(upbeat music) >> Hello everyone. Welcome to theCube's presentation of the "AWS Startup Showcase." The topic this episode is AI and machine learning, top startups building foundational model infrastructure. This is season three, episode one of the ongoing series covering exciting startups from the AWS ecosystem. And this time we're talking about AI and machine learning. I'm your host, John Furrier. I'm excited I'm joined today by Robert Nishihara, who's the co-founder and CEO of a hot startup called Anyscale. He's here to talk about Ray, the open source project, Anyscale's infrastructure for foundation as well. Robert, thank you for joining us today. >> Yeah, thanks so much as well. >> I've been following your company since the founding pre pandemic and you guys really had a great vision scaled up and in a perfect position for this big wave that we all see with ChatGPT and OpenAI that's gone mainstream. Finally, AI has broken out through the ropes and now gone mainstream, so I think you guys are really well positioned. I'm looking forward to to talking with you today. But before we get into it, introduce the core mission for Anyscale. Why do you guys exist? What is the North Star for Anyscale? >> Yeah, like you mentioned, there's a tremendous amount of excitement about AI right now. You know, I think a lot of us believe that AI can transform just every different industry. So one of the things that was clear to us when we started this company was that the amount of compute needed to do AI was just exploding. Like to actually succeed with AI, companies like OpenAI or Google or you know, these companies getting a lot of value from AI, were not just running these machine learning models on their laptops or on a single machine. They were scaling these applications across hundreds or thousands or more machines and GPUs and other resources in the Cloud. And so to actually succeed with AI, and this has been one of the biggest trends in computing, maybe the biggest trend in computing in, you know, in recent history, the amount of compute has been exploding. And so to actually succeed with that AI, to actually build these scalable applications and scale the AI applications, there's a tremendous software engineering lift to build the infrastructure to actually run these scalable applications. And that's very hard to do. So one of the reasons many AI projects and initiatives fail is that, or don't make it to production, is the need for this scale, the infrastructure lift, to actually make it happen. So our goal here with Anyscale and Ray, is to make that easy, is to make scalable computing easy. So that as a developer or as a business, if you want to do AI, if you want to get value out of AI, all you need to know is how to program on your laptop. Like, all you need to know is how to program in Python. And if you can do that, then you're good to go. Then you can do what companies like OpenAI or Google do and get value out of machine learning. >> That programming example of how easy it is with Python reminds me of the early days of Cloud, when infrastructure as code was talked about was, it was just code the infrastructure programmable. That's super important. That's what AI people wanted, first program AI. That's the new trend. And I want to understand, if you don't mind explaining, the relationship that Anyscale has to these foundational models and particular the large language models, also called LLMs, was seen with like OpenAI and ChatGPT. Before you get into the relationship that you have with them, can you explain why the hype around foundational models? Why are people going crazy over foundational models? What is it and why is it so important? >> Yeah, so foundational models and foundation models are incredibly important because they enable businesses and developers to get value out of machine learning, to use machine learning off the shelf with these large models that have been trained on tons of data and that are useful out of the box. And then, of course, you know, as a business or as a developer, you can take those foundational models and repurpose them or fine tune them or adapt them to your specific use case and what you want to achieve. But it's much easier to do that than to train them from scratch. And I think there are three, for people to actually use foundation models, there are three main types of workloads or problems that need to be solved. One is training these foundation models in the first place, like actually creating them. The second is fine tuning them and adapting them to your use case. And the third is serving them and actually deploying them. Okay, so Ray and Anyscale are used for all of these three different workloads. Companies like OpenAI or Cohere that train large language models. Or open source versions like GPTJ are done on top of Ray. There are many startups and other businesses that fine tune, that, you know, don't want to train the large underlying foundation models, but that do want to fine tune them, do want to adapt them to their purposes, and build products around them and serve them, those are also using Ray and Anyscale for that fine tuning and that serving. And so the reason that Ray and Anyscale are important here is that, you know, building and using foundation models requires a huge scale. It requires a lot of data. It requires a lot of compute, GPUs, TPUs, other resources. And to actually take advantage of that and actually build these scalable applications, there's a lot of infrastructure that needs to happen under the hood. And so you can either use Ray and Anyscale to take care of that and manage the infrastructure and solve those infrastructure problems. Or you can build the infrastructure and manage the infrastructure yourself, which you can do, but it's going to slow your team down. It's going to, you know, many of the businesses we work with simply don't want to be in the business of managing infrastructure and building infrastructure. They want to focus on product development and move faster. >> I know you got a keynote presentation we're going to go to in a second, but I think you hit on something I think is the real tipping point, doing it yourself, hard to do. These are things where opportunities are and the Cloud did that with data centers. Turned a data center and made it an API. The heavy lifting went away and went to the Cloud so people could be more creative and build their product. In this case, build their creativity. Is that kind of what's the big deal? Is that kind of a big deal happening that you guys are taking the learnings and making that available so people don't have to do that? >> That's exactly right. So today, if you want to succeed with AI, if you want to use AI in your business, infrastructure work is on the critical path for doing that. To do AI, you have to build infrastructure. You have to figure out how to scale your applications. That's going to change. We're going to get to the point, and you know, with Ray and Anyscale, we're going to remove the infrastructure from the critical path so that as a developer or as a business, all you need to focus on is your application logic, what you want the the program to do, what you want your application to do, how you want the AI to actually interface with the rest of your product. Now the way that will happen is that Ray and Anyscale will still, the infrastructure work will still happen. It'll just be under the hood and taken care of by Ray in Anyscale. And so I think something like this is really necessary for AI to reach its potential, for AI to have the impact and the reach that we think it will, you have to make it easier to do. >> And just for clarification to point out, if you don't mind explaining the relationship of Ray and Anyscale real quick just before we get into the presentation. >> So Ray is an open source project. We created it. We were at Berkeley doing machine learning. We started Ray so that, in order to provide an easy, a simple open source tool for building and running scalable applications. And Anyscale is the managed version of Ray, basically we will run Ray for you in the Cloud, provide a lot of tools around the developer experience and managing the infrastructure and providing more performance and superior infrastructure. >> Awesome. I know you got a presentation on Ray and Anyscale and you guys are positioning as the infrastructure for foundational models. So I'll let you take it away and then when you're done presenting, we'll come back, I'll probably grill you with a few questions and then we'll close it out so take it away. >> Robert: Sounds great. So I'll say a little bit about how companies are using Ray and Anyscale for foundation models. The first thing I want to mention is just why we're doing this in the first place. And the underlying observation, the underlying trend here, and this is a plot from OpenAI, is that the amount of compute needed to do machine learning has been exploding. It's been growing at something like 35 times every 18 months. This is absolutely enormous. And other people have written papers measuring this trend and you get different numbers. But the point is, no matter how you slice and dice it, it' a astronomical rate. Now if you compare that to something we're all familiar with, like Moore's Law, which says that, you know, the processor performance doubles every roughly 18 months, you can see that there's just a tremendous gap between the needs, the compute needs of machine learning applications, and what you can do with a single chip, right. So even if Moore's Law were continuing strong and you know, doing what it used to be doing, even if that were the case, there would still be a tremendous gap between what you can do with the chip and