and it is extremely cost efficient and power efficient as well. >> Well. >> Now, the second element that really is important is in a day, developers deeply value the time it takes to build these models, and they don't want to build models from scratch. And this is where SageMaker, which is, even going to Kaggle uses, this is what it is, number one, enterprise ML platform. What it did to traditional machine learning, where tens of thousands of customers use StageMaker today, including the ones I mentioned, is that what used to take like months to build these models have dropped down to now a matter of days, if not less. Now, a generative AI, the cost of building these models, if you look at the landscape, the model parameter size had jumped by more than thousand X in the past three years, thousand x. And that means the training is like a really big distributed systems problem. How do you actually scale these model training? How do you actually ensure that you utilize these efficiently? Because these machines are very expensive, let alone they consume a lot of power. So, this is where SageMaker capability to build, automatically train, tune, and deploy models really concern this, especially with this distributor training infrastructure, and those are some of the reasons why some of the leading generative AI startups are actually leveraging it, because they do not want a giant infrastructure team, which is constantly tuning and fine tuning, and keeping these clusters alive. >> It sounds like a lot like what startups are doing with the cloud early days, no data center, you move to the cloud. So, this is the trend we're seeing, right? You guys are making it easier for developers with Hugging Face, I get that. I love that GitHub for machine learning, large language models are complex and expensive to build, but not anymore, you got Trainium and Inferentia, developers can get faster time to value, but then you got the transformers data sets, token libraries, all that optimized for generator. This is a perfect storm for startups. Jon Turow, a former AWS person, who used to work, I think for you, is now a VC at Madrona Venture, he and I were talking about the generator AI landscape, it's exploding with startups. Every alpha entrepreneur out there is seeing this as the next frontier, that's the 20 mile stairs, next 10 years is going to be huge. What is the big thing that's happened? 'Cause some people were saying, the founder of Yquem said, "Oh, the start ups won't be real, because they don't all have AI experience." John Markoff, former New York Times writer told me that, AI, there's so much work done, this is going to explode, accelerate really fast, because it's almost like it's been waiting for this moment. What's your reaction? >> I actually think there is going to be an explosion of startups, not because they need to be AI startups, but now finally AI is really accessible or going to be accessible, so that they can create remarkable applications, either for enterprises or for disrupting actually how customer service is being done or how creative tools are being built. And I mean, this is going to change in many ways. When we think about generative AI, we always like to think of how it generates like school homework or arts or music or whatnot, but when you look at it on the practical side, generative AI is being actually used across various industries. I'll give an example of like Autodesk. Autodesk is a customer who runs an AWS and SageMaker. They already have an offering that enables generated design, where designers can generate many structural designs for products, whereby you give a specific set of constraints and they actually can generate a structure accordingly. And we see similar kind of trend across various industries, where it can be around creative media editing or various others. I have the strong sense that literally, in the next few years, just like now, conventional machine learning is embedded in every application, every mobile app that we see, it is pervasive, and we don't even think twice about it, same way, like almost all apps are built on cloud. Generative AI is going to be part of every startup, and they are going to create remarkable experiences without needing actually, these deep generative AI scientists. But you won't get that until you actually make these models accessible. And I also don't think one model is going to rule the world, then you want these developers to have access to broad range of models. Just like, go back to the early days of deep learning. Everybody thought it is going to be one framework that will rule the world, and it has been changing, from Caffe to TensorFlow to PyTorch to various other things. And I have a suspicion, we had to enable developers where they are, so. >> You know, Dave Vellante and I have been riffing on this concept called super cloud, and a lot of people have co-opted to be multicloud, but we really were getting at this whole next layer on top of say, AWS. You guys are the most comprehensive cloud, you guys are a super cloud, and even Adam and I are talking about ISVs evolving to ecosystem partners. I mean, your top customers have ecosystems building on top of it. This feels like a whole nother AWS. How are you guys leveraging the history of AWS, which by the way, had the same trajectory, startups came in, they didn't want to provision a data center, the heavy lifting, all the things that have made Amazon successful culturally. And day one thinking is, provide the heavy lifting, undifferentiated heavy lifting, and make it faster for developers to program code. AI's got the same thing. How are you guys taking this to the next level, because now, this is an opportunity for the competition to change the game and take it over? This is, I'm sure, a conversation, you guys have a lot of things going on in AWS that makes you unique. What's the internal and external positioning around how you take it to the next level? >> I mean, so I agree with you that generative AI has a very, very strong potential in terms of what it can enable in terms of next generation application. But this is where Amazon's experience and expertise in putting these foundation models to work internally really has helped us quite a bit. If you look at it, like amazon.com search is like a very, very important application in terms of what is the customer impact on number of customers who use that application openly, and the amount of dollar impact it does for an organization. And we have been doing it silently for a while now. And the same thing is true for like Alexa too, which actually not only uses it for natural language understanding other city, even national leverages is set for creating stories and various other examples. And now, our approach to it from AWS is we actually look at it as in terms of the same three tiers like we did in machine learning, because when you look at generative AI, we genuinely see three sets of customers. One is, like really deep technical expert practitioner startups. These are the startups that are creating the next generation models like the likes of stability AIs or Hugging Face with Bloom or AI21. And they generally want to build their own models, and they want the best price performance of their infrastructure for training and inference. That's where our investments in silicon and hardware and networking innovations, where Trainium and Inferentia really plays a big role. And we can nearly do that, and that is one. The second middle tier is where I do think developers don't want to spend time building their own models, let alone, they actually want the model to be useful to that data. They don't need their models to create like high school homeworks or various other things. What they generally want is, hey, I had this data from my enterprises that I want to fine tune and make it really work only for this, and make it work remarkable, can be for tech summarization, to generate a report, or it can be for better Q&A, and so forth. This is where we are. Our investments in the middle tier with SageMaker, and our partnership with Hugging Face and AI21 and co here are all going to very meaningful. And you'll see us investing, I mean, you already talked about CodeWhisperer, which is an open preview, but we are also partnering with a whole lot of top ISVs, and you'll see more on this front to enable the next wave of generated AI apps too, because this is an area where we do think lot of innovation is yet to be done. It's like day one for us in this space, and we want to enable that huge ecosystem to flourish. >> You know, one of the things Dave Vellante and I were talking about in our first podcast we just did on Friday, we're going to do weekly, is we highlighted the AI ChatGPT example as a horizontal use case, because everyone loves it, people are using it in all their different verticals, and horizontal scalable cloud plays perfectly into it. So I have to ask you, as you look at what AWS is going to bring to the table, a lot's changed over the past 13 years with AWS, a lot more services are available, how should someone rebuild or re-platform and refactor their application of business with AI, with AWS? What are some of the tools that you see and recommend? Is it Serverless, is it SageMaker, CodeWhisperer? What do you think's going to shine brightly within the AWS stack, if you will, or service list, that's going to be part of this? As you mentioned, CodeWhisperer and SageMaker, what else should people be looking at as they start tinkering and getting all these benefits, and scale up their ups? >> You know, if we were a startup, first, I would really work backwards from the customer problem I try to solve, and pick and choose, bar, I don't need to deal with the undifferentiated heavy lifting, so. And that's where the answer is going to change. If you look at it then, the answer is not going to be like a one size fits all, so you need a very strong, I mean, granted on the compute front, if you can actually completely accurate it, so unless, I will always recommend it, instead of running compute for running your ups, because it takes care of all the undifferentiated heavy lifting, but on the data, and that's where we provide a whole variety of databases, right from like relational data, or non-relational, or dynamo, and so forth. And of course, we also have a deep analytical stack, where data directly flows from our relational databases into data lakes and data virus. And you can get value along with partnership with various analytical providers. The area where I do think fundamentally things are changing on what people can do is like, with CodeWhisperer, I was literally trying to actually program a code on sending a message through Twilio, and I was going to pull up to read a documentation, and in my ID, I was actually saying like, let's try sending a message to Twilio, or let's actually update a Route 53 error code. All I had to do was type in just a comment, and it actually started generating the sub-routine. And it is going to be a huge time saver, if I were a developer. And the goal is for us not to actually do it just for AWS developers, and not to just generate the code, but make sure the code is actually highly secure and follows the best practices. So, it's not always about machine learning, it's augmenting with automated reasoning as well. And generative AI is going to be changing, and not just in how people write code, but also how it actually gets built and used as well. You'll see a lot more stuff coming on this front. >> Swami, thank you for your time. I know you're super busy. Thank you for sharing on the news and giving commentary. Again, I think this is a AWS moment and industry moment, heavy lifting, accelerated value, agility. AIOps is going to be probably redefined here. Thanks for sharing your commentary. And we'll see you next time, I'm looking forward to doing more follow up on this. It's going to be a big wave. Thanks. >> Okay. Thanks again, John, always a pleasure. >> Okay. This is SiliconANGLE's breaking news commentary. I'm John Furrier with SiliconANGLE News, as well as host of theCUBE. Swami, who's a leader in AWS, has been on theCUBE multiple times. We've been tracking the growth of how Amazon's journey has just been exploding past five years, in particular, past three. You heard the numbers, great performance, great reviews. This is a watershed moment, I think, for the industry, and it's going to be a lot of fun for the next 10 years. Thanks for watching. (bright music)

(light music) >> Hi everybody. Welcome to this Cube Conversation. I'm really pleased to announce a collaboration with Rob Strechay. He's a guest cube analyst, and we'll be working together to extract the signal from the noise. Rob is a long-time product pro, working at a number of firms including AWS, HP, HPE, NetApp, Snowplow. I did a stint as an analyst at Enterprise Strategy Group. Rob, good to see you. Thanks for coming into our Marlboro Studios. >> Well, thank you for having me. It's always great to be here. >> I'm really excited about working with you. We've known each other for a long time. You've been in the Cube a bunch. You know, you're in between gigs, and I think we can have a lot of fun together. Covering events, covering trends. So. let's get into it. What's happening out there? We're sort of exited the isolation economy. Things were booming. Now, everybody's tapping the brakes. From your standpoint, what are you seeing out there? >> Yeah. I'm seeing that people are really looking how to get more out of their data. How they're bringing things together, how they're looking at the costs of Cloud, and understanding how are they building out their SaaS applications. And understanding that when they go in and actually start to use Cloud, it's not only just using the base services anymore. They're looking at, how do I use these platforms as a service? Some are easier than others, and they're trying to understand, how do I get more value out of that relationship with the Cloud? They're also consolidating the number of Clouds that they have, I would say to try to better optimize their spend, and getting better pricing for that matter. >> Are you seeing people unhook Clouds, or just reduce maybe certain Cloud activities and going maybe instead of 60/40 going 90/10? >> Correct. It's more like the 90/10 type of rule where they're starting to say, Hey I'm not going to get rid of Azure or AWS or Google. I'm going to move a portion of this over that I was using on this one service. Maybe I got a great two-year contract to start with on this platform as a service or a database as a service. I'm going to unhook from that and maybe go with an independent. Maybe with something like a Snowflake or a Databricks on top of another Cloud, so that I can consolidate down. But it also gives them more flexibility as well. >> In our last breaking analysis, Rob, we identified six factors that were reducing Cloud consumption. There were factors and customer tactics. And I want to get your take on this. So, some of the factors really, you got fewer mortgage originations. FinTech, obviously big Cloud user. Crypto, not as much activity there. Lower ad spending means less Cloud. And then one of 'em, which you kind of disagreed with was less, less analytics, you know, fewer... Less frequency of calculations. I'll come back to that. But then optimizing compute using Graviton or AMD instances moving to cheaper storage tiers. That of course makes sense. And then optimize pricing plans. Maybe going from On Demand, you know, to, you know, instead of pay by the drink, buy in volume. Okay. So, first of all, do those make sense to you with the exception? We'll come back and talk about the analytics piece. Is that what you're seeing from customers? >> Yeah, I think so. I think that was pretty much dead on with what I'm seeing from customers and the ones that I go out and talk to. A lot of times they're trying to really monetize their, you know, understand how their business utilizes these Clouds. And, where their spend is going in those Clouds. Can they use, you know, lower tiers of storage? Do they really need the best processors? Do they need to be using Intel or can they get away with AMD or Graviton 2 or 3? Or do they need to move in? And, I think when you look at all of these Clouds, they always have pricing curves that are arcs from the newest to the oldest stuff. And you can play games with that. And understanding how you can actually lower your costs by looking at maybe some of the older generation. Maybe your application was written 10 years ago. You don't necessarily have to be on the best, newest processor for that application per se. >> So last, I want to come back to this whole analytics piece. Last June, I think it was June, Dev Ittycheria, who's the-- I call him Dev. Spelled Dev, pronounced Dave. (chuckles softly) Same pronunciation, different spelling. Dev Ittycheria, CEO of Mongo, on the earnings call. He was getting, you know, hit. Things were starting to get a little less visible in terms of, you know, the outlook. And people were pushing him like... Because you're in the Cloud, is it easier to dial down? And he said, because we're the document database, we support transaction applications. We're less discretionary than say, analytics. Well on the Snowflake earnings call, that same month or the month after, they were all over Slootman and Scarpelli. Oh, the Mongo CEO said that they're less discretionary than analytics. And Snowflake was an interesting comment. They basically said, look, we're the Cloud. You can dial it up, you can dial it down, but the area under the curve over a period of time is going to be the same, because they get their customers to commit. What do you say? You disagreed with the notion that people are running their calculations less frequently. Is that because they're trying to do a better job of targeting customers in near real time? What are you seeing out there? >> Yeah, I think they're moving away from using people and more expensive marketing. Or, they're trying to figure out what's my Google ad spend, what's my Meta ad spend? And what they're trying to do is optimize that spend. So, what is the return on advertising, or the ROAS as they would say. And what they're looking to do is understand, okay, I have to collect these analytics that better understand where are these people coming from? How do they get to my site, to my store, to my whatever? And when they're using it, how do they they better move through that? What you're also seeing is that analytics is not only just for kind of the retail or financial services or things like that, but then they're also, you know, using that to make offers in those categories. When you move back to more, you know, take other companies that are building products and SaaS delivered products. They may actually go and use this analytics for making the product better. And one of the big reasons for that is maybe they're dialing back how many product managers they have. And they're looking to be more data driven about how they actually go and build the product out or enhance the product. So maybe they're, you know, an online video service and they want to understand why people are either using or not using the whiteboard inside the product. And they're collecting a lot of that product analytics in a big way so that they can go through that. And they're doing it in a constant manner. This first party type tracking within applications is growing rapidly by customers. >> So, let's talk about who wins in that. So, obviously the Cloud guys, AWS, Google and Azure. I want to come back and unpack that a little bit. Databricks and Snowflake, we reported on our last breaking analysis, it kind of on a collision course. You know, a couple years ago we were thinking, okay, AWS, Snowflake and Databricks, like perfect sandwich. And then of course they started to become more competitive. My sense is they still, you know, compliment each other in the field, right? But, you know, publicly, they've got bigger aspirations, they get big TAMs that they're going after. But it's interesting, the data shows that-- So, Snowflake was off the charts in terms of spending momentum and our EPR surveys. Our partner down in New York, they kind of came into line. They're both growing in terms of market presence. Databricks couldn't get to IPO. So, we don't have as much, you know, visibility on their financials. You know, Snowflake obviously highly transparent cause they're a public company. And then you got AWS, Google and Azure. And it seems like AWS appears to be more partner friendly. Microsoft, you know, depends on what market you're in. And Google wants to sell BigQuery. >> Yeah. >> So, what are you seeing in the public Cloud from a data platform perspective? >> Yeah. I think that was pretty astute in what you were talking about there, because I think of the three, Google is definitely I think a little bit behind in how they go to market with their partners. Azure's done a fantastic job of partnering with these companies to understand and even though they may have Synapse as their go-to and where they want people to go to do AI and ML. What they're looking at is, Hey, we're going to also be friendly with Snowflake. We're also going to be friendly with a Databricks. And I think that, Amazon has always been there because that's where the market has been for these developers. So, many, like Databricks' and the Snowflake's have gone there first because, you know, Databricks' case, they built out on top of S3 first. And going and using somebody's object layer other than AWS, was not as simple as you would think it would be. Moving between those. >> So, one of the financial meetups I said meetup, but the... It was either the CEO or the CFO. It was either Slootman or Scarpelli talking at, I don't know, Merrill Lynch or one of the other financial conferences said, I think it was probably their Q3 call. Snowflake said 80% of our business goes through Amazon. And he said to this audience, the next day we got a call from Microsoft. Hey, we got to do more. And, we know just from reading the financial statements that Snowflake is getting concessions from Amazon, they're buying in volume, they're renegotiating their contracts. Amazon gets it. You know, lower the price, people buy more. Long term, we're all going to make more money. Microsoft obviously wants to get into that game with Snowflake. They understand the momentum. They said Google, not so much. And I've had customers tell me that they wanted to use Google's AI with Snowflake, but they can't, they got to go to to BigQuery. So, honestly, I haven't like vetted that so. But, I think it's true. But nonetheless, it seems like Google's a little less friendly with the data platform providers. What do you think? >> Yeah, I would say so. I think this is a place that Google looks and wants to own. Is that now, are they doing the right things long term? I mean again, you know, you look at Google Analytics being you know, basically outlawed in five countries in the EU because of GDPR concerns, and compliance and governance of data. And I think people are looking at Google and BigQuery in general and saying, is it the best place for me to go? Is it going to be in the right places where I need it? Still, it's still one of the largest used databases out there just because it underpins a number of the Google services. So you almost get, like you were saying, forced into BigQuery sometimes, if you want to use the tech on top. >> You do strategy. >> Yeah. >> Right? You do strategy, you do messaging. Is it the right call by Google? I mean, it's not a-- I criticize Google sometimes. But, I'm not sure it's the wrong call to say, Hey, this is our ace in the hole. >> Yeah. >> We got to get people into BigQuery. Cause, first of all, BigQuery is a solid product. I mean it's Cloud native and it's, you know, by all, it gets high marks. So, why give the competition an advantage? Let's try to force people essentially into what is we think a great product and it is a great product. The flip side of that is, they're giving up some potential partner TAM and not treating the ecosystem as well as one of their major competitors. What do you do if you're in that position? >> Yeah, I think that that's a fantastic question. And the question I pose back to the companies I've worked with and worked for is, are you really looking to have vendor lock-in as your key differentiator to your service? And I think when you start to look at these companies that are moving away from BigQuery, moving to even, Databricks on top of GCS in Google, they're looking to say, okay, I can go there if I have to evacuate from GCP and go to another Cloud, I can stay on Databricks as a platform, for instance. So I think it's, people are looking at what platform as a service, database as a service they go and use. Because from a strategic perspective, they don't want that vendor locking. >> That's where Supercloud becomes interesting, right? Because, if I can run on Snowflake or Databricks, you know, across Clouds. Even Oracle, you know, they're getting into business with Microsoft. Let's talk about some of the Cloud players. So, the big three have reported. >> Right. >> We saw AWSs Cloud growth decelerated down to 20%, which is I think the lowest growth rate since they started to disclose public numbers. And they said they exited, sorry, they said January they grew at 15%. >> Yeah. >> Year on year. Now, they had some pretty tough compares. But nonetheless, 15%, wow. Azure, kind of mid thirties, and then Google, we had kind of low thirties. But, well behind in terms of size. And Google's losing probably almost $3 billion annually. But, that's not necessarily a bad thing by advocating and investing. What's happening with the Cloud? Is AWS just running into the law, large numbers? Do you think we can actually see a re-acceleration like we have in the past with AWS Cloud? Azure, we predicted is going to be 75% of AWS IAS revenues. You know, we try to estimate IAS. >> Yeah. >> Even though they don't share that with us. That's a huge milestone. You'd think-- There's some people who have, I think, Bob Evans predicted a while ago that Microsoft would surpass AWS in terms of size. You know, what do you think? >> Yeah, I think that Azure's going to keep to-- Keep growing at a pretty good clip. I think that for Azure, they still have really great account control, even though people like to hate Microsoft. The Microsoft sellers that are out there making those companies successful day after day have really done a good job of being in those accounts and helping people. I was recently over in the UK. And the UK market between AWS and Azure is pretty amazing, how much Azure there is. And it's growing within Europe in general. In the states, it's, you know, I think it's growing well. I think it's still growing, probably not as fast as it is outside the U.S. But, you go down to someplace like Australia, it's also Azure. You hear about Azure all the time. >> Why? Is that just because of the Microsoft's software state? It's just so convenient. >> I think it has to do with, you know, and you can go with the reasoning they don't break out, you know, Office 365 and all of that out of their numbers is because they have-- They're in all of these accounts because the office suite is so pervasive in there. So, they always have reasons to go back in and, oh by the way, you're on these old SQL licenses. Let us move you up here and we'll be able to-- We'll support you on the old version, you know, with security and all of these things. And be able to move you forward. So, they have a lot of, I guess you could say, levers to stay in those accounts and be interesting. At least as part of the Cloud estate. I think Amazon, you know, is hitting, you know, the large number. Laws of large numbers. But I think that they're also going through, and I think this was seen in the layoffs that they were making, that they're looking to understand and have profitability in more of those services that they have. You know, over 350 odd services that they have. And you know, as somebody who went there and helped to start yet a new one, while I was there. And finally, it went to beta back in September, you start to look at the fact that, that number of services, people, their own sellers don't even know all of their services. It's impossible to comprehend and sell that many things. So, I think what they're going through is really looking to rationalize a lot of what they're doing from a services perspective going forward. They're looking to focus on more profitable services and bringing those in. Because right now it's built like a layer cake where you have, you know, S3 EBS and EC2 on the bottom of the layer cake. And then maybe you have, you're using IAM, the authorization and authentication in there and you have all these different services. And then they call it EMR on top. And so, EMR has to pay for that entire layer cake just to go and compete against somebody like Mongo or something like that. So, you start to unwind the costs of that. Whereas Azure, went and they build basically ground up services for the most part. And Google kind of falls somewhere in between in how they build their-- They're a sort of layer cake type effect, but not as many layers I guess you could say. >> I feel like, you know, Amazon's trying to be a platform for the ecosystem. Yes, they have their own products and they're going to sell. And that's going to drive their profitability cause they don't have to split the pie. But, they're taking a piece of-- They're spinning the meter, as Ziyas Caravalo likes to say on every time Snowflake or Databricks or Mongo or Atlas is, you know, running on their system. They take a piece of the action. Now, Microsoft does that as well. But, you look at Microsoft and security, head-to-head competitors, for example, with a CrowdStrike or an Okta in identity. Whereas, it seems like at least for now, AWS is a more friendly place for the ecosystem. At the same time, you do a lot of business in Microsoft. >> Yeah. And I think that a lot of companies have always feared that Amazon would just throw, you know, bodies at it. And I think that people have come to the realization that a two pizza team, as Amazon would call it, is eight people. I think that's, you know, two slices per person. I'm a little bit fat, so I don't know if that's enough. But, you start to look at it and go, okay, if they're going to start out with eight engineers, if I'm a startup and they're part of my ecosystem, do I really fear them or should I really embrace them and try to partner closer with them? And I think the smart people and the smart companies are partnering with them because they're realizing, Amazon, unless they can see it to, you know, a hundred million, $500 million market, they're not going to throw eight to 16 people at a problem. I think when, you know, you could say, you could look at the elastic with OpenSearch and what they did there. And the licensing terms and the battle they went through. But they knew that Elastic had a huge market. Also, you had a number of ecosystem companies building on top of now OpenSearch, that are now domain on top of Amazon as well. So, I think Amazon's being pretty strategic in how they're doing it. I think some of the-- It'll be interesting. I think this year is a payout year for the cuts that they're making to some of the services internally to kind of, you know, how do we take the fat off some of those services that-- You know, you look at Alexa. I don't know how much revenue Alexa really generates for them. But it's a means to an end for a number of different other services and partners. >> What do you make of this ChatGPT? I mean, Microsoft obviously is playing that card. You want to, you want ChatGPT in the Cloud, come to Azure. Seems like AWS has to respond. And we know Google is, you know, sharpening its knives to come up with its response. >> Yeah, I mean Google just went and talked about Bard for the first time this week and they're in private preview or I guess they call it beta, but. Right at the moment to select, select AI users, which I have no idea what that means. But that's a very interesting way that they're marketing it out there. But, I think that Amazon will have to respond. I think they'll be more measured than say, what Google's doing with Bard and just throwing it out there to, hey, we're going into beta now. I think they'll look at it and see where do we go and how do we actually integrate this in? Because they do have a lot of components of AI and ML underneath the hood that other services use. And I think that, you know, they've learned from that. And I think that they've already done a good job. Especially for media and entertainment when you start to look at some of the ways that they use it for helping do graphics and helping to do drones. I think part of their buy of iRobot was the fact that iRobot was a big user of RoboMaker, which is using different models to train those robots to go around objects and things like that, so. >> Quick touch on Kubernetes, the whole DevOps World we just covered. The Cloud Native Foundation Security, CNCF. The security conference up in Seattle last week. First time they spun that out kind of like reinforced, you know, AWS spins out, reinforced from reinvent. Amsterdam's coming up soon, the CubeCon. What should we expect? What's hot in Cubeland? >> Yeah, I think, you know, Kubes, you're going to be looking at how OpenShift keeps growing and I think to that respect you get to see the momentum with people like Red Hat. You see others coming up and realizing how OpenShift has gone to market as being, like you were saying, partnering with those Clouds and really making it simple. I think the simplicity and the manageability of Kubernetes is going to be at the forefront. I think a lot of the investment is still going into, how do I bring observability and DevOps and AIOps and MLOps all together. And I think that's going to be a big place where people are going to be looking to see what comes out of CubeCon in Amsterdam. I think it's that manageability ease of use. >> Well Rob, I look forward to working with you on behalf of the whole Cube team. We're going to do more of these and go out to some shows extract the signal from the noise. Really appreciate you coming into our studio. >> Well, thank you for having me on. Really appreciate it. >> You're really welcome. All right, keep it right there, or thanks for watching. This is Dave Vellante for the Cube. And we'll see you next time. (light music)

>> Hello everyone and welcome back to theCUBE, where we're live from Dallas, Texas here at Super computing 2022. I am joined by my cohost David Nicholson. Thank you so much for being here with me and putting up with my trashy jokes all day. >> David: Thanks for having me. >> Yeah. Yes, we are going to be talking about AI this morning and I'm very excited that our guest has has set the stage for us here quite well. Please welcome Andrea Booker. Andrea, thank you so much for being here with us. >> Absolutely. Really excited to be here. >> Savannah: How's your show going so far? >> It's been really cool. I think being able to actually see people in person but also be able to see the latest technologies and and have the live dialogue that connects us in a different way than we have been able to virtually. >> Savannah: Oh yeah. No, it's all, it's all about that human connection and that it is driving towards our first question. So as we were just chit chatting. You said you are excited about making AI real and humanizing that. >> Andrea: Absolutely. >> What does that mean to you? >> So I think when it comes down to artificial intelligence it means so many different things to different people. >> Savannah: Absolutely. >> I was talking to my father the other day for context, he's in his late seventies, right. And I'm like, oh, artificial intelligence, this or that, and he is like, machines taking over the world. Right. >> Savannah: Very much the dark side. >> A little bit Terminator. And I'm like, well, not so much. So that was a fun discussion. And then you flip it to the other side and I'm talking to my 11 year old daughter and she's like, Alexa make sure you know my song preferences. Right. And that's the other very real way in which it's kind of impacting our lives. >> Savannah: Yeah. >> Right. There's so many different use cases that I don't think everyone understands how that resonates. Right. It's the simple things from, you know, recommend Jason Engines when you're on Amazon and it suggests just a little bit more. >> Oh yeah. >> I'm a little bit to you that one, right. To stuff that's more impactful in regards to getting faster diagnoses from your doctors. Right. Such peace of mind being able to actually hear that answer faster know how to go tackle something. >> Savannah: Great point, yeah. >> You know, and, and you know, what's even more interesting is from a business perspective, you know the projections are over the next five years about 90% of customers are going to use AI applications in in some fashion, right. >> Savannah: Wow. >> And the reason why that's interesting is because if you look at it today, only about 15% of of them are doing so. Right. So we're early. So when we're talking growth and the opportunity, it's, it's amazing. >> Yeah. I can, I can imagine. So when you're talking to customers, what are are they excited? Are they nervous? Are you educating them on how to apply Dell technology to advance their AI? Where are they off at because we're so early? >> Yeah well, I think they're figuring it out what it means to them, right? >> Yeah. Because there's so many different customer applications of it, right? You have those in which, you know, are on on the highest end in which that our new XE products are targeting that when they think of it. You know, I I, I like to break it down in this fashion in which artificial intelligence can actually save human lives, right? And this is those extreme workloads that I'm talking about. We actually can develop a Covid vaccine faster, right. Pandemic tracking, you know with global warming that's going on. And we have these extreme weather events with hurricanes and tsunamis and all these things to be able to get advanced notice to people to evacuate, to move. I mean, that's a pretty profound thing. And it is, you know so it could be used in that way to save lives, right? >> Absolutely. >> Which is it's the natural outgrowth of the speeds and feeds discussions that we might have internally. It's, it's like, oh, oh, speed doubled. Okay. Didn't it double last year? Yeah. Doubled last year too. So it's four x now. What does that mean to your point? >> Andrea: Yeah, yeah. >> Savannah: Yeah. >> Being able to deliver faster insight insights that are meaningful within a timeframe when otherwise they wouldn't be meaningful. >> Andrea: Yeah. >> If I tell you, within a two month window whether it's going to rain this weekend, that doesn't help you. In hindsight, we did the calculation and we figured out it's going to be 40 degrees at night last Thursday >> Knowing it was going to completely freeze here in Dallas to our definition in Texas but we prepare better to back to bring clothes. >> We were talking to NASA about that yesterday too. I mean, I think it's, it's must be fascinating for you to see your technology deployed in so many of these different use cases as well. >> Andrea: Absolutely, absolutely. >> It's got to be a part of one of the more >> Andrea: Not all of them are extreme, right? >> Savannah: Yeah. >> There's also examples of, you know natural language processing and what it does for us you know, the fact that it can break down communication barriers because we're global, right? We're all in a global environment. So if you think about conference calls in which we can actually clearly understand each other and what the intent is, and the messaging brings us closer in different ways as well. Which, which is huge, right? You don't want things lost in translation, right? So it, it helps on so many fronts. >> You're familiar with the touring test idea of, of, you know whether or not, you know, the test is if you can't discern within a certain number of questions that you're interacting with an AI versus a real human, then it passes the touring test. I think there should be a natural language processing test where basically I say, fine >> Andrea: You see if people was mad or not. >> You tell me, you tell me. >> I love this idea, David. >> You know? >> Yeah. This is great. >> Okay. AI lady, >> You tell me what I meant. >> Yeah, am I actually okay? >> How far from, that's silly example but how far do you think we are from that? I mean, what, what do you seeing out there in terms of things where you're kind of like, whoa, they did this with technology I'm responsible for, that was impressive. Or have you heard of things that are on the horizon that, you know, again, you, you know they're the big, they're the big issues. >> Yeah. >> But any, anything kind of interesting and little >> I think we're seeing it perfected and tweaked, right? >> Yeah. >> You know, I think going back to my daughter it goes from her screaming at Alexa 'cause she did hear her right the first time to now, oh she understands and modifies, right? Because we're constantly tweaking that technology to have a better experience with it. And it's a continuum, right? The voice to text capabilities, right. You know, I I'd say early on it got most of those words, right Right now it's, it's getting pretty dialed in. Right. >> Savannah: That's a great example. >> So, you know, little things, little things. >> Yeah. I think I, I love the, the this thought of your daughter as the example of training AI. What, what sort of, you get to look into the future quite a bit, I'm sure with your role. >> Andrea: Absolutely. >> Where, what is she going to be controlling next? >> The world. >> The world. >> No, I mean if you think about it just from a generational front, you know technology when I was her age versus what she's experiencing, she lives and breathes it. I mean, that's the generational change. So as these are coming out, you have new folks growing with it that it's so natural that they are so open to adopting it in their common everyday behaviors. Right? >> Savannah: Yeah. >> But they'd they never, over time they learn, oh well how it got there is 'cause of everything we're doing now, right. >> Savannah: Yeah. >> You know, one, one fun example, you know as my dad was like machines are taking over the world is not, not quite right. Even if when you look at manufacturing, there's a difference in using AI to go build a digital simulation of a factory to be able to optimize it and design it right before you're laying the foundation that saves cost, time and money. That's not taking people's jobs in that extreme event. >> Right. >> It's really optimizing for faster outcomes and, and and helping our customers get there which is better for everyone. >> Savannah: Yeah and safer too. I mean, using the factory example, >> Totally safer. >> You're able to model out what a workplace injury might be or what could happen. Or even the ergonomics of how people are using. >> Andrea: Yeah, should it be higher so they don't have to bend over? Right. >> Exactly. >> There's so many fantastic positive ways. >> Yeah so, so for your dad, you know, I mean it's going to help us, it's going to make, it's going to take away when I. Well I'm curious what you think, David when I think about AI, I think it's going to take out a lot of the boring things in life that, that we don't like >> Andrea: Absolutely. Doing. The monotony and the repetitive and let us optimize our creative selves maybe. >> However, some of the boring things are people's jobs. So, so it is, it it it will, it will it will push a transition in our economy in the global economy, in my opinion. That would be painful for some, for some period of time. But overall beneficial, >> Savannah: Yes. But definitely as you know, definitely there will be there will be people who will be disrupted and, you know. >> Savannah: Tech's always kind of done that. >> We No, but we need, I, I think we need to make sure that the digital divide doesn't get so wide that you know that, that people might not be negative, negatively affected. And, but, but I know that like organizations like Dell I believe what you actually see is, >> Andrea: Yeah. >> No, it's, it's elevating people. It's actually taking away >> Andrea: Easier. >> Yeah. It's, it's, it's allowing people to spend their focus on things that are higher level, more interesting tasks. >> Absolutely. >> David: So a net, A net good. But definitely some people disrupted. >> Yes. >> I feel, I feel disrupted. >> I was going to say, are, are we speaking for a friend or for ourselves here today on stage? >> I'm tired of software updates. So maybe if you could, if you could just standardize. So AI and ML. >> Andrea: Yeah. >> People talk about machine learning and, and, and and artificial intelligence. How would you differentiate the two? >> Savannah: Good question. >> It it, it's, it's just the different applications and the different workloads of it, right? Because you actually have artificial intelligence you have machine learning in which the learn it's learning from itself. And then you have like the deep learning in which it's diving deeper in in its execution and, and modeling. And it really depends on the workload applications as long as well as how large the data set is that's feeding into it for those applications. Right. And that really leads into the, we have to make sure we have the versatility in our offerings to be able to meet every dimension of that. Right. You know our XE products that we announced are really targeted for that, those extreme AI HPC workloads. Right. Versus we also have our entire portfolio products that we make sure we have GPU diversity throughout for the other applications that may be more edge centric or telco centric, right? Because AI isn't just these extreme situations it's also at the edge. It's in the cloud, it's in the data center, right? So we want to make sure we have, you know versatility in our offerings and we're really meeting customers where they're at in regards to the implementation and and the AI workloads that they have. >> Savannah: Let's dig in a little bit there. So what should customers expect with the next generation acceleration trends that Dell's addressing in your team? You had three exciting product announcements here >> Andrea: We did, we did. >> Which is very exciting. So you can talk about that a little bit and give us a little peek. >> Sure. So, you know, for, for the most extreme applications we have the XE portfolio that we built upon, right? We already had the XC 85 45 and we've expanded that out in a couple ways. The first of which is our very first XC 96 88 way offering in which we have Nvidia's H 100 as well as 8 100. 'Cause we want choice, right? A choice between performance, power, what really are your needs? >> Savannah: Is that the first time you've combined? >> Andrea: It's the first time we've had an eight way offering. >> Yeah. >> Andrea: But we did so mindful that the technology is emerging so much from a thermal perspective as well as a price and and other influencers that we wanted that choice baked into our next generation of product as we entered the space. >> Savannah: Yeah, yeah. >> The other two products we have were both in the four way SXM and OAM implementation and we really focus on diversifying and not only from vendor partnerships, right. The XC 96 40 is based off Intel Status Center max. We have the XE 86 40 that is going to be in or Nvidia's NB length, their latest H 100. But the key differentiator is we have air cold and we have liquid cold, right? So depending on where you are from that data center journey, I mean, I think one of the common themes you've heard is thermals are going up, performance is going up, TBPs are going up power, right? >> Savannah: Yeah. >> So how do we kind of meet in the middle to be able to accommodate for that? >> Savannah: I think it's incredible how many different types of customers you're able to accommodate. I mean, it's really impressive. I feel lucky we've gotten to see these products you're describing. They're here on the show floor. There's millions of dollars of hardware literally sitting in your booth. >> Andrea: Oh yes. >> Which is casual only >> Pies for you. Yeah. >> Yeah. We were, we were chatting over there yesterday and, and oh, which, which, you know which one of these is more expensive? And the response was, they're both expensive. It was like, okay perfect >> But assume the big one is more. >> David: You mentioned, you mentioned thermals. One of the things I've been fascinated by walking around is all of the different liquid cooling solutions. >> Andrea: Yeah. >> And it's almost hysterical. You look, you look inside, it looks like something from it's like, what is, what is this a radiator system for a 19th century building? >> Savannah: Super industrial? >> Because it looks like Yeah, yeah, exactly. Exactly, exactly. It's exactly the way to describe it. But just the idea that you're pumping all of this liquid over this, over this very, very valuable circuitry. A lot of the pitches have to do with, you know this is how we prevent disasters from happening based on the cooling methods. >> Savannah: Quite literally >> How, I mean, you look at the power requirements of a single rack in a data center, and it's staggering. We've talked about this a lot. >> Savannah: Yeah. >> People who aren't kind of EV you know electric vehicle nerds don't appreciate just how much power 90 kilowatts of power is for an individual rack and how much heat that can generate. >> Andrea: Absolutely. >> So Dell's, Dell's view on this is air cooled water cooled figure it out fit for for function. >> Andrea: Optionality, optionality, right? Because our customers are a complete diverse set, right? You have those in which they're in a data center 10 to 15 kilowatt racks, right? You're not going to plum a liquid cool power hungry or air power hungry thing in there, right? You might get one of these systems in, in that kind of rack you know, architecture, but then you have the middle ground the 50 to 60 is a little bit of choice. And then the super extreme, that's where liquid cooling makes sense to really get optimized and have the best density and, and the most servers in that solution. So that's why it really depends, and that's why we're taking that approach of diversity, of not only vendors and, and choice but also implementation and ways to be able to address that. >> So I think, again, again, I'm, you know electric vehicle nerd. >> Yeah. >> It's hysterical when you, when you mention a 15 kilowatt rack at kind of flippantly, people don't realize that's way more power than the average house is consuming. >> Andrea: Yeah, yeah >> So it's like your entire house is likely more like five kilowatts on a given day, you know, air conditioning. >> Andrea: Maybe you have still have solar panel. >> In Austin, I'm sorry >> California, Austin >> But, but, but yeah, it's, it's staggering amounts of power staggering amounts of heat. There are very real problems that you guys are are solving for to drive all of these top line value >> Andrea: Yeah. >> Propositions. It's super interesting. >> Savannah: It is super interesting. All right, Andrea, last question. >> Yes. Yes. >> Dell has been lucky to have you for the last decade. What is the most exciting part about you for the next decade of your Dell career given the exciting stuff that you get to work on. >> I think, you know, really working on what's coming our way and working with my team on that is is just amazing. You know, I can't say it enough from a Dell perspective I have the best team. I work with the most, the smartest people which creates such a fun environment, right? So then when we're looking at all this optionality and and the different technologies and, and, and you know partners we work with, you know, it's that coming together and figuring out what's that best solution and then bringing our customers along that journey. That kind of makes it fun dynamic that over the next 10 years, I think you're going to see fantastic things. >> David: So I, before, before we close, I have to say that's awesome because this event is also a recruiting event where some of these really really smarts students that are surrounding us. There were some sirens going off. They're having competitions back here. >> Savannah: Yeah, yeah, yeah. >> So, so when they hear that. >> Andrea: Where you want to be. >> David: That's exactly right. That's exactly right. >> Savannah: Well played. >> David: That's exactly right. >> Savannah: Well played. >> Have fun. Come on over. >> Well, you've certainly proven that to us. Andrea, thank you so much for being with us This was such a treat. David Nicholson, thank you for being here with me and thank you for tuning in to theCUBE a lot from Dallas, Texas. We are all things HPC and super computing this week. My name's Savannah Peterson and we'll see you soon. >> Andrea: Awesome.