what you need in order to do machine learning. And so given this graph, what we've seen, and what has been clear to us since we started this company, is that doing AI requires scaling. There's no way around it. It's not a nice to have, it's really a requirement. And so that led us to start Ray, which is the open source project that we started to make it easy to build these scalable Python applications and scalable machine learning applications. And since we started the project, it's been adopted by a tremendous number of companies. Companies like OpenAI, which use Ray to train their large models like ChatGPT, companies like Uber, which run all of their deep learning and classical machine learning on top of Ray, companies like Shopify or Spotify or Instacart or Lyft or Netflix, ByteDance, which use Ray for their machine learning infrastructure. Companies like Ant Group, which makes Alipay, you know, they use Ray across the board for fraud detection, for online learning, for detecting money laundering, you know, for graph processing, stream processing. Companies like Amazon, you know, run Ray at a tremendous scale and just petabytes of data every single day. And so the project has seen just enormous adoption since, over the past few years. And one of the most exciting use cases is really providing the infrastructure for building training, fine tuning, and serving foundation models. So I'll say a little bit about, you know, here are some examples of companies using Ray for foundation models. Cohere trains large language models. OpenAI also trains large language models. You can think about the workloads required there are things like supervised pre-training, also reinforcement learning from human feedback. So this is not only the regular supervised learning, but actually more complex reinforcement learning workloads that take human input about what response to a particular question, you know is better than a certain other response. And incorporating that into the learning. There's open source versions as well, like GPTJ also built on top of Ray as well as projects like Alpa coming out of UC Berkeley. So these are some of the examples of exciting projects in organizations, training and creating these large language models and serving them using Ray. Okay, so what actually is Ray? Well, there are two layers to Ray. At the lowest level, there's the core Ray system. This is essentially low level primitives for building scalable Python applications. Things like taking a Python function or a Python class and executing them in the cluster setting. So Ray core is extremely flexible and you can build arbitrary scalable applications on top of Ray. So on top of Ray, on top of the core system, what really gives Ray a lot of its power is this ecosystem of scalable libraries. So on top of the core system you have libraries, scalable libraries for ingesting and pre-processing data, for training your models, for fine tuning those models, for hyper parameter tuning, for doing batch processing and batch inference, for doing model serving and deployment, right. And a lot of the Ray users, the reason they like Ray is that they want to run multiple workloads. They want to train and serve their models, right. They want to load their data and feed that into training. And Ray provides common infrastructure for all of these different workloads. So this is a little overview of what Ray, the different components of Ray. So why do people choose to go with Ray? I think there are three main reasons. The first is the unified nature. The fact that it is common infrastructure for scaling arbitrary workloads, from data ingest to pre-processing to training to inference and serving, right. This also includes the fact that it's future proof. AI is incredibly fast moving. And so many people, many companies that have built their own machine learning infrastructure and standardized on particular workflows for doing machine learning have found that their workflows are too rigid to enable new capabilities. If they want to do reinforcement learning, if they want to use graph neural networks, they don't have a way of doing that with their standard tooling. And so Ray, being future proof and being flexible and general gives them that ability. Another reason people choose Ray in Anyscale is the scalability. This is really our bread and butter. This is the reason, the whole point of Ray, you know, making it easy to go from your laptop to running on thousands of GPUs, making it easy to scale your development workloads and run them in production, making it easy to scale, you know, training to scale data ingest, pre-processing and so on. So scalability and performance, you know, are critical for doing machine learning and that is something that Ray provides out of the box. And lastly, Ray is an open ecosystem. You can run it anywhere. You can run it on any Cloud provider. Google, you know, Google Cloud, AWS, Asure. You can run it on your Kubernetes cluster. You can run it on your laptop. It's extremely portable. And not only that, it's framework agnostic. You can use Ray to scale arbitrary Python workloads. You can use it to scale and it integrates with libraries like TensorFlow or PyTorch or JAX or XG Boost or Hugging Face or PyTorch Lightning, right, or Scikit-learn or just your own arbitrary Python code. It's open source. And in addition to integrating with the rest of the machine learning ecosystem and these machine learning frameworks, you can use Ray along with all of the other tooling in the machine learning ecosystem. That's things like weights and biases or ML flow, right. Or you know, different data platforms like Databricks, you know, Delta Lake or Snowflake or tools for model monitoring for feature stores, all of these integrate with Ray. And that's, you know, Ray provides that kind of flexibility so that you can integrate it into the rest of your workflow. And then Anyscale is the scalable compute platform that's built on top, you know, that provides Ray. So Anyscale is a managed Ray service that runs in the Cloud. And what Anyscale does is it offers the best way to run Ray. And if you think about what you get with Anyscale, there are fundamentally two things. One is about moving faster, accelerating the time to market. And you get that by having the managed service so that as a developer you don't have to worry about managing infrastructure, you don't have to worry about configuring infrastructure. You also, it provides, you know, optimized developer workflows. Things like easily moving from development to production, things like having the observability tooling, the debug ability to actually easily diagnose what's going wrong in a distributed application. So things like the dashboards and the other other kinds of tooling for collaboration, for monitoring and so on. And then on top of that, so that's the first bucket, developer productivity, moving faster, faster experimentation and iteration. The second reason that people choose Anyscale is superior infrastructure. So this is things like, you know, cost deficiency, being able to easily take advantage of spot instances, being able to get higher GPU utilization, things like faster cluster startup times and auto scaling. Things like just overall better performance and faster scheduling. And so these are the kinds of things that Anyscale provides on top of Ray. It's the managed infrastructure. It's fast, it's like the developer productivity and velocity as well as performance. So this is what I wanted to share about Ray in Anyscale. >> John: Awesome. >> Provide that context. But John, I'm curious what you think. >> I love it. I love the, so first of all, it's a platform because that's the platform architecture right there. So just to clarify, this is an Anyscale platform, not- >> That's right. >> Tools. So you got tools in the platform. Okay, that's key. Love that managed service. Just curious, you mentioned Python multiple times, is that because of PyTorch and TensorFlow or Python's the most friendly with machine learning or it's because it's very common amongst all developers? >> That's a great question. Python is the language that people are using to do machine learning. So it's the natural starting point. Now, of course, Ray is actually designed in a language agnostic way and there are companies out there that use Ray to build scalable Java applications. But for the most part right now we're focused on Python and being the best way to build these scalable Python and machine learning applications. But, of course, down the road there always is that potential. >> So if you're slinging Python code out there and you're watching that, you're watching this video, get on Anyscale bus quickly. Also, I just, while you were giving the presentation, I couldn't help, since you mentioned OpenAI, which by the way, congratulations 'cause they've had great scale, I've noticed in their rapid growth 'cause they were the fastest company to the number of users than anyone in the history of the computer industry, so major successor, OpenAI and ChatGPT, huge fan. I'm not a skeptic at all. I think it's just the beginning, so congratulations. But I actually typed into ChatGPT, what are the top three benefits of Anyscale and came up with scalability, flexibility, and ease of use. Obviously, scalability is what you guys are called. >> That's pretty good. >> So that's what they came up with. So they nailed it. Did you have an inside prompt training, buy it there? Only kidding. (Robert laughs) >> Yeah, we hard coded that one. >> But that's the kind of thing that came up really, really quickly if I asked it to write a sales document, it probably will, but this is the future interface. This is why people are getting excited about the foundational models and the large language models because it's allowing the interface with the user, the consumer, to be more human, more natural. And this is clearly will be in every application in the future. >> Absolutely. This is how people are going to interface with software, how they're going to interface with products in the future. It's not just something, you know, not just a chat bot that you talk to. This is going to be how you get things done, right. How you use your web browser or how you use, you know, how you use Photoshop or how you use other products. Like you're not going to spend hours learning all the APIs and how to use them. You're going to talk to it and tell it what you want it to do. And of course, you know, if it doesn't understand it, it's going to ask clarifying questions. You're going to have a conversation and then it'll figure it out. >> This is going to be one of those things, we're going to look back at this time Robert and saying, "Yeah, from that company, that was the beginning of that wave." And just like AWS and Cloud Computing, the folks who got in early really were in position when say the pandemic came. So getting in early is a good thing and that's what everyone's talking about is getting in early and playing around, maybe replatforming or even picking one or few apps to refactor with some staff and managed services. So people are definitely jumping in. So I have to ask you the ROI cost question. You mentioned some of those, Moore's Law versus what's going on in the industry. When you look at that kind of scale, the first thing that jumps out at people is, "Okay, I love it. Let's go play around." But what's it going to cost me? Am I going to be tied to certain GPUs? What's the landscape look like from an operational standpoint, from the customer? Are they locked in and the benefit was flexibility, are you flexible to handle any Cloud? What is the customers, what are they looking at? Basically, that's my question. What's the customer looking at? >> Cost is super important here and many of the companies, I mean, companies are spending a huge amount on their Cloud computing, on AWS, and on doing AI, right. And I think a lot of the advantage of Anyscale, what we can provide here is not only better performance, but cost efficiency. Because if we can run something faster and more efficiently, it can also use less resources and you can lower your Cloud spending, right. We've seen companies go from, you know, 20% GPU utilization with their current setup and the current tools they're using to running on Anyscale and getting more like 95, you know, 100% GPU utilization. That's something like a five x improvement right there. So depending on the kind of application you're running, you know, it's a significant cost savings. We've seen companies that have, you know, processing petabytes of data every single day with Ray going from, you know, getting order of magnitude cost savings by switching from what they were previously doing to running their application on Ray. And when you have applications that are spending, you know, potentially $100 million a year and getting a 10 X cost savings is just absolutely enormous. So these are some of the kinds of- >> Data infrastructure is super important. Again, if the customer, if you're a prospect to this and thinking about going in here, just like the Cloud, you got infrastructure, you got the platform, you got SaaS, same kind of thing's going to go on in AI. So I want to get into that, you know, ROI discussion and some of the impact with your customers that are leveraging the platform. But first I hear you got a demo. >> Robert: Yeah, so let me show you, let me give you a quick run through here. So what I have open here is the Anyscale UI. I've started a little Anyscale Workspace. So Workspaces are the Anyscale concept for interactive developments, right. So here, imagine I'm just, you want to have a familiar experience like you're developing on your laptop. And here I have a terminal. It's not on my laptop. It's actually in the cloud running on Anyscale. And I'm just going to kick this off. This is going to train a large language model, so OPT. And it's doing this on 32 GPUs. We've got a cluster here with a bunch of CPU cores, bunch of memory. And as that's running, and by the way, if I wanted to run this on instead of 32 GPUs, 64, 128, this is just a one line change when I launch the Workspace. And what I can do is I can pull up VS code, right. Remember this is the interactive development experience. I can look at the actual code. Here it's using Ray train to train the torch model. We've got the training loop and we're saying that each worker gets access to one GPU and four CPU cores. And, of course, as I make the model larger, this is using deep speed, as I make the model larger, I could increase the number of GPUs that each worker gets access to, right. And how that is distributed across the cluster. And if I wanted to run on CPUs instead of GPUs or a different, you know, accelerator type, again, this is just a one line change. And here we're using Ray train to train the models, just taking my vanilla PyTorch model using Hugging Face and then scaling that across a bunch of GPUs. And, of course, if I want to look at the dashboard, I can go to the Ray dashboard. There are a bunch of different visualizations I can look at. I can look at the GPU utilization. I can look at, you know, the CPU utilization here where I think we're currently loading the model and running that actual application to start the training. And some of the things that are really convenient here about Anyscale, both I can get that interactive development experience with VS code. You know, I can look at the dashboards. I can monitor what's going on. It feels, I have a terminal, it feels like my laptop, but it's actually running on a large cluster. And I can, with however many GPUs or other resources that I want. And so it's really trying to combine the best of having the familiar experience of programming on your laptop, but with the benefits, you know, being able to take advantage of all the resources in the Cloud to scale. And it's like when, you know, you're talking about cost efficiency. One of the biggest reasons that people waste money, one of the silly reasons for wasting money is just forgetting to turn off your GPUs. And what you can do here is, of course, things will auto terminate if they're idle. But imagine you go to sleep, I have this big cluster. You can turn it off, shut off the cluster, come back tomorrow, restart the Workspace, and you know, your big cluster is back up and all of your code changes are still there. All of your local file edits. It's like you just closed your laptop and came back and opened it up again. And so this is the kind of experience we want to provide for our users. So that's what I wanted to share with you. >> Well, I think that whole, couple of things, lines of code change, single line of code change, that's game changing. And then the cost thing, I mean human error is a big deal. People pass out at their computer. They've been coding all night or they just forget about it. I mean, and then it's just like leaving the lights on or your water running in your house. It's just, at the scale that it is, the numbers will add up. That's a huge deal. So I think, you know, compute back in the old days, there's no compute. Okay, it's just compute sitting there idle. But you know, data cranking the models is doing, that's a big point. >> Another thing I want to add there about cost efficiency is that we make it really easy to use, if you're running on Anyscale, to use spot instances and these preemptable instances that can just be significantly cheaper than the on-demand instances. And so when we see our customers go from what they're doing before to using Anyscale and they go from not using these spot instances 'cause they don't have the infrastructure around it, the fault tolerance to handle the preemption and things like that, to being able to just check a box and use spot instances and save a bunch of money. >> You know, this was my whole, my feature article at Reinvent last year when I met with Adam Selipsky, this next gen Cloud is here. I mean, it's not auto scale, it's infrastructure scale. It's agility. It's flexibility. I think this is where the world needs to go. Almost what DevOps did for Cloud and what you were showing me that demo had this whole SRE vibe. And remember Google had site reliability engines to manage all those servers. This is kind of like an SRE vibe for data at scale. I mean, a similar kind of order of magnitude. I mean, I might be a little bit off base there, but how would you explain it? >> It's a nice analogy. I mean, what we are trying to do here is get to the point where developers don't think about infrastructure. Where developers only think about their application logic. And where businesses can do AI, can succeed with AI, and build these scalable applications, but they don't have to build, you know, an infrastructure team. They don't have to develop that expertise. They don't have to invest years in building their internal machine learning infrastructure. They can just focus on the Python code, on their application logic, and run the stuff out of the box. >> Awesome. Well, I appreciate the time. Before we wrap up here, give a plug for the company. I know you got a couple websites. Again, go, Ray's got its own website. You got Anyscale. You got an event coming up. Give a plug for the company looking to hire. Put a plug in for the company. >> Yeah, absolutely. Thank you. So first of all, you know, we think AI is really going to transform every industry and the opportunity is there, right. We