(upbeat music) >> Hi everyone. Welcome to theCUBE special presentation here in Palo Alto, California. I'm John Furrier, host of theCUBE. We got two great guests, one for calling in from Germany, or videoing in from Germany, one from Maryland. We've got VMware and AWS. This is the customer successes with VMware Cloud on AWS Showcase: Accelerating Business Transformation. Here in the Showcase at Samir Kadoo, worldwide VMware strategic alliance solution architect leader with AWS. Samir, great to have you. And Daniel Rethmeier, principal architect global AWS synergy at VMware. Guys, you guys are working together, you're the key players in this relationship as it rolls out and continues to grow. So welcome to theCUBE. >> Thank you, greatly appreciate it. >> Great to have you guys both on. As you know, we've been covering this since 2016 when Pat Gelsinger, then CEO, and then then CEO AWS at Andy Jassy did this. It kind of got people by surprise, but it really kind of cleaned out the positioning in the enterprise for the success of VM workloads in the cloud. VMware's had great success with it since and you guys have the great partnerships. So this has been like a really strategic, successful partnership. Where are we right now? You know, years later, we got this whole inflection point coming, you're starting to see this idea of higher level services, more performance are coming in at the infrastructure side, more automation, more serverless, I mean and AI. I mean, it's just getting better and better every year in the cloud. Kind of a whole 'nother level. Where are we? Samir, let's start with you on the relationship. >> Yeah, totally. So I mean, there's several things to keep in mind, right? So in 2016, right, that's when the partnership between AWS and VMware was announced. And then less than a year later, that's when we officially launched VMware Cloud on AWS. Years later, we've been driving innovation, working with our customers, jointly engineering this between AWS and VMware. Day in, day out, as far as advancing VMware Cloud on AWS. You know, even if you look at the innovation that takes place with the solution, things have modernized, things have changed, there's been advancements. You know, whether it's security focus, whether it's platform focus, whether it's networking focus, there's been modifications along the way, even storage, right, more recently. One of the things to keep in mind is we're looking to deliver value to our customers together. These are our joint customers. So there's hundreds of VMware and AWS engineers working together on this solution. And then factor in even our sales teams, right? We have VMware and AWS sales teams interacting with each other on a constant daily basis. We're working together with our customers at the end of the day too. Then we're looking to even offer and develop jointly engineered solutions specific to VMware Cloud on AWS. And even with VMware to other platforms as well. Then the other thing comes down to is where we have dedicated teams around this at both AWS and VMware. So even from solutions architects, even to our sales specialists, even to our account teams, even to specific engineering teams within the organizations, they all come together to drive this innovation forward with VMware Cloud on AWS and the jointly engineered solution partnership as well. And then I think one of the key things to keep in mind comes down to we have nearly 600 channel partners that have achieved VMware Cloud on AWS service competency. So think about it from the standpoint, there's 300 certified or validated technology solutions, they're now available to our customers. So that's even innovation right off the top as well. >> Great stuff. Daniel, I want to get to you in a second upon this principal architect position you have. In your title, you're the global AWS synergy person. Synergy means bringing things together, making it work. Take us through the architecture, because we heard a lot of folks at VMware explore this year, formerly VMworld, talking about how the workloads on IT has been completely transforming into cloud and hybrid, right? This is where the action is. Where are you? Is your customers taking advantage of that new shift? You got AIOps, you got ITOps changing a lot, you got a lot more automation, edges right around the corner. This is like a complete transformation from where we were just five years ago. What's your thoughts on the relationship? >> So at first, I would like to emphasize that our collaboration is not just that we have dedicated teams to help our customers get the most and the best benefits out of VMware Cloud and AWS, we are also enabling us mutually. So AWS learns from us about the VMware technology, where VMware people learn about the AWS technology. We are also enabling our channel partners and we are working together on customer projects. So we have regular assembles globally and also virtually on Slack and the usual suspect tools working together and listening to customers. That's very important. Asking our customers where are their needs? And we are driving the solution into the direction that our customers get the best benefits out of VMware Cloud on AWS. And over the time, we really have involved the solution. As Samir mentioned, we just added additional storage solutions to VMware Cloud on AWS. We now have three different instance types that cover a broad range of workloads. So for example, we just edited the I4i host, which is ideally for workloads that require a lot of CPU power, such as, you mentioned it, AI workloads. >> Yeah, so I want to get us just specifically on the customer journey and their transformation, you know, we've been reporting on Silicon angle in theCUBE in the past couple weeks in a big way that the ops teams are now the new devs, right? I mean that sounds a little bit weird, but IT operations is now part of a lot more DataOps, security, writing code, composing. You know, with open source, a lot of great things are changing. Can you share specifically what customers are looking for when you say, as you guys come in and assess their needs, what are they doing, what are some of the things that they're doing with VMware on AWS specifically that's a little bit different? Can you share some of and highlights there? >> That's a great point, because originally, VMware and AWS came from very different directions when it comes to speaking people and customers. So for example, AWS, very developer focused, whereas VMware has a very great footprint in the ITOps area. And usually these are very different teams, groups, different cultures, but it's getting together. However, we always try to address the customer needs, right? There are customers that want to build up a new application from the scratch and build resiliency, availability, recoverability, scalability into the application. But there are still a lot of customers that say, "Well, we don't have all of the skills to redevelop everything to refactor an application to make it highly available. So we want to have all of that as a service. Recoverability as a service, scalability as a service. We want to have this from the infrastructure." That was one of the unique selling points for VMware on-premise and now we are bringing this into the cloud. >> Samir, talk about your perspective. I want to get your thoughts, and not to take a tangent, but we had covered the AWS re:MARS, actually it was Amazon re:MARS, machine learning automation, robotics and space was really kind of the confluence of industrial IoT, software, physical. And so when you look at like the IT operations piece becoming more software, you're seeing things about automation, but the skill gap is huge. So you're seeing low code, no code, automation, you know, "Hey Alexa, deploy a Kubernetes cluster." Yeah, I mean that's coming, right? So we're seeing this kind of operating automation meets higher level services, meets workloads. Can you unpack that and share your opinion on what you see there from an Amazon perspective and how it relates to this? >> Yeah. Yeah, totally, right? And you know, look at it from the point of view where we said this is a jointly engineered solution, but it's not migrating to one option or the other option, right? It's more or less together. So even with VMware Cloud on AWS, yes it is utilizing AWS infrastructure, but your environment is connected to that AWS VPC in your AWS account. So if you want to leverage any of the native AWS services, so any of the 200 plus AWS services, you have that option to do so. So that's going to give you that power to do certain things, such as, for example, like how you mentioned with IoT, even with utilizing Alexa, or if there's any other service that you want to utilize, that's the joining point between both of the offerings right off the top. Though with digital transformation, right, you have to think about where it's not just about the technology, right? There's also where you want to drive growth in the underlying technology even in your business. Leaders are looking to reinvent their business, they're looking to take different steps as far as pursuing a new strategy, maybe it's a process, maybe it's with the people, the culture, like how you said before, where people are coming in from a different background, right? They may not be used to the cloud, they may not be used to AWS services, but now you have that capability to mesh them together. >> Okay. >> Then also- >> Oh, go ahead, finish your thought. >> No, no, no, I was going to say what it also comes down to is you need to think about the operating model too, where it is a shift, right? Especially for that vStor admin that's used to their on-premises environment. Now with VMware Cloud on AWS, you have that ability to leverage a cloud, but the investment that you made and certain things as far as automation, even with monitoring, even with logging, you still have that methodology where you can utilize that in VMware Cloud on AWS too. >> Daniel, I want to get your thoughts on this because at Explore and after the event, as we prep for CubeCon and re:Invent coming up, the big AWS show, I had a couple conversations with a lot of the VMware customers and operators, and it's like hundreds of thousands of users and millions of people talking about and peaked on VMware, interested in VMware. The common thread was one person said, "I'm trying to figure out where I'm going to put my career in the next 10 to 15 years." And they've been very comfortable with VMware in the past, very loyal, and they're kind of talking about, I'm going to be the next cloud, but there's no like role yet. Architects, is it solution architect, SRE? So you're starting to see the psychology of the operators who now are going to try to make these career decisions. Like what am I going to work on? And then it's kind of fuzzy, but I want to get your thoughts, how would you talk to that persona about the future of VMware on, say, cloud for instance? What should they be thinking about? What's the opportunity? And what's going to happen? >> So digital transformation definitely is a huge change for many organizations and leaders are perfectly aware of what that means. And that also means to some extent, concerns with your existing employees. Concerns about do I have to relearn everything? Do I have to acquire new skills and trainings? Is everything worthless I learned over the last 15 years of my career? And the answer is to make digital transformation a success, we need not just to talk about technology, but also about process, people, and culture. And this is where VMware really can help because if you are applying VMware Cloud on AWS to your infrastructure, to your existing on-premise infrastructure, you do not need to change many things. You can use the same tools and skills, you can manage your virtual machines as you did in your on-premise environment, you can use the same managing and monitoring tools, if you have written, and many customers did this, if you have developed hundreds of scripts that automate tasks and if you know how to troubleshoot things, then you can use all of that in VMware Cloud on AWS. And that gives not just leaders, but also the architects at customers, the operators at customers, the confidence in such a complex project. >> The consistency, very key point, gives them the confidence to go. And then now that once they're confident, they can start committing themselves to new things. Samir, you're reacting to this because on your side, you've got higher level services, you've got more performance at the hardware level. I mean, a lot improvements. So, okay, nothing's changed, I can still run my job, now I got goodness on the other side. What's the upside? What's in it for the customer there? >> Yeah, so I think what it comes down to is they've already been so used to or entrenched with that VMware admin mentality, right? But now extending that to the cloud, that's where now you have that bridge between VMware Cloud on AWS to bridge that VMware knowledge with that AWS knowledge. So I will look at it from the point of view where now one has that capability and that ability to just learn about the cloud. But if they're comfortable with certain aspects, no one's saying you have to change anything. You can still leverage that, right? But now if you want to utilize any other AWS service in conjunction with that VM that resides maybe on-premises or even in VMware Cloud on AWS, you have that option to do so. So think about it where you have that ability to be someone who's curious and wants to learn. And then if you want to expand on the skills, you certainly have that capability to do so. >> Great stuff, I love that. Now that we're peeking behind the curtain here, I'd love to have you guys explain, 'cause people want to know what's goes on behind the scenes. How does innovation get happen? How does it happen with the relationships? Can you take us through a day in the life of kind of what goes on to make innovation happen with the joint partnership? Do you guys just have a Zoom meeting, do you guys fly out, you write code, go do you ship things? I mean, I'm making it up, but you get the idea. How does it work? What's going on behind the scenes? >> So we hope to get more frequently together in-person, but of course we had some difficulties over the last two to three years. So we are very used to Zoom conferences and Slack meetings. You always have to have the time difference in mind if you are working globally together. But what we try, for example, we have regular assembles now also in-person, geo-based, so for AMEA, for the Americas, for APJ. And we are bringing up interesting customer situations, architectural bits and pieces together. We are discussing it always to share and to contribute to our community. >> What's interesting, you know, as events are coming back, Samir, before you weigh in this, I'll comment as theCUBE's been going back out to events, we're hearing comments like, "What pandemic? We were more productive in the pandemic." I mean, developers know how to work remotely and they've been on all the tools there, but then they get in-person, they're happy to see people, but no one's really missed the beat. I mean, it seems to be very productive, you know, workflow, not a lot of disruption. More, if anything, productivity gains. >> Agreed, right? I think one of the key things to keep in mind is even if you look at AWS's, and even Amazon's leadership principles, right? Customer obsession, that's key. VMware is carrying that forward as well. Where we are working with our customers, like how Daniel said and meant earlier, right? We might have meetings at different time zones, maybe it's in-person, maybe it's virtual, but together we're working to listen to our customers. You know, we're taking and capturing that feedback to drive innovation in VMware Cloud on AWS as well. But one of the key things to keep in mind is yes, there has been the pandemic, we might have been disconnected to a certain extent, but together through technology, we've been able to still communicate, work with our customers, even with VMware in between, with AWS and whatnot, we had that flexibility to innovate and continue that innovation. So even if you look at it from the point of view, right? VMware Cloud on AWS Outposts, that was something that customers have been asking for. We've been able to leverage the feedback and then continue to drive innovation even around VMware Cloud on AWS Outposts. So even with the on-premises environment, if you're looking to handle maybe data sovereignty or compliance needs, maybe you have low latency requirements, that's where certain advancements come into play, right? So the key thing is always to maintain that communication track. >> In our last segment we did here on this Showcase, we listed the accomplishments and they were pretty significant. I mean geo, you got the global rollouts of the relationship. It's just really been interesting and people can reference that, we won't get into it here. But I will ask you guys to comment on, as you guys continue to evolve the relationship, what's in it for the customer? What can they expect next? Because again, I think right now, we're at an inflection point more than ever. What can people expect from the relationship and what's coming up with re:Invent? Can you share a little bit of kind of what's coming down the pike? >> So one of the most important things we have announced this year, and we will continue to evolve into that direction, is independent scale of storage. That absolutely was one of the most important items customer asked for over the last years. Whenever you are requiring additional storage to host your virtual machines, you usually in VMware Cloud on AWS, you have to add additional nodes. Now we have three different node types with different ratios of compute, storage, and memory. But if you only require additional storage, you always have to get also additional compute and memory and you have to pay for it. And now with two solutions which offer choice for the customers, like FS6 wanted a ONTAP and VMware Cloud Flex Storage, you now have two cost effective opportunities to add storage to your virtual machines. And that offers opportunities for other instance types maybe that don't have local storage. We are also very, very keen looking forward to announcements, exciting announcements, at the upcoming events. >> Samir, what's your reaction take on what's coming down on your side? >> Yeah, I think one of the key things to keep in mind is we're looking to help our customers be agile and even scaled with their needs, right? So with VMware Cloud on AWS, that's one of the key things that comes to mind, right? There are going to be announcements, innovations, and whatnot with upcoming events. But together, we're able to leverage that to advance VMware cloud on AWS. To Daniel's point, storage for example, even with host offerings. And then even with decoupling storage from compute and memory, right? Now you have the flexibility where you can do all of that. So to look at it from the standpoint where now with 21 regions where we have VMware Cloud on AWS available as well, where customers can utilize that as needed when needed, right? So it comes down to, you know, transformation will be there. Yes, there's going to be maybe where workloads have to be adapted where they're utilizing certain AWS services, but you have that flexibility and option to do so. And I think with the continuing events, that's going to give us the options to even advance our own services together. >> Well you guys are in the middle of it, you're in the trenches, you're making things happen, you've got a team of people working together. My final question is really more of a kind of a current situation, kind of future evolutionary thing that you haven't seen this before. I want to get both of your reaction to it. And we've been bringing this up in the open conversations on theCUBE is in the old days, let's go back this generation, you had ecosystems, you had VMware had an ecosystem, AWS had an ecosystem. You know, we have a product, you have a product, biz dev deals happen, people sign relationships, and they do business together and they sell each other's products or do some stuff. Now it's more about architecture, 'cause we're now in a distributed large scale environment where the role of ecosystems are intertwining and you guys are in the middle of two big ecosystems. You mentioned channel partners, you both have a lot of partners on both sides, they come together. So you have this now almost a three dimensional or multidimensional ecosystem interplay. What's your thoughts on this? Because it's about the architecture, integration is a value, not so much innovations only. You got to do innovation, but when you do innovation, you got to integrate it, you got to connect it. So how do you guys see this as an architectural thing, start to see more technical business deals? >> So we are removing dependencies from individual ecosystems and from individual vendors. So a customer no longer has to decide for one vendor and then it is a very expensive and high effort project to move away from that vendor, which ties customers even closer to specific vendors. We are removing these obstacles. So with VMware Cloud on AWS, moving to the cloud, firstly it's not a dead end. If you decide at one point in time because of latency requirements or maybe some compliance requirements, you need to move back into on-premise, you can do this. If you decide you want to stay with some of your services on-premise and just run a couple of dedicated services in the cloud, you can do this and you can man manage it through a single pane of glass. That's quite important. So cloud is no longer a dead end, it's no longer a binary decision, whether it's on-premise or the cloud, it is the cloud. And the second thing is you can choose the best of both worlds, right? If you are migrating virtual machines that have been running in your on-premise environment to VMware Cloud on AWS either way in a very, very fast cost effective and safe way, then you can enrich, later on enrich these virtual machines with services that are offered by AWS, more than 200 different services ranging from object-based storage, load balancing, and so on. So it's an endless, endless possibility. >> We call that super cloud in the way that we generically defining it where everyone's innovating, but yet there's some common services. But the differentiation comes from innovation where the lock in is the value, not some spec, right? Samir, this is kind of where cloud is right now. You guys are not commodity, amazon's completely differentiating, but there's some commodity things happen. You got storage, you got compute, but then you got now advances in all areas. But partners innovate with you on their terms. >> Absolutely. >> And everybody wins. >> Yeah, I 100% agree with you. I think one of the key things, you know, as Daniel mentioned before, is where it's a cross education where there might be someone who's more proficient on the cloud side with AWS, maybe more proficient with the VMware's technology. But then for partners, right? They bridge that gap as well where they come in and they might have a specific niche or expertise where their background, where they can help our customers go through that transformation. So then that comes down to, hey, maybe I don't know how to connect to the cloud, maybe I don't know what the networking constructs are, maybe I can leverage that partner. That's one aspect to go about it. Now maybe you migrated that workload to VMware Cloud on AWS. Maybe you want to leverage any of the native AWS services or even just off the top, 200 plus AWS services, right? But it comes down to that skillset, right? So again, solutions architecture at the back of the day, end of the day, what it comes down to is being able to utilize the best of both worlds. That's what we're giving our customers at the end of the day. >> I mean, I just think it's a refactoring and innovation opportunity at all levels. I think now more than ever, you can take advantage of each other's ecosystems and partners and technologies and change how things get done with keeping the consistency. I mean, Daniel, you nailed that, right? I mean you don't have to do anything. You still run it. Just spear the way you're working on it and now do new things. This is kind of a cultural shift. >> Yeah, absolutely. And if you look, not every customer, not every organization has the resources to refactor and re-platform everything. And we give them a very simple and easy way to move workloads to the cloud. Simply run them and at the same time, they can free up resources to develop new innovations and grow their business. >> Awesome. Samir, thank you for coming on. Daniel, thank you for coming to Germany. >> Thank you. Oktoberfest, I know it's evening over there, weekend's here. And thank you for spending the time. Samir, give you the final word. AWS re:Invent's coming up. We're preparing, we're going to have an exclusive with Adam, with Fryer, we'd do a curtain raise, and do a little preview. What's coming down on your side with the relationship and what can we expect to hear about what you got going on at re:Invent this year? The big show? >> Yeah, so I think Daniel hit upon some of the key points, but what I will say is we do have, for example, specific sessions, both that VMware's driving and then also that AWS is driving. We do have even where we have what are called chalk talks. So I would say, and then even with workshops, right? So even with the customers, the attendees who are there, whatnot, if they're looking to sit and listen to a session, yes that's there, but if they want to be hands-on, that is also there too. So personally for me as an IT background, been in sysadmin world and whatnot, being hands-on, that's one of the key things that I personally am looking forward. But I think that's one of the key ways just to learn and get familiar with the technology. >> Yeah, and re:Invent's an amazing show for the in-person. You guys nail it every year. We'll have three sets this year at theCUBE and it's becoming popular. We have more and more content. You guys got live streams going on, a lot of content, a lot of media. So thanks for sharing that. Samir, Daniel, thank you for coming on on this part of the Showcase episode of really the customer successes with VMware Cloud on AWS, really accelerating business transformation with AWS and VMware. I'm John Furrier with theCUBE, thanks for watching. (upbeat music)

>>Hi everyone. Welcome to the Cube special presentation here in Palo Alto, California. I'm John Foer, host of the Cube. We've got two great guests, one for calling in from Germany, our videoing in from Germany, one from Maryland. We've got VMware and aws. This is the customer successes with VMware cloud on AWS showcase, accelerating business transformation here in the showcase with Samir Candu Worldwide. VMware strategic alliance solution, architect leader with AWS Samir. Great to have you and Daniel Re Myer, principal architect global AWS synergy at VMware. Guys, you guys are, are working together. You're the key players in the re relationship as it rolls out and continues to grow. So welcome to the cube. >>Thank you. Greatly appreciate it. >>Great to have you guys both on, As you know, we've been covering this since 2016 when Pat Geling, then CEO and then then CEO AWS at Andy Chasy did this. It kind of got people by surprise, but it really kind of cleaned out the positioning in the enterprise for the success. OFM workloads in the cloud. VMware's had great success with it since, and you guys have the great partnerships. So this has been like a really strategic, successful partnership. Where are we right now? You know, years later we got this whole inflection point coming. You're starting to see, you know, this idea of higher level services, more performance are coming in at the infrastructure side. More automation, more serverless, I mean, and a, I mean it's just getting better and better every year in the cloud. Kinda a whole nother level. Where are we, Samir? Let's start with you on, on the relationship. >>Yeah, totally. So I mean, there's several things to keep in mind, right? So in 2016, right, that's when the partnership between AWS and VMware was announced, and then less than a year later, that's when we officially launched VMware cloud on aws. Years later, we've been driving innovation, working with our customers, jointly engineering this between AWS and VMware day in, day out. As far as advancing VMware cloud on aws. You know, even if you look at the innovation that takes place with a solution, things have modernized, things have changed, there's been advancements, you know, whether it's security focus, whether it's platform focus, whether it's networking focus, there's been modifications along the way, even storage, right? More recently, one of the things to keep in mind is we're looking to deliver value to our customers together. These are our joint customers. So there's hundreds of VMware and AWS engineers working together on this solution. >>And then factor in even our sales teams, right? We have VMware and AWS sales teams interacting with each other on a constant daily basis. We're working together with our customers at the end of the day too. Then we're looking to even offer and develop jointly engineered solutions specific to VMware cloud on aws, and even with VMware's, other platforms as well. Then the other thing comes down to is where we have dedicated teams around this at both AWS and VMware. So even from solutions architects, even to our sales specialists, even to our account teams, even to specific engineering teams within the organizations, they all come together to drive this innovation forward with VMware cloud on AWS and the jointly engineered solution partnership as well. And then I think one of the key things to keep in mind comes down to we have nearly 600 channel partners that have achieved VMware cloud on AWS service competency. So think about it from the standpoint there's 300 certified or validated technology solutions, they're now available to our customers. So that's even innovation right off the top as well. >>Great stuff. Daniel, I wanna get to you in a second. Upon this principal architect position you have in your title, you're the global a synergy person. Synergy means bringing things together, making it work. Take us through the architecture, because we heard a lot of folks at VMware explore this year, formerly world, talking about how the, the workloads on it has been completely transforming into cloud and hybrid, right? This is where the action is. Where are you? Is your customers taking advantage of that new shift? You got AI ops, you got it. Ops changing a lot, you got a lot more automation edges right around the corner. This is like a complete transformation from where we were just five years ago. What's your thoughts on the >>Relationship? So at at, at first, I would like to emphasize that our collaboration is not just that we have dedicated teams to help our customers get the most and the best benefits out of VMware cloud on aws. We are also enabling US mutually. So AWS learns from us about the VMware technology, where VMware people learn about the AWS technology. We are also enabling our channel partners and we are working together on customer projects. So we have regular assembled globally and also virtually on Slack and the usual suspect tools working together and listening to customers, that's, that's very important. Asking our customers where are their needs? And we are driving the solution into the direction that our customers get the, the best benefits out of VMware cloud on aws. And over the time we, we really have involved the solution. As Samia mentioned, we just added additional storage solutions to VMware cloud on aws. We now have three different instance types that cover a broad range of, of workload. So for example, we just added the I four I host, which is ideally for workloads that require a lot of CPU power, such as you mentioned it, AI workloads. >>Yeah. So I wanna guess just specifically on the customer journey and their transformation. You know, we've been reporting on Silicon angle in the queue in the past couple weeks in a big way that the OPS teams are now the new devs, right? I mean that sounds OP a little bit weird, but operation IT operations is now part of the, a lot more data ops, security writing code composing, you know, with open source, a lot of great things are changing. Can you share specifically what customers are looking for when you say, as you guys come in and assess their needs, what are they doing? What are some of the things that they're doing with VMware on AWS specifically that's a little bit different? Can you share some of and highlights there? >>That, that's a great point because originally VMware and AWS came from very different directions when it comes to speaking people at customers. So for example, aws very developer focused, whereas VMware has a very great footprint in the IT ops area. And usually these are very different, very different teams, groups, different cultures, but it's, it's getting together. However, we always try to address the customers, right? There are customers that want to build up a new application from the scratch and build resiliency, availability, recoverability, scalability into the application. But there are still a lot of customers that say, well we don't have all of the skills to redevelop everything to refactor an application to make it highly available. So we want to have all of that as a service, recoverability as a service, scalability as a service. We want to have this from the infrastructure. That was one of the unique selling points for VMware on premise and now we are bringing this into the cloud. >>Samir, talk about your perspective. I wanna get your thoughts, and not to take a tangent, but we had covered the AWS remar of, actually it was Amazon res machine learning automation, robotics and space. It was really kinda the confluence of industrial IOT software physical. And so when you look at like the IT operations piece becoming more software, you're seeing things about automation, but the skill gap is huge. So you're seeing low code, no code automation, you know, Hey Alexa, deploy a Kubernetes cluster. Yeah, I mean, I mean that's coming, right? So we're seeing this kind of operating automation meets higher level services meets workloads. Can you unpack that and share your opinion on, on what you see there from an Amazon perspective and how it relates to this? >>Yeah, totally. Right. And you know, look at it from the point of view where we said this is a jointly engineered solution, but it's not migrating to one option or the other option, right? It's more or less together. So even with VMware cloud on aws, yes it is utilizing AWS infrastructure, but your environment is connected to that AWS VPC in your AWS account. So if you wanna leverage any of the native AWS services, so any of the 200 plus AWS services, you have that option to do so. So that's gonna give you that power to do certain things, such as, for example, like how you mentioned with iot, even with utilizing Alexa or if there's any other service that you wanna utilize, that's the joining point between both of the offerings. Right off the top though, with digital transformation, right? You, you have to think about where it's not just about the technology, right? There's also where you want to drive growth in the underlying technology. Even in your business leaders are looking to reinvent their business. They're looking to take different steps as far as pursuing a new strategy. Maybe it's a process, maybe it's with the people, the culture, like how you said before, where people are coming in from a different background, right? They may not be used to the cloud, they may not be used to AWS services, but now you have that capability to mesh them together. Okay. Then also, Oh, >>Go ahead, finish >>Your thought. No, no, I was gonna say, what it also comes down to is you need to think about the operating model too, where it is a shift, right? Especially for that VS four admin that's used to their on-premises at environment. Now with VMware cloud on aws, you have that ability to leverage a cloud, but the investment that you made and certain things as far as automation, even with monitoring, even with logging, yeah. You still have that methodology where you can utilize that in VMware cloud on AWS two. >>Danielle, I wanna get your thoughts on this because at at explore and, and, and after the event, now as we prep for Cuban and reinvent coming up the big AWS show, I had a couple conversations with a lot of the VMware customers and operators and it's like hundreds of thousands of, of, of, of users and millions of people talking about and and peaked on VM we're interested in v VMware. The common thread was one's one, one person said, I'm trying to figure out where I'm gonna put my career in the next 10 to 15 years. And they've been very comfortable with VMware in the past, very loyal, and they're kind of talking about, I'm gonna be the next cloud, but there's no like role yet architects, is it Solution architect sre. So you're starting to see the psychology of the operators who now are gonna try to make these career decisions, like how, what am I gonna work on? And it's, and that was kind of fuzzy, but I wanna get your thoughts. How would you talk to that persona about the future of VMware on, say, cloud for instance? What should they be thinking about? What's the opportunity and what's gonna happen? >>So digital transformation definitely is a huge change for many organizations and leaders are perfectly aware of what that means. And that also means in, in to to some extent, concerns with your existing employees. Concerns about do I have to relearn everything? Do I have to acquire new skills? And, and trainings is everything worthless I learned over the last 15 years of my career? And the, the answer is to make digital transformation a success. We need not just to talk about technology, but also about process people and culture. And this is where VMware really can help because if you are applying VMware cloud on a, on AWS to your infrastructure, to your existing on-premise infrastructure, you do not need to change many things. You can use the same tools and skills, you can manage your virtual machines as you did in your on-premise environment. You can use the same managing and monitoring tools. If you have written, and many customers did this, if you have developed hundreds of, of scripts that automate tasks and if you know how to troubleshoot things, then you can use all of that in VMware cloud on aws. And that gives not just leaders, but but also the architects at customers, the operators at customers, the confidence in, in such a complex project, >>The consistency, very key point, gives them the confidence to go and, and then now that once they're confident they can start committing themselves to new things. Samir, you're reacting to this because you know, on your side you've got higher level services, you got more performance at the hardware level. I mean, lot improvement. So, okay, nothing's changed. I can still run my job now I got goodness on the other side. What's the upside? What's in it for the, for the, for the customer there? >>Yeah, so I think what it comes down to is they've already been so used to or entrenched with that VMware admin mentality, right? But now extending that to the cloud, that's where now you have that bridge between VMware cloud on AWS to bridge that VMware knowledge with that AWS knowledge. So I will look at it from the point of view where now one has that capability and that ability to just learn about the cloud, but if they're comfortable with certain aspects, no one's saying you have to change anything. You can still leverage that, right? But now if you wanna utilize any other AWS service in conjunction with that VM that resides maybe on premises or even in VMware cloud on aws, you have that option to do so. So think about it where you have that ability to be someone who's curious and wants to learn. And then if you wanna expand on the skills, you certainly have that capability to do so. >>Great stuff. I love, love that. Now that we're peeking behind