can be the infrastructure that enables all of that to happen, that makes it easy for companies to succeed with AI, and get value out of AI. Now we have, if you're interested in learning more about Ray, Ray has been emerging as the standard way to build scalable applications. Our adoption has been exploding. I mentioned companies like OpenAI using Ray to train their models. But really across the board companies like Netflix and Cruise and Instacart and Lyft and Uber, you know, just among tech companies. It's across every industry. You know, gaming companies, agriculture, you know, farming, robotics, drug discovery, you know, FinTech, we see it across the board. And all of these companies can get value out of AI, can really use AI to improve their businesses. So if you're interested in learning more about Ray and Anyscale, we have our Ray Summit coming up in September. This is going to highlight a lot of the most impressive use cases and stories across the industry. And if your business, if you want to use LLMs, you want to train these LLMs, these large language models, you want to fine tune them with your data, you want to deploy them, serve them, and build applications and products around them, give us a call, talk to us. You know, we can really take the infrastructure piece, you know, off the critical path and make that easy for you. So that's what I would say. And, you know, like you mentioned, we're hiring across the board, you know, engineering, product, go-to-market, and it's an exciting time. >> Robert Nishihara, co-founder and CEO of Anyscale, congratulations on a great company you've built and continuing to iterate on and you got growth ahead of you, you got a tailwind. I mean, the AI wave is here. I think OpenAI and ChatGPT, a customer of yours, have really opened up the mainstream visibility into this new generation of applications, user interface, roll of data, large scale, how to make that programmable so we're going to need that infrastructure. So thanks for coming on this season three, episode one of the ongoing series of the hot startups. In this case, this episode is the top startups building foundational model infrastructure for AI and ML. I'm John Furrier, your host. Thanks for watching. (upbeat music)

(upbeat instrumental) >> Hello and welcome to this CUBE conversation. I'm John Furrier, host of theCUBE, here in Palo Alto, California. Got a great conversation with Robert Nishihara who's the co-founder and CEO of Anyscale. Robert, great to have you on this CUBE conversation. It's great to see you. We did your first Ray Summit a couple years ago and congratulations on your venture. Great to have you on. >> Thank you. Thanks for inviting me. >> So you're first time CEO out of Berkeley in Data. You got the Databricks is coming out of there. You got a bunch of activity coming from Berkeley. It's like a, it really is kind of like where a lot of innovations going on data. Anyscale has been one of those startups that has risen out of that scene. Right? You look at the success of what the Data lakes are now. Now you've got the generative AI. This has been a really interesting innovation market. This new wave is coming. Tell us what's going on with Anyscale right now, as you guys are gearing up and getting some growth. What's happening with the company? >> Yeah, well one of the most exciting things that's been happening in computing recently, is the rise of AI and the excitement about AI, and the potential for AI to really transform every industry. Now of course, one of the of the biggest challenges to actually making that happen is that doing AI, that AI is incredibly computationally intensive, right? To actually succeed with AI to actually get value out of AI. You're typically not just running it on your laptop, you're often running it and scaling it across thousands of machines, or hundreds of machines or GPUs, and to, so organizations and companies and businesses that do AI often end up building a large infrastructure team to manage the distributed systems, the computing to actually scale these applications. And that's a, that's a, a huge software engineering lift, right? And so, one of the goals for Anyscale is really to make that easy. To get to the point where, developers and teams and companies can succeed with AI. Can build these scalable AI applications, without really you know, without a huge investment in infrastructure with a lot of, without a lot of expertise in infrastructure, where really all they need to know is how to program on their laptop, how to program in Python. And if you have that, then that's really all you need to succeed with AI. So that's what we've been focused on. We're building Ray, which is an open source project that's been starting to get adopted by tons of companies, to actually train these models, to deploy these models, to do inference with these models, you know, to ingest and pre-process their data. And our goals, you know, here with the company are really to make Ray successful. To grow the Ray community, and then to build a great product around it and simplify the development and deployment, and productionization of machine learning for, for all these businesses. >> It's a great trend. Everyone wants developer productivity seeing that, clearly right now. And plus, developers are voting literally on what standards become. As you look at how the market is open source driven, a lot of that I love the model, love the Ray project love the, love the Anyscale value proposition. How big are you guys now, and how is that value proposition of Ray and Anyscale and foundational models coming together? Because it seems like you guys are in a perfect storm situation where you guys could get a real tailwind and draft off the the mega trend that everyone's getting excited. The new toy is ChatGPT. So you got to look at that and say, hey, I mean, come on, you guys did all the heavy lifting. >> Absolutely. >> You know how many people you are, and what's the what's the proposition for you guys these days? >> You know our company's about a hundred people, that a bit larger than that. Ray's been going really quickly. It's been, you know, companies using, like OpenAI uses Ray to train their models, like ChatGPT. Companies like Uber run all their deep learning you know, and classical machine learning on top of Ray. Companies like Shopify, Spotify, Netflix, Cruise, Lyft, Instacart, you know, Bike Dance. A lot of these companies are investing heavily in Ray for their machine learning infrastructure. And I think it's gotten to the point where, if you're one of these, you know type of businesses, and you're looking to revamp your machine learning infrastructure. If you're looking to enable new capabilities, you know make your teams more productive, increase, speed up the experimentation cycle, you know make it more performance, like build, you know, run applications that are more scalable, run them faster, run them in a more cost efficient way. All of these types of companies are at least evaluating Ray and Ray is an increasingly common choice there. I think if they're not using Ray, if many of these companies that end up not using Ray, they often end up building their own infrastructure. So Ray has been, the growth there has been incredibly exciting over the, you know we had our first in-person Ray Summit just back in August, and planning the next one for, for coming September. And so when you asked about the value proposition, I think there's there's really two main things, when people choose to go with Ray and Anyscale. One reason is about moving faster, right? It's about developer productivity, it's about speeding up the experimentation cycle, easily getting their models in production. You know, we hear many companies say that they, you know they, once they prototype a model, once they develop a model, it's another eight weeks, or 12 weeks to actually get that model in production. And that's a reason they talk to us. We hear companies say that, you know they've been training their models and, and doing inference on a single machine, and they've been sort of scaling vertically, like using bigger and bigger machines. But they, you know, you can only do that for so long, and at some point you need to go beyond a single machine and that's when they start talking to us. Right? So one of the main value propositions is around moving faster. I think probably the phrase I hear the most is, companies saying that they don't want their machine learning people to have to spend all their time configuring infrastructure. All this is about productivity. >> Yeah. >> The other. >> It's the big brains in the company. That are being used to do remedial tasks that should be automated right? I mean that's. >> Yeah, and I mean, it's hard stuff, right? It's also not these people's area of expertise, and or where they're adding the most value. So all of this is around developer productivity, moving faster, getting to market faster. The other big value prop and the reason people choose Ray and choose Anyscale, is around just providing superior infrastructure. This is really, can we scale more? You know, can we run it faster, right? Can we run it in a more cost effective way? We hear people saying that they're not getting good GPU utilization with the existing tools they're using, or they can't scale beyond a certain point, or you know they don't have a way to efficiently use spot instances to save costs, right? Or their clusters, you know can't auto scale up and down fast enough, right? These are all the kinds of things that Ray and Anyscale, where Ray and Anyscale add value and solve these kinds of problems. >> You know, you bring up great points. Auto scaling concept, early days, it was easy getting more compute. Now it's complicated. They're built into more integrated apps in the cloud. And you mentioned those companies that you're working with, that's impressive. Those are like the big hardcore, I call them hardcore. They have a good technical teams. And as the wave starts to move from these companies that were hyper scaling up all the time, the mainstream are just developers, right? So you need an interface in, so I see the dots connecting with you guys and I want to get your reaction. Is that how you see it? That you got the alphas out there kind of kicking butt, building their own stuff, alpha developers and infrastructure. But mainstream just wants programmability. They want that heavy lifting taken care of for them. Is that kind of how you guys see it? I mean, take us through that. Because to get crossover to be democratized, the automation's got to be there. And for developer productivity to be in, it's got to be coding and programmability. >> That's right. Ultimately for AI to really be successful, and really you know, transform every industry in the way we think it has the potential to. It has to be easier to use, right? And that is, and being easier to use, there's many dimensions to that. But an important one is that as a developer to do AI, you shouldn't have to be an expert in distributed systems. You shouldn't have to be an expert in infrastructure. If you do have to be, that's going to really limit the number of people who can do this, right? And I think there are so many, all of the companies we talk to, they don't want to be in the business of building and managing infrastructure. It's not that they can't do it. But it's going to slow them down, right? They want to allocate their time and their energy toward building their product, right? To building a better product, getting their product to market faster. And if we can take the infrastructure work off of the critical path for them, that's going to speed them up, it's going to simplify their lives. And I think that is critical for really enabling all of these companies to succeed with AI. >> Talk about the customers you guys are talking to right now, and how that translates over. Because I think you hit a good thread there. Data infrastructure is critical. Managed services are coming online, open sources continuing to grow. You have these people building their own, and then if they abandon it or don't scale it properly, there's kind of consequences. 'Cause it's a system you mentioned, it's a distributed system architecture. It's not as easy as standing up a monolithic app these days. So when you guys go to the marketplace and talk to customers, put the customers in buckets. So you got the ones that are kind of leaning in, that are pretty peaked, probably working with you now, open source. And then what's the customer profile look like as you go mainstream? Are they looking to manage service, looking for more architectural system, architecture approach? What's the, Anyscale progression? How do you engage with your customers? What are they telling you? >> Yeah, so many of these companies, yes, they're looking for managed infrastructure 'cause they want to move faster, right? Now the kind of these profiles of these different customers, they're three main workloads that companies run on Anyscale, run with Ray. It's training related workloads, and it is serving and deployment related workloads, like actually deploying your models, and it's batch processing, batch inference related workloads. Like imagine you want to do computer vision on tons and tons of, of images or videos, or you want to do natural language processing on millions of documents or audio, or speech or things like that, right? So the, I would say the, there's a pretty large variety of use cases, but the most common you know, we see tons of people working with computer vision data, you know, computer vision problems, natural language processing problems. And it's across many different industries. We work with companies doing drug discovery, companies doing you know, gaming or e-commerce, right? Companies doing robotics or agriculture. So there's a huge variety of the types of industries that can benefit from AI, and can really get a lot of value out of AI. And, but the, but the problems are the same problems that they all want to solve. It's like how do you make your team move faster, you know succeed with AI, be more productive, speed up the experimentation, and also how do you do this in a more performant way, in a faster, cheaper, in a more cost efficient, more scalable way. >> It's almost like the cloud game is coming back to AI and these foundational models, because I was just on a podcast, we recorded our weekly podcast, and I was just riffing with Dave Vellante, my co-host on this, were like, hey, in the early days of Amazon, if you want to build an app, you just, you have to build a data center, and then you go to now you go to the cloud, cloud's easier, pay a little money, penny's on the dollar, you get your app up and running. Cloud computing is born. With foundation models in generative AI. The old model was hard, heavy lifting, expensive, build out, before you get to do anything, as you mentioned time. So I got to think that you're pretty much in a good position with this foundational model trend in generative AI because I just looked at the foundation map, foundation models, map of the ecosystem. You're starting to see layers of, you got the tooling, you got platform, you got cloud. It's filling out really quickly. So why is Anyscale important to this new trend? How do you talk to people when they ask you, you know what does ChatGPT mean for Anyscale? And how does the financial foundational model growth, fit into your plan? >> Well, foundational models are hugely important for the industry broadly. Because you're going to have these really powerful models that are trained that you know, have been trained on tremendous amounts of data. tremendous amounts of computes, and that are useful out of the box, right? That people can start to use, and query, and get value out of, without necessarily training these huge models themselves. Now Ray fits in and Anyscale fit in, in a number of places. First of all, they're useful for creating these foundation models. Companies like OpenAI, you know, use Ray for this purpose. Companies like Cohere use Ray for these purposes. You know, IBM. If you look at, there's of course also open source versions like GPTJ, you know, created using Ray. So a lot of these large language models, large foundation models benefit from training on top of Ray. And, but of course for every company training and creating these huge foundation models, you're going to have many more that are fine tuning these models with their own data. That are deploying and serving these models for their own applications, that are building other application and business logic around these models. And that's where Ray also really shines, because Ray you know, is, can provide common infrastructure for all of these workloads. The training, the fine tuning, the serving, the data ingest and pre-processing, right? The hyper parameter tuning, the and and so on. And so where the reason Ray and Anyscale are important here, is that, again, foundation models are large, foundation models are compute intensive, doing you know, using both creating and using these foundation models requires tremendous amounts of compute. And there there's a big infrastructure lift to make that happen. So either you are using Ray and Anyscale to do this, or you are building the infrastructure and managing the infrastructure yourself. Which you can do, but it's, it's hard. >> Good luck with that. I always say good luck with that. I mean, I think if you really need to do, build that hardened foundation, you got to go all the way. And I think this, this idea of composability is interesting. How is Ray working with OpenAI for instance? Take, take us through that. Because I think you're going to see a lot of people talking about, okay I got trained models, but I'm going to have not one, I'm going to have many. There's big debate that OpenAI is going to be the mother of all LLMs, but now, but really people are also saying that to be many more, either purpose-built or specific. The fusion and these things come together there's like a blending of data, and that seems to be a value proposition. How does Ray help these guys get their models up? Can you take, take us through what Ray's doing for say OpenAI and others, and how do you see the models interacting with each other? >> Yeah, great question. So where, where OpenAI uses Ray right now, is for the training workloads. Training both to create ChatGPT and models like that. There's both a supervised learning component, where you're pre-training this model on doing supervised pre-training with example data. There's also a reinforcement learning component, where you are fine-tuning the model and continuing to train the model, but based on human feedback, based on input from humans saying that, you know this response to this question is better than this other response to this question, right? And so Ray provides the infrastructure for scaling the training across many, many GPUs, many many machines, and really running that in an efficient you know, performance fault tolerant way, right? And so, you know, open, this is not the first version of OpenAI's infrastructure, right? They've gone through iterations where they did start with building the infrastructure themselves. They were using tools like MPI. But at some point, you know, given the complexity, given the scale of what they're trying to do, you hit a wall with MPI and that's going to happen with a lot of other