the curtain here, I'd love to have you guys explain, cuz people wanna know what's goes on in behind the scenes. How does innovation get happen? How does it happen with the relationship? Can you take us through a day in the life of kind of what goes on to make innovation happen with the joint partnership? You guys just have a zoom meeting, Do you guys fly out, you write go do you ship thing? I mean I'm making it up, but you get the idea, what's the, what's, how does it work? What's going on behind the scenes? >>So we hope to get more frequently together in person, but of course we had some difficulties over the last two to three years. So we are very used to zoom conferences and and Slack meetings. You always have to have the time difference in mind if we are working globally together. But what we try, for example, we have reg regular assembled now also in person geo based. So for emia, for the Americas, for aj. And we are bringing up interesting customer situations, architectural bits and pieces together. We are discussing it always to share and to contribute to our community. >>What's interesting, you know, as, as events are coming back to here, before you get, you weigh in, I'll comment, as the cube's been going back out to events, we are hearing comments like what, what pandemic we were more productive in the pandemic. I mean, developers know how to work remotely and they've been on all the tools there, but then they get in person, they're happy to see people, but there's no one's, no one's really missed the beat. I mean it seems to be very productive, you know, workflow, not a lot of disruption. More if anything, productivity gains. >>Agreed, right? I think one of the key things to keep in mind is, you know, even if you look at AWS's and even Amazon's leadership principles, right? Customer obsession, that's key. VMware is carrying that forward as well. Where we are working with our customers, like how Daniel said met earlier, right? We might have meetings at different time zones, maybe it's in person, maybe it's virtual, but together we're working to listen to our customers. You know, we're taking and capturing that feedback to drive innovation and VMware cloud on AWS as well. But one of the key things to keep in mind is yes, there have been, there has been the pandemic, we might have been disconnected to a certain extent, but together through technology we've been able to still communicate work with our customers. Even with VMware in between, with AWS and whatnot. We had that flexibility to innovate and continue that innovation. So even if you look at it from the point of view, right? VMware cloud on AWS outposts, that was something that customers have been asking for. We've been been able to leverage the feedback and then continue to drive innovation even around VMware cloud on AWS outposts. So even with the on premises environment, if you're looking to handle maybe data sovereignty or compliance needs, maybe you have low latency requirements, that's where certain advancements come into play, right? So the key thing is always to maintain that communication track. >>And our last segment we did here on the, on this showcase, we listed the accomplishments and they were pretty significant. I mean go, you got the global rollouts of the relationship. It's just really been interesting and, and people can reference that. We won't get into it here, but I will ask you guys to comment on, as you guys continue to evolve the relationship, what's in it for the customer? What can they expect next? Cuz again, I think right now we're in at a, an inflection point more than ever. What can people expect from the relationship and what's coming up with reinvent? Can you share a little bit of kind of what's coming down the pike? >>So one of the most important things we have announced this year, and we will continue to evolve into that direction, is independent scale of storage. That absolutely was one of the most important items customer asked us for over the last years. Whenever, whenever you are requiring additional storage to host your virtual machines, you usually in VMware cloud on aws, you have to add additional notes. Now we have three different note types with different ratios of compute, storage and memory. But if you only require additional storage, you always have to get also additional compute and memory and you have to pay. And now with two solutions which offer choice for the customers, like FS six one, NetApp onap, and VMware cloud Flex Storage, you now have two cost effective opportunities to add storage to your virtual machines. And that offers opportunities for other instance types maybe that don't have local storage. We are also very, very keen looking forward to announcements, exciting announcements at the upcoming events. >>Samir, what's your, what's your reaction take on the, on what's coming down on your side? >>Yeah, I think one of the key things to keep in mind is, you know, we're looking to help our customers be agile and even scale with their needs, right? So with VMware cloud on aws, that's one of the key things that comes to mind, right? There are gonna be announcements, innovations and whatnot with outcoming events. But together we're able to leverage that to advance VMware cloud on AWS to Daniel's point storage, for example, even with host offerings. And then even with decoupling storage from compute and memory, right now you have the flexibility where you can do all of that. So to look at it from the standpoint where now with 21 regions where we have VMware cloud on AWS available as well, where customers can utilize that as needed when needed, right? So it comes down to, you know, transformation will be there. Yes, there's gonna be maybe where workloads have to be adapted where they're utilizing certain AWS services, but you have that flexibility and option to do so. And I think with the continuing events that's gonna give us the options to even advance our own services together. >>Well you guys are in the middle of it, you're in the trenches, you're making things happen, you've got a team of people working together. My final question is really more of a kind of a current situation, kind of future evolutionary thing that you haven't seen this before. I wanna get both of your reaction to it. And we've been bringing this up in, in the open conversations on the cube is in the old days it was going back this generation, you had ecosystems, you had VMware had an ecosystem they did best, had an ecosystem. You know, we have a product, you have a product, biz dev deals happen, people sign relationships and they do business together and they, they sell to each other's products or do some stuff. Now it's more about architecture cuz we're now in a distributed large scale environment where the role of ecosystems are intertwining. >>And this, you guys are in the middle of two big ecosystems. You mentioned channel partners, you both have a lot of partners on both sides. They come together. So you have this now almost a three dimensional or multidimensional ecosystem, you know, interplay. What's your thoughts on this? And, and, and because it's about the architecture, integration is a value, not so much. Innovation is only, you gotta do innovation, but when you do innovation, you gotta integrate it, you gotta connect it. So what is, how do you guys see this as a, as an architectural thing, start to see more technical business deals? >>So we are, we are removing dependencies from individual ecosystems and from individual vendors. So a customer no longer has to decide for one vendor and then it is a very expensive and high effort project to move away from that vendor, which ties customers even, even closer to specific vendors. We are removing these obstacles. So with VMware cloud on aws moving to the cloud, firstly it's, it's not a dead end. If you decide at one point in time because of latency requirements or maybe it's some compliance requirements, you need to move back into on-premise. You can do this if you decide you want to stay with some of your services on premise and just run a couple of dedicated services in the cloud, you can do this and you can mana manage it through a single pane of glass. That's quite important. So cloud is no longer a dead and it's no longer a binary decision, whether it's on premise or the cloud. It it is the cloud. And the second thing is you can choose the best of both works, right? If you are migrating virtual machines that have been running in your on-premise environment to VMware cloud on aws, by the way, in a very, very fast cost effective and safe way, then you can enrich later on enrich these virtual machines with services that are offered by aws. More than 200 different services ranging from object based storage, load balancing and so on. So it's an endless, endless possibility. >>We, we call that super cloud in, in a, in a way that we be generically defining it where everyone's innovating, but yet there's some common services. But the differentiation comes from innovation where the lock in is the value, not some spec, right? Samir, this is gonna where cloud is right now, you guys are, are not commodity. Amazon's completely differentiating, but there's some commodity things. Having got storage, you got compute, but then you got now advances in all areas. But partners innovate with you on their terms. Absolutely. And everybody wins. >>Yeah. And a hundred percent agree with you. I think one of the key things, you know, as Daniel mentioned before, is where it it, it's a cross education where there might be someone who's more proficient on the cloud side with aws, maybe more proficient with the viewers technology, but then for partners, right? They bridge that gap as well where they come in and they might have a specific niche or expertise where their background, where they can help our customers go through that transformation. So then that comes down to, hey, maybe I don't know how to connect to the cloud. Maybe I don't know what the networking constructs are. Maybe I can leverage that partner. That's one aspect to go about it. Now maybe you migrated that workload to VMware cloud on aws. Maybe you wanna leverage any of the native AWS services or even just off the top 200 plus AWS services, right? But it comes down to that skill, right? So again, solutions architecture at the back of, back of the day, end of the day, what it comes down to is being able to utilize the best of both worlds. That's what we're giving our customers at the end of the >>Day. I mean, I just think it's, it's a, it's a refactoring and innovation opportunity at all levels. I think now more than ever, you can take advantage of each other's ecosystems and partners and technologies and change how things get done with keeping the consistency. I mean, Daniel, you nailed that, right? I mean, you don't have to do anything. You still run the fear, the way you working on it and now do new things. This is kind of a cultural shift. >>Yeah, absolutely. And if, if you look, not every, not every customer, not every organization has the resources to refactor and re-platform everything. And we gave, we give them a very simple and easy way to move workloads to the cloud. Simply run them and at the same time they can free up resources to develop new innovations and, and grow their business. >>Awesome. Samir, thank you for coming on. Danielle, thank you for coming to Germany, Octoberfest, I know it's evening over there, your weekend's here. And thank you for spending the time. Samir final give you the final word, AWS reinvents coming up. Preparing. We're gonna have an exclusive with Adam, but Fry, we do a curtain raise, a dual preview. What's coming down on your side with the relationship and what can we expect to hear about what you got going on at reinvent this year? The big show? >>Yeah, so I think, you know, Daniel hit upon some of the key points, but what I will say is we do have, for example, specific sessions, both that VMware's driving and then also that AWS is driving. We do have even where we have what I call a chalk talks. So I would say, and then even with workshops, right? So even with the customers, the attendees who are there, whatnot, if they're looking for to sit and listen to a session, yes that's there. But if they wanna be hands on, that is also there too. So personally for me as an IT background, you know, been in CIS admin world and whatnot, being hands on, that's one of the key things that I personally am looking forward. But I think that's one of the key ways just to learn and get familiar with the technology. Yeah, >>Reinvents an amazing show for the in person. You guys nail it every year. We'll have three sets this year at the cube. It's becoming popular. We more and more content. You guys got live streams going on, a lot of content, a lot of media, so thanks, thanks for sharing that. Samir Daniel, thank you for coming on on this part of the showcase episode of really the customer successes with VMware Cloud Ons, really accelerating business transformation withs and VMware. I'm John Fur with the cube, thanks for watching. Hello everyone. Welcome to this cube showcase, accelerating business transformation with VMware cloud on it's a solution innovation conversation with two great guests, Fred and VP of commercial services at aws and NA Ryan Bard, who's the VP and general manager of cloud solutions at VMware. Gentlemen, thanks for joining me on this showcase. >>Great to be here. >>Hey, thanks for having us on. It's a great topic. You know, we, we've been covering this VMware cloud on abus since, since the launch going back and it's been amazing to watch the evolution from people saying, Oh, it's the worst thing I've ever seen. It's what's this mean? And depress work were, we're kind of not really on board with kind of the vision, but as it played out as you guys had announced together, it did work out great for VMware. It did work out great for a D and it continues two years later and I want just get an update from you guys on where you guys see this has been going. I'll see multiple years. Where is the evolution of the solution as we are right now coming off VMware explorer just recently and going in to reinvent, which is only a couple weeks away, feels like tomorrow. But you know, as we prepare a lot going on, where are we with the evolution of the solution? >>I mean, first thing I wanna say is, you know, PBO 2016 was a someon moment and the history of it, right? When Pat Gelsinger and Andy Jessey came together to announce this and I think John, you were there at the time I was there, it was a great, great moment. We launched the solution in 2017, the year after that at VM Word back when we called it Word, I think we have gone from strength to strength. One of the things that has really mattered to us is we have learned froms also in the processes, this notion of working backwards. So we really, really focused on customer feedback as we build a service offering now five years old, pretty remarkable journey. You know, in the first years we tried to get across all the regions, you know, that was a big focus because there was so much demand for it. >>In the second year we started going really on enterprise grade features. We invented this pretty awesome feature called Stretch clusters, where you could stretch a vSphere cluster using VSA and NSX across two AZs in the same region. Pretty phenomenal four nine s availability that applications start started to get with that particular feature. And we kept moving forward all kinds of integration with AWS direct connect transit gateways with our own advanced networking capabilities. You know, along the way, disaster recovery, we punched out two, two new services just focused on that. And then more recently we launched our outposts partnership. We were up on stage at Reinvent, again with Pat Andy announcing AWS outposts and the VMware flavor of that VMware cloud and AWS outposts. I think it's been significant growth in our federal sector as well with our federal and high certification more recently. So all in all, we are super excited. We're five years old. The customer momentum is really, really strong and we are scaling the service massively across all geos and industries. >>That's great, great update. And I think one of the things that you mentioned was how the advantages you guys got from that relationship. And, and this has kind of been the theme for AWS since I can remember from day one. Fred, you guys do the heavy lifting as as, as you always say for the customers here, VMware comes on board, takes advantage of the AWS and kind of just doesn't miss a beat, continues to move their workloads that everyone's using, you know, vSphere and these are, these are big workloads on aws. What's the AWS perspective on this? How do you see it? >>Yeah, it's pretty fascinating to watch how fast customers can actually transform and move when you take the, the skill set that they're familiar with and the advanced capabilities that they've been using on Preem and then overlay it on top of the AWS infrastructure that's, that's evolving quickly and, and building out new hardware and new instances we'll talk about. But that combined experience between both of us on a jointly engineered solution to bring the best security and the best features that really matter for those workloads drive a lot of efficiency and speed for the, for the customer. So it's been well received and the partnership is stronger than ever from an engineering standpoint, from a business standpoint. And obviously it's been very interesting to look at just how we stay day one in terms of looking at new features and work and, and responding to what customers want. So pretty, pretty excited about just seeing the transformation and the speed that which customers can move to bmc. Yeah, >>That's what great value publish. We've been talking about that in context too. Anyone building on top of the cloud, they can have their own supercloud as we call it. If you take advantage of all the CapEx and and investment Amazon's made and AWS has made and, and and continues to make in performance IAS and pass all great stuff. I have to ask you guys both as you guys see this going to the next level, what are some of the differentiations you see around the service compared to other options on the market? What makes it different? What's the combination? You mentioned jointly engineered, what are some of the key differentiators of the service compared to others? >>Yeah, I think one of the key things Fred talked about is this jointly engineered notion right from day one. We were the earlier doctors of AWS Nitro platform, right? The reinvention of E two back five years ago. And so we have been, you know, having a very, very strong engineering partnership at that level. I think from a VMware customer standpoint, you get the full software defined data center or compute storage networking on EC two, bare metal across all regions. You can scale that elastically up and down. It's pretty phenomenal just having that consistency globally, right on aws EC two global regions. Now the other thing that's a real differentiator for us that customers tell us about is this whole notion of a managed service, right? And this was somewhat new to VMware, but we took away the pain of this undifferentiated heavy lifting where customers had to provision rack, stack hardware, configure the software on top, and then upgrade the software and the security batches on top. >>So we took, took away all of that pain as customers transitioned to VMware cloud and aws. In fact, my favorite story from last year when we were all going through the lock for j debacle industry was just going through that, right? Favorite proof point from customers was before they put even race this issue to us, we sent them a notification saying we already patched all of your systems, no action from you. The customers were super thrilled. I mean these are large banks, many other customers around the world, super thrilled they had to take no action, but a pretty incredible industry challenge that we were all facing. >>Nora, that's a great, so that's a great point. You know, the whole managed service piece brings up the security, you kind of teasing at it, but you know, there's always vulnerabilities that emerge when you are doing complex logic. And as you grow your solutions, there's more bits. You know, Fred, we were commenting before we came on camera, there's more bits than ever before and, and at at the physics layer too, as well as the software. So you never know when there's gonna be a zero day vulnerability out there. Just, it happens. We saw one with fornet this week, this came outta the woodwork. But moving fast on those patches, it's huge. This brings up the whole support angle. I wanted to ask you about how you guys are doing that as well, because to me we see the value when we, when we talk to customers on the cube about this, you know, it was a real, real easy understanding of how, what the cloud means to them with VMware now with the aws. But the question that comes up that we wanna get more clarity on is how do you guys handle support together? >>Well, what's interesting about this is that it's, it's done mutually. We have dedicated support teams on both sides that work together pretty seamlessly to make sure that whether there's a issue at any layer, including all the way up into the app layer, as you think about some of the other workloads like sap, we'll go end to end and make sure that we support the customer regardless of where the particular issue might be for them. And on top of that, we look at where, where we're improving reliability in, in as a first order of, of principle between both companies. So from an availability and reliability standpoint, it's, it's top of mind and no matter where the particular item might land, we're gonna go help the customer resolve. That works really well >>On the VMware side. What's been the feedback there? What's the, what are some of the updates? >>Yeah, I think, look, I mean, VMware owns and operates the service, but we have a phenomenal backend relationship with aws. Customers call VMware for the service for any issues and, and then we have a awesome relationship with AWS on the backend for support issues or any hardware issues. The BASKE management that we jointly do, right? All of the hard problems that customers don't have to worry about. I think on the front end, we also have a really good group of solution architects across the companies that help to really explain the solution. Do complex things like cloud migration, which is much, much easier with VMware cloud aws, you know, we are presenting that easy button to the public cloud in many ways. And so we have a whole technical audience across the two companies that are working with customers every single day. >>You know, you had mentioned, I've got a list here, some of the innovations the, you mentioned the stretch clustering, you know, getting the GOs working, Advanced network, disaster recovery, you know, fed, Fed ramp, public sector certifications, outposts, all good. You guys are checking the boxes every year. You got a good, good accomplishments list there on the VMware AWS side here in this relationship. The question that I'm interested in is what's next? What recent innovations are you doing? Are you making investments in what's on the lists this year? What items will be next year? How do you see the, the new things, the list of accomplishments, people wanna know what's next. They don't wanna see stagnant growth here, they wanna see more action, you know, as as cloud kind of continues to scale and modern applications cloud native, you're seeing more and more containers, more and more, you know, more CF C I C D pipe pipelining with with modern apps, put more pressure on the system. What's new, what's the new innovations? >>Absolutely. And I think as a five yearold service offering innovation is top of mind for us every single day. So just to call out a few recent innovations that we announced in San Francisco at VMware Explorer. First of all, our new platform i four I dot metal, it's isolate based, it's pretty awesome. It's the latest and greatest, all the speeds and feeds that we would expect from VMware and aws. At this point in our relationship. We announced two different storage options. This notion of working from customer feedback, allowing customers even more price reductions, really take off that storage and park it externally, right? And you know, separate that from compute. So two different storage offerings there. One is with AWS Fsx, with NetApp on tap, which brings in our NetApp partnership as well into the equation and really get that NetApp based, really excited about this offering as well. >>And the second storage offering for VMware cloud Flex Storage, VMware's own managed storage offering. Beyond that, we have done a lot of other innovations as well. I really wanted to talk about VMware cloud Flex Compute, where previously customers could only scale by hosts and a host is 36 to 48 cores, give or take. But with VMware cloud Flex Compute, we are now allowing this notion of a resource defined compute model where customers can just get exactly the V C P memory and storage that maps to the applications, however small they might be. So this notion of granularity is really a big innovation that that we are launching in the market this year. And then last but not least, talk about ransomware. Of course it's a hot topic in industry. We are seeing many, many customers ask for this. We are happy to announce a new ransomware recovery with our VMware cloud DR solution. >>A lot of innovation there and the way we are able to do machine learning and make sure the workloads that are covered from snapshots and backups are actually safe to use. So there's a lot of differentiation on that front as well. A lot of networking innovations with Project Knot star for ability to have layer flow through layer seven, you know, new SaaS services in that area as well. Keep in mind that the service already supports managed Kubernetes for containers. It's built in to the same clusters that have virtual machines. And so this notion of a single service with a great TCO for VMs and containers and sort of at the heart of our office, >>The networking side certainly is a hot area to keep innovating on. Every year it's the same, same conversation, get better, faster networking, more, more options there. The flex computes. Interesting. If you don't mind me getting a quick clarification, could you explain the Drew screen resource defined versus hardware defined? Because this is kind of what we had saw at Explore coming out, that notion of resource defined versus hardware defined. What's the, what does that mean? >>Yeah, I mean I think we have been super successful in this hardware defined notion. We we're scaling by the hardware unit that we present as software defined data centers, right? And so that's been super successful. But we, you know, customers wanted more, especially customers in different parts of the world wanted to start even smaller and grow even more incrementally, right? Lower their costs even more. And so this is the part where resource defined starts to be very, very interesting as a way to think about, you know, here's my bag of resources exactly based on what the customers request for fiber machines, five containers, its size exactly for that. And then as utilization grows, we elastically behind the scenes, we're able to grow it through policies. So that's a whole different dimension. It's a whole different service offering that adds value and customers are comfortable. They can go from one to the other, they can go back to that post based model if they so choose to. And there's a jump off point across these two different economic models. >>It's kind of cloud of flexibility right there. I like the name Fred. Let's get into some of the examples of customers, if you don't mind. Let's get into some of the ex, we have some time. I wanna unpack a little bit of what's going on with the customer deployments. One of the things we've heard again on the cube is from customers is they like the clarity of the relationship, they love the cloud positioning of it. And then what happens is they lift and shift the workloads and it's like, feels great. It's just like we're running VMware on AWS and then they would start consuming higher level services, kind of that adoption next level happens and because it it's in the cloud, so, So can you guys take us through some recent examples of customer wins or deployments where they're using VMware cloud on AWS on getting started, and then how do they progress once they're there? How does it evolve? Can you just walk us through a couple of use cases? >>Sure. There's a, well there's a couple. One, it's pretty interesting that, you know, like you said, as there's more and more bits you need better and better hardware and networking. And we're super excited about the I four and the capabilities there in terms of doubling and or tripling what we're doing around a lower variability on latency and just improving all the speeds. But what customers are doing with it, like the college in New Jersey, they're accelerating their deployment on a, on onboarding over like 7,400 students over a six to eight month period. And they've really realized a ton of savings. But what's interesting is where and how they can actually grow onto additional native services too. So connectivity to any other services is available as they start to move and migrate into this. The, the options there obviously are tied to all the innovation that we have across any services, whether it's containerized and with what they're doing with Tanu or with any other container and or services within aws. >>So there's, there's some pretty interesting scenarios where that data and or the processing, which is moved quickly with full compliance, whether it's in like healthcare or regulatory business is, is allowed to then consume and use things, for example, with tech extract or any other really cool service that has, you know, monthly and quarterly innovations. So there's things that you just can't, could not do before that are coming out and saving customers money and building innovative applications on top of their, their current app base in, in a rapid fashion. So pretty excited about it. There's a lot of examples. I think I probably don't have time to go into too, too many here. Yeah. But that's actually the best part is listening to customers and seeing how many net new services and new applications are they actually building on top of this platform. >>Nora, what's your perspective from the VMware sy? So, you know, you guys have now a lot of headroom to offer customers with Amazon's, you know, higher level services and or whatever's homegrown where's being rolled out? Cuz you now have a lot of hybrid too, so, so what's your, what's your take on what, what's happening in with customers? >>I mean, it's been phenomenal, the, the customer adoption of this and you know, banks and many other highly sensitive verticals are running production grade applications, tier one applications on the service over the last five years. And so, you know, I have a couple of really good examples. S and p Global is one of my favorite examples. Large bank, they merge with IHS market, big sort of conglomeration. Now both customers were using VMware cloud and AWS in different ways. And with the, with the use case, one of their use cases was how do I just respond to these global opportunities without having to invest in physical data centers? And then how do I migrate and consolidate all my data centers across the global, which there were many. And so one specific example for this company was how they migrated thousand 1000 workloads to VMware cloud AWS in just six weeks. Pretty phenomenal. If you think about everything that goes into a cloud migration process, people process technology and the beauty of the technology going from VMware point A to VMware point B, the the lowest cost, lowest risk approach to adopting VMware, VMware cloud, and aws. So that's, you know, one of my favorite examples. There are many other examples across other verticals that we continue to see. The good thing is we are seeing rapid expansion across the globe that constantly entering new markets with the limited number of regions and progressing our roadmap there. >>Yeah, it's great to see, I mean the data center migrations go from months, many, many months to weeks. It's interesting to see some of those success stories. So congratulations. One >>Of other, one of the other interesting fascinating benefits is the sustainability improvement in terms of being green. So the efficiency gains that we have both in current generation and new generation processors and everything that we're doing to make sure that when a customer can be elastic, they're also saving power, which is really critical in a lot of regions worldwide at this point in time. They're, they're seeing those benefits. If you're running really inefficiently in your own data center, that is just a, not a great use of power. So the actual calculators and the benefits to these workloads is, are pretty phenomenal just in being more green, which I like. We just all need to do our part there. And, and this is a big part of it here. >>It's a huge, it's a huge point about the sustainability. Fred, I'm glad you called that out. The other one I would say is supply chain issues. Another one you see that constrains, I can't buy hardware. And the third one is really obvious, but no one really talks about it. It's security, right? I mean, I remember interviewing Stephen Schmidt with that AWS and many years ago, this is like 2013, and you know, at that time people were saying the cloud's not secure. And he's like, listen, it's more secure in the cloud on premise. And if you look at the security breaches, it's all about the on-premise data center vulnerabilities, not so much hardware. So there's a lot you gotta to stay current on, on the isolation there is is hard. So I think, I think the security and supply chain, Fred is, is another one. Do you agree? >>I I absolutely agree. It's, it's hard to manage supply chain nowadays. We put a lot of effort into that and I think we have a great ability to forecast and make sure that we can lean in and, and have the resources that are available and run them, run them more efficiently. Yeah, and then like you said on the security point, security is job one. It is, it is the only P one. And if you think of how we build our infrastructure from Nitro all the way up and how we respond and work with our partners and our customers, there's nothing more important. >>And naron your point earlier about the managed service patching and being on top of things, it's really gonna get better. All right, final question. I really wanna thank you for your time on this showcase. It's really been a great conversation. Fred, you had made a comment earlier. I wanna kind of end with kind of a curve ball and put you eyes on the spot. We're talking about a modern, a new modern shift. It's another, we're seeing another inflection point, we've been documenting it, it's almost like cloud hitting another inflection point with application and open source growth significantly at the app layer. Continue to put a lot of pressure and, and innovation in the infrastructure side. So the question is for you guys each to answer is what's the same and what's different in today's market? So it's kind of like we want more of the same here, but also things have changed radically and better here. What are the, what's, what's changed for the better and where, what's still the same kind of thing hanging around that people are focused on? Can you share your perspective? >>I'll, I'll, I'll, I'll tackle it. You know, businesses are complex and they're often unique that that's the same. What's changed is how fast you can innovate. The ability to combine manage services and new innovative services and build new applications is so much faster today. Leveraging world class hardware that you don't have to worry about that's elastic. You, you could not do that even five, 10 years ago to the degree you can today, especially with innovation. So innovation is accelerating at a, at a rate that most people can't even comprehend and understand the, the set of services that are available to them. It's really fascinating to see what a one pizza team of of engineers can go actually develop in a week. It is phenomenal. So super excited about this space and it's only gonna continue to accelerate that. That's my take. All right. >>You got a lot of platform to compete on with, got a lot to build on then you're Ryan, your side, What's your, what's your answer to that question? >>I think we are seeing a lot of innovation with new applications that customers are constant. I think what we see is this whole notion of how do you go from desktop to production to the secure supply chain and how can we truly, you know, build on the agility that developers desire and build all the security and the pipelines to energize that motor production quickly and efficiently. I think we, we are seeing, you know, we are at the very start of that sort of of journey. Of course we have invested in Kubernetes the means to an end, but there's so much more beyond that's happening in industry. And I think we're at the very, very beginning of this transformations, enterprise transformation that many of our customers are going through and we are inherently part of it. >>Yeah. Well gentlemen, I really appreciate that we're seeing the same thing. It's more the same here on, you know, solving these complexities with distractions. Whether it's, you know, higher level services with large scale infrastructure at, at your fingertips. Infrastructures, code, infrastructure to be provisioned, serverless, all the good stuff happen in Fred with AWS on your side. And we're seeing customers resonate with this idea of being an operator, again, being a cloud operator and developer. So the developer ops is kind of, DevOps is kind of changing too. So all for the better. Thank you for spending the time and we're seeing again, that traction with the VMware customer base and of us getting, getting along great together. So thanks for sharing your perspectives, >>I appreciate it. Thank you so >>Much. Okay, thank you John. Okay, this is the Cube and AWS VMware showcase, accelerating business transformation. VMware cloud on aws, jointly engineered solution, bringing innovation to the VMware customer base, going to the cloud and beyond. I'm John Fur, your host. Thanks for watching. Hello everyone. Welcome to the special cube presentation of accelerating business transformation on vmc on aws. I'm John Furrier, host of the Cube. We have dawan director of global sales and go to market for VMware cloud on adb. This is a great showcase and should be a lot of fun. Ashish, thanks for coming on. >>Hi John. Thank you so much. >>So VMware cloud on AWS has been well documented as this big success for VMware and aws. As customers move their workloads into the cloud, IT operations of VMware customers has signaling a lot of change. This is changing the landscape globally is on cloud migration and beyond. What's your take on this? Can you open this up with the most important story around VMC on aws? >>Yes, John. The most important thing for our customers today is the how they can safely and swiftly move their ID infrastructure and applications through cloud. Now, VMware cloud AWS is a service that allows all vSphere based workloads to move to cloud safely, swiftly and reliably. Banks can move their core, core banking platforms, insurance companies move their core insurance platforms, telcos move their goss, bss, PLA platforms, government organizations are moving their citizen engagement platforms using VMC on aws because this is one platform that allows you to move it, move their VMware based platforms very fast. Migrations can happen in a matter of days instead of months. Extremely securely. It's a VMware manage service. It's very secure and highly reliably. It gets the, the reliability of the underlyings infrastructure along with it. So win-win from our customers perspective. >>You know, we reported on this big news in 2016 with Andy Chas, the, and Pat Geling at the time, a lot of people said it was a bad deal. It turned out to be a great deal because not only could VMware customers actually have a cloud migrate to the cloud, do it safely, which was their number one concern. They didn't want to have disruption to their operations, but also position themselves for what's beyond just shifting to the cloud. So I have to ask you, since you got the finger on the pulse here, what are we seeing in the market when it comes to migrating and modern modernizing in the cloud? Because that's the next step. They go to the cloud, you guys have done that, doing it, then they go, I gotta modernize, which means kind of upgrading or refactoring. What's your take on that? >>Yeah, absolutely. Look, the first step is to help our customers assess their infrastructure and licensing and entire ID operations. Once we've done the assessment, we then create their migration plans. A lot of our customers are at that inflection point. They're, they're looking at their real estate, ex data center, real estate. They're looking at their contracts with colocation vendors. They really want to exit their data centers, right? And VMware cloud and AWS is a perfect solution for customers who wanna exit their data centers, migrate these applications onto the AWS platform using VMC on aws, get rid of additional real estate overheads, power overheads, be socially and environmentally conscious by doing that as well, right? So that's the migration story, but to your point, it doesn't end there, right? Modernization is a critical aspect of the entire customer journey as as well customers, once they've migrated their ID applications and infrastructure on cloud get access to all the modernization services that AWS has. They can correct easily to our data lake services, to our AIML services, to custom databases, right? They can decide which applications they want to keep and which applications they want to refactor. They want to take decisions on containerization, make decisions on service computing once they've come to the cloud. But the most important thing is to take that first step. You know, exit data centers, come to AWS using vmc or aws, and then a whole host of modernization options available to them. >>Yeah, I gotta say, we had this right on this, on this story, because you just pointed out a big thing, which was first order of business is to make sure to leverage the on-prem investments that those customers made and then migrate to the cloud where they can maintain their applications, their data, their infrastructure operations that they're used to, and then be in position to start getting modern. So I have to ask you, how are you guys specifically, or how is VMware cloud on s addressing these needs of the customers? Because what happens next is something that needs to happen faster. And sometimes the skills might not be there because if they're running old school, IT ops now they gotta come in and jump in. They're gonna use a data cloud, they're gonna want to use all kinds of machine learning, and there's a lot of great goodness going on above the stack there. So as you move with the higher level services, you know, it's a no brainer, obviously, but they're not, it's not yesterday's higher level services in the cloud. So how are, how is this being addressed? >>Absolutely. I think you hit up on a very important point, and that is skills, right? When our customers are operating, some of the most critical applications I just mentioned, core banking, core insurance, et cetera, they're most of the core applications that our customers have across industries, like even, even large scale ERP systems, they're actually sitting on VMware's vSphere platform right now. When the customer wants to migrate these to cloud, one of the key bottlenecks they face is skill sets. They have the trained manpower for these core applications, but for these high level services, they may not, right? So the first order of business is to help them ease this migration pain as much as possible by not wanting them to, to upscale immediately. And we VMware cloud and AWS exactly does that. I mean, you don't have to do anything. You don't have to create new skill set for doing this, right? Their existing skill sets suffice, but at the same time, it gives them that, that leeway to build that skills roadmap for their team. DNS is invested in that, right? Yes. We want to help them build those skills in the high level services, be it aml, be it, be it i t be it data lake and analytics. We want to invest in them, and we help our customers through that. So that ultimately the ultimate goal of making them drop data is, is, is a front and center. >>I wanna get into some of the use cases and success stories, but I want to just reiterate, hit back your point on the skill thing. Because if you look at what you guys have done at aws, you've essentially, and Andy Chassey used to talk about this all the time when I would interview him, and now last year Adam was saying the same thing. You guys do all the heavy lifting, but if you're a VMware customer user or operator, you are used to things. You don't have to be relearn to be a cloud architect. Now you're already in the game. So this is like almost like a instant path to cloud skills for the VMware. There's hundreds of thousands of, of VMware architects and operators that now instantly become cloud architects, literally overnight. Can you respond to that? Do you agree with that? And then give an example. >>Yes, absolutely. You know, if you have skills on the VMware platform, you know, know, migrating to AWS using via by cloud and AWS is absolutely possible. You don't have to really change the skills. The operations are exactly the same. The management systems are exactly the same. So you don't really have to change anything but the advantages that you get access to all the other AWS services. So you are instantly able to integrate with other AWS services and you become a cloud architect immediately, right? You are able to solve some of the critical problems that your underlying IT infrastructure has immediately using this. And I think that's a great value proposition for our customers to use this service. >>And just one more point, I want just get into something that's really kind of inside baseball or nuanced VMC or VMware cloud on AWS means something. Could you take a minute to explain what on AWS means? Just because you're like hosting and using Amazon as a, as a work workload? Being on AWS means something specific in your world, being VMC on AWS mean? >>Yes. This is a great question, by the way, You know, on AWS means that, you know, VMware's vse platform is, is a, is an iconic enterprise virtualization software, you know, a disproportionately high market share across industries. So when we wanted to create a cloud product along with them, obviously our aim was for them, for the, for this platform to have the goodness of the AWS underlying infrastructure, right? And, and therefore, when we created this VMware cloud solution, it it literally use the AWS platform under the eighth, right? And that's why it's called a VMs VMware cloud on AWS using, using the, the, the wide portfolio of our regions across the world and the strength of the underlying infrastructure, the reliability and, and, and sustainability that it offers. And therefore this product is called VMC on aws. >>It's a distinction I think is worth noting, and it does reflect engineering and some levels of integration that go well beyond just having a SaaS app and, and basically platform as a service or past services. So I just wanna make sure that now super cloud, we'll talk about that a little bit in another interview, but I gotta get one more question in before we get into the use cases and customer success stories is in, in most of the VM world, VMware world, in that IT world, it used to, when you heard migration, people would go, Oh my God, that's gonna take months. And when I hear about moving stuff around and doing cloud native, the first reaction people might have is complexity. So two questions for you before we move on to the next talk. Track complexity. How are you addressing the complexity issue and how long these migrations take? Is it easy? Is it it hard? I mean, you know, the knee jerk reaction is month, You're very used to that. If they're dealing with Oracle or other old school vendors, like, they're, like the old guard would be like, takes a year to move stuff around. So can you comment on complexity and speed? >>Yeah. So the first, first thing is complexity. And you know, what makes what makes anything complex is if you're, if you're required to acquire new skill sets or you've gotta, if you're required to manage something differently, and as far as VMware cloud and AWS on both these aspects, you don't have to do anything, right? You don't have to acquire new skill sets. Your existing idea operation skill sets on, on VMware's platforms are absolutely fine and you don't have to manage it any differently like, than what you're managing your, your ID infrastructure today. So in both these aspects, it's exactly the same and therefore it is absolutely not complex as far as, as far as, as far as we cloud and AWS is concerned. And the other thing is speed. This is where the huge differentiation is. You have seen that, you know, large banks and large telcos have now moved their workloads, you know, literally in days instead of months. >>Because because of VMware cloud and aws, a lot of time customers come to us with specific deadlines because they want to exit their data centers on a particular date. And what happens, VMware cloud and AWS is called upon to do that migration, right? So speed is absolutely critical. The reason is also exactly the same because you are using the exactly the same platform, the same management systems, people are available to you, you're able to migrate quickly, right? I would just reference recently we got an award from President Zelensky of Ukraine for, you know, migrating their entire ID digital infrastructure and, and that that happened because they were using VMware cloud database and happened very swiftly. >>That's been a great example. I mean, that's one political, but the economic advantage of getting outta the data center could be national security. You mentioned Ukraine, I mean Oscar see bombing and death over there. So clearly that's a critical crown jewel for their running their operations, which is, you know, you know, world mission critical. So great stuff. I love the speed thing. I think that's a huge one. Let's get into some of the use cases. One of them is, the first one I wanted to talk about was we just hit on data, data center migration. It could be financial reasons on a downturn or our, or market growth. People can make money by shifting to the cloud, either saving money or making money. You win on both sides. It's a, it's a, it's almost a recession proof, if you will. Cloud is so use case for number one data center migration. Take us through what that looks like. Give an example of a success. Take us through a day, day in the life of a data center migration in, in a couple minutes. >>Yeah. You know, I can give you an example of a, of a, of a large bank who decided to migrate, you know, their, all their data centers outside their existing infrastructure. And they had, they had a set timeline, right? They had a set timeline to migrate the, the, they were coming up on a renewal and they wanted to make sure that this set timeline is met. We did a, a complete assessment of their infrastructure. We did a complete assessment of their IT applications, more than 80% of their IT applications, underlying v vSphere platform. And we, we thought that the right solution for them in the timeline that they wanted, right, is VMware cloud ands. And obviously it was a large bank, it wanted to do it safely and securely. It wanted to have it completely managed, and therefore VMware cloud and aws, you know, ticked all the boxes as far as that is concerned. >>I'll be happy to report that the large bank has moved to most of their applications on AWS exiting three of their data centers, and they'll be exiting 12 more very soon. So that's a great example of, of, of the large bank exiting data centers. There's another Corolla to that. Not only did they manage to manage to exit their data centers and of course use and be more agile, but they also met their sustainability goals. Their board of directors had given them goals to be carbon neutral by 2025. They found out that 35% of all their carbon foot footprint was in their data centers. And if they moved their, their ID infrastructure to cloud, they would severely reduce the, the carbon footprint, which is 35% down to 17 to 18%. Right? And that meant their, their, their, their sustainability targets and their commitment to the go to being carbon neutral as well. >>And that they, and they shift that to you guys. Would you guys take that burden? A heavy lifting there and you guys have a sustainability story, which is a whole nother showcase in and of itself. We >>Can Exactly. And, and cause of the scale of our, of our operations, we are able to, we are able to work on that really well as >>Well. All right. So love the data migration. I think that's got real proof points. You got, I can save money, I can, I can then move and position my applications into the cloud for that reason and other reasons as a lot of other reasons to do that. But now it gets into what you mentioned earlier was, okay, data migration, clearly a use case and you laid out some successes. I'm sure there's a zillion others. But then the next step comes, now you got cloud architects becoming minted every, and you got managed services and higher level services. What happens next? Can you give us an example of the use case of the modernization around the NextGen workloads, NextGen applications? We're starting to see, you know, things like data clouds, not data warehouses. We're not gonna data clouds, it's gonna be all kinds of clouds. These NextGen apps are pure digital transformation in action. Take us through a use case of how you guys make that happen with a success story. >>Yes, absolutely. And this is, this is an amazing success story and the customer here is s and p global ratings. As you know, s and p global ratings is, is the world leader as far as global ratings, global credit ratings is concerned. And for them, you know, the last couple of years have been tough as far as hardware procurement is concerned, right? The pandemic has really upended the, the supply chain. And it was taking a lot of time to procure hardware, you know, configure it in time, make sure that that's reliable and then, you know, distribute it in the wide variety of, of, of offices and locations that they have. And they came to us. We, we did, again, a, a, a alar, a fairly large comprehensive assessment of their ID infrastructure and their licensing contracts. And we also found out that VMware cloud and AWS is the right solution for them. >>So we worked there, migrated all their applications, and as soon as we migrated all their applications, they got, they got access to, you know, our high level services be our analytics services, our machine learning services, our, our, our, our artificial intelligence services that have been critical for them, for their growth. And, and that really is helping them, you know, get towards their next level of modern applications. Right Now, obviously going forward, they will have, they will have the choice to, you know, really think about which applications they want to, you know, refactor or which applications they want to go ahead with. That is really a choice in front of them. And, but you know, the, we VMware cloud and AWS really gave them the opportunity to first migrate and then, you know, move towards modernization with speed. >>You know, the speed of a startup is always the kind of the Silicon Valley story where you're, you know, people can make massive changes in 18 months, whether that's a pivot or a new product. You see that in startup world. Now, in the enterprise, you can see the same thing. I noticed behind you on your whiteboard, you got a slogan that says, are you thinking big? I know Amazon likes to think big, but also you work back from the customers and, and I think this modern application thing's a big deal because I think the mindset has always been constrained because back before they moved to the cloud, most IT, and, and, and on-premise data center shops, it's slow. You gotta get the hardware, you gotta configure it, you gotta, you gotta stand it up, make sure all the software is validated on it, and loading a database and loading oss, I mean, mean, yeah, it got easier and with scripting and whatnot, but when you move to the cloud, you have more scale, which means more speed, which means it opens up their capability to think differently and build product. What are you seeing there? Can you share your opinion on that epiphany of, wow, things are going fast, I got more time to actually think about maybe doing a cloud native app or transforming this or that. What's your, what's your reaction to that? Can you share your opinion? >>Well, ultimately we, we want our customers to utilize, you know, most of our modern services, you know, applications should be microservices based. When desired, they should use serverless applic. So list technology, they should not have monolithic, you know, relational database contracts. They should use custom databases, they should use containers when needed, right? So ultimately, we want our customers to use these modern technologies to make sure that their IT infrastructure, their licensing, their, their entire IT spend is completely native to cloud technologies. They work with the speed of a startup, but it's important for them to, to, to get to the first step, right? So that's why we create this journey for our customers, where you help them migrate, give them time to build the skills, they'll help them mo modernize, take our partners along with their, along with us to, to make sure that they can address the need for our customers. That's, that's what our customers need today, and that's what we are working backwards from. >>Yeah, and I think that opens up some big ideas. I'll just say that the, you know, we're joking, I was joking the other night with someone here in, in Palo Alto around serverless, and I said, you know, soon you're gonna hear words like architectural list. And that's a criticism on one hand, but you might say, Hey, you know, if you don't really need an architecture, you know, storage lists, I mean, at the end of the day, infrastructure is code means developers can do all the it in the coding cycles and then make the operations cloud based. And I think this is kind of where I see the dots connecting. Final thought here, take us through what you're thinking around how this new world is evolving. I mean, architecturals kind of a joke, but the point is, you know, you have to some sort of architecture, but you don't have to overthink it. >>Totally. No, that's a great thought, by the way. I know it's a joke, but it's a great thought because at the end of the day, you know, what do the customers really want? They want outcomes, right? Why did service technology come? It was because there was an outcome that they needed. They didn't want to get stuck with, you know, the, the, the real estate of, of a, of a server. They wanted to use compute when they needed to, right? Similarly, what you're talking about is, you know, outcome based, you know, desire of our customers and, and, and that's exactly where the word is going to, Right? Cloud really enforces that, right? We are actually, you know, working backwards from a customer's outcome and using, using our area the breadth and depth of our services to, to deliver those outcomes, right? And, and most of our services are in that path, right? When we use VMware cloud and aws, the outcome is a, to migrate then to modernize, but doesn't stop there, use our native services, you know, get the business outcomes using this. So I think that's, that's exactly what we are going through >>Actually, should actually, you're the director of global sales and go to market for VMware cloud on Aus. I wanna thank you for coming on, but I'll give you the final minute. Give a plug, explain what is the VMware cloud on Aus, Why is it great? Why should people engage with you and, and the team, and what ultimately is this path look like for them going forward? >>Yeah. At the end of the day, we want our customers to have the best paths to the cloud, right? The, the best path to the cloud is making sure that they migrate safely, reliably, and securely as well as with speed, right? And then, you know, use that cloud platform to, to utilize AWS's native services to make sure that they modernize their IT infrastructure and applications, right? We want, ultimately that our customers, customers, customer get the best out of, you know, utilizing the, that whole application experience is enhanced tremendously by using our services. And I think that's, that's exactly what we are working towards VMware cloud AWS is, is helping our customers in that journey towards migrating, modernizing, whether they wanna exit a data center or whether they wanna modernize their applications. It's a essential first step that we wanna help our customers with >>One director of global sales and go to market with VMware cloud on neighbors. He's with aws sharing his thoughts on accelerating business transformation on aws. This is a showcase. We're talking about the future path. We're talking about use cases with success stories from customers as she's thank you for spending time today on this showcase. >>Thank you, John. I appreciate it. >>Okay. This is the cube, special coverage, special presentation of the AWS Showcase. I'm John Furrier, thanks for watching.

(upbeat music) >> Hi everyone. Welcome to theCUBE special presentation here in Palo Alto, California. I'm John Furrier, host of theCUBE. We got two great guests, one for calling in from Germany, or videoing in from Germany, one from Maryland. We've got VMware and AWS. This is the customer successes with VMware Cloud on AWS Showcase: Accelerating Business Transformation. Here in the Showcase at Samir Kadoo, worldwide VMware strategic alliance solution architect leader with AWS. Samir, great to have you. And Daniel Rethmeier, principal architect global AWS synergy at VMware. Guys, you guys are working together, you're the key players in this relationship as it rolls out and continues to grow. So welcome to theCUBE. >> Thank you, greatly appreciate it. >> Great to have you guys both on. As you know, we've been covering this since 2016 when Pat Gelsinger, then CEO, and then then CEO AWS at Andy Jassy did this. It kind of got people by surprise, but it really kind of cleaned out the positioning in the enterprise for the success of VM workloads in the cloud. VMware's had great success with it since and you guys have the great partnerships. So this has been like a really strategic, successful partnership. Where are we right now? You know, years later, we got this whole inflection point coming, you're starting to see this idea of higher level services, more performance are coming in at the infrastructure side, more automation, more serverless, I mean and AI. I mean, it's just getting better and better every year in the cloud. Kind of a whole 'nother level. Where are we? Samir, let's start with you on the relationship. >> Yeah, totally. So I mean, there's several things to keep in mind, right? So in 2016, right, that's when the partnership between AWS and VMware was announced. And then less than a year later, that's when we officially launched VMware Cloud on AWS. Years later, we've been driving innovation, working with our customers, jointly engineering this between AWS and VMware. You know, one of the key things... Together, day in, day out, as far as advancing VMware Cloud on AWS. You know, even if you look at the innovation that takes place with the solution, things have modernized, things have changed, there's been advancements. You know, whether it's security focus, whether it's platform focus, whether it's networking focus, there's been modifications along the way, even storage, right, more recently. One of the things to keep in mind is we're looking to deliver value to our customers together. These are our joint customers. So there's hundreds of VMware and AWS engineers working together on this solution. And then factor in even our sales teams, right? We have VMware and AWS sales teams interacting with each other on a constant daily basis. We're working together with our customers at the end of the day too. Then we're looking to even offer and develop jointly engineered solutions specific to VMware Cloud on AWS. And even with VMware to other platforms as well. Then the other thing comes down to is where we have dedicated teams around this at both AWS and VMware. So even from solutions architects, even to our sales specialists, even to our account teams, even to specific engineering teams within the organizations, they all come together to drive this innovation forward with VMware Cloud on AWS and the jointly engineered solution partnership as well. And then I think one of the key things to keep in mind comes down to we have nearly 600 channel partners that have achieved VMware Cloud on AWS service competency. So think about it from the standpoint, there's 300 certified or validated technology solutions, they're now available to our customers. So that's even innovation right off the top as well. >> Great stuff. Daniel, I want to get to you in a second upon this principal architect position you have. In your title, you're the global AWS synergy person. Synergy means bringing things together, making it work. Take us through the architecture, because we heard a lot of folks at VMware explore this year, formerly VMworld, talking about how the workloads on IT has been completely transforming into cloud and hybrid, right? This is where the action is. Where are you? Is your customers taking advantage of that new shift? You got AIOps, you got ITOps changing a lot, you got a lot more automation, edges right around the corner. This is like a complete transformation from where we were just five years ago. What's your thoughts on the relationship? >> So at first, I would like to emphasize that our collaboration is not just that we have dedicated teams to help our customers get the most and the best benefits out of VMware Cloud and AWS, we are also enabling us mutually. So AWS learns from us about the VMware technology, where VMware people learn about the AWS technology. We are also enabling our channel partners and we are working together on customer projects. So we have regular assembles globally and also virtually on Slack and the usual suspect tools working together and listening to customers. That's very important. Asking our customers where are their needs? And we are driving the solution into the direction that our customers get the best benefits out of VMware Cloud on AWS. And over the time, we really have involved the solution. As Samir mentioned, we just added additional storage solutions to VMware Cloud on AWS. We now have three different instance types that cover a broad range of workloads. So for example, we just edited the I4i host, which is ideally for workloads that require a lot of CPU power, such as, you mentioned it, AI workloads. >> Yeah, so I want to get us just specifically on the customer journey and their transformation, you know, we've been reporting on Silicon angle in theCUBE in the past couple weeks in a big way that the ops teams are now the new devs, right? I mean that sounds a little bit weird, but IT operations is now part of a lot more DataOps, security, writing code, composing. You know, with open source, a lot of great things are changing. Can you share specifically what customers are looking for when you say, as you guys come in and assess their needs, what are they doing, what are some of the things that they're doing with VMware on AWS specifically that's a little bit different? Can you share some of and highlights there? >> That's a great point, because originally, VMware and AWS came from very different directions when it comes to speaking people and customers. So for example, AWS, very developer focused, whereas VMware has a very great footprint in the ITOps area. And usually these are very different teams, groups, different cultures, but it's getting together. However, we always try to address the customer needs, right? There are customers that want to build up a new application from the scratch and build resiliency, availability, recoverability, scalability into the application. But there are still a lot of customers that say, "Well, we don't have all of the skills to redevelop everything to refactor an application to make it highly available. So we want to have all of that as a service. Recoverability as a service, scalability as a service. We want to have this from the infrastructure." That was one of the unique selling points for VMware on-premise and now we are bringing this into the cloud. >> Samir, talk about your perspective. I want to get your thoughts, and not to take a tangent, but we had covered the AWS re:MARS, actually it was Amazon re:MARS, machine learning automation, robotics and space was really kind of the confluence of industrial IoT, software, physical. And so when you look at like the IT operations piece becoming more software, you're seeing things about automation, but the skill gap is huge. So you're seeing low code, no code, automation, you know, "Hey Alexa, deploy a Kubernetes cluster." Yeah, I mean that's coming, right? So we're seeing this kind of operating automation meets higher level services, meets workloads. Can you unpack that and share your opinion on what you see there from an Amazon perspective and how it relates to this? >> Yeah. Yeah, totally, right? And you know, look at it from the point of view where we said this is a jointly engineered solution, but it's not migrating to one option or the other option, right? It's more or less together. So even with VMware Cloud on AWS, yes it is utilizing AWS infrastructure, but your environment is connected to that AWS VPC in your AWS account. So if you want to leverage any of the native AWS services, so any of the 200 plus AWS services, you have that option to do so. So that's going to give you that power to do certain things, such as, for example, like how you mentioned with IoT, even with utilizing Alexa, or if there's any other service that you want to utilize, that's the joining point between both of the offerings right off the top. Though with digital transformation, right, you have to think about where it's not just about the technology, right? There's also where you want to drive growth in the underlying technology even in your business. Leaders are looking to reinvent their business, they're looking to take different steps as far as pursuing a new strategy, maybe it's a process, maybe it's with the people, the culture, like how you said before, where people are coming in from a different background, right? They may not be used to the cloud, they may not be used to AWS services, but now you have that capability to mesh them together. >> Okay. >> Then also- >> Oh, go ahead, finish your thought. >> No, no, no, I was going to say what it also comes down to is you need to think about the operating model too, where it is a shift, right? Especially for that vStor admin that's used to their on-premises environment. Now with VMware Cloud on AWS, you have that ability to leverage a cloud, but the investment that you made and certain things as far as automation, even with monitoring, even with logging, you still have that methodology where you can utilize that in VMware Cloud on AWS too. >> Daniel, I want to get your thoughts on this because at Explore and after the event, as we prep for CubeCon and re:Invent coming up, the big AWS show, I had a couple conversations with a lot of the VMware customers and operators, and it's like hundreds of thousands of users and millions of people talking about and peaked on VMware, interested in VMware. The common thread was one person said, "I'm trying to figure out where I'm going to put my career in the next 10 to 15 years." And they've been very comfortable with VMware in the past, very loyal, and they're kind of talking about, I'm going to be the next cloud, but there's no like role yet. Architects, is it solution architect, SRE? So you're starting to see the psychology of the operators who now are going to try to make these career decisions. Like what am I going to work on? And then it's kind of fuzzy, but I want to get your thoughts, how would you talk to that persona about the future of VMware on, say, cloud for instance? What should they be thinking about? What's the opportunity? And what's going to happen? >> So digital transformation definitely is a huge change for many organizations and leaders are perfectly aware of what that means. And that also means to some extent, concerns with your existing employees. Concerns about do I have to relearn everything? Do I have to acquire new skills and trainings? Is everything worthless I learned over the last 15 years of my career? And the answer is to make digital transformation a success, we need not just to talk about technology, but also about process, people, and culture. And this is where VMware really can help because if you are applying VMware Cloud on AWS to your infrastructure, to your existing on-premise infrastructure, you do not need to change many things. You can use the same tools and skills, you can manage your virtual machines as you did in your on-premise environment, you can use the same managing and monitoring tools, if you have written, and many customers did this, if you have developed hundreds of scripts that automate tasks and if you know how to troubleshoot things, then you can use all of that in VMware Cloud on AWS. And that gives not just leaders, but also the architects at customers, the operators at customers, the confidence in such a complex project. >> The consistency, very key point, gives them the confidence to go. And then now that once they're confident, they can start committing themselves to new things. Samir, you're reacting to this because on your side, you've got higher level services, you've got more performance at the hardware level. I mean, a lot improvements. So, okay, nothing's changed, I can still run my job, now I got goodness on the other side. What's the upside? What's in it for the customer there? >> Yeah, so I think what it comes down to is they've already been so used to or entrenched with that VMware admin mentality, right? But now extending that to the cloud, that's where now you have that bridge between VMware Cloud on AWS to bridge that VMware knowledge with that AWS knowledge. So I will look at it from the point of view where now one has that capability and that ability to just learn about the cloud. But if they're comfortable with certain aspects, no one's saying you have to change anything. You can still leverage that, right? But now if you want to utilize any other AWS service in conjunction with that VM that resides maybe on-premises or even in VMware Cloud on AWS, you have that option to do so. So think about it where you have that ability to be someone who's curious and wants to learn. And then if you want to expand on the skills, you certainly have that capability to do so. >> Great stuff, I love that. Now that we're peeking behind the curtain here, I'd love to have you guys explain, 'cause people want to know what's goes on behind the scenes. How does innovation get happen? How does it happen with the relationships? Can you take us through a day in the life of kind of what goes on to make innovation happen with the joint partnership? Do you guys just have a Zoom meeting, do you guys fly out, you write code, go do you ship things? I mean, I'm making it up, but you get the idea. How does it work? What's going on behind the scenes? >> So we hope to get more frequently together in-person, but of course we had some difficulties over the last two to three years. So we are very used to Zoom conferences and Slack meetings. You always have to have the time difference in mind if you are working globally together. But what we try, for example, we have regular assembles now also in-person, geo-based, so for AMEA, for the Americas, for APJ. And we are bringing up interesting customer situations, architectural bits and pieces together. We are discussing it always to share and to contribute to our community. >> What's interesting, you know, as events are coming back, Samir, before you weigh in this, I'll comment as theCUBE's been going back out to events, we're hearing comments like, "What pandemic? We were more productive in the pandemic." I mean, developers know how to work remotely and they've been on all the tools there, but then they get in-person, they're happy to see people, but no one's really missed the beat. I mean, it seems to be very productive, you know, workflow, not a lot of disruption. More, if anything, productivity gains. >> Agreed, right? I think one of the key things to keep in mind is even if you look at AWS's, and even Amazon's leadership principles, right? Customer obsession, that's key. VMware is carrying that forward as well. Where we are working with our customers, like how Daniel said and meant earlier, right? We might have meetings at different time zones, maybe it's in-person, maybe it's virtual, but together we're working to listen to our customers. You know, we're taking and capturing that feedback to drive innovation in VMware Cloud on AWS as well. But one of the key things to keep in mind is yes, there has been the pandemic, we might have been disconnected to a certain extent, but together through technology, we've been able to still communicate, work with our customers, even with VMware in between, with AWS and whatnot, we had that flexibility to innovate and continue that innovation. So even if you look at it from the point of view, right? VMware Cloud on AWS Outposts, that was something that customers have been asking for. We've been able to leverage the feedback and then continue to drive innovation even around VMware Cloud on AWS Outposts. So even with the on-premises environment, if you're looking to handle maybe data sovereignty or compliance needs, maybe you have low latency requirements, that's where certain advancements come into play, right? So the key thing is always to maintain that communication track. >> In our last segment we did here on this Showcase, we listed the accomplishments and they were pretty significant. I mean geo, you got the global rollouts of the relationship. It's just really been interesting and people can reference that, we won't get into it here. But I will ask you guys to comment on, as you guys continue to evolve the relationship, what's in it for the customer? What can they expect next? Because again, I think right now, we're at an inflection point more than ever. What can people expect from the relationship and what's coming up with re:Invent? Can you share a little bit of kind of what's coming down the pike? >> So one of the most important things we have announced this year, and we will continue to evolve into that direction, is independent scale of storage. That absolutely was one of the most important items customer asked for over the last years. Whenever you are requiring additional storage to host your virtual machines, you usually in VMware Cloud on AWS, you have to add additional nodes. Now we have three different node types with different ratios of compute, storage, and memory. But if you only require additional storage, you always have to get also additional compute and memory and you have to pay for it. And now with two solutions which offer choice for the customers, like FS6 wanted a ONTAP and VMware Cloud Flex Storage, you now have two cost effective opportunities to add storage to your virtual machines. And that offers opportunities for other instance types maybe that don't have local storage. We are also very, very keen looking forward to announcements, exciting announcements, at the upcoming events. >> Samir, what's your reaction take on what's coming down on your side? >> Yeah, I think one of the key things to keep