companies in this space. And at that point you don't have many other options other than to use Ray or to build your own infrastructure. >> That's awesome. And then your vision on this data interaction, because the old days monolithic models were very rigid. You couldn't really interface with them. But we're kind of seeing this future of data fusion, data interaction, data blending at large scale. What's your vision? How do you, what's your vision of where this goes? Because if this goes the way people think. You can have this data chemistry kind of thing going on where people are integrating all kinds of data with each other at large scale. So you need infrastructure, intelligence, reasoning, a lot of code. Is this something that you see? What's your vision in all this? Take us through. >> AI is going to be used everywhere right? It's, we see this as a technology that's going to be ubiquitous, and is going to transform every business. I mean, imagine you make a product, maybe you were making a tool like Photoshop or, or whatever the, you know, tool is. The way that people are going to use your tool, is not by investing, you know, hundreds of hours into learning all of the different, you know specific buttons they need to press and workflows they need to go through it. They're going to talk to it, right? They're going to say, ask it to do the thing they want it to do right? And it's going to do it. And if it, if it doesn't know what it's want, what it's, what's being asked of it. It's going to ask clarifying questions, right? And then you're going to clarify, and you're going to have a conversation. And this is going to make many many many kinds of tools and technology and products easier to use, and lower the barrier to entry. And so, and this, you know, many companies fit into this category of trying to build products that, and trying to make them easier to use, this is just one kind of way it can, one kind of way that AI will will be used. But I think it's, it's something that's pretty ubiquitous. >> Yeah. It'll be efficient, it'll be efficiency up and down the stack, and will change the productivity equation completely. You just highlighted one, I don't want to fill out forms, just stand up my environment for me. And then start coding away. Okay well this is great stuff. Final word for the folks out there watching, obviously new kind of skill set for hiring. You guys got engineers, give a plug for the company, for Anyscale. What are you looking for? What are you guys working on? Give a, take the last minute to put a plug in for the company. >> Yeah well if you're interested in AI and if you think AI is really going to be transformative, and really be useful for all these different industries. We are trying to provide the infrastructure to enable that to happen, right? So I think there's the potential here, to really solve an important problem, to get to the point where developers don't need to think about infrastructure, don't need to think about distributed systems. All they think about is their application logic, and what they want their application to do. And I think if we can achieve that, you know we can be the foundation or the platform that enables all of these other companies to succeed with AI. So that's where we're going. I think something like this has to happen if AI is going to achieve its potential, we're looking for, we're hiring across the board, you know, great engineers, on the go-to-market side, product managers, you know people who want to really, you know, make this happen. >> Awesome well congratulations. I know you got some good funding behind you. You're in a good spot. I think this is happening. I think generative AI and foundation models is going to be the next big inflection point, as big as the pc inter-networking, internet and smartphones. This is a whole nother application framework, a whole nother set of things. So this is the ground floor. Robert, you're, you and your team are right there. Well done. >> Thank you so much. >> All right. Thanks for coming on this CUBE conversation. I'm John Furrier with theCUBE. Breaking down a conversation around AI and scaling up in this new next major inflection point. This next wave is foundational models, generative AI. And thanks to ChatGPT, the whole world's now knowing about it. So it really is changing the game and Anyscale is right there, one of the hot startups, that is in good position to ride this next wave. Thanks for watching. (upbeat instrumental)

>>Well, hello everybody. John Walls here and continuing our coverage here at AWS Reinvent 22 on the queue. We continue our segments here in the Global Startup program, which of course is sponsored by AWS Startup Showcase, and with us to talk about any scale as the co-founder and CEO of the company, Robert and n, you are Robert. Good to see you. Thanks for joining us. >>Yeah, great. And thank you. >>You bet. Yeah. Glad to have you aboard here. So let's talk about Annie Scale, first off, for those at home and might not be familiar with what you do. Yeah. Because you've only been around for a short period of time, you're telling me >>Company's about >>Three years now. Three >>Years old, >>Yeah. Yeah. So tell us all about it. Yeah, >>Absolutely. So one of the biggest things happening in computing right now is the proliferation of ai. AI is just spreading throughout every industry has the potential to transform every industry. But the thing about doing AI is that it's incredibly computationally intensive. So if you wanna do do ai, you're not, you're probably not just doing it on your laptop, you're doing it across many machines, many gpu, many compute resources, and that's incredibly hard to do. It requires a lot of software engineering expertise, a lot of infrastructure expertise, a lot of cloud computing expertise to build the software infrastructure and distributed systems to really scale AI across all of the, across the cloud. And to do it in a way where you're really getting value out of ai. And so that is the, the problem statement that AI has tremendous potential. It's incredibly hard to do because of the, the scale required. >>And what we are building at any scale is really trying to make that easy. So trying to get to the point where, as a developer, if you know how to program on your laptop, then if you know how to program saying Python on your laptop, then that's enough, right? Then you can do ai, you can get value out of it, you can scale it, you can build the kinds of, you know, incredibly powerful applica AI applications that companies like Google and, and Facebook and others can build. But you don't have to learn about all of the distributed systems and infrastructure. It just, you know, we'll handle that for you. So that's, if we're successful, you know, that's what we're trying to achieve here. >>Yeah. What, what makes AI so hard to work with? I mean, you talk about the complexity. Yeah. A lot of moving parts. I mean, literally moving parts, but, but what is it in, in your mind that, that gets people's eyes spinning a little bit when they, they look at great potential. Yeah. But also they look at the downside of maybe having to work your way through Pike mere of sorts. >>So, so the potential is definitely there, but it's important to remember that a lot of AI initiatives fail. Like a lot of initiative AI initiatives, something like 80 or 90% don't make it out of, you know, the research or prototyping phase and inter production. Hmm. So, some of the things that are hard about AI and the reasons that AI initiatives can fail, one is the scale required, you know, moving. It's one thing to develop something on your laptop, it's another thing to run it across thousands of machines. So that's scale, right? Another is the transition from development and prototyping to production. Those are very different, have very different requirements. Absolutely. A lot of times it's different teams within a company. They have different tech stacks, different software they're using. You know, we hear companies say that when they move from develop, you know, once they prototype and develop a model, it could take six to 12 weeks to get that model in production. >>And that often involves rewriting a lot of code and handing it off to another team. So the transition from development to production is, is a big challenge. So the scale, the development to production handoff. And then lastly, a big challenge is around flexibility. So AI's a fast moving field, you see new developments, new algorithms, new models coming out all the time. And a lot of teams we work with, you know, they've, they've built infrastructure. They're using products out there to do ai, but they've found that it's sort of locking them into rigid workflows or specific tools, and they don't have the flexibility to adopt new algorithms or new strategies or approaches as they're being developed as they come out. And so they, but their developers want the flexibility to use the latest tools, the latest strategies. And so those are some of the main problems we see. It's really like, how do you scale scalability? How do you move easily from development and production and back? And how do you remain flexible? How do you adapt and, and use the best tools that are coming out? And so those are, yeah, just those are and often reasons that people start to use Ray, which is our open source project in any scale, which is our, our product. So tell >>Me about Ray, right? Yeah. Opensource project. I think you said you worked on it >>At Berkeley. That's right. Yeah. So before this company, I did a PhD in machine learning at Berkeley. And one of the challenges that we were running into ourselves, we were trying to do machine learning. We actually weren't infrastructure or distributed systems people, but we found ourselves in order to do machine learning, we found ourselves building all sorts of tools, ad hoc tools and systems to scale the machine learning, to be able to run it in a reasonable amount of time and to be able to leverage the compute that we needed. And it wasn't just us people all across, you know, machine learning researchers, machine learning practitioners were building their own tooling and infrastructure. And that was one of the things that we felt was really holding back progress. And so that's how we slowly and kind of gradually got into saying, Hey, we could build better tools here. >>We could build, we could try to make this easier to do so that all of these people don't have to build their own infrastructure. They can focus on the actual machine learning applications that they're trying to build. And so we started, Ray started this open source project for basically scaling Python applications and scaling machine learning applications. And, well, initially we were running around Berkeley trying to get all of our friends to try it out and, and adopt it and, you know, and give us feedback. And if it didn't work, we would debug it right away. And that slow, you know, that gradually turned into more companies starting to adopt it, bigger teams starting to adopt it, external contributors starting to, to contribute back to the open source project and make it better. And, you know, before you know it, we were hosting meetups, giving to talks, running tutorials, and the project was just taking off. And so that's a big part of what we continue to develop today at any scale, is like really fostering this open source community, growing the open source user base, making sure Ray is just the best way to scale Python applications and, and machine learning applications. >>So, so this was a graduate school project That's right. You say on, on your way to getting your doctorate and now you commercializing now, right? Yeah. I mean, so you're being able to offer it, first off, what a journey that was, right? I mean, who would've thought Absolutely. I guess you probably did think that at some point, but >>No, you know, when we started, when we were working on Ray, we actually didn't anticipate becoming a company, or we at least just weren't looking that far ahead. We were really excited about solving this problem of making distributed computing easy, you know, getting to the point where developers just don't have to learn about infrastructure and distributed systems, but get all the benefits. And of course, it wasn't until, you know, later on as we were graduating from Berkeley and we wanted to continue really taking this project further and, and really solving this problem that it, we realized it made sense to start a company. >>So help me out, like, like what, what, and I might have missed this, so I apologize if I did, but in terms of, of Ray's that building block and essential for your, your ML or AI work down the road, you know, what, what is it doing for me or what, what will it allow me to do in either one of those realms that I, I can't do now? >>Yeah. And so, so like why use Ray versus not using Ray? Yeah, I think the, the answer is that you, you know, if you're doing ai, you need to scale. It's becoming, if you don't find that to be the case today, you probably will tomorrow, you know, or the day after that. And so it's really increasingly, it's a requirement. It's not an option. And so if you're scaling, if you're trying to build these scalable applications you are building, you're either going to use Ray or, or something like Ray or you're going to build the infrastructure yourself and building the infrastructure yourself, that's a long journey. >>So why take that on, right? >>And many of the companies we work with don't want to be in the business of building and managing infrastructure. No. Because, you know, if they, they want their their best engineers to build their product, right? To, to get their product to market faster. >>I want, I want you to do that for me. >>Right? Exactly. And so, you know, we can really accelerate what these teams can do and, you know, and if we can make the infrastructure something they just don't have to think about, that's, that's why you would choose to use Ray. >>Okay. You know, between a and I and ml are, are they different animals in terms of what you're trying to get done or what Ray can do? >>Yeah, and actually I should say like, it's not just, you know, teams that are new teams that are starting out, that are using Ray, many companies that have built, already built their own infrastructure will then switch to using Ray. And to give you a few examples, like Uber runs all their deep learning on Ray, okay. And, you know, open ai, which is really at the frontier of training large models and, and you know, pushing the boundaries of, of ai, they train their largest models using Ray. You know, companies like Shopify rebuilt their entire machine learning platform using Ray, >>But they started somewhere else. >>They had, this is all, you know, like, it's not like the v1, you know, of their, of their machine learning infrastructure. This is like, they did it a different way before, this is like the second version or the third iteration of of, of how they're doing it. And they realize often it's because, you know, I mean in the case of, of Uber, just to give you one example, they built a system called hova for scaling deep learning on a bunch of GPUs. Right Now, as you scale deep learning on GPUs for them, the bottleneck shifted away from, you know, as you scale GPU's training, the bottleneck shifted away from training and to the data ingest and pre-processing. And they wanted to scale data ingest and pre-processing on CPUs. So now Hova, it's a deep learning framework. It doesn't do the data ingest and pre-processing on CPUs, but you can, if you run Hova on top of Ray, you can scale training on GPUs. >>And then Ray has another library called Ray Data you can, that lets you scale the ingest and pre-processing on CPUs. You can pipeline them together. And that allowed them to train larger models on more data before, just to take one example, ETA prediction, if you get in an Uber, it tells you what time you're supposed to arrive. Sure. That uses a deep learning model called d eta. And before they were able to train on about two weeks worth of data. Now, you know, using Ray and for scaling the data, ingestive pre-processing and training, they can train on much more data. You know, you can get more accurate ETA predictions. So that's just one example of the kind of benefit they were able to get. Right. Also, because it's running on top of, of Ray and Ray has this ecosystem of libraries, you know, they can also use Ray's hyper parameter tuning library to do hyper parameter tuning for their deep learning models. >>They can also use it for inference and you know, because these are all built on top of Ray, they inherit the like, elasticity and fault tolerance of running on top of Ray. So really it simplifies things on the infrastructure side cuz there's just, if you have Ray as common infrastructure for your machine learning workloads, there's just one system to, to kind of manage and operate. And if you are, it simplifies things for the end users like the developers because from their perspective, they're just writing a Python application. They don't have to learn how to use three different distributed systems and stitch them together and all of this. >>So aws, before I let you go, how do they come into play here for you? I mean, are you part of the showcase, a startup showcase? So obviously a major partner and major figure in the offering that you're presenting >>People? Yeah, well you can run. So any scale is a managed ray service. Like any scale is just the best way to run Ray and deploy Ray. And we run on top of aws. So many of our customers are, you know, using Ray through any scale on aws. And so we work very closely together and, and you know, we have, we have joint customers and basically, and you know, a lot of the value that any scale is adding on top of Ray is around the production story. So basically, you know, things like high availability, things like failure handling, retry alerting, persistence, reproducibility, these are a lot of the value, the values of, you know, the value that our platform adds on top of the open source project. A lot of stuff as well around collaboration, you know, imagine you are, you, something goes wrong with your application, your production job, you want to debug it, you can just share the URL with your, your coworker. They can click a button, reproduce the exact same thing, look at the same logs, you know, and, and, and figure out what's going on. And also a lot around, one thing that's, that's important for a lot of our customers is efficiency around cost. And so we >>Support every customer. >>Exactly. A lot of people are spending a lot of money on, on aws. Yeah. Right? And so any scale supports running out of the box on cheaper like spot instances, these preempt instances, which, you know, just reduce costs by quite a bit. And so things like that. >>Well, the company is any scale and you're on the show floor, right? So if you're having a chance to watch this during reinvent, go down and check 'em out. Robert Ashihara joining us here, the co-founder and ceo and Robert, thanks for being with us. Yeah. Here on the cube. Really enjoyed it. Me too. Thanks so much. Boy, three years graduate program and boom, here you are, you know, with off to the enterprise you go. Very nicely done. All right, we're gonna continue our coverage here on the Cube with more here from Las Vegas. We're the Venetian, we're AWS Reinvent 22 and you're watching the Cube, the leader in high tech coverage.