in mind is we're looking to help our customers be agile and even scaled with their needs, right? So with VMware Cloud on AWS, that's one of the key things that comes to mind, right? There are going to be announcements, innovations, and whatnot with upcoming events. But together, we're able to leverage that to advance VMware cloud on AWS. To Daniel's point, storage for example, even with host offerings. And then even with decoupling storage from compute and memory, right? Now you have the flexibility where you can do all of that. So to look at it from the standpoint where now with 21 regions where we have VMware Cloud on AWS available as well, where customers can utilize that as needed when needed, right? So it comes down to, you know, transformation will be there. Yes, there's going to be maybe where workloads have to be adapted where they're utilizing certain AWS services, but you have that flexibility and option to do so. And I think with the continuing events, that's going to give us the options to even advance our own services together. >> Well you guys are in the middle of it, you're in the trenches, you're making things happen, you've got a team of people working together. My final question is really more of a kind of a current situation, kind of future evolutionary thing that you haven't seen this before. I want to get both of your reaction to it. And we've been bringing this up in the open conversations on theCUBE is in the old days, let's go back this generation, you had ecosystems, you had VMware had an ecosystem, AWS had an ecosystem. You know, we have a product, you have a product, biz dev deals happen, people sign relationships, and they do business together and they sell each other's products or do some stuff. Now it's more about architecture, 'cause we're now in a distributed large scale environment where the role of ecosystems are intertwining and you guys are in the middle of two big ecosystems. You mentioned channel partners, you both have a lot of partners on both sides, they come together. So you have this now almost a three dimensional or multidimensional ecosystem interplay. What's your thoughts on this? Because it's about the architecture, integration is a value, not so much innovations only. You got to do innovation, but when you do innovation, you got to integrate it, you got to connect it. So how do you guys see this as an architectural thing, start to see more technical business deals? >> So we are removing dependencies from individual ecosystems and from individual vendors. So a customer no longer has to decide for one vendor and then it is a very expensive and high effort project to move away from that vendor, which ties customers even closer to specific vendors. We are removing these obstacles. So with VMware Cloud on AWS, moving to the cloud, firstly it's not a dead end. If you decide at one point in time because of latency requirements or maybe some compliance requirements, you need to move back into on-premise, you can do this. If you decide you want to stay with some of your services on-premise and just run a couple of dedicated services in the cloud, you can do this and you can man manage it through a single pane of glass. That's quite important. So cloud is no longer a dead end, it's no longer a binary decision, whether it's on-premise or the cloud, it is the cloud. And the second thing is you can choose the best of both worlds, right? If you are migrating virtual machines that have been running in your on-premise environment to VMware Cloud on AWS either way in a very, very fast cost effective and safe way, then you can enrich, later on enrich these virtual machines with services that are offered by AWS, more than 200 different services ranging from object-based storage, load balancing, and so on. So it's an endless, endless possibility. >> We call that super cloud in the way that we generically defining it where everyone's innovating, but yet there's some common services. But the differentiation comes from innovation where the lock in is the value, not some spec, right? Samir, this is kind of where cloud is right now. You guys are not commodity, amazon's completely differentiating, but there's some commodity things happen. You got storage, you got compute, but then you got now advances in all areas. But partners innovate with you on their terms. >> Absolutely. >> And everybody wins. >> Yeah, I 100% agree with you. I think one of the key things, you know, as Daniel mentioned before, is where it's a cross education where there might be someone who's more proficient on the cloud side with AWS, maybe more proficient with the VMware's technology. But then for partners, right? They bridge that gap as well where they come in and they might have a specific niche or expertise where their background, where they can help our customers go through that transformation. So then that comes down to, hey, maybe I don't know how to connect to the cloud, maybe I don't know what the networking constructs are, maybe I can leverage that partner. That's one aspect to go about it. Now maybe you migrated that workload to VMware Cloud on AWS. Maybe you want to leverage any of the native AWS services or even just off the top, 200 plus AWS services, right? But it comes down to that skillset, right? So again, solutions architecture at the back of the day, end of the day, what it comes down to is being able to utilize the best of both worlds. That's what we're giving our customers at the end of the day. >> I mean, I just think it's a refactoring and innovation opportunity at all levels. I think now more than ever, you can take advantage of each other's ecosystems and partners and technologies and change how things get done with keeping the consistency. I mean, Daniel, you nailed that, right? I mean you don't have to do anything. You still run it. Just spear the way you're working on it and now do new things. This is kind of a cultural shift. >> Yeah, absolutely. And if you look, not every customer, not every organization has the resources to refactor and re-platform everything. And we give them a very simple and easy way to move workloads to the cloud. Simply run them and at the same time, they can free up resources to develop new innovations and grow their business. >> Awesome. Samir, thank you for coming on. Daniel, thank you for coming to Germany. >> Thank you. Oktoberfest, I know it's evening over there, weekend's here. And thank you for spending the time. Samir, give you the final word. AWS re:Invent's coming up. We're preparing, we're going to have an exclusive with Adam, with Fryer, we'd do a curtain raise, and do a little preview. What's coming down on your side with the relationship and what can we expect to hear about what you got going on at re:Invent this year? The big show? >> Yeah, so I think Daniel hit upon some of the key points, but what I will say is we do have, for example, specific sessions, both that VMware's driving and then also that AWS is driving. We do have even where we have what are called chalk talks. So I would say, and then even with workshops, right? So even with the customers, the attendees who are there, whatnot, if they're looking to sit and listen to a session, yes that's there, but if they want to be hands-on, that is also there too. So personally for me as an IT background, been in sysadmin world and whatnot, being hands-on, that's one of the key things that I personally am looking forward. But I think that's one of the key ways just to learn and get familiar with the technology. >> Yeah, and re:Invent's an amazing show for the in-person. You guys nail it every year. We'll have three sets this year at theCUBE and it's becoming popular. We have more and more content. You guys got live streams going on, a lot of content, a lot of media. So thanks for sharing that. Samir, Daniel, thank you for coming on on this part of the Showcase episode of really the customer successes with VMware Cloud on AWS, really accelerating business transformation with AWS and VMware. I'm John Furrier with theCUBE, thanks for watching. (upbeat music)

(upbeat music) >> Welcome back everyone to Supercloud22, I'm John Furrier, host of theCUBE here in Palo Alto. For this next ecosystem's segment we have Muddu Sudhakar, who is the co-founder and CEO of Aisera, a friend of theCUBE, Cube alumni, serial entrepreneur, multiple exits, been on multiple times with great commentary. Muddu, thank you for coming on, and supporting our- >> Also thank you for having me, John. >> Yeah, thank you. Great handshake there, I love to do it. One, I wanted you here because, two reasons, one is, congratulations on your new funding. >> Thank you. >> For $90 million, Series D funding. >> Series D funding. >> So, huge validation in this market. >> It is. >> You have been experienced software so, it's a real testament to your team. But also, you're kind of in the Supercloud vortex. This new wave that Supercloud is part of is, I call it the pretext to what's coming with multi-clouds. It is the next level. >> I see. >> Structural change and we have been reporting on it, Dave and I, and we are being challenged. So, we decided to open it up. >> Very good, I would love it. >> And have a conversation rather than waiting eight months to prove that we are right. Which, we are right, but that is a long story. >> You're always right. (both laughs) >> What do you think of Supercloud, that's going on? What is the big trend? Because its public cloud is great, so there is no conflict there. >> Right. >> It's got great business, it's integrated, IaaS, to SaaS, PaaS, all in the beginning, or the middle. All that is called good. Now you have on-premise high rate cloud. >> Right. >> Edge is right around the corner. Exploding in new capabilities. So, complexity is still here. >> That's right, I think, you nailed it. We talk about hybrid cloud, and multi cloud. Supercloud is kind of elevates the message even better. Because you still have to leave for some of our clouds, public clouds. There will be some of our clouds, still running on the Edge. That's where, the Edge cloud comes in. Some will still be on-prem. So, the Supercloud as a concept is beyond hybrid and multi cloud. To me, I will run some of our cloud on Amazon. Some could be on Aisera, some could be running only on Edge, right? >> Mm hm >> And we still have, what we call remote executors. Some leaders of service now. You have, what we call the mid-server, is what I think it was called. Where you put in a small code and run it. >> Yeah. >> So, I think all those things will be running on-prem environment and VMware cloud, et cetera. >> And if you look back at, I think it has been five years now, maybe four or five years since Andy Jassy at reInvent announced Outposts. Think that was the moment in time that Dave and I took this pause back and said "Okay, that's Amazon." who listens to their customers. Acknowledging Hybrid. >> Right. >> Then we saw the rise of Snowflakes, the Databricks, specialty clouds. You start to see people who are building on top of AWS. But at MongoDB, it is a database, now they are a full blown, large scale data platform. These companies took advantage of the public cloud to build, as Jerry Chen calls it, "Castles in the cloud." >> Right. >> That seems to be happening in all areas. What do you think about that? >> Right, so what is driving the cloud? To me, we talk about machine learning in AI, right? Versus clouded options. We used to call it lift and shift. The outposts and lift and shift. Initially this was to get the data into the cloud. I think if you see, the vendor that I like the most, is, I'm not picking any favorite but, Microsoft Azure, they're thinking like your Supercloud, right? Amazon is other things, but Azure is a lot more because they run on-prem. They are also on Azure CloudFront, Amazon CloudFront. So I think, Azure and Amazon are doing a lot more in the area of Supercloud. What is really helping is the machine learning environment, needs Superclouds. Because I will be running some on the Edge, some compute, some will be running on the public cloud, some could be running on my data center. So, I think the Supercloud is really suited for AI and automation really well. >> Yeah, it is a good point about Microsoft, too. And I think Microsoft's existing install base saved Azure. >> Okay. >> They brought Office 365, Sequel Server, cause their customers weren't leaving Microsoft. They had the productivity thing nailed down as well as the ability to catch up >> That's right. >> To AWS. So, natural extension to on-premise with Microsoft. >> I think... >> Tell us- >> Your Supercloud is what Microsoft did. Right? Azure. If you think of, like, they had an Office 365, their SharePoint, their Dynamics, taking all of those properties, running on the Azure. And still giving the migration path into a data center. Is Supercloud. So, the early days Supercloud came from Azure. >> Well, that's a good point, we will certainly debate that. I will also say that Snowflake built on AWS. >> That's right. >> Okay, and became a super powerhouse with the data business. As did Databricks. >> That's right. >> Then went to Azure >> That's right. >> So, you're seeing kind of the Playbook. >> Right. >> Go fast on Cloud Native, the native cloud. Get that fly wheel going, then get going, somewhere else. >> It is, and to that point I think you and me are talking, right? If you are to start at one cloud and go to another cloud, the amount of work as a vendor for us to use for implement. Today, like we use all three clouds, including the Gov Cloud. It's a lot of work. So, what will happen, the next toolkit we use? Even services like Elastic. People will not, the word commoditize, is not the word, but people will create an abstraction layer, even for S3. >> Explain that, explain that in detail. So, elastic? What do you mean by that? >> Yeah, so what that means is today, Elasticsearch, if you do an Elasticsearch on Amazon, if I go to Azure, I don't want enter another Elasticsearch layer. Ideally I want us to write an abstracted search layer. So, that when I move my services into a different cloud I don't want to re-compute and re-calculate everything. That's a lot of work. Particularly once you have a production customer, if I were to shift the workloads, even to the point of infrastructure, take S3, if I read infrastructure to S3 and tomorrow I go to Azure. Azure will have its own objects store. I don't want to re-validate that. So what will happen is digital component, Kubernetes is already there, we want storage, we want network layer, we want VPM services, elastic as well as all fundamental stuff, including MongoDB, should be abstracted to run. On the Superclouds. >> Okay, well that is a little bit of a unicorn fantasy. But let's break that down. >> Sure. >> Do you think that's possible? >> It is. Because I think, if I am on MongoDB, I should be able to give a horizontal layer to MongoDB that is optimized for all three of them. I don't want MongoDB. >> First of all, everyone will buy that. >> Sure. >> I'm skeptical that that's possible. Given where we are at right now. So, you're saying that a vendor will provide an abstraction layer. >> No, I'm saying that either MongoDB, itself will do it, or a third party layer will come as a service which will abstract all this layer so that we will write to an AP layer. >> So what do you guys doing? How do you handle multiple clouds? You guys are taking that burden on, because it makes sense, you should build the abstraction layer. Not rely on a third party vendor right? >> We are doing it because there is no third party available offer it. But if you offer a third party tomorrow, I will use that as a Supercloud service. >> If they're 100% reliable? >> That's right. That's exactly it. >> They have to do the work. >> They have to do the work because if today I am doing it because no one else is offering it- >> Okay so what people might not know is that you are an angel investor as well as an entrepreneur been very successful, so you're rich, you have a lot of money. If I were a startup and I said, Muddu, I want to build this abstraction layer. What would be funding advice that you would give me as an entrepreneur? As a company to do that? >> I would do it like an Apigee that Google acquired, you should create an Apigee-like layer, for infrastructure upfront services, I think that is a very good option. >> And you think that is viable? >> It is very much viable. >> Would that be part of Supercloud architecture, in your opinion? >> It is. Right? And that will abstract all the clouds to some level. Like it is like Kubernetes abstract, so that if I am running on Kubernetes I can transfer to any cloud. >> Yeah >> But that should go from computer into other infrastructures. >> It's seems to me, Muddu, and I want to get your thoughts about this whole Supercloud defacto standard opportunity. It feels like we are waiting for a moment where there is some sort of defacto unification, whether it is in the distraction layer, or a standards body. There is no W3C here going on. I mean, W3C was for web consortium, for world wide web. The Supercloud seems to be having the same impact the web had. Transformative, disruptive, re-factoring business operations. Is there a standardized body or an opportunity for a defacto? Like Kubernetes was a great example of a unification around something for orchestration. Is there a better version in the Supercloud model where we need a standard? >> Yes and no. The reason is because by the time you come to standard, take time to look what happened. First, we started with VMs, then became Docker and Containers then we came to Kubernetes. So it goes through a journey. I think the next few years will be stood on SuperCloud let's make customers happy, let's make enough services going, and then the standards will come. Standards will be almost 2-3 years later. So I don't think standards should happen right now. Right now, all we need is, we need enough start ups to create the super layer abstraction, with the goal in mind of AI automation. The reason, AI is because AI needs to be able to run that. Automated because running a work flow is, I can either run a workflow in the cloud services, I can run it on on-prem, I can run it on database, so you have two good applications, take AI and automation with Supercloud and make enough enough noise on that make enough applications, then the standards will come. >> On this project we have been with SuperCloud these past day we have heard a lot of people talking. The themes that developers are okay, they are doing great. Open source is booming. >> Yes >> Cloud Native's got major traction. Developers are going fast and they love it, shifting left, all these great things. They're putting a lot of data, DevOps and the security teams, they're the ones who are leveling up. We are hearing a lot of conversations around how they can be faster. What is your view on this as relative to that Supercloud nirvana getting there? How are DevOps and security teams leveling up to devs? >> A couple of things. I think that in the world of DevSecOps and security ops. The reason security is important, right? Given what is going on, but you don't need to do security the manual way. I think that whole new operation that you and me talked about, AI ops should happen. Where the AI ops is for service operation, for performance, for incident or for security. Nobody thinks of AI security. So, the DevOps people should think more world of AI ops, so that I can predict, prevent things before they happen. Then the security will be much better. So AI ops with Supercloud will probably be that nirvana. But that is what should happen. >> In the AI side of things, what you guys are doing, what are you learning, on scale, relative to data? Is there, you said machine learning needs data, it needs scale operation. What's your view on the automation piece of all this? >> I think to me, the data is the single, underrated, unsung kind of hero in the whole machine learning. Everyone talks about AI and machine learning algorithms. Algorithms are as important, but even more important is data. Lack of data I can't do algorithms. So my advice to customers is don't lose your data. That is why I see, Frank, my old boss, setting everything up into the data cloud, in Snowflake. Data is so important, store the data, analyze the data. Data is the new AI. You and me talk so many times- >> Yeah >> It's underrated, people are not anticipating how important it is. But the data is coming from logs, events, whether there is knowledge documents, any data in any form. I think keep the data, analyze the data, data patterns, and then things like SuperCloud can really take advantage of that. >> So, in the Supercloud equation one of the things that has come up is that the native clouds do great. Their IaaS to SaaS is interactions that solve a lot of problems. There is integration that is good. >> Right. >> Now when you go off cloud, you get regions, get latency issues- >> Right >> You have more complexity. So what's the trade off in the Supercloud journey, if you had to guess? And just thinking out loud here, what would be some of the architectural trade offs of how you do it, what's the sequence? What's the order of operations to get Superclouding going? >> Yeah, very good questions here. I think once you start going from the public cloud, the clouds there scale to lets say, even a regional data center onto an Edge, latency will kick in. The lack of computer function will kick in. So there I think everything should become asynchronous, right? You will run the application in a limited environment. You should anticipate for small memories, small compute, long latencies, but still following should happen. So some operations should become the old-school following, like, it's like the email. I send an email, it's an asynchronous thing, I made a sponsor, I think most of message passing should go back to the old-school architectures They should become asynchronous where thing can rely. I think, as long as algorithms can take that into Edge, I think that Superclouds can really bridge between the public cloud to the edge. >> Muddu, thanks for coming, we really appreciate your insights here. You've always been a great friend, great commentator. If you weren't the CEO and a famous angel investor, we would certainly love to have you as a theCUBE analyst, here on theCUBE. >> I am always available for you. (John laughs) >> When you retire, you can come back. Final point, we've got time left. We'll give you a chance to talk about the company. I'm really intrigued by the success of your ninety million dollar financing realm because we are in a climate where people aren't getting those kinds of investments. It's usually down-rounds. >> Okay >> 409 adjustments, people are struggling. You got an up-round and you got a big number. Why the success? What is going on with the company? Why are you guys getting such great validation? Goldman Sachs, Thoma Bravo, Zoom, these are big names, these are the next gen winners. >> It is. >> Why are they picking you? Why are they investing in you? >> I think it is not one thing, it is many things. First all, I think it is a four-year journey for us where we are right now. So, the company started late 2017. It is getting the right customers, partners, employees, team members. So it is a lot hard work went in. So a lot of thanks to the Aisera community for where we are. Why customers and where we are? Look, fundamentally there is a problem to solve. Like, what Aisera is trying to solve is can we automate customer service? Whether internal employees, external customer support. Do it for IT, HR, sales, marketing, all the way to ops. Like you talk about DevSecOps, I don't want thousands of tune ups for ops. If I can make that job better, >> Yeah >> I want to, any job I want to automate. I call it, elevate the human, right? >> Yeah. >> And that's the reason- >> 'Cause you're saying people have to learn specialty tools, and there are consequences to that. >> Right, and to me, people should focus on more important tasks and use AI as a tool to automate those things right? It's like thinking of offering Apple City as Alexa as a service, that is how we are trying to offer customer service, like, right? And if it can do that consistently, and reduce costs, cost is a big reason why customers like us a lot, we have eliminated the cost in this down economy, I will amplify our message even more, right? I am going to take a bite out of their expense. Whether it is tool expense, it's on resources. Second, is user productivity And finally, experience. People want experience. >> Final question, folks out there, first of all, what do you think about Supercloud? And if someone asks you what is this Supercloud thing? How would you answer? >> Supercloud, is, to me, beyond multi cloud and hybrid cloud. It is to bridge applications that are build in Supercloud can run on all clouds seamlessly. You don't need to compile them, re-clear them. Supercloud is one place to build, develop, and deploy. >> Great, Muddu. Thank you for coming on. Supercloud22 here breaking it down with the ecosystem commentary, we have a lot of people coming to the small group of experts in our network, bringing you in open conversation around the future of cloud computing and applications globally. And again, it is all about the next generation cloud. This is theCUBE, thanks for watching. (upbeat music)

(gentle music) >> Hey, everyone. Welcome back to New York City. Lisa Martin and John Furrier here with theCUBE, covering AWS Summit NYC. This is a series of summits this year. There's about 15 of them globally. We are excited to be here with a couple of guests. We have an alumni back with us. Couple of guests from Caylent, Stephen Garden joins us, the Executive Chairman, and Valerie Henderson, Chief Revenue Officer. Guys, welcome to the program. >> Thank you. >> Thank you. Thank you for having us. >> Great to have you, welcome back. >> Appreciate it, from 2016. >> 2016, it's been a minute. >> Yep. >> But that was before Caylent. Talk to us about Caylent, what do you guys do? What do you deliver? How are you affiliated with AWS? >> Sure, so we were founded in 2015, initially as a container management product. So our roots are very deeply centered around Cloud native. We've since evolved and become a Cloud native consultancy. We're all in with AWS. We were actually just awarded AWS Premier Partner a couple of weeks ago, so we're pretty pumped about that, but we're about 250 people now, across North and South America. And our goal is really to work with customers that are looking to innovate and evolve and use AWS as a catalyst to build new products for their business. >> As a catalyst, I like that. Valerie, talk about the customer. Obviously so much tumbled in the last couple of years. Still going through it. >> Yeah, of course. >> How have customer conversations evolved and changed in the last couple of years, from your perspective? >> Yeah, I think from my perspective it is such a unique time and it's a time that is constantly changing. And I think change breeds opportunity, and I feel like customers see that, and they're leaning in. They want the opportunity to create new revenue streams, do more, more efficiently, and I think that's the key. And the questions are really asking, how can we take our data, and turn it into something that we can monetize? How can we be smarter with what we have? And I think it's an incredible time to be in the space that we're in. Every conversation I have is really forward thinking, and about the business. And I've been in this space for a while, and that was not always that case. And I think now people are shifting that IT shop to IP shop, and that's so key, from my perspective. >> Interesting, interesting shift there. Every company has to be a data company these days, to be competitive, the last couple of years it was, how did we survive? Pivot, pivot, pivot. But to be a data company, means you have to be able to extract the value and insights from that data and act on it, to your point, develop new products, new revenue streams, new opportunities. How do you enable companies, and maybe this is a question that you can both answer, to truly become data companies? >> The whole model from a service's perspective is not a do-for model, it is a do-with model. And any time we go into a customer, it's like, where are they on the curve? From monolith application, to microservices, where do they sit today? And I think when you dig in, you assess, you deeply understand where they are, you can get them to where they want to be, and build a plan. And the way our model works is, we're doing it with them, and what that means is we're enabling them, documentation, we're supporting them, that if we're not there, they're going to be able to carry it forward and continue to do more. So, that's so so important. I'd love Stephen's take on it. >> Yeah, I think the other trend that we're seeing in data more recently is that customers need to share their information with other partners, collaborate. And AWS is just the perfect platform to be able to do that, enable that sharing. And you're seeing even businesses like Snowflake build a data Cloud on top of AWS. So, I think that's a new angle that we're seeing which is really bringing together way more innovation- >> What about that data clean-room trend that's going on, Snowflake's doing a lot of that. But some of them have a little lock in spec there, versus being open, security, privacy, governance, what's the balance between open sharing and the requirements you need to be secure and compliant? >> Yeah, I think very simplistically, the information that you are using to deliver your product and service to customers generally safer, more public and available, the information that's confidential to your business behind the scenes, obviously, you use the right protocols to lock it out. But it is a very hot topic in today's world, especially with Web3 and people seeking to get their information back, so... >> So you mentioned you guys around since 2015, if you go back in time, it seems like yesterday, but Cloud time, it's like two generations ago. Why is data now more relevant? Is it because the technology's gotten better and easier, or more maturization of the client's understanding, or being full with data, having a data problem and hence an opportunity? Or is it open source has evolved? Or all three, what's your reaction to that? Why is it exploding now when it's been around for a while? >> It keeps exponentially growing, right? The more and more data. There was a stat four or five years ago about, hey, we're taking more photographs in a single year now than all of mankind, leading up to that date, but I think just the sheer quantities and the way people are managing it now, and being able to actually capture information points of everything across their entire business, just presents a much bigger opportunity to be able to take and form decisions of the back of that. >> So do you see the customers having more data full problems, that they're having more data? So that's... And in that one >> 100%. >> Of the consequences of not leveraging it? >> Yeah, it's what to do. Yeah, absolutely, and if you think about when you wake up in the morning if you ask Alexa what the weather is, and like, you're creating data, in every engagement with the world. So I think it's this explosion of it, but then it exists, and what do you do, and having a strategy. I still think one of the biggest gaps is people, and talent, and expertise to do the work, frankly. Which is, the hypothesis of Caylent existing. >> Yeah, I think a data concept and application, because what's the weather to Alexa, is an application of what's the weather, it's a request, but it's actually the data's built into the app. >> It's built in. >> So data as code is a new trend. >> Yes, yeah, yeah, and I think it's funny to answer the question. There's more data points surrounding how to leverage your data, and I'm like, it's crazy, I think you're really seeing that working- >> We have an old data warehouse, we can't get the weather data, although it's there somewhere. But that's the problem. Getting the data, in the applications, this is not... Wasn't around 10 years ago. No one was talking like that. Now it's more standard. That sounds like DevOps to me, a DevOps problem. >> Yeah, moving from the monolithic to the microservice is wild, and just the way that people are building applications today. The users, their customers are demanding more from the service, and AWS is able to deliver that. >> What are some of your customers doing with you guys, can you give some examples and scope the scale of your relationship with the customers, vis-a-vis AWS and the Cloud, how they're using you guys and the Cloud. >> Yeah, yeah, for sure, a customer of ours, Allergen, which is an incredible organization, really had a large effort to modernize. And they actually have a data lab within their company called Allergen Data Labs, and they leveraged us to truly just modernize this containerization effort. How they can do more with less, and that serverless experience. So, I think from my perspective what we're seeing is also a need to be thoughtful about DevOps retooling and tooling because talent wants to work with the best toolset, the hottest stuff on the street, and again, to keep talent is key, in any organization's success. >> Valerie, how does Caylent help with that from a talent perspective? Obviously there's talent shortage, we're also still in the great resignation. >> Oh my gosh. >> How do you help organizations bridge the gap so that they can glean insights from data and be competitive and win? >> Yeah, we actually just published a case study with Novus which was bought by SEI, which is a huge financial firm. Where they said, "Listen, it's human nature to say I have a gap, and I need to fill it, I'm going to hire somebody." That's human nature to say, okay, this is what we're going to do. But the reality is, I think companies are starting to see the advantage of using a partner and say, okay, I could hire one person or I could bring in a partner who's going to have a team of five, works incrementally for a period of time, does with, helps coach my team up, document all of that, and I think that they're seeing value from that. And ultimately, it's not that we don't want them to eventually hire. When they do hire, we want that person to come in and have the best experience. >> And sometimes the people aren't even available, right? >> Correct, yeah. >> So you have a combination of managed services, a plethora of managed services that are also involved with the customers. So, it's that integration, scale, and partnering and sharing. You mentioned sharing data earlier, how do you guys view that integration piece, 'cause if you have a modern architecture, you got to have that decomposed, decoupled but integrated approach. >> Yeah, we really believe that the whole world of project services and managed services is coming together as one. So we have a single delivery model which we're really passionate about. And we look at it as an embedded team within our customers, embedded DevOps to support them, basically on anything that could be from a modernizing a new application through to addressing a more traditional Cloud architecture framework that's in place. But yeah, the trick to it is, as Val said earlier is the do with approach, not just do for, right? I think customers need to learn about the Cloud. They need to understand the technology that they're using. They want to have that understanding. And we found a way of fitting in our services to help them accelerate that part. >> So Valerie, I got to ask you the question. So, in sports you talk about the modern era of baseball or whatever, we're in the modern era of Cloud, going next generation. We call it Super Cloud, a concept that Dave and I put out at re:Invent. If someone asks you, what does the modern era look like? As you look at your customer base and the data you guys have, how would you describe this modern era? What is it made up of? Is it outcomes versus solutions? Is it technology that's decentralized? How do you talk about it? What is the modern era, if you were- >> Not to oversimplify it, but I'm going to, the idea that somebody could come into work and all they have to think about is business outcomes and the data points that they need to achieve said business outcomes. I'm the biggest fan of measure what matters, I think it is an incredibly powerful methodology. And I think anybody who thinks about running business, they know that it's a scale. The amount of companies that are in that place is very small right now. So I think modern era is really that running an IT company to an IP company. >> So Stephen, if you unpack that, what's under the covers to make that happen? Automation, machines, what's your assessment of that outcome, which by the way was well said. Beautiful, beautiful comment. What makes that happen? >> I think it is around automation. It is around do once and then apply many times. That is key. Obviously it's a fundamental principle of the Cloud, is that consistency in that repeatability. So when you can simplify services down to a point, click, deploy, I think you're in a much better position to be able to move quickly and then not have to worry about anything under the hood and just focus, like Val said, on the business outcomes. >> That's more creative. They're focusing on the problems, to not do the rock fetches and the heavy lifting that's not differentiated. >> I find that what gives people energy generates opportunity. And I think when people hit those roadblocks of, these things don't work together. There's all these interdependencies. It's really challenging. So I love what's happening. I think there's never been a better time to be in this business. >> Not a dull moment, That's for darn sure. >> Not a dull moment. >> Valerie, talk about outcomes. You mentioned a couple of customers that you're working with, some case studies. It is all about outcomes these days. That's the conversations that we have with the entire ecosystem is all about business outcomes. What are some of those key transformative business outcomes that Caylent is helping customers to achieve? >> Yeah, to me one thing that is key is, anytime I'm meeting with a customer, I want to understand who their customers are. I'm like, who is your customer? And how can we create a better experience for that customer. Whether it's their end users or their external customers. And I think that is a huge element. What we're seeing is that sassification of, how do I make it easier for my customers to procure and engage with my platform? And a lot of what we're doing right now is helping clients with that. And it's not a flip of a switch, it's not a click of a button, it's complicated. But that is what we are here to help, help simplify, help create that understanding of what's possible. >> How do you guys talk to your customers, take a minute to give a plug for the company. What are you looking for? What's the stats? How many employees you guys hiring, and what's the pitch to customers? >> Yeah, so I think every organization is on their journey to the Cloud now. It's gotten to that point where if you're not working with a public Cloud provider, you're part of a very, very small group. We like to say that we'll meet customers where they are, and help evolve them as a business, help evolve their teams. And that's what we mean when we say do with, so it's a pretty broad spectrum. We're big in healthcare. We're big in FinTech. We've worked with a lot of startup customers. We have about 250 customers today, 250 employees. And we're scaling rapidly. We've grown that from about 50 employees a year ago. >> Oh, wow. >> Yes, when I started, we were just around 60 people and we're at 260 today. >> And why are people working with you? What are you guys, solving a problem? Are you enabling them? What's the pitch? >> Without a doubt, I love that. Being in sales my whole career, somebody asking me for a pitch is my favorite. >> Okay, let's go. >> Yeah, yeah, the true value prop of what we do is all of the above. We enable, we help customers do more faster, but again, we do not want customers to walk away from an engagement with us saying, oh no, we don't know what to do. We want them to feel empowered. I still think the biggest gap from everything being in that IP business outcome is people. And for us, we're so passionate about that, and building a company that really truly believes that. And that's part of who we are as a company and our value system. >> And the digital transformation, ultimately what they're going through, you get them there faster. They get the outcomes and they're operational. >> Absolutely, and also to be clear, when a customer has a great experience working with you, they want to tell other people about the experience. And for us, like the referrals that we get, the partnership with Amazon is so key. >> What are some reactions after you go through an engagement? We've been riffing on this concept of Super Cloud where you're starting to see people build on top of, not the AWSs, but their partners that work with them. And so the customers are getting their own Cloud experience at scale. What are some of the comments you hear from your successful customers? What are some anecdotal feedback? >> Yeah, yeah. >> I'm so glad we did this because now I'm selling more, I'm doing this, what are some of the things that they're thinking? >> Yeah, yeah, I think ultimately the consistent theme that we get is, "I'm so glad that I didn't let fear hold me back from engaging a partner," because a lack of control scares a lot of customers. It does. And I think customers that are willing to say, "Okay, I'm going to have a little faith, trust in the process." They thank us. They do, and we've seen that across the board. I think that crossing that chasm is not to be underestimated without a doubt. >> Great story, congratulations. >> Oh, thank you. >> Well, there's nothing more powerful and potent than the voice of the customer. >> Without a doubt. And really you have to listen. >> Yes, yes, definitely. Stephen, Valerie, thank you so much for joining Dave and me on the program today, talking about Caylent, what you guys are doing for customers with AWS, empowering, enabling, collaboration. I love it, thank you. >> Yeah, thank you both. >> All right, our pleasure. For John Furrier, I'm Lisa Martin. You're watching theCUBE live in New York City, we are at AWSO in NYC, John and I will be right back with our next guest. (gentle music)

>>Hello, everyone. Welcome to the cubes presentation of the 80, but startup showcases MarTech is the focus. And this is all about the emerging cloud scale customer experience. This is season two, episode three of the ongoing series covering the exciting, fast growing startups from the cloud AWS ecosystem to talk about the future and what's available now, where are the actions? I'm your host John fur. Today. We joined by Cub alumni, Jerry Chen partner at Greylock ventures. Jerry. Great to see you. Thanks for coming on, >>John. Thanks for having me back. I appreciate you welcome there for season two. Uh, as a, as a guest star, >><laugh>, you know, Hey, you know, season two, it's not a one and done it's continued coverage. We, we got the episodic, uh, cube flicks model going >>Here. Well, you know, congratulations, the, the coverage on this ecosystem around AWS has been impressive, right? I think you and I have talked a long time about AWS and the ecosystem building. It just continues to grow. And so the coverage you did last season, all the events of this season is, is pretty amazing from the data security to now marketing. So it's, it's great to >>Watch. And 12 years now, the cube been running. I remember 2013, when we first met you in the cube, we just left VMware just getting into the venture business. And we were just riffing the next 80. No one really kind of knew how big it would be. Um, but we were kinda riffing on. We kind of had a sense now it's happening. So now you start to see every vertical kind of explode with the right digital transformation and disruption where you see new incumbents. I mean, new Newton brands get replaced the incumbent old guard. And now in MarTech, it's ripe for, for disruption because web two has gone on to web 2.5, 3, 4, 5, um, cookies are going away. You've got more governance and privacy challenges. There's a slew of kind of ad tech baggage, but yet lots of new data opportunities. Jerry, this is a huge, uh, thing. What's your take on this whole MarTech cloud scale, uh, >>Market? I, I think, I think to your point, John, that first the trends are correct and the bad and the good or good old days, the battle days MarTech is really about your webpage. And then email right there. There's, there's the emails, the only channel and the webpage was only real estate and technology to care about fast forward, you know, 10 years you have webpages, mobile apps, VR experiences, car experiences, your, your, your Alexa home experiences. Let's not even get to web three web 18, whatever it is. Plus you got text messages, WhatsApp, messenger, email, still great, et cetera. So I think what we've seen is both, um, explosion and data, uh, explosion of channel. So sources of data have increases and the fruits of the data where you can reach your customers from text, email, phone calls, etcetera have exploded too. So the previous generation created big company responses, Equa, you know, that exact target that got acquired by Oracle or, or, um, Salesforce, and then companies like, um, you know, MailChimp that got acquired as well, but into it, you're seeing a new generation companies for this new stack. So I, I think it's exciting. >>Yeah. And you mentioned all those things about the different channels and stuff, but the key point is now the generation shifts going on, not just technical generation, uh, and platform and tools, it's the people they're younger. They don't do email. They have, you know, proton mail accounts, zillion Gmail accounts, just to get the freebie. Um, they're like, they're, they'll do subscriptions, but not a lot. So the generational piece on the human side is huge. Okay. And then you got the standards, bodies thrown away, things like cookies. Sure. So all this is makes it for a complicated, messy situation. Um, so out of this has to come a billion dollar startup in my mind, >>I, I think multiple billion dollars, but I think you're right in the sense that how we want engage with the company branch, either consumer brands or business brands, no one wants to pick a phone anymore. Right? Everybody wants to either chat or DM people on Twitter. So number one, the, the way we engage is different, both, um, where both, how like chat or phone, but where like mobile device, but also when it's the moment when we need to talk to a company or brand be it at the store, um, when I'm shopping in real life or in my car or at the airport, like we want to reach the brands, the brands wanna reach us at the point of decision, the point of support, the point of contact. And then you, you layer upon that the, the playing field, John of privacy security, right? All these data silos in the cloud, the, the, the, the game has changed and become even more complicated with the startup. So the startups are gonna win. Will do, you know, the collect, all the data, make us secure in private, but then reach your customers when and where they want and how they want it. >>So I gotta ask you, because you had a great podcast just this week, published and snowflake had their event going on the data cloud, there's a new kind of SAS platform vibe going on. You're starting to see it play out. Uh, and one of the things I, I noticed on your podcast with the president of Hashi Corp, who was on people should listen to that podcast. It's on gray matter, which is the Greylocks podcast, uh, plug for you guys. He mentioned he mentions the open source dynamic, right? Sure. And, and I like what he, things, he said, he said, software business has changed forever. It's my words. Now he said infrastructure, but I'm saying software in general, more broader infrastructure and software as a category is all open source. One game over no debate. Right. You agree? >>I, I think you said infrastructure specifically starts at open source, but I would say all open source is one more or less because open source is in every bit of software. Right? And so from your operating system to your car, to your mobile phone, open source, not necessarily as a business model or, or, or whatever, we can talk about that. But open source as a way to build software distribute, software consume software has one, right? It is everywhere. So regardless how you make money on it, how you build software, an open source community ha has >>One. Okay. So let's just agree. That's cool. I agree with that. Let's take it to the next level. I'm a company starting a company to sell to big companies who pay. I gotta have a proprietary advantage. There's gotta be a way. And there is, I know you've talked about it, but I have my opinion. There is needs to be a way to be proprietary in a way that allows for that growth, whether it's integration, it's not gonna be on software license or maybe support or new open source model. But how does startups in the MarTech this area in general, when they disrupt or change the category, they gotta get value creation going. What's your take on, on building. >>You can still build proprietary software on top of open source, right? So there's many companies out there, um, you know, in a company called rock set, they've heavily open source technology like Rock's DB under the hood, but they're running a cloud database. That's proprietary snowflake. You talk about them today. You know, it's not open source technology company, but they use open source software. I'm sure in the hoods, but then there's open source companies, data break. So let's not confus the two, you can still build proprietary software. There's just components of open source, wherever we go. So number one is you can still build proprietary IP. Number two, you can get proprietary data sources, right? So I think increasingly you're seeing companies fight. I call this systems intelligence, right, by getting proprietary data, to train your algorithms, to train your recommendations, to train your applications, you can still collect data, um, that other competitors don't have. >>And then it can use the data differently, right? The system of intelligence. And then when you apply the system intelligence to the end user, you can create value, right? And ultimately, especially marketing tech, the highest level, what we call the system of engagement, right? If, if the chat bot the mobile UI, the phone, the voice app, etcetera, if you own the system of engagement, be a slack, or be it, the operating system for a phone, you can also win. So still multiple levels to play John in multiple ways to build proprietary advantage. Um, just gotta own system record. Yeah. System intelligence, system engagement. Easy, right? Yeah. >>Oh, so easy. Well, the good news is the cloud scale and the CapEx funded there. I mean, look at Amazon, they've got a ton of open storage. You mentioned snowflake, but they're getting a proprietary value. P so I need to ask you MarTech in particular, that means it's a data business, which you, you pointed out and we agree. MarTech will be about the data of the workflows. How do you get those workflows what's changing and how these companies are gonna be building? What's your take on it? Because it's gonna be one of those things where it might be the innovation on a source of data, or how you handle two parties, ex handling encrypted data sets. I don't know. Maybe it's a special encryption tool, so we don't know what it is. What's your what's, what's your outlook on this area? >>I, I, I think that last point just said is super interesting, super genius. It's integration or multiple data sources. So I think either one, if it's a data business, do you have proprietary data? Um, one number two with the data you do have proprietary, not how do you enrich the data and do you enrich the data with, uh, a public data set or a party data set? So this could be cookies. It could be done in Brad street or zoom info information. How do you enrich the data? Number three, do you have machine learning models or some other IP that once you collected the data, enriched the data, you know, what do you do with the data? And then number four is once you have, um, you know, that model of the data, the customer or the business, what do you deal with it? Do you email, do you do a tax? >>Do you do a campaign? Do you upsell? Do you change the price dynamically in our customers? Do you serve a new content on your website? So I think that workflow to your point is you can start from the same place, what to do with the data in between and all the, on the out the side of this, this pipeline is where a MarTech company can have then. So like I said before, it was a website to an email go to website. You know, we have a cookie fill out a form. Yeah. I send you an email later. I think now you, you can't just do a website to email, it's a website plus mobile apps, plus, you know, in real world interaction to text message, chat, phone, call Twitter, a whatever, you know, it's >>Like, it's like, they're playing checkers in web two and you're talking 3d chess. <laugh>, I mean, there's a level, there's a huge gap between what's coming. And this is kind of interesting because now you mentioned, you know, uh, machine learning and data, and AI is gonna factor into all this. You mentioned, uh, you know, rock set. One of your portfolios has under the hood, you know, open source and then use proprietary data and cloud. Okay. That's a configuration, that's an architecture, right? So architecture will be important in terms of how companies posture in this market, cuz MarTech is ripe for innovation because it's based on these old technologies, but there's tons of workflows, but you gotta have the data. Right. And so if I have the best journey map from a client that goes to a website, but then they go and they do something in the organic or somewhere else. If I don't have that, what good is it? It's like a blind spot. >>Correct. So I think you're seeing folks with the data BS, snowflake or data bricks, or an Amazon that S three say, Hey, come to my data cloud. Right. Which, you know, Snowflake's advertising, Amazon will say the data cloud is S3 because all your data exists there anyway. So you just, you know, live on S3 data. Bricks will say, S3 is great, but only use Amazon tools use data bricks. Right. And then, but on top of that, but then you had our SaaS companies like Oracle, Salesforce, whoever, and say, you know, use our qua Marketo, exact target, you know, application as a system record. And so I think you're gonna have a battle between, do I just work my data in S3 or where my data exists or gonna work my data, some other application, like a Marketo Ella cloud Z target, um, or, you know, it could be a Twilio segment, right. Was combination. So you'll have this battle between these, these, these giants in the cloud, easy, the castles, right. Versus, uh, the, the, the, the contenders or the, or the challengers as we call >>'em. Well, great. Always chat with the other. We always talk about castles in the cloud, which is your work that you guys put out, just an update on. So check out greylock.com. They have castles on the cloud, which is a great thesis on and a map by the way ecosystem. So you guys do a really good job props to Jerry and the team over at Greylock. Um, okay. Now I gotta ask kind of like the VC private equity sure. Market question, you know, evaluations. Uh, first of all, I think it's a great time to do a startup. So it's a good time to be in the VC business. I think the next two years, you're gonna find some nice gems, but also you gotta have that cleansing period. You got a lot of overvaluation. So what happened with the markets? So there's gonna be a lot of M and a. So the question is what are some of the things that you see as challenges for product teams in particular that might have that killer answer in MarTech, or might not have the runway if there's no cash, um, how do people partner in this modern era, cuz scale's a big deal, right? Mm-hmm <affirmative> you can measure everything. So you get the combination of a, a new kind of M and a market coming, a potential growth market for the right solution. Again, value's gotta be be there. What's your take on this market? >>I, I, I think you're right. Either you need runway, so cash to make it through, through this next, you know, two, three years, whatever you think the market Turmo is or two, you need scale, right? So if you're at a company of scale and you have enough data, you can probably succeed on your own. If not, if you're kind of in between or early to your point, either one focus, a narrower wedge, John, just like we say, just reduce the surface area. And next two years focus on solving one problem. Very, very well, or number two in this MarTech space, especially there's a lot of partnership and integration opportunities to create a complete solution together, to compete against kind of the incumbents. Right? So I think they're folks with the data, they're folks doing data, privacy, security, they're post focusing their workflow or marketing workflows. You're gonna see either one, um, some M and a, but I definitely can see a lot of Coopers in partnership. And so in the past, maybe you would say, I'm just raise another a hundred million dollars and do what you're doing today. You might say, look, instead of raising more money let's partner together or, or merge or find a solution. So I think people are gonna get creative. Yeah. Like said scarcity often is good. Yeah. I think forces a lot more focus and a lot more creativity. >>Yeah. That's a great point. I'm glad you brought that up up. Cause I didn't think you were gonna go there. I was gonna ask that biz dev activity is going to be really fundamental because runway combined with the fact that, Hey, you know, if you know, get real or you're gonna go under is a real issue. So now people become friends. They're like, okay, if we partner, um, it's clearly a good way to go if you can get there. So what advice would you give companies? Um, even most experienced, uh, founders and operators. This is a different market, right? It's a different kind of velocity, obviously architectural data. You mentioned some of those key things. What's the posture to partner. What's your advice? What's the combat man manual to kind of compete in this new biz dev world where some it's a make or break time, either get the funding, get the customers, which is how you get funding or you get a biz dev deal where you combine forces, uh, go to market together or not. What's your advice? >>I, I think that the combat manual is either you're partnering for one or two things, either one technology or two customers or sometimes both. So it would say which partnerships, youre doing for technology EG solution completers. Like you have, you know, this puzzle piece, I have this puzzle piece data and data privacy and let's work together. Um, or number two is like, who can help you with customers? And that's either a, I, they can be channel for you or, or vice versa or can share customers and you can actually go to market together and find customers jointly. So ideally you're partner for one, if not the other, sometimes both. And just figure out where in your life cycle do you need? Um, friends. >>Yeah. Great. My final question, Jerry, first of all, thanks for coming on and sharing your in insight as usual. Always. Awesome final question for the folks watching that are gonna be partnering and buying product and services from these startups. Um, there's a select few great ones here and obviously every other episode as well, and you've got a bunch you're investing in this, it's actually a good market for the ones that are lean companies that are lean and mean have value. And the cloud scale does provide that. So a lot of companies are getting it right, they're gonna break through. So they're clearly gonna be getting customers the buyer side, how should they be looking through the lens right now and looking at companies, what should they look for? Um, and they like to take chances with seeing that. So it's not so much, they gotta be vetted, but you know, how do they know the winners from the pretenders? >>You know, I, I think the customers are always smart. I think in the, in the, in the past in market market tech, especially they often had a budget to experiment with. I think you're looking now the customers, the buyer technologies are looking for a hard ROI, like a return on investment. And before think they might experiment more, but now they're saying, Hey, are you gonna help me save money or increase revenue or some hardcore metric that they care about? So I think, um, the startups that actually have a strong ROI, like save money or increased revenue and can like point empirically how they do that will, will, you know, rise to the top of, of the MarTech landscape. And customers will see that they're they're, the customers are smart, right? They're savvy buyers. They, they, they, they, they can smell good from bad and they're gonna see the strong >>ROI. Yeah. And the other thing too, I like to point out, I'd love to get your reaction real quick is a lot of the companies have DNA, any open source or they have some community track record where communities now, part of the vetting. I mean, are they real good people? >>Yeah. I, I think open stores, like you said, in the community in general, like especially all these communities that move on slack or discord or something else. Right. I think for sure, just going through all those forums, slack communities or discord communities, you can see what's a good product versus next versus bad. Don't go to like the other sites. These communities would tell you who's working. >>Well, we got a discord channel on the cube now had 14,000 members. Now it's down to six, losing people left and right. We need a moderator, um, to get on. If you know anyone on discord, anyone watching wants to volunteer to be the cube discord, moderator. Uh, we could use some help there. Love discord. Uh, Jerry. Great to see you. Thanks for coming on. What's new at Greylock. What's some of the things happening. Give a quick plug for the firm. When you guys working on, I know there's been some cool things happening, new investments, people moving. >>Yeah. Look we're we're Greylock partners, seed series a firm. I focus at enterprise software. I have a team with me that also does consumer investing as well as crypto investing like all firms. So, but we're we're seed series a occasionally later stage growth. So if you're interested, uh, FA me@jkontwitterorjgreylock.com. Thank you, John. >>Great stuff, Jerry. Thanks for coming on. This is the Cube's presentation of the, a startup showcase. MarTech is the series this time, emerging cloud scale customer experience where the integration and the data matters. This is season two, episode three of the ongoing series covering the hottest cloud startups from the ADWS ecosystem. Um, John farrier, thanks for watching.

>>The cube presents HPE discover 2022 brought to you by HPE. >>Welcome back to the Venetian convention center. You're watching the Cube's coverage of HPE discover 2022. The first discover live discover in three years, 2019 was the last one. The cube we were just talking about. This has been at H HP discover. Now HPE since 2011, my co-host John furrier. We're pleased to welcome Phil Maru. Who's the executive vice president and general manager of HPE Aruba. And he's joined by David Hughes, the chief product and technology officer at HPE Aruba gentleman. Welcome to the cube. Good to see you. Thank you. Thank >>You. >>Okay, so you guys talk a lot, Phil, about the intelligent edge. Yep. Okay. What do you, what do you mean by that? >>Yeah, so we, well, we're kind of focused on, is providing technology to customers that sits out at the edge and typically the edge would be, uh, any location out of the data center or out of the cloud. So for the most part, our customers would deploy our technology either in their office premises or maybe retail premises shops, uh, maybe deploying out of the home where their employees are on a factory floor. And we're really talking about technology to connect both people and devices back to, um, systems and technology throughout an organization. So, but >>I, I, you know, sometimes I call it the near edge and the far edge yeah. Near, near edge. Maybe as we saw home Depot up on the stage yesterday far, Edge's like space. Right. You're including all of that. Right. That's >>Edge. >>Yeah. And actually we, we, we, you know, we've got a broad range of technology that actually works within the data center as well. So, you know, what we are focused on is providing, uh, network technology, software and services. And, you know, for the most part, our heritage is at the edge, but it's more pervasive than that. So >>If you have the edge, you got connectivity and power, that's an edge. How much, um, is the physical world being connected now you're seeing robotics automation. Yeah. Ex and with machine learning specifically in compute, really driving a new acceleration at the edge. What you, how do you guys view that? What's your reaction? Yeah. >>I think, look, it, I think as connectivity is improving and that's both in terms of wifi connectivity, so, you know, wifi technology continues to, uh, advance and also you've got this new kind of private 5g area, just generally connectivity is becoming more pervasive and that's helping some industries that haven't previously embraced it. And I think industrial is, is one of the big ones. So, you know, historically it was difficult for kind of car manufacturers to really enable a factory floor. But now the connectivity is connectivity is better. That gives them the opportunity to be able to really change how they do things. So >>David, if you do take an outside in view, mm-hmm <affirmative>, uh, and, and, and when you talk to customers, what are they telling you and how is that informing your product strategy? >>Yeah, well, you >>Know, I think there's, there's several themes we hear. One is, you know, it's really important, better work from anywhere they wanna enable their employees, um, to get the same experience, whether they're at home or on the road or in their branch office or at headquarters. Um, you know, people are also concerned that as they deploy, deploy all of this IOT and pursuit of digital transformation, they don't want those devices to be a weak point where someone breaks into one device and moves naturally, um, across the network. So they want to have this great experience for their customers and their users, but they wanna make sure that they're not compromising security, um, in any way. And so it's about getting that balance between ease of use and, and security. That's one of the primary things we hear, >>You know, Dave, one of the things we talked about many, many years ago was when hybrid and was starting to come out multi-cloud was on the, on the table early on. Uh, we were, we were saying, Hey, the data center is just a big edge, right? I mean, if you have cloud operations and you see what's going on with GreenLake here now, the momentum hybrid cloud is cloud operations, right? An edge off data centers to a big edge on premises. And you got the edge as you have cloud operations, like say GreenLake, plugging in partners and diverse environments. You're connecting, not just branch offices that are per perimeter based. You have no perimeter and you have now other companies connecting mm-hmm <affirmative> so you got data and you got network. How do you guys see that transition as GreenLake has a very big ecosystem part of it, partners and whatnot. >>Yeah. So, you know, I think for us, um, the ecosystem of partners that we have is critical in terms of delivering what our customers need. And, you know, I think one of the really important areas is around verticals. So, um, you know, when you think about different verticals, they have similar problems, but you need to tailor the solutions. Um, to each of those, you know, we are talking a bit about devices and people. When you look at say a healthcare environment, there can be 30 devices there for each patient. And, um, so there's connecting all those devices securely, but we have partners that will help pull all of that together that may be focused on, um, you know, medical environment that may focused on stadiums. They may be focused on industrial. Um, so having partners that understand those verticals and working closely with them to deliver solutions is important in our go to market. >>So another kind of product question and related to what you just said, David, I got connectivity, speed, reliability, cost security, or maybe a missing something. But you, you said earlier, you gonna gotta balance those. How do you do that? And do you do that for the specific use cases? Like for instance, you just mentioned stadiums and 81 and how do you balance those and, and do you tailor those for the use cases? >>Yeah, well, I think it depends on the customer and different people have different views about where they need to be. So some people are, are so afraid about security. They wanna be air gapped and completely separate than the internet. That would be one extreme mm-hmm <affirmative> other people, you know, look at it and see what's happening with COVID with everyone working from home with people being able to work from Starbucks or the airport. And they're beginning to think, well, why is the branch that much different? And so what I think we are seeing is, you know, a reevaluation of how people connect to, um, the apps they're using and, uh, you know, you, you, you've probably for sure heard people talking about zero trust, talking about micro segmentation. You know, I think what we we see is that people wanna be able to build a network in a way where rather than any device being able to talk to any device or any person, which is where the internet started, we wanna build to build networks where people or devices can only talk to the destinations that are necessary for them to do their job. >>And so a lot of the technology that we are building into the network is really about making security intrinsic by limiting what can talk to what that's >>Actually micro, micro segmentations, zero trust, um, these all point to a modern, the modern network, as you say, Antonio Neri was just on the cube, talking about programmability, substrate, the words like that come to mind, what is the modern network look like? I mean, you have to be agile. You have to be programmable. You have to have security. Can you describe in your words, what does the modern network these days need to look like? How should customers think about architecting them? What are some of the table stakes and what are some of the differentiators that customers need to do to have a modern network? >>Yeah, well, you covered off a coup a few quarter, one there with clarity and so on. So let me pick one that you didn't mention. And, and I, you know, I think we are seeing, you know, a lot of interest around network as a service. And, you know, when we think about network as a service, we think about it broadly, um, you know, for consumers, we're getting more and more used to buying things as a service versus buying a thing. When you, when you get Alexa, you care about how well she answers your questions, you don't care about what CPU is or how much Ram Alexa has. And likewise with networking, people are caring about the outcomes of keeping their employees connected, keeping their, their devices and systems running. And so what for us, what NASA is all about is that shift of thinking about a network as being a collection of devices that get managed to being a framework for connectivity and running it from the point of view of those outcomes. >>And so whether, you know, it's about CapEx versus OPEX or about do it yourself, managing the network yourself versus outsourcing that, um, or it's about the, you know, Greenfield versus brownfield, each of our customers has got a different starting point, but they're all getting heading towards this destination of being able to treat their network as a service. And so that is, you know, a key area of innovation for us and whether it's big customers like home Depot that you heard about yesterday, um, where we kind of manage everything for them on a, as on a store basis, um, for connectivity, um, or, you know, the recent, um, skew based nest that we launched, which is a really scalable foundation for our partners to build nest offerings around. Um, we see this as a key part of network modernization. Yeah. >>And one of the things, again, that's great stuff. Uh, infrastructure is code, which was really kind of pioneer the DevOps movement in cloud kind of as platform level. And you got data ops now and AI at the top of the stack, we were always wondering when network as code was gonna come, uh, and where you actually have it, where it's programmable. I mean, we all know what policies do do. They're good. That's all great network as code. >>Yeah. >>And that's the concept that's like DevOps, it's like, make it work just seamlessly, just be always on. And >>Yeah. And smart, you know, people are always looking for the, for the easy button. Um, and so they want, they want things to operate easily. They want it to be easy to manage. And, you know, I actually think there's a little bit of a, um, a conflict between networkers code and the easy button, right? So it depends on the class of customers. Some customers like financials, for instance, have a huge software development organizations that are extremely capable that could, that can go with program ability that want things as code. But the majority of the, of, of the verticals that we deal with, um, don't have those big captive software organizations. And so they're really looking for automation and simplicity and they wanna outsource that problem. So in Aruba central, we have invested a lot to make it really easy for our customers to, um, get what they need, you know, is that movement of zero code. It's more like zero code. They want, they want something packaged now >>The headless networks. Yeah. Low code, no code >>Kind of thing. Yeah, that's right. And, you know, obviously for people that have the sophistication that want to, um, do the most advanced things, we have APIs. And so we support that kind of programmable way of doing things. But I'd say that that's that's, those are more specialized customers. So >>Phil, yeah. Uh, is that the strategy? I mean, David listed off a number of, of factors here is that Aruba's strategy to modernize networks to actually create the easy button through network as a service is as simple as dial tone. Is that how we >>Should think? I mean, the way I think about the strategy is I think about it as a triangle, really, along the bottom, we've got the products and services that we offer and we continue to add more products and services. We either buy companies such as silver peak a couple of years ago, or we build, uh, additional products and by, and by the way, that's in response to customers who are frustrated with some other suppliers and wanna move on mass over to, uh, companies like ourselves. So at the bottom layer of the product and services, and then the other side of the triangle one would be NAS, which we talked about, which is kind of move to buying network and as a service. And then the other side of the triangle is the platform, which for us is river central, which is part of HP GreenLake. And that's really all about, you know, kind of making it easy for customers to manage networks and Aruba central right now has got about 120,000 live customers on it. It connects to about 2 million devices and it's collecting a lot of data as well. So we anonymously collect data from all of our customers. We've got one and a half billion data points in the platform. And what we do is we let that data kind of look for anomalies and spot problems on the network before they happen for customers. >>So Aruba central predated, uh, uh, GreenLake GreenLake. Yeah. And, and so did you write to GreenLake through GreenLake APIs? How, what was the engineering work to accomplish that? >>Yeah, so really, um, Aruba central is kind of the Genesis of the GreenLake platform. So we took Aruba central and made it more generic okay. To build the GreenLake cloud platform. And you know, what we've done very recently is bring, bring Aruba into that unified infrastructure, along with storage and compute. So the same sign-on applies across all of HP's, um, products, the same way of managing licenses, managing devices. And so it provides us, uh, great foundation going forwards to, um, solve more comprehensively. Our customers automation requires. >>So, so just a quick follow. So Aruba actually was the main spring of GreenLake from the standpoint of okay. Sing, like you said, single sign on a platform that could evolve and become more, more generic. Yes. So, okay. So that was a nice little, um, bonus of the acquisition, you know, it's now the whole company >><laugh> Aruba taking over. >>Yeah. There's been a lot of work to, to, uh, you know, make it generic and, and widely applicable. Right. Yeah. Um, so, but >>You were purpose >>Built for yeah. Well it's foundational. Yes. So foundational for GreenLake, they built on top of it. Yeah. So you mentioned the data points, billions of data points. So I gotta ask you, cuz we're seeing this, um, copy more and more with machine learning, driving a lot of acceleration, cuz you can do simulations with machine learning and compute. We had Neil McDonal done earlier. He's a compute guy, you got networking. So with all this, um, these services and devices being put on and off the network humans, can't actually figure this out. You can discover what's on the network. How are you guys viewing the discovery and monitoring because there's no perimeter okay. On the network anymore. So I want to know what's out there. Um, how do you get through it? How does machine learning and AI play into this? >>Yeah. I mean, what we are trying to do is obviously flag trends for customers and say, Hey look, you know, we can either see something happening with your network. So there's a particular issue over here and we need to, I dunno, free up more capacity to solve that. Or we're looking at how their network is running and then comparing that with anonymized data from all of our other customers as well. So we're just helping find those problems. But yeah, you're right. I mean, I think it is becoming more of an issue for organizations, you know, how do you manage the network, >>But you see machine learning and AI playing a big part. >>Yeah, yeah. Yeah. I think, uh, AI massively and, and other technology advances as well that we make. So recently we, uh, also announced the availability of location awareness within our access points. And that might sound like a simple thing. But when network, when companies build out their networks, they often lose or they potentially could lose the records as to, well, where were the access points that we laid out and actually where are they not within, you know, 20 feet, but where actually are they? So we introduced kind of location, finding technology as well into our, uh, access points to make it easy for >>Customers. So Aruba one of the best, if not the best acquisition. I think that HP E has made, um, it's made by three par was, you know, good. It saved the storage business. Okay. That was more of a defensive play. Uh, but to see Aruba, it's a growth business. You guys report on it every quarter. Yeah. It's obviously a key ingredient to enable uh, uh, GreenLake and, and a that's another example, nimble was similar. We're much smaller sort of more narrow, but taking the AI ops piece and bringing it over. So it's, it was great to see HPE executing on some of its M and a as opposed to just leaving them alone and not really leveraging 'em. So guys, yeah. Congratulations really appreciate you guys coming on and explaining that. Congratulations on all the, all the great work and thanks for coming on the cube. Okay. >>Thank you guys. Yeah. Thanks for having us. >>All right, John, and I'll be back right after this short break. You're watching the cube, the leader in enterprise tech coverage from HPE Las Vegas, 2022. We'll be right back.

(upbeat music) >> Welcome back, everyone. It's theCUBE's coverage here in Las Vegas. I'm John Furrier, your host of theCUBE with re:MARS. Amazon re:MARS stands for machine learning, automation, robotics, and space. Lot of great content, accomplishment. AI meets meets robotics and space, industrial IoT, all things data. And we've got two great guests here to unpack the AI side of it. Manoj Suvarna, Managing Director at AI Ecosystem at Deloitte and Arte Merritt, Conversational AI Lead at AWS. Manoj, it's great to see you CUBE alumni. Art, welcome to theCUBE. >> Thanks for having me. I appreciate it. >> So AI's the big theme. Actually, the big disconnect in the industry has been the industrial OT versus IT, and that's happening. Now you've got space and robotics meets what we know is machine learning and AI which we've been covering. This is the confluence of the new IoT market. >> It absolutely is. >> What's your opinion on that? >> Yeah, so actually it's taking IoT beyond the art of possible. One area that we have been working very closely with AWS. We're strategic alliance with them. And for the past six years, we have been investing a lot in transformations. Transformation as it relate to the cloud, transformation as it relate to data modernization. The new edge is essentially on AI and machine learning. And just this week, we announced a new solution which is more focused around enhancing contact center intelligence. So think about the edge of the contact center, where we all have experiences around dealing with customer service and how to really take that to the next level, challenges that clients are facing in every part of that business. So clearly. >> Well, Conversational AI is a good topic. Talk about the relationship with Deloitte and Amazon for a second around AI because you guys have some great projects going on right now. That's well ahead of the curve on solving the scale problem 'cause there's a scale and problem, practical problem and then scale. What's the relationship with Amazon and Deloitte? >> We have a great alliance and relationship. Deloitte brings that expertise to help folks build high quality, highly effective conversational AI and enterprises are implementing these solutions to really try to improve the overall customer experience. So they want to help agents improve productivity, gain insights into the reasons why folks are calling but it's really to provide that better user experience being available 24/7 on channels users prefer to interact. And the solutions that Deloitte is building are highly advanced, super exciting. Like when we show demos of them to potential customers, the eyes light up and they want those solutions. >> John: Give an example when their eyes light up. What are you showing there? >> One solution, it's called multimodal interfaces. So what this is, is when you're call into like a voice IVR, Deloitte's solution will send the folks say a mobile app or a website. So the person can interact with both the phone touching on the screen and the voice and it's all kept in sync. So imagine you call the doctor's office or say I was calling a airline and I want to change my flight or sorry, change the seat. If they were to say, seat 20D is available. Well, I don't know what that means, but if you see the map while you're talking, you can say, oh, 20D is the aisle. I'm going to select that. So Deloitte's doing those kind of experiences. It's incredible. >> Manoj, this is where the magic comes into play when you bring data together and you have integration like this. Asynchronously or synchronously, it's all coming together. You have different platforms, phone, voice, silo databases potentially, the old way. Now, the new ways integrating. What makes it all work? What's the key to success? >> Yeah, it's certainly not a trivial feat. Bringing together all of these ecosystems of relationships, technologies all put together. We cannot do it alone. This is where we partner with AWS with some of our other partners like Salesforce and OneReach and really trying to bring a symphony of some of these solutions to bear. When you think about, going back to the example of contact center, the challenges that the pandemic posed in the last couple of years was the fact that who's a humongous rise in volume of number of calls. You can imagine people calling in asking for all kinds of different things, whether it's airlines whether it is doctor's office and retail. And then couple with that is the fact that there's the labor shortage. And how do you train agents to get them to be productive enough to be able to address hundreds or thousands of these calls? And so that's where we have been starting to, we have invested in those solutions bringing those technologies together to address real client problems, not just slideware but actual production environments. And that's where we launched this solution called TrueServe as of this week, which is really a multimodal solution that is built with preconceived notions of technologies and libraries where we can then be industry agnostic and be able to deliver those experiences to our clients based on whatever vertical or industry they're in. >> Take me through the client's engagement here because I can imagine they want to get a practical solution. They're going to want to have it up and running, not like a just a chatbot, but like they completely integrated system. What's the challenge and what's the outcome first set of milestones that you see that they do first? Do they just get the data together? Are they deploying a software solution? What's the use cases? >> There's a couple different use cases. We see there's the self-service component that we're talking about with the chatbots or voice IVR solutions. There's also use cases for helping the agents, so real-time agent assist. So you call into a contact center, it's transcribed in real time, run through some sort of knowledge base to give the agents possible answers to help the user out, tying in, say the Salesforce data, CRM data, to know more about the user. Like if I was to call the airline, it's going to say, "Are you calling about your flight to San Francisco tomorrow?" It knows who I am. It leverages that stuff. And then the key piece is the analytics knowing why folks are calling, not just your metrics around, length of calls or deflections, but what were the reasons people were calling in because you can use that data to improve your underlying products or services. These are the things that enterprise are looking for and this is where someone like Deloitte comes in, brings that expertise, speeds up the time to market and really helps the customers. >> Manoj, what was the solution you mentioned that you guys announced? >> Yeah, so this is called Deloitte TrueServe. And essentially, it's a combination of multiple different solutions combinations from AWS, from Salesforce, from OneReach. All put together with our joint engineering and really delivering that capability. Enhancing on that is the analytics component, which is really critical, especially because when you think about the average contact center, less than 10% of the data gets analyzed today, and how do you then extract value out of that data and be able to deliver business outcomes. >> I was just talking to some of the other day about Zoom. Everyone records their zoom meetings, and no one watches them. I mean, who's going to wade through that. Call center is even more high volume. We're talking about massive data. And so will you guys automate that? Do you go through every single piece of data, every call and bring it down? Is that how it works? >> Go ahead. >> There's just some of the things you can do. Analyze the calls for common themes, like figuring out like topic modeling, what are the reasons people are calling in. Summarizing that stuff so you can see what those underlying issues are. And so that could be, like I was mentioning, improving the product or service. It could also be for helping train the agents. So here's how to answer that question. And it could even be reinforcing positive experiences maybe an agent had a particular great call and that could be a reference for other folks. >> Yeah, and also during the conversation, when you think about within 60 to 90 seconds, how do you identify the intonation, the sentiments of the client customer calling in and be able to respond in real time for the challenges that they might be facing and the ability to authenticate the customer at the same time be able to respond to them. I think that is the advancements that we are seeing in the market. >> I think also your point about the data having residual values also excellent because this is a long tail of value in this data, like for predictions and stuff. So NASA was just on before you guys came on, talking about the Artemis project and all the missions and they have to run massive amounts of simulations. And this is where I've kind of seen the dots connect here. You can run with AI, run all the heavy lifting without human touching it to get that first ingestion or analysis, and then iterating on the data based upon what else happens. >> Manoj: Absolutely. >> This is now the new normal, right? Is this? >> It is. And it's transverse towards across multiple domains. So the example we gave you was around Conversational AI. We're now looking at that for doing predictive analytics. Those are some examples that we are doing jointly with AWS SageMaker. We are working on things like computer vision with some of the capabilities and what computer vision has to offer. And so when you think about the continuum of possibilities of what we can bring together from a tools, technology, services perspective, really the sky is the limit in terms of delivering these real experiences to our clients. >> So take me through a customer. Pretending I'm a customer, I get it. I got to do this. It's a competitive advantage. What are the outcomes that they are envisioning? What are some of the patterns you're seeing with customers? What outcomes are they expecting and what kind of high level upside you see them envisioning coming out of the data? >> So when you think about the CxOs today and the board, a lot of them are thinking about, okay, how do you build more efficiency in those system? How do you enable a technology or solution for them to not only increase their top line but as well as their bottom line? How do you enhance the customer experience, which in this case is spot on because when you think about, when customers go repeat to a vendor, it's based on quality, it's based on price. Customer experience is now topping that where your first experience, whether it's through a chat or a virtual assistant or a phone call is going to determine the longevity of that customer with you as a vendor. And so clearly, when you think about how clients are becoming AI fuel, this is where we are bringing in new technologies, new solutions to really push the art to the limit and the art of possible. >> You got a playbook too to do this? >> Yeah, yeah, absolutely. We have done that. And in fact, we are now taking that to the next level up. So something that I've mentioned about this before, which is how do you trust an AI system as it's building up. >> Hold on, I need to plug in. >> Yeah, absolutely. >> I put this here for a reason to remind me. No, but also trust is a big thing. Just put that trustworthy. This is an AI ethics question. >> Arte: It's a big. >> Let's get into it. This is huge. Data's data. Data can be biased from coming in >> Part of it, there are concerns you have to look at the bias in the data. It's also how you communicate through these automated channels, being empathetic, building trust with the customer, being concise in the answers and being accessible to all sorts of different folks and how they might communicate. So it's definitely a big area. >> I mean, you think about just normal life. We all lived situations where we got a text message from a friend or someone close to us where, what the hell, what are you saying? And they had no contextual bad feelings about it or, well, there's misunderstandings 'cause the context isn't there 'cause you're rapid fire them on the subway. I'm riding my bike. I stop and text, okay, I'm okay. Church response could mean I'm busy or I'm angry. Like this is now what you said about empathy. This is now a new dynamic in here. >> Oh, the empathy is huge, especially if you're say a financial institution or building that trust with folks and being empathetic. If someone's reaching out to a contact center, there's a good chance they're upset about something. So you have to take that. >> John: Calm them down first. >> Yeah, and not being like false like platitude kind of things, like really being empathetic, being inclusive in the language. Those are things that you have conversation designers and linguistics folks that really look into that. That's why having domain expertise from folks like Deloitte come in to help with that. 'Cause maybe if you're just building the chat on your own, you might not think of those things. But the folks with the domain expertise will say like, Hey, this is how you script it. It's the power of words, getting that message across clearly. >> The linguistics matter? >> Yeah, yeah. >> It does. >> By vertical too, I mean, you could pick any the tribe, whatever orientation and age, demographics, genders. >> All of those things that we take for granted as a human. When you think about trust, when you think about bias, when you think about ethics, it just gets amplified. Because now you're dealing with millions and millions of data points that may or may not be the right direction in terms of somebody's calling in depending on what age group they're in. Some questions might not be relevant for that age group. Now a human can determine that, but a bot cannot. And so how do you make sure that when you look at this data coming in, how do you build models that are ethically aware of the contextual algorithms and the alignment with it and also enabling that experience to be much enhanced than taking it backwards, and that's really. >> I can imagine it getting better with as people get scaled up a bit 'cause then you're going to have to start having AI to watch the AI at some point, as they say. Where are we in the progress in the industry right now? Because I know there's been a lot of news stories around, ethics and AI and bias and it's a moving train actually, but still problems are going to be solved. Are we at the tipping point yet? Are we still walking in before we crawl or crawling before we walk? I should say, I mean, where are we? >> I think we are in between a crawling or walk phase. And the reason for that is because it varies depending on whether you're regulated industry or unregulated. In the regulated industry, there are compliance regulations requirements, whether it's government whether it's banking, financial institutions where they have to meet Sarbanes-Oxley and all kinds of compliance requirements, whereas an unregulated industry like retail and consumer, it is anybody's gain. And so the reality of it is that there is more of an awareness now. And that's one of the reasons why we've been promoting this jointly with AWS. We have a framework that we have established where there are multiple pillars of trust, bias, privacy, and security that companies and organizations need to think about. Our data scientists, ML engineers need to be familiar with it, but because while they're super great in terms of model building and development, when it comes to the business, when it comes to the client or a customer, it is super important for them to trust this platform, this algorithm. And that is where we are trying to build that momentum, bring that awareness. One of my colleagues has written this book "Trustworthy AI". We're trying to take the message out to the market to say, there is a framework. We can help you get there. And certainly that's what we are doing. >> Just call Deloitte up and you're going to take care of them. >> Manoj: Yeah. >> On the Amazon side, Amazon Web Services. I always interview Swami every year at re:Invent and he always get the updates. He's been bullish on this for a long time on this Conversational AI. What's the update on the AWS side? Where are you guys at? What's the current trends that you're riding? What wave are you riding right now? >> So some of the trends we see in customer interest, there's a couple of things. One is the multimodal interfaces we we're just chatting about where the voice IVA is synced with like a web or mobile experience, so you take that full advantage of the device. The other is adding additional AI into the Conversational AI. So one example is a customer that included intelligent document processing as part of the chatbot. So instead of typing your name and address, take a photo of your driver's license. It was an insurance onboarding chatbot, so you could take a photo of your existing insurance policy. It'll extract that information to build the new insurance policy. So folks get excited about that. And the third area we see interest is what's called multi-bot orchestration. And this is where you can have one main chatbot. Marshall user across different sub-chatbots based on the use case persona or even language. So those things get people really excited and then AWS is launching all sorts of new features. I don't know which one is coming out. >> I know something's coming out tomorrow. He's right at corner. He's big smile on his face. He wouldn't tell me. It's good. >> We have for folks like the closer alliance relationships, we we're able to get previews. So there a preview of all the new stuff. And I don't know what I could, it's pretty exciting stuff. >> You get in trouble if you spill the beans here. Don't, be careful. I'll watch you. We'll talk off camera. All exciting stuff. >> Yeah, yeah. I think the orchestrator bot is interesting. Having the ability to orchestrate across different contextual datasets is interesting. >> One of the areas where it's particularly interesting is in financial services. Imagine a bank could have consumer accounts, merchant accounts, investment banking accounts. So if you were to chat with the chatbot and say I want to open account, well, which account do you mean? And so it's able to figure out that context to navigate folks to those sub-chatbots behind the scenes. And so it's pretty interesting style. >> Awesome. Manoj while we're here, take a minute to quickly give a plug for Deloitte. What your program's about? What customers should expect if they work with you guys on this project? Give a quick commercial for Deloitte. >> Yeah, no, absolutely. I mean, Deloitte has been continuing to lead the AI field organization effort across our client base. If you think about all the Fortune 100, Fortune 500, Fortune 2000 clients, we certainly have them where they are in advanced stages of multiple deployments for AI. And we look at it all the way from strategy to implementation to operational models. So clients don't have to do it alone. And we are continuing to build our ecosystem of relationships, partnerships like the alliances that we have with AWS, building the ecosystem of relationships with other emerging startups, to your point about how do you continue to innovate and bring those technologies to your clients in a trustworthy environment so that we can deliver it in production scale. That is essentially what we're driving. >> Well, Arte, there's a great conversation and the AI will take over from here as we end the segment. I see a a bot coming on theCUBE later and there might be CUBE be replaced with robots. >> Right, right, right, exactly. >> I'm John Furrier, calling from Palo Alto. >> Someday, CUBE bot. >> You can just say, Alexa do my demo for me or whatever it is. >> Or digital twin for John. >> We're going to have a robot on earlier do a CUBE interview and that's Dave Vellante. He'd just pipe his voice in and be fun. Well, thanks for coming on, great conversation. >> Thank you. Thanks for having us. >> CUBE coverage here at re:MARS in Las Vegas. Back to the event circle. We're back in the line. Got re:Inforce and don't forget re:Invent at the end of the year. CUBE coverage of this exciting show here. Machine learning, automation, robotics, space. That's MARS, it's re:MARS. I'm John Furrier. Thanks for watching. (gentle music)

(upbeat music) >> Okay, welcome back everyone. CUBE's coverage of re:MARS, here in Las Vegas, in person. I'm John Furrier, host of theCUBE. This is the analyst panel wrap up analysis of the keynote, the show, past one and a half days. We got two great guests here. We got Andy Thurai, Vice President, Principal Consultant, Constellation Research. Larry Carvalho, Principal Consultant at RobustCloud LLC. Congratulations going out on your own. >> Thank you. >> Andy, great to see you. >> Great to see you as well. >> Guys, thanks for coming out. So this is the session where we break down and analyze, you guys are analysts, industry analysts, you go to all the shows, we see each other. You guys are analyzing the landscape. What does this show mean to you guys? 'Cause this is not obvious to the normal tech follower. The insiders see the confluence of robotics, space, automation and machine learning. Obviously, it's IoTs, industrials, it's a bunch of things. But there's some dots to connect. Let's start with you, Larry. What do you see here happening at this show? >> So you got to see how Amazon started, right? When AWS started. When AWS started, it primarily took the compute storage, networking of Amazon.com and put it as a cloud service, as a service, and started selling the heck out of it. This is a stage later now that Amazon.com has done a lot of physical activity, and using AIML and the robotics, et cetera, it's now the second phase of innovation, which is beyond digital transformation of back office processes, to the transformation of physical processes where people are now actually delivering remotely and it's an amazing area. >> So back office's IT data center kind of vibe. >> Yeah. >> You're saying front end, industrial life. >> Yes. >> Life as we know it. >> Right, right. I mean, I just stopped at a booth here and they have something that helps anybody who's stuck in the house who cannot move around. But with Alexa, order some water to bring them wherever they are in the house where they're stuck in their bed. But look at the innovation that's going on there right at the edge. So I think those are... >> John: And you got the Lunar, got the sex appeal of the space, Lunar Outpost interview, >> Yes. >> those guys. They got Rover on Mars. They're going to have be colonizing the moon. >> Yes. >> I made a joke, I'm like, "Well, I left a part back on earth, I'll be right back." (Larry and Andy laugh) >> You can't drive back to the office. So a lot of challenges. Andy, what's your take of the show? Take us your analysis. What's the vibe, what's your analysis so far? >> It's a great show. So, as Larry was saying, one of the thing was that when Amazon started, right? So they were more about cloud computing. So, which means is they try to commoditize more of data center components or compute components. So that was working really well for what I call it as a compute economy, right? >> John: Mm hmm. >> And I call the newer economy as more of a AIML-based data economy. So when you move from a compute economy into a data economy, there are things that come into the forefront that never existed before, never popular before. Things like your AIML model creation, model training, model movement, model influencing, all of the above, right? And then of course the robotics has come long way since then. And then some of what they do at the store, or the charging, the whole nine yards. So, the whole concept of all of these components, when you put them on re:Invent, such a big show, it was getting lost. So that's why they don't have it for a couple of years. They had it one year. And now all of a sudden they woke up and say, "You know what? We got to do this!" >> John: Yeah. >> To bring out this critical components that we have, that's ripe, mature for the world to next component. So that's why- I think they're pretty good stuff. And some of the robotics things I saw in there, like one of them I posted on my Twitter, it's about the robot dog, sniffing out the robot rover, which I thought was pretty hilarious. (All laugh) >> Yeah, this is the thing. You're seeing like the pandemic put everything on hold on the last re:Mars, and then the whole world was upside down. But a lot of stuff pulled forward. You saw the call center stuff booming. You saw the Zoomification of our workplace. And I think a lot of people got to the realization that this hybrid, steady-state's here. And so, okay. That settles that. But the digital transformation of actually physical work? >> Andy: Yeah. >> Location, the walk in and out store right over here we've seen that's the ghost store in Seattle. We've all been there. In fact, I was kind of challenged, try to steal something. I'm like, okay- (Larry laughs) I'm pulling all my best New Jersey moves on everyone. You know? >> Andy: You'll get charged for it. >> I couldn't get away with it. Two double packs, drop it, it's smart as hell. Can't beat the system. But, you bring that to where the AI machine learning, and the robotics meet, robots. I mean, we had robots here on theCUBE. So, I think this robotics piece is a huge IoT, 'cause we've been covering industrial IoT for how many years, guys? And you could know what's going on there. Huge cyber threats. >> Mm hmm. >> Huge challenges, old antiquated OT technology. So I see a confluence in the collision between that OT getting decimated, to your point. And so, do you guys see that? I mean, am I just kind of seeing mirage? >> I don't see it'll get decimated, it'll get replaced with a newer- >> John: Dave would call me out on that. (Larry laughs) >> Decimated- >> Microsoft's going to get killed. >> I think it's going to have to be reworked. And just right now, you want do anything in a shop floor, you have to have a physical wire connected to it. Now you think about 5G coming in, and without a wire, you get minute details, you get low latency, high bandwidth. And the possibilities are endless at the edge. And I think with AWS, they got Outposts, they got Snowcone. >> John: There's a threat to them at the edge. Outpost is not doing well. You talk to anyone out there, it's like, you can't find success stories. >> Larry: Yeah. >> I'm going to get hammered by Amazon people, "Oh, what're you're saying that?" You know, EKS for example, with serverless is kicking ass too. So, I mean I'm not saying Outpost was wrong answer, it was a right at the time, what, four years ago that came out? >> Yeah. >> Okay, so, but that doesn't mean it's just theirs. You got Dell Technologies want some edge action. >> Yeah. >> So does HPE. >> Yes. >> So you got a competitive edge situation. >> I agree with that and I think that's definitely not Amazon's strong point, but like everything, they try to make it easy to use. >> John: Yeah. >> You know, you look at the AIML and they got Canvas. So Canvas says, hey, anybody can do AIML. If they can do that for the physical robotic processes, or even like with Outpost and Snowcone, that'll be good. I don't think they're there yet, and they don't have the presence in the market, >> John: Yeah. >> like HPE and, >> John: Well, let me ask you guys this question, because I think this brings up the next point. Will the best technology win or will the best solution win? Because if cloud's a platform and all software's open source, which you can make those assumptions, you then say, hey, they got this killer robotics thing going on with Artemis and Moonshot, they're trying to colonize the moon, but oh, they discovered a killer way to solve a big problem. Does something fall out of this kind of re:Mars environment, that cracks the code and radically changes and disrupts the IoT game? That's my open question. I don't know the answer. I'd love to get your take on what might be possible, what wild card's out there around, disrupting the edge. >> So one thing I see the way, so when IoT came into the world of play, it's when you're digitizing the physical world, it's IoT that does digitalization part of that actually, right? >> But then it has its own set of problems. >> John: Yeah. >> You're talking about you installing sensor everywhere, right? And not only installing your own sensor, but also you're installing competitor sensors. So in a given square feet how many sensors can you accommodate? So there are physical limitations on liabilities of bandwidth and networking all of that. >> John: And integration. >> As well. >> John: Your point. >> Right? So when that became an issue, this is where I was talking to the robotic guys here, a couple of companies, and one of the use cases they were talking about, which I thought was pretty cool, is, rather than going the sensor route, you go the robot route. So if you have either a factor that you want to map out, you put as many sensors on your robot, whatever that is, and then you make it go around, map the whole thing, and then you also do a surveillance in the whole nine yards. So, you can either have a fixed sensors or you can have moving sensors. So you can have three or four robots. So initially, when I was asking them about the price of it, when they were saying about a hundred thousand dollars, I was like, "Who would buy that?" (John and Larry laugh) >> When they then explained that, this is the use case, oh, that makes sense, because if you had to install, entire factory floor sensors, you're talking about millions of dollars. >> John: Yeah. >> But if you do the moveable sensors in this way, it's a lot cheaper. >> John: Yeah, yeah. >> So it's based on your use case, what are your use cases? What are you trying to achieve? >> The general purpose is over. >> Yeah. >> Which you're getting at, and that the enablement, this is again, this is the cloud scale open question- >> Yep. >> it's, okay, the differentiations isn't going to be open source software. That's open. >> It's going to be in the, how you configure it. >> Yes. >> What workflows you might have, the data streams. >> I think, John, you're bringing up a very good point about general purpose versus special purpose. Yesterday Zoox was on the stage and when they talked about their vehicle, it's made just for self-driving. You walk around in Vegas, over here, you see a bunch of old fashioned cars, whether they're Ford or GM- >> and they put all these devices around it, but you're still driving the same car. >> John: Yeah, exactly. >> You can retrofit those, but I don't think that kind of IoT is going to work. But if you redo the whole thing, we are going to see a significant change in how IoT delivers value all the way from the industrial to home, to healthcare, mining, agriculture, it's going to have to redo. I'll go back to the OT question. There are some OT guys, I know Rockwell and Siemens, some of them are innovating faster. The ones who innovate faster to keep up with the IT side, as well as the MLAI model are going to be the winners on that one. >> John: Yeah, I agree. Andy, your thoughts on manufacturing, you brought up the sensor thing. Robotics ultimately is, end of the day, an opportunity there. Obviously machine learning, we know what that does. As we move into these more autonomous builds, what does that look like? And is Amazon positioned well there? Obviously they have big manufacturers. Some are saying that they might want to get out of that business too, that Jassy's evaluating that some are saying. So, where does this all lead for that robotics manufacturing lifestyle, walk in, grab my food? 'Cause it's all robotics and AI at the end of the day, I got sensors, I got cameras, I got non-humans moving heavy lifting stuff, fixing the moon will be done by robots, not humans. So it's all coming. What's your analysis? >> Well, so, the point about robotics is on how far it has come, it is unbelievable, right? Couple of examples. One was that I was just talking to somebody, was explaining to them, to see that robot dog over there at the Boston Dynamics one- >> John: Yeah. >> climbing up and down the stairs. >> Larry: Yeah. >> That's more like the dinosaur movie opening the doors scene. (John and Larry laugh) It's like that for me, because the coordinated things, it is able to go walk up and down, that's unbelievable. But okay, it does that, and then there was also another video which is going on viral on the internet. This guy kicks the dog, robot dog, and then it falls down and it gets back up, and the sentiment that people were feeling for the dog, (Larry laughs) >> you can't, it's a robot, but people, it just comes at that level- >> John: Empathy, for a non-human. >> Yeah. >> But you see him, hey you, get off my lawn, you know? It's like, where are we? >> It has come to that level that people are able to kind of not look at that as a robot, but as more like a functioning, almost like a pet-level, human-level being. >> John: Yeah. >> And you saw that the human-like walking robot there as well. But to an extent, in my view, they are all still in an experimentation, innovation phase. It doesn't made it in the industrial terms yet. >> John: Yeah, not yet, it's coming. >> But, the problem- >> John: It's coming fast. That's what I'm trying to figure out is where you guys see Amazon and the industry relative to what from the fantasy coming reality- >> Right. >> of space in Mars, which is, it's intoxicating, let's face it. People love this. The nerds are all here. The geeks are all here. It's a celebration. James Hamilton's here- >> Yep. >> trying to get him on theCUBE. And he's here as a civilian. Jeff Barr, same thing. I'm here, not for Amazon, I bought a ticket. No, you didn't buy a ticket. (Larry laughs) >> I'm going to check on that. But, he's geeking out. >> Yeah. >> They're there because they want to be here. >> Yeah. >> Not because they have to work here. >> Well, I mean, the thing is, the innovation velocity has increased, because, in the past, remember, the smaller companies couldn't innovate because they don't have the platform. Now Compute is a platform available at the scale you want, AI is available at the scale. Every one of them is available at the scale you want. So if you have an idea, it's easy to innovate. The innovation velocity is high. But where I see most of the companies failing, whether startup or big company, is that you don't find the appropriate use case to solve, and then don't sell it to the right people to buy that. So if you don't find the right use case or don't sell the right value proposition to the actual buyer, >> John: Mm hmm. >> then why are you here? What are you doing? (John laughs) I mean, you're not just an invention, >> John: Eh, yeah. >> like a telephone kind of thing. >> Now, let's get into next talk track. I want to get your thoughts on the experience here at re:Mars. Obviously AWS and the Amazon people kind of combined effort between their teams. The event team does a great job. I thought the event, personally, was first class. The coffee didn't come in late today, I was complaining about that, (Larry laughs) >> people complaining out there, at CUBE reviews. But world class, high bar on the quality of the event. But you guys were involved in the analyst program. You've been through the walkthrough, some of the briefings. I couldn't do that 'cause I'm doing theCUBE interviews. What would you guys learn? What were some of the key walkaways, impressions? Amazon's putting all new teams together, seems on the analyst relations. >> Larry: Yeah. >> They got their mojo booming. They got three shows now, re:Mars, re:inforce, re:invent. >> Andy: Yeah. >> Which will be at theCUBE at all three. Now we got that coverage going, what's it like? What was the experience like? Did you feel it was good? Where do they need to improve? How would you grade the Amazon team? >> I think they did a great job over here in just bringing all the physical elements of the show. Even on the stage, where they had robots in there. It made it real and it's not just fake stuff. And every, or most of the booths out there are actually having- >> John: High quality demos. >> high quality demos. (John laughs) >> John: Not vaporware. >> Yeah, exactly. Not vaporware. >> John: I won't say the name of the company. (all laugh) >> And even the sessions were very good. They went through details. One thing that stood out, which is good, and I cover Low Code/No Code, and Low Code/No Code goes across everything. You know, you got DevOps No Low-Code Low-Code. You got AI Low Code/No Code. You got application development Low Code/No Code. What they have done with AI with Low Code/No Code is very powerful with Canvas. And I think that has really grown the adoption of AI. Because you don't have to go and train people what to do. And then, people are just saying, Hey, let me kick the tires, let me use it. Let me try it. >> John: It's going to be very interesting to see how Amazon, on that point, handles this, AWS handles this data tsunami. It's cause of Snowflake. Snowflake especially running the table >> Larry: Yeah. >> on the old Hadoop world. I think Dave had a great analysis with other colleagues last week at Snowflake Summit. But still, just scratching the surface. >> Larry: Yeah. >> The question is, how shared that ecosystem, how will that morph? 'Cause right now you've got Data Bricks, you've got Snowflake and a handful of others. Teradata's got some new chops going on there and a bunch of other folks. Some are going to win and lose in this downturn, but still, the scale that's needed is massive. >> So you got data growing so much, you were talking earlier about the growth of data and they were talking about the growth. That is a big pie and the pie can be shared by a lot of folks. I don't think- >> John: And snowflake pays AWS, remember that? >> Right, I get it. (John laughs) >> I get it. But they got very unique capabilities, just like Netflix has very unique capabilities. >> John: Yeah. >> They also pay AWS. >> John: Yeah. >> Right? But they're competing on prime. So I really think the cooperation is going to be there. >> John: Yeah. >> The pie is so big >> John: Yeah. >> that there's not going to be losers, but everybody could be winners. >> John: I'd be interested to follow up with you guys after next time we have an event together, we'll get you back on and figure out how do you measure this transitions? You went to IDC, so they had all kinds of ways to measure shipments. >> Larry: Yep. >> Even Gartner had fumbled for years, the Magic Quadrant on IaaS and PaaS when they had the market share. (Larry laughs) And then they finally bundled PaaS and IaaS together after years of my suggesting, thank you very much Gartner. (Larry laughs) But that just performs as the landscape changes so does the scoreboard. >> Yep. >> Right so, how do you measure who's winning and who's losing? How can we be critical of Amazon so they can get better? I mean, Andy Jassy always said to me, and Adam Salassi same way, we want to hear how bad we're doing so we can get better. >> Yeah. >> So they're open-minded to feedback. I mean, not (beep) posting on them, but they're open to critical feedback. What do you guys, what feedback would you give Amazon? Are they winning? I see them number one clearly over Azure, by miles. And even though Azure's kicking ass and taking names, getting back in the game, Microsoft's still behind, by a long ways, in some areas. >> Andy: Yes. In some ways. >> So, the scoreboard's changing. What's your thoughts on that? >> So, look, I mean, at the end of the day, when it comes to compute, right, Amazon is a clear winner. I mean, there are others who are catching up to it, but still, they are the established leader. And it comes with its own advantages because when you're trying to do innovation, when you're trying to do anything else, whether it's a data collection, we were talking about the data sensors, the amount of data they are collecting, whether it's the store, that self-serving store or other innovation projects, what they have going on. The storage compute and process of that requires a ton of compute. And they have that advantage with them. And, as I mentioned in my last article, one of my articles, when it comes to AIML and data programs, there is a rich and there is a poor. And the rich always gets richer because they, they have one leg up already. >> John: Yeah. >> I mean the amount of model training they have done, the billion or trillion dollar trillion parametrization, fine tuning of the model training and everything. They could do it faster. >> John: Yeah. >> Which means they have a leg up to begin with. So unless you are given an opportunity as a smaller, mid-size company to compete at them at the same level, you're going to start at the negative level to begin with. You have a lot of catch up to do. So, the other thing about Amazon is that they, when it comes to a lot of areas, they admit that they have to improve in certain areas and they're open and willing and listen to the people. >> Where are you, let's get critical. Let's do some critical analysis. Where does Amazon Websters need to get better? In your opinion, what criticism would you, in a constructive way, share? >> I think on the open source side, they need to be more proactive in, they are already, but they got to get even better than what they are. They got to engage with the community. They got to be able to talk on the open source side, hey, what are we doing? Maybe on the hardware side, can they do some open-sourcing of that? They got graviton. They got a lot of stuff. Will they be able to share the wealth with other folks, other than just being on an Amazon site, on the edge with their partners. >> John: Got it. >> If they can now take that, like you said, compute with what they have with a very end-to-end solution, the full stack. And if they can extend it, that's going to be really beneficial for them. >> Awesome. Andy, final word here. >> So one area where I think they could improve, which would be a game changer would be, right now, if you look at all of their solutions, if you look at the way they suggest implementation, the innovations, everything that comes out, comes out across very techy-oriented. The persona is very techy-oriented. Very rarely their solutions are built to the business audience or to the decision makers. So if I'm, say, an analyst, if I want to build, a business analyst rather, if I want to build a model, and then I want to deploy that or do some sort of application, mobile application, or what have you, it's a little bit hard. It's more techy-oriented. >> John: Yeah, yeah. >> So, if they could appeal or build a higher level abstraction of how to build and deploy applications for business users, or even build something industry specific, that's where a lot of the legacy companies succeeded. >> John: Yeah. >> Go after manufacturing specific or education. >> Well, we coined the term 'Supercloud' last re:Invent, and that's what we see. And Jerry Chen at Greylock calls it Castles in the Cloud, you can create these moats >> Yep. >> on top of the CapEx >> Yep. >> of Amazon. >> Exactly. >> And ride their back. >> Yep. >> And the difference in what you're paying and what you're charging, if you're good, like a Snowflake or a Mongo. I mean, Mongo's, they're just as big as Snow, if not bigger on Amazon than Snowflake is. 'Cause they use a lot of compute. No one turns off their database. (John laughs) >> Snowflake a little bit different, a little nuanced point, but, this is the new thing. You see Goldman Sachs, you got Capital One. They're building their own kind of, I call them sub clouds, but Dave Vellante says it's a Supercloud. And that essentially is the model. And then once you have a Supercloud, you say, great, I'm going to make sure it works on Azure and Google. >> Andy: Yep. >> And Alibaba if I have to. So, we're kind of seeing a playbook. >> Andy: Mm hmm. >> But you can't get it wrong 'cause it scales. >> Larry: Yeah, yeah. >> You can't scale the wrong answer. >> Andy: Yeah. >> So that seems to be what I'm watching is, who gets it right? Product market fit. Then if they roll it out to the cloud, then it becomes a Supercloud, and that's pure product market fit. So I think that's something that I've seen some people trying to figure out. And then, are you a supplier to the Superclouds? Like a Dell? Or you become an enabler? >> Andy: Yeah. >> You know, what's Dell Technologies do? >> Larry: Yeah. >> I mean, how do the box movers compete? >> Larry: I, the whole thing is now hybrid and you're going to have to see just, you said. (Larry laughs) >> John: Hybrid's a steady-state. I don't need to. >> Andy: I mean, >> By the way we're (indistinct), we can't get the chips, cause Broadcom and Apple bought 'em all. (Larry laughs) I mean there's a huge chip problem going on. >> Yes. I agree. >> Right now. >> I agree. >> I mean all these problems when you attract to a much higher level, a lot of those problems go away because you don't care about what they're using underlying as long as you deliver my solution. >> Larry: Yes. >> Yeah, it could be significantly, a little bit faster than what it used to be. But at the end of the day, are you solving my specific use case? >> John: Yeah. >> Then I'm willing to wait a little bit longer. >> John: Yeah. Time's on our side and now they're getting the right answers. Larry, Andy, thanks for coming on. This great analyst session turned into more of a podcast vibe, but you know what? (Larry laughs) To chill here at re:Mars, thanks for coming on, and we unpacked a lot. Thanks for sharing. >> Both: Thank you. >> Appreciate it. We'll get you back on. We'll get you in the rotation. We'll take it virtual. Do a panel. Do a panel, do some panels around this. >> Larry: Absolutely. >> Andy: Oh this not virtual, this physical. >> No we're live right now! (all laugh) We get back to Palo Alto. You guys are influencers. Thanks for coming on. You guys are moving the market, congratulations. Take a minute, quick minute each to plug any work you're doing for the people watching. Larry, what are you working on? Andy? You go after Larry, what you're working on. >> Yeah. So since I started my company, RobustCloud, since I left IDC about a year ago, I'm focused on edge computing, cloud-native technologies, and Low Code/No Code. And basically I help companies put their business value together. >> All right, Andy, what are you working on? >> I do a lot of work on the AIML areas. Particularly, last few of my reports are in the AI Ops incident management and ML Ops areas of how to generally improve your operations. >> John: Got it, yeah. >> In other words, how do you use the AIML to improve your IT operations? How do you use IT Ops to improve your AIML efficiency? So those are the- >> John: The real hardcore business transformation. >> Yep. >> All right. Guys, thanks so much for coming on the analyst session. We do keynote review, breaking down re:Mars after day two. We got a full day tomorrow. I'm John Furrier with theCUBE. See you next time. (pleasant music)

>>Okay, welcome back everyone. This is the Cube's coverage of S reinve rein Mars. I said reinvent all my VES months away. Re Mars machine learning, automation, robotics, and space. I'm John feer, host of the cube, an exciting guest here, bringing on special guest more robot robots are welcome on the cube. We're gonna have that segment here. Mike Dooley co-founder and CEO of Labrador systems. Mike, welcome to the cube. Thanks. >>Coming on. Thank, thank you so much. Yeah. Labrador systems. We're a company is developing a new type of assistive robot for people in the home. And you know, our mission is really to help people live independently. And so we're about to show a robot that's it looks like my, what used to be in a warehouse or other places, but it's being designed to be both robust enough to operate in real world settings, help people that may be aging and using a Walker wheelchair. A cane could have early onset health conditions like Parkinson's and things like that. So >>Let me, let me set this up first, before you get into the, the demo, because I think here at re Mars, one of the things that's coming outta the show besides the cool vibe, right? Is that materials handling? Isn't the only thing you've seen with robotics. Yeah. You're seeing a lot more life industrial impact. And this is an example of one of that, isn't >>It? Yeah. We just actually got an award. It's a Joseph EGL Bergo was the first person to actually put robots in factories and automation. And in doing that, um, he set up grant for robots going beyond that, to help people live in it. So we're the first recipient of that. But yeah, I think that robots, they're not the, what you think about with Rosie yet. We're the wrong way from that, but they're, they can do really meaningful things. >>And before we get the demo, your mission hearing, what you're gonna show here is a lot of hard work and we know how hard it is. What's the mission. What's the vision. >>The mission is to help people live more independently on their own terms. Uh, we're, there's, it's an innate part of the human condition that at some point in our lives, it becomes more difficult to move ourselves or move things around it. And that is a huge impact on our independence. So when we're putting this robot in pilots, we're helping people try to regain degrees of independence, be more active deal with whatever situation they want, but under their terms and have, have control over their life. >>Okay, well, let's get into it. May I offer you a glass of water? Well, you >>Know, I have a robot that just happens to be really good at delivering things, including water. Um, we just actually pulled these out of our refrigerator on our last demo. So why don't we bring over the retriever? And so we're gonna command it to come on in. So this is a Labrador retriever. These robots have been in homes. This robot itself has been in homes, helping people do activities like this. It's able to sort of go from place to place it automatically navigates itself. Uh, just like we've been called a self-driving shelf, um, as an example, but it's meant to be very friendly, can come to a position like this could be by my armchair and it would automatically park. And then I could do something like I can pick up, okay, I want some water and maybe I want to drink it out of a cup and I can do this. And if I have a cough or something else, cough drops. My phone, all sorts of things can be in there. Um, so the purpose of the retriever is really to be this extra pair of hands, to keep things close by and move things. And it can automatically adjust to any hide or position. And if I, even if I block it like a safety, it, it >>Stops. And someone who say disabled or can't move is recovering or has some as aging or whatever the case is. This comes to them. It's autonomous in it sense. Is that that what works or yeah. Is it guided? How does >>It, it works on a series of bus stops. So the in robotics, we call those way points. But when we're talking to people, the bus stops are the places you want it to go. You have a bus stop by the front door, your kitchen sink, the refrigerator, your armchair, the laundry machine, you won't closet it. <laugh>. And with that simple metaphor, we, we train the robot in a couple hours. We create all these routes, just like a subway map. And then the robot is autonomous. So I can hit a button. I can hit my cell phone, or I can say Alexa ass lab, one to come to the kitchen. The robot will autonomously navigate through everything, go around the pets park itself. And it raises and lowers to bring things with and reach. So I'm sitting and it might lower itself down. So I can just comfortably get something at the kitchen. I, it could just go right to the level of the countertop. So it's very easy for someone that has an issue to move things with with limited, uh, challenges. >>And this really illustrates to this show again. Yeah. Talk about the impact here. Cause we're at a historic moment in robotics. >>We are. Yeah. >>What's your reaction to that? Tell your, share your vision >>On that. I've been in robotics for 25 years. Um, and I started, I actually started working actually at Lego and launching Lego Mindstorms, the end of the nineties. So I have like CEO just last night again, they gush over like you did that. Yeah. <laugh> and again, I'm pretty old school. And so we've my career. If I've been working through from toys onto like robotic floor cleaners, the algorithms that are on Roomba today came from the startup that we were all part of. We're, we're moving things to be bigger and bigger and have a bigger >>Impact. What's it feel like? I mean, cuz I mean I can see the experience and by the way, it's hardcore robotics communities out there, but now it's still mainstream. It's opening up the aperture of robotics. Yeah. It's the prime time is right now and it's an inflection point. >>Well, and it's also a point where we desperately need it. So we have incredible work for shortages <laugh> and it's not that we're, these robots are not to take people jobs away it's to do the work that people don't want to do and try to make, you know, free them up for things that are more important. Yeah. In senior care, that's the high touch we want caregivers to be helping people get outta their bed, help them safely move from place to place things that robots aren't at yet. Yeah. But for getting the garbage, for getting a drink or giving the person the freedom to say, do I wanna ask my caregiver or my spouse to do that? Or do I wanna do it myself? And so robots can be incredibly liberating experience if they're, if they're done in the right way and they're done well, >>It's a choice. It actually comes down to choice. I remember this argument way back when, oh, ATM's gonna kill the bank teller. In fact more bank tellers emerged. Right. And so there's choices come out there and, but there's still more advances to do. What is you, what do you see as milestones for the industry as you start to seeing better handling better voice activation cameras on board. I noticed some cameras in there. Yeah. So we're starting to see the, some of the smaller, faster, cheaper >>It's it's especially yeah. Faster. Cheaper is what we're after. So can we redo? So like the gyros that are on this type of robot used to be like in the tens of thousands of dollars 20 or 30 years ago. And, and then when you started seeing Roomba and the floor cleaners come out, those started what happened was basically the gyro on here that what's happening in consumer electronics, the ability for the iPhone to play, you know, the game in turn and, and do portrait and landscape. That actually is what enables all these robots that clean your floors to do very tight angles. What we're doing is this migration of consumer electronics then gets robbed and, and adopted over in that. So it's really about it's I, it's not that you're gonna see things radically change. It's just that you're gonna see more and more applications get more sophisticated and become more affordable. Our target is to bring this for a few hundred dollars a month into people's homes. Yeah. Yeah. Um, and make that economy work for as many people as >>Possible. Yeah. Mike, what a great, great illustration of great point there now on your history looking forward. Okay. Smaller Fest are cheaper. Yeah. You're gonna see a human aspect. So technology's kind of getting out of the way now you got a lot in the cloud, you got machine learning, big thing here. There's a human creative side now gonna be a big part of this. Yeah. Can you talk about like how you see that unfolding? Because again, younger people gonna come in, you got a lot more things pre-built I just saw a swaping on stage saying, oh, we, we write subroutines automatically the machine learning like, oh my God, that's so cool. Like, so more is coming for, to, for builders, right. To build what's the playbook gonna look like? How do you see the human aspect, creative crafting building? >>I, I it's, you know, it's a hard Fu future to predict. I think the issue is that humans are always gonna have to be more clever than the AI <laugh>, you know, I, I can't say that enough is that AI can solve some things and it can get smarter and smarter. You task that over and then let's work on the things that can't do. And I think that's intellectually challenging. Like, and I, and I think we have a long way to go, uh, to sort of keep on pushing that forward. So the whole mission is people get to do more interesting things with their life, more dynamic. Think about what the machines should be working on. Yeah. And then move on to the next things. >>Well, a lot of good healthcare implications. Yeah. Uh, senior living people who are themselves, >>All those are place. Yeah. >>Now that you have, um, this kind of almost a perfect storm of innovation coming, and I just think it's gonna be the beginning. You're gonna see a lot of young people come in. Yeah. And a lot of people in school now going down to the elementary school level yeah. Are really immersed in robotics. They're born with it. And certainly as they get older, what kind of disciplines do you see coming into robot? I used to be pretty clear. Yeah. Right. Nerdy, builder, builder. Now it's like what? I got Mac and rice. My code. >>Yeah. My, my co-founder and CEO has a good example. Anybody we interview, we say we really like it. If you think of yourself as an astronaut, going on to a space mission. And, and it's really appropriate being here at R Mars is that normally the astronaut has one specialty, but they have to know enough of the other skills to be able to help out. In case of an emergency robotics is so complex. There's so there's mechanical, there's electrical, there's software, they're perceptual, there's user interface, all of those Fs together. So when we're trying to do a demo and something goes wrong, I can't say why. I only do mechanical. Yeah. You got it. You really have to have a system. So I think if any system architects, people that if you're gonna, if, if you're gonna be, if mechanical is your thing, you better learn a little bit electrical and software. Yeah. If software is your thing, you better not just write code because you need to understand where you're >>Your back. Well, the old days you have to know for trend to run any instrumentation in the old days. So same kind of vibe. So what does that impact on the teamwork side? Because now I can imagine, okay, you got some general purpose knowledge, so math, science, all the disciplines, but the specialties there, I love that right now. Teamwork. Yeah. Because you, you know, I could be a generalism at some point. There's another component I'm gonna need to call my teammate for. >>Yeah. Yeah. And you have to have, yeah. So it, yeah, we're a small team, so it's a little bit easier right now, but even the technology. So like there's a, what, this is, this runs on Linux and that runs on Ross, which is a robotics operating system. The modules are, are the, are sorry, the modules, I mean redundant there, but the, the part that makes the robot go, okay, I'm gonna command it to go here. It's gonna go around it, see an obstacle. This module kicks in, even the elements become module. So that's part of how teams work is that, and, and Amazon has a rule around that is that everything has to have an API. Yeah. I have to be able to express my work and the way that somebody else can come in and talk to it in a very easy way. So you're also going away from like, sort of like the hidden code that only I touch you can't have ownership of that. You have to let your team understand how it works and let them control it and edit it. Well, >>Super exciting. Dan, first of all, great to bring robots on the cube set. Thanks to your team here. Doing that. Yeah. Um, talk about the company. Um, put a plug in, what are you guys doing? Sure. Raising money, getting more staff, more sales. We're give, give a commercial. >>Yeah. So we, we closed the seed round. So we've been around it's actually five years next month. Um, did pre-seed and then we closed the seed round that we announced back at CS. So we debuted the retriever for the first time we had it under wraps. We had it in people's homes for a year before we did that. Um, I, Amazon was one of our early investors and they actually co-led on this last round, along with our friends at iRobot. So yeah. Uh, so we've raised that we're right in the next phase of deploying this, especially going more into senior living now that that's opening up with COVID coming down and looking at helping these workforce issues where there's that crisis. So we'll be raising later this year. So we're starting to sort of do the preview for series a. We're starting to take those pre-orders for robots and for Lois. And then our goal is we're and we're actually already at the factory. So we've been converting this, these there's a version of this robot underway right now at the factory that will probably have engineering units at the end of this year. Yeah. Goal is for, uh, full production with all the supply chain issues for second half of, of next >>Year. Yeah. Well, congratulations. It's a great product. And I gotta ask you what's on the roadmap, how you see this product unfolding. What's the wishlist look like if you had all the dough in the world, what would you do next? What would you be putting on there? Sure. If you had the magic wand what's happening, >>It's a couple variables. I think it's scale. So it's driving the, this whole thing is designed to go down in cost, which improves basically accessibility. More people can afford it. The health system, Medicare, those sorts of folks. See it one. So basically get us into reduction and get us into volume is one part, I think the other ones is adding layers. I, what we, when we see our presentation and the speech we're doing tomorrow, we see this as a force multiplier for a lot of other things in healthcare. So if I bring the blood pressure cuff, like we have on the retrieval, I can be a physical reminder to take your medication, to take the, my, my readings, or we are just con having a conversation with some of our friends of Amazon is bringing an echo show to you when you want to have a conversation and take it away. >>When you don't think about that metaphor of how do I wanna live my life and what do I have control over? And then on top of it, the sensors on the robot, they're pretty sophisticated. So in my case, my mom is still around she's 91, but now in a hospital beded wheelchair could, we've seen her walking differently early, early on, and using things like Intel, real sense and, and computer vision and AI to detect things and just say to her, don't even tell anybody else, we're noticing this. Do you wanna share this with your doctor? Yeah. That's the world. I think that what we're trying to do is lay this out as version 1.0, so that when folks like us are around, it'll something like decades from now, life is so much more better for the options and choices we have. It's >>Really interesting. You know, I liked, um, kind of the theme here. There's a lot of day to day problems that people like to solve. And then there's like the new industrial problems that are emerging that are opportunities. And then there's the save the world kind of vibe. <laugh>, there's help people make things positive, right. You know, solve the climate problems, help people. And so we're kind of at this new era and it's beyond just like sustainability and, you know, bias. That's all gotta get done a new tipping point around the human aspect of >>Things. And you do it economically. I think sometimes you think that, okay, well, you're just doing this cuz you're, you're socially motivated and doesn't, you don't care how many you sell it to just so you can accomplish it. It's their link. The, the cheaper that we can make this, the more people you can impact. I think you're talking about the kids today is the work we did at Lego. In the end of the nineties, you made a, a robotics kit for 200 bucks and millions of kids. Yeah. Did that. And >>Grape pie. I mean, you had accessories to it. Make a developer friendly. >>Yeah, no, exactly. And we're getting all those requests. So I think that's the thing is like, get a new platform, learn what it's like to have this sort of capability and then let the market drive. It, let the people sort of the folks who are gonna be using it that are in a wheelchair, are dealing with Parkinson's or Ms, or other issues. What can we add to that ecosystem? So you it's, it's all about being very human centric in that. Yeah. And making the other parts of the economy make it work for them, make it so that the health system, they get an ROI on this so that, Hey, this is a good thing to put into people's homes. >>And well, I think you have the nice, attractive value proposition to investors. Obviously robotics is super cool and really relevant. Cool, cool. And relevant to me always is nice to have that. So check that, then you got the economics on price, pressure, prove the price down lower. Yeah. Open up the Tams of the market. Right. Make it more viable economically. >>Yeah, definitely. And then, and what we're having, what's driving us that wasn't around seven when we started this about four and a half years ago. Uh, my joke and I don't mean to offend them, but after doing pitching the vision of this in six months, don't be, >>Don't be afraid. We're do we, >>My, my joke. And I'm sort to see more bold about is that VCs don't think they're gonna get old. They're just gonna get rich. And so the idea is that they didn't see themselves in this position and we not Gloo and doom, you can work out, you can be active, but we're living older, longer. We are it's. My mom is born in the depression. She's been in a wheelchair for five years. She might be around for a good, another 10 or 15. And that's wonderful for her, but her need for care is really high. >>Yeah. And the pressure on the family too, there's always, there's always collateral damage on all these impacts. >>There's 53 million unpaid family caregivers in the us. Yeah. Just in the time that we've grown, been doing this, it's grown 4% a year and it's a complicated thing. And it's, it's not just the pressure on you to help your mom or dad or whoever. It's the frustration on their face when they have to always ask for that help. So it's, it's twofold. It's give them some freedom back so they can make a choice. Like my classic example is my mom wants tea. My dad's trying to watch the game. He, she asks for it. It's not hot enough. Sends it back. And that's a currency. Yeah, yeah. That she's losing and, and it's frustration as opposed to give her a choice to say, I'm gonna do this on my own. And I that's just, >>You wanna bring the computer out, do a FaceTime with the family, send it back. Or you mentioned the Alexa there's so many use cases. Oh >>No. We talked about, uh, we talked about putting like a, a device with a CA with a screen on it so she could chat and see pictures. And it says, I don't want to have this in my bedroom. That's my private space. Yeah. But if we could have the robot, bring it in when it's appropriate and take it on go the retriever that's that's >>The whole go fetch what I need right now. That's and then go lie down. Yeah. >>That's what I, I called >>Labrador. Doesn't lie down >>Actually. But well, it lowers down, it lowers down about 25 inches. That's about lying. >>Down's super exciting. And congratulations. I know, um, how passionate you are. It's obvious. Yeah. And being in the business so long, so many accomplishment you had. Yeah. But now is a whole new Dawn. A new era here. >>Yeah. Oh yeah. No, I, we just, it was real. It was on impromptu. It wasn't scheduled. There's a, a post circle on LinkedIn where all the robots got together. <laugh> you know, and they were seeing to hang out. No, and you're seeing stuff that wasn't possible. You look at this and you go, well, what's the big thing. It's a box on wheels. It's like, it wasn't possible to navigate something around the complexity of a home 10 years ago for the price we're doing. Yeah. It wasn't possible to wa have things that walk or spot that can go through construction sites. I, I think people don't realize it's it. It really is changing. And then we're, I think every five years you're gonna be seeing this more bold deployment of these things hitting our lives. It's >>It's super cool. And that's why this show's so popular. It's not obvious to mainstream, but you look at the confluence of all those forces coming together. Yeah. It's just a wonderful thing. Thanks for coming on. Appreciate >>It really, really appreciate you for this >>Time. Great success. Great demo. Mike, do cofounder, the CEO of Labrador systems. Check him out. They have the retriever, uh, future of robotics here. It's all impact all life on the planet. And more space. Two is to keep coverage here at re Mars, stay tuned for more live coverage. After this short break.

>>Okay, welcome back. Everyone's Cube's coverage here in Las Vegas for Amazon re Mars machine learning, automation, robotics, and space out. John fir host of the queue. Got a great set of guests here talking about AI, Jason Montgomery CTO and co-founder man and Ryans CEO, founder guys. Thanks for coming on. We're just chatting, lost my train of thought. Cuz we were chatting about something else, your history with DataRobot and, and your backgrounds entrepreneurs. Welcome to the queue. Thanks >>Tur. Thanks for having >>Us. So first, before we get into the conversation, tell me about the company. You guys have a history together, multiple startups, multiple exits. What are you guys working on? Obviously AI is hot here as part of the show. M is Mars machine learning, which we all know is the basis for AI. What's the story. >>Yeah, really. We're we're here for two of the letters and Mars. We're here for the machine learning and the automation part. So at the high level, man is a no code AI application development platform. And basically anybody could log in and start making AI applications. It could be anything from just texting it with the Twilio integration to tell you that you're doing great or that you need to exercise more to integrating with zenes to get support tickets classified. >>So Jason, we were talking too about before he came on camera about the cloud and how you can spin up resources. The data world is coming together and I, and I like to see two flash points. The, I call it the 2010 big data era that began and then failed Hadoop crashed and burned. Yeah. Then out of the, out of the woodwork came data robots and the data stacks and the snowflakes >>Data break snowflake. >>And now you have that world coming back at scale. So we're now seeing a huge era of, I need to stand up infrastructure and platform to do all this heavy lifting. I don't have time to do. Right. That sounds like what you guys are doing. Is that kind of the case? >>That's absolutely correct. Yeah. Typically you would have to hire a whole team. It would take you months to sort of get the infrastructure automation in place, the dev ops DevOps pipelines together. And to do the automation to spin up, spin down, scale up scale down requires a lot of special expertise with, you know, Kubernetes. Yeah. And a lot of the other data pipelines and a lot of the AWS technologies. So we automate a lot of that. So >>If, if DevOps did what they did, infrastructure has code. Yeah. Data has code. This is kind of like that. It's not data ops per se. Is there a category? How do you see this? Cuz it's you could say data ops, but that's also it's DevOps dev. It's a lot going on. Oh yeah. It's not just seeing AI ops, right? There's a lot more, what, what would you call this? >>It's a good question. I don't know if we've quite come up with the name. I know >>It's not data ops. It's not >>Like we call it AI process automation >>SSPA instead of RPA, >>What RPA promised to be. Yes, >>Exactly. But what's the challenge. The number one problem is it's I would say not, not so much all on ever on undifferent heavy lifting. It's a lot of heavy lifting that for sure. Yes. What's involved. What's the consequences of not going this way. If I want to do it myself, can you take me through the, the pros and cons of what the scale scope, the scale of without you guys? >>Yeah. Historically you needed to curate all your data, bring it together and have some sort of data lake or something like that. And then you had to do really a lot of feature engineering and a lot of other sort of data science on the back end and automate the whole thing and deploy it and get it out there. It's a, it's a pretty rigorous and, and challenging problem that, you know, we there's a lot of automation platforms for, but they typically focus on data scientists with these large language models we're using they're pre-trained. So you've sort of taken out that whole first step of all that data collection to start out and you can basically start prototyping almost instantly because they've already got like 6 billion parameters, 10 billion parameters in them. They understand the human language really well. And a lot of other problems. I dunno if you have anything you wanna add to that, Ryan, but >>Yeah, I think the other part is we deal with a lot of organizations that don't have big it teams. Yeah. And it would be impossible quite frankly, for them to ever do something like deploy text, track as an example. Yeah. They're just not gonna do it, but now they can come to us. They know the problem they want solved. They know that they have all these invoices as an example and they wanna run it through a text track. And now with us they can just drag and drop and say, yeah, we want tech extract. Then we wanted to go through this. This is what we >>Want. Expertise is a huge problem. And the fact that it's changing too, right? Yeah. Put that out there. You guys say, you know, cybersecurity challenges. We guys do have a background on that. So you know, all the cutting edge. So this just seems to be this it, I hate to say transformation. Cause I not the word I'm looking for, I'd say stuck in the mud kind of scenario where they can't, they have to get bigger, faster. Yeah. And the scale is bigger and they don't have the people to do it. So you're seeing the rise of managed service. You mentioned Kubernetes, right? I know this young 21 year old kid, he's got a great business. He runs a managed service. Yep. Just for Kubernetes. Why? Because no, one's there to stand up the clusters. >>Yeah. >>It's a big gap. >>So this, you have these sets of services coming in now, where, where do you guys fit into that conversation? If I'm the customer? My problem is what, what is my, what is my problem that I need you guys for? What does it look like to describe my problem? >>Typically you actually, you, you kind of know that your employees are spending a lot of time, a lot of hours. So I'll just give you a real example. We have a customer that they were spending 60 hours a week just reviewing these accounts, payable, invoices, 60 hours a week on that. And they knew there had to be a better way. So manual review manual, like when we got their data, they were showing us these invoices and they had to have their people circle the total on the invoice, highlight the customer name, the >>Person who quit the next day. Right? >>No like they, they, Hey, you know, they had four people doing this, I think. And the point is, is they come to us and we say, well, you know, AI can, can just basically using something like text track can just do this. And then we can enrich those outputs from text track with the AI. So that's where the transformers come in. And when we showed them that and got them up and running in about 30 minutes, they were mind blown. Yeah. And now this is a company that doesn't have a big it department. So the >>Kind, and they had the ability to quantify the problem >>They knew. And, and in this case it was actually a business user. It was not a technical >>In is our she consequence technical it's hours. She consequences that's wasted. Manual, labor wasted. >>Exactly. Yeah. And, and to their point, it was look, we have way more high, valuable tasks that our people could be doing yeah. Than doing this AP thing. It takes 60 hours. And I think that's really important to remember about AI. What're I don't think it's gonna automate away people's jobs. Yeah. What it's going to do is it's going to free us up to focus on what really matters and focus on the high value stuff. And that's what people should >>Be doing. I know it's a cliche. I'm gonna say it again. Cause I keep saying, cause I keep saying for people to listen, the bank teller argument always was the big thing. Oh yeah. They're gonna get killed by the ATM machine. No, they're opening up more branches. That's right. That's right. So it's like, come on. People let's get, get over that. So I, I definitely agree with that. Then the question, next question is what's your secret sauce? I'm the customer I'm gonna like that value proposition. You make something go away. It's a pain relief. Then there's the growth side. Okay. You can solve from problems. Now I want this, the, the vitamin you got aspirin. And I want the vitamin. What's the growth angle for you guys with your customers. What's the big learnings. Once they get the beach head with problem solving. >>I think it, it, it it's the big one is let's say that we start with the account payable thing because it's so our platform's so approachable. They go in and then they start tinkering with the initial, we'll call it a template. So they might say, Hey, you know what, actually, in this edge case, I'm gonna play with this. And not only do I want it to go to our accounting system, but if it's this edge case, I want it to email me. So they'll just drag and drop an email block into our canvas. And now they're making it >>Their own. There is the no code, low code's situation. They're essentially building a notification engine under the covers. They have no idea what they're doing. That's >>Right. They get the, they just know that, Hey, you know what? When, when like the amount's over $10,000, I want an email. They know that's what they want. They don't, they don't know that's the notification engine. Of >>Course that's value email. Exactly. I get what I wanted. All right. So tell me about the secret sauce. What's under the covers. What's the big, big, big scale, valuable, valuable, secret sauce. >>I would say part of it. And, and honestly, the reason that we're able to do this now is transformer architecture. When the transformer papers came out and then of course the attention is all you need paper, those kind of unlocked it and made this all possible. Beyond that. I think the other secret sauce we've been doing this a long time. >>So we kind of, we know we're in the paid points. We went to those band points. Cause we weren't data scientists or ML people. >>Yeah. >>Yeah. You, you walked the snow and no shoes on in the winter. That's right. These kids now got boots on. They're all happy. You've installed machines. You've loaded OSS on, on top of rack switches. Yeah. I mean, it's unbelievable how awesome it's right now to be a developer and now a business user's doing the low code. Yep. If you have the system architecture set up, so back to the data engineering side, you guys had the experience got you here. This is a big discussion right now. We're having in, in, on the cube and many conversations like the server market, you had that go away through Amazon and Google was one of the first, obviously the board, but the idea that servers could be everywhere. So the SRE role came out the site reliability engineer, right. Which was one guy or gal and zillions of servers. Now you're seeing the same kind of role with data engineering. And then there's not a lot of people that fit the requirement of being a data engineer. It's like, yeah, it's very unique. Cause you're dealing with a system architecture, not data science. So start to see the role of this, this, this new persona, because they're taking on all the manual challenges of doing that. You guys are kind of replaced that I think. Well, do you agree with it about the data engineer? First of all? >>I think, yeah. Well and it's different cuz there's the older data engineer and then there's sort of the newer cloud aware one who knows how to use all the cloud technologies. And so when you're trying, we've tried to hire some of those and it's like, okay, you're really familiar with old database technology, but can you orchestrate that in a serverless environment with a lot of AWS technology for instance. And it's, and that's hard though. They don't, they don't, there's not a lot of people who know that space, >>So there's no real curriculum out there. That's gonna teach you how to handle, you know, ETL. And also like I got I'm on stream data from this source. Right. I'm using sequel I'm I got put all together. >>Yeah. So it's yeah, it's a lot of just not >>Data science. It's >>Figure that out. So its a large language models too. We don't have to worry about some of the data there too. It's it's already, you know, codified in the model. And then as we collect data, as people use our platform, they can then curate data. They want to annotate or enrich the model with so that it works better as it goes. So we're kind of curating, collecting the data as it's used. So as it evolves, it just gets better. >>Well, you guys obviously have a lot of experience together and congratulations on the venture. Thank you. What's going on here at re Mars. Why are you here? What's the pitch. What's the story. Where's your, you got two letters. You got the, you got the M for the machine learning and AI and you got the, a for automation. What's the ecosystem here for you? What are you doing? >>Well, I mean, I think you, you kind of said it right. We're here because the machine learning and the automation part, >>But >>More, more widely than that. I mean we work very, very closely with Amazon on a number of front things like text track, transcribe Alexa, basically all these AWS services are just integrations within our system. So you might want to hook up your AI to an Alexa so that you could say, Hey Alexa, tell me updates about my LinkedIn feed. I don't know, whatever, whatever your hearts content >>Is. Well what about this cube transcription? >>Yeah, exactly. A hundred percent. >>Yeah. We could do that. You know, feed all this in there and then we could do summarization of everything >>Here, >>Q and a extraction >>And say, Hey, these guys are >>Technicals. Yeah, >>There you go. No, they mentioned Kubernetes. We didn't say serverless chef puppet. Those are words straight, you know, and no linguistics matters right into that's a service that no one's ever gonna build. >>Well, and actually on that point, really interesting. We work with some healthcare companies and when you're basically, when people call in and they call into the insurance, they have a question about their, what like is this gonna be covered? And what they want to key in on are things like I just went to my doctor and got a cancer diagnosis. So the, the, the relevant thing here is they just got this diagnosis. And why is that important? Well, because if you just got a diagnosis, they want to start a certain triage to make you successful with your treatments. Because obviously there's an >>Incentive to do time. That time series matters and, and data exactly. And machine learning reacts to it. But also it could be fed back old data. It used to be time series to store it. Yeah. But now you could reuse it to see how to make the machine learning better. Are you guys doing anything, anything around that, how to make that machine learning smarter, look doing look backs or maybe not the right word, but because you have data, I might as well look back at it's happened. >>So part of, part of our platform and part of what we do is as people use these applications, to your point, there's lots of data that's getting generated, but we capture all that. And that becomes now a labeled data set within our platform. And you can take that label data set and do something called fine tuning, which just makes the underlying model more and more yours. It's proprietary. The more you do it. And it's more accurate. Usually the more you do it. >>So yeah, we keep all that. I wanna ask your reaction on this is a good point. The competitive advantage in the intellectual property is gonna be the workflows. And so the data is the IP. If this refinement happens, that becomes intellectual property. Yeah. That's kind of not software. It's the data modeling. It's the data itself is worth something. Are you guys seeing that? >>Yeah. And actually how we position the company is man team is a control plane and you retain ownership of the data plane. So it is your intellectual property. Yeah. It's in your system, it's in your AWS environment. >>That's not what everyone else is doing. Everyone wants to be the control plane and the data plan. We >>Don't wanna own your data. We don't, it's a compliance and security nightmare. Yeah. >>Let's be, Real's the question. What do you optimize for? Great. And I think that's a fair, a fair bet. Given the fact that clients want to be more agile with their data anyway, and the more restrictions you put on them, why would that this only gets you in trouble? Yeah. I could see that being a and plus lock. In's gonna be a huge factor. Yeah. I think this is coming fast and no one's talking about it in the press, but everyone's like run to silos, be a silo and that's not how data works. No. So the question is how do you create siloing of data for say domain specific applications while maintaining a horizontally scalable data plan or control plan that seems to be kind of disconnected everyone to lock in their data. What do you guys think about that? This industry transition we're in now because it seems people are reverting back to fourth grade, right. And to, you know, back to silos. >>Yeah. I think, well, I think the companies probably want their silo of data, their IP. And so as they refine their models and, and we give them the ability to deploy it in their own stage maker and their own VPC, they, they retain and own it. They can actually get rid of us and they still have that model. Now they may have to build, you know, a lot of pipelines and other technology to support it. But well, >>Your lock in is usability. Exactly. And value. Yeah. Value proposition is the lock in bingo. That's not counterintuitive. Exactly. Yeah. You say, Hey, more value. How do I wanna get rid of it? Valuable. I'll pay for it. Right. As long as you have multiple value, step up. And that's what cloud does. I mean, think that's the thing about cloud. That's gonna make all this work. In my opinion, the value enablement is much higher. Yeah. So good business model. Anything else here at the show that you observed that you like, that you think people would be interested in? What's the most important story coming out of the, the holistic, if you zoom up and look at re Mars, what's, what's coming out of the vibe. >>You know, one thing that I think about a lot is we're, you know, we have Artis here, humanity hopefully soon gonna be going to Mars. And I think that's really, really exciting. And I also think when we go to Mars, we're probably not gonna send a bunch of software engineers up there. >>Right. So like robots will do break fix now. So, you know, we're good. It's gone. So services are gonna be easy. >>Yeah. But I, oh, >>I left that device back at earth. I just think that's not gonna be good. Just >>Replicated it in one. I think there's like an eight >>Minute, the first monopoly on next day delivery in space. >>They'll just have a spaceship that sends out drones to Barss. Yeah. But I think that when we start going back to the moon and we go to Mars, people are gonna think, Hey, I need this application now to solve this problem that I didn't anticipate having. And in science fiction, we kind of saw this with like how, right? Like you had this AI on this computer or this, on this spaceship that could do all this stuff. We need that. And I haven't seen that here yet. >>No, it's not >>Here yet. And >>It's right now I think getting the hardware right first. Yep. But we did a lot of reporting on this with the D O D and the tactile edge, you know, military applications. It's a fundamental, I won't say it's a tech, religious argument. Like, do you believe in agile realtime data or do you believe in democratizing multi-vendor, you know, capability? I think, I think the interesting needs to sort itself out because sometimes multi vendor multi-cloud might not work for an application that needs this database or this application at the edge. >>Right. >>You know, so if you're in space, the back haul, it matters. >>It really does. Yeah. >>Yeah. Not a good time to go back and get that highly available data. You mean highly, is it highly available or there's two terms highly available, which means real time and available. Yeah. Available means it's on a dis, right? >>Yeah. >>So that's a big challenge. Well guys, thanks for coming on. Plug for the company. What are you guys up to? How much funding do you have? How old are you staff hiring? What's some of the details. >>We're about 45 people right now. We are a globally distributed team. So we hire every like from every country, pretty much we are fully remote. So if you're looking for that, hit us up, definitely always look for engineers, looking for more data scientists. We're very, very well funded as well. And yeah. So >>You guys headquarters out, you guys headquartered. >>So a lot of us live in Columbus, Ohio that's technically HQ, but like I said, we we're in pretty much every continent except in Antarctica. So >>You're for all virtual. >>Yeah. A hundred percent virtual, a hundred percent. >>Got it. Well, congratulations and love to hear that Datadog story at another time >>Or DataBot >>Yeah. I mean data, DataBot sorry. Let's get, get all confused >>Data dog data company. >>Well, thanks for coming on and congratulations for your success and thanks for sharing. Yeah. >>Thanks for having us for having >>Pleasure to be here. It's a cube here at rebars. I'm John furier host. Thanks for watching more coming back after this short break.