>> Welcome back to the Seaport in Boston and this is theCUBE's coverage of Red Hat Summit 2022. I'm Dave Vellante with my co-host Paul Gillin. Rob Belson is here as the Developer Relations Lead at Verizon. Robbie great to see you. Thanks for coming on theCUBE. >> Thanks for having me. >> So Verizon and developer relations. Talk about your role there. Really interesting. >> Absolutely. If you think about our mobile edge computing portfolio in Verizon 5G Edge, suddenly the developer is a more important persona than ever for actually adopting the cloud itself and adopting the mobile edge. So the question then quickly became how do we go after developers and how do we tell stories that ultimately resonate with them? And so my role has been spearheading our developer relations and experience efforts, which is all about meeting developers in the channels where they actually are, building content that resonates with them. Building out architectures that showcase how do you actually use the technology in the wild? And then ultimately creating automation assets that make their lives easier in deploying to the mobile edge. >> So, you know, telcos get a bad rap, when you're thinking it's amazing what you guys do. You put out all this capital infrastructure, big outlays. You know, we use our phones to drop a call. People like, "Ah, freaking Verizon." But it's amazing what we can actually do too. You think about the pandemic, the shift that the telcos had to go through to landlines to support home, never missed a beat. And yet at the same time you're providing all this infrastructure for people to come over the top, the cost forbid is going down, right? Your cost are going up and yet now we're doing this big 5G buildup. So I feel like there's a renaissance about to occur in edge computing that the telcos are going to lead new forms of monetization new value that you're going to be able to add, new services, new applications. The future's got to be exciting for you guys and it's going to be developer-led, isn't it? >> Absolutely. I mean it's been such an exciting time to be a part of our mobile edge computing portfolio. If you think back to late 2019 we were really asking the question with the advent of high speed 5G mobile networks, how can you drive more immersive experiences from the cloud in a cloud native way without compromising on the tools you know and love? And that's ultimately what caused us to really work with the likes of AWS and others to think about what does a mobile edge computing portfolio look like? So we started with 5G Edge with AWS Wavelength. So taking the compute and storage services you know and love in AWS and bringing it to the edge of our 4G and 5G networks. But then we start to think, well, wait a minute. Why stop at public networks? Let's think about private networks. How can we bring the cloud and private networks together? So you turn back to late 2021 we announced Verizon 5G Edge with AWS Outposts but we didn't even stop there. We said, "Well, interest's cool, but what about network APIs? We've been talking about the ability and the programmability of the 5G network but what does that actually look like to the developers? And one great example is our Edge Discovery Service. So you think about the proliferation of the edge 17 Wavelength Zones today in the US. Well, what edge is the right edge? You think about maybe the airline industry if the closest exit might be behind you absolutely applies to service discovery. So we've built an API that helps answer that seemingly basic question but is the fundamental building block for everything to workload orchestration, workload distribution. A basic network building block has become so important to some of these new sources of revenue streams, as we mentioned, but also the ability to disintermediate that purpose built hardware. You think about the future of autonomous mobile robots either ground and aerial robotics. Well, you want to make those devices as cheap as possible but you don't want to compromise on performance. And that mobile edge layer is going to become so critical for that connectivity, but also the compute itself. >> So I just kind of gave my little narrative up front about telco, but that purpose built hardware that you're talking about is exceedingly reliable. I mean, it's hardened, it's fossilized and so now as you just disaggregate that and go to a more programmable infrastructure, how are you able to and what gives you confidence that you're going to be able to maintain that reliability that I joke about? Oh, but it's so reliable. The network has amazing reliability. How are you able to maintain that? Is that just the pace of technology is now caught up, I wonder if you can explain that? >> I think it's really cool as I see reliability and sort of geo distribution as inextricably linked. So in a world where to get that best in class latency you needed to go to one place and one place only. Well, now you're creating some form of single source of failure whether it's the power, whether it's the compute itself, whether it's the networking, but with a more geo distributed footprint, particularly in the mobile edge more choices for where to deliver that immersive experience you're naturally driving an increase in reliability. But again, infra alone it's not going to do the job. You need the network APIs. So it's the convergence of the cloud and network and infra and the automation behind it that's been incredibly powerful. And as a great example, the work we've been doing in hybrid MEC the ability to converge within one single architecture, the private network, the public network, the AWS Outposts, the AWS Wavelength all in one has been such a fantastic journey and Red Hat has been a really important part in that journey. >> From the perspective of the developer when they're building a full cloud to edge application, where does Verizon pick up? Where do they start working primarily with you versus with their cloud provider? >> Absolutely. And I think you touched on a really important point. I think when you often think about the edge it's thought of as an either, or. Is it the edge? Is it the cloud? Is it both? It's an and I can't emphasize that enough. What we've seen from the customers greenfield or otherwise it's about extending an application or services that were never intended to live at the edge, to the edge itself, to deliver a more performant experience. And for certain control plane operations, metadata, backend operations analytics that can absolutely stay in the cloud itself. And so our role is to be a trusted partner in some of our enterprise customers' journeys. Of course, they can lean on the cloud provider in select cases, but we're an absolutely critical mode of support as you think about what are those architectures? How do you integrate the network APIs? And through our developer relations efforts, we've put a major role in helping to shape what those patterns really look like in the wild. >> When they're developing for 5G I mean, the availability of 5G of particularly you know, the high bandwidth 5G is pretty spotty right now. Mostly urban areas. How should they be thinking in the future developing an application roll out two years from now about where 5G will be at that point? >> Absolutely. I think one of the most important things in this case is the interoperability of our edge computing portfolio with both 4G and 5G. Whenever somebody asks me about the performance of 5G they ask how fast? Or for edge computing. It's always about benchmark. It's not an absolute value. It's always about benchmarking the performance to that next best alternative. What were you going to get if you didn't have edge computing in your back pocket? And so along that line of thought having the option to go either through 4G or 5G, having a mobile edge computing portfolio that works for both modes of connectivity even CAN-AM IoT is incredibly powerful. >> So it sounds like 4G is going to be with us for quite a while still? >> And I think it's an important part of the architecture. >> Yeah. >> Robert, tell us about the developer that's building these applications. Where does that individual come from? What's their persona? >> Oh, boy I think there's a number of different personas and flavors. I've seen everything from the startup in the back of a garage working hard to try to figure out what could I do for a next generation media and entertainment experience but also large enterprises. And I think a great area where we saw this was our 5G Edge Computing Challenge that we hosted last year. Believe it or not 100 submissions from over 22 countries, all building on Verizon 5G Edge. It was so exciting to see because so many different use cases across public safety, healthcare, media and entertainment. And what we found was that education is so important. A lot of developers have great ideas but if you don't understand the fundamentals of the infrastructure you get bogged down in networking and setting up your environment. And that's why we think that developer education is so important. We want to make it easy and in fact, the 5G Edge portfolio was designed in such a way that we'll abstract the complexities of the network away so you can focus on building your application and that's such a central theme and focus for how we approach the development. >> So what kind of services are you exposing via APIs? >> Absolutely, so first and foremost, as you think about 5G Edge with say AWS Wavelength, the infra there are APIs that are exposed by AWS to launch your infra, to patch your infrastructure, to automate your infrastructure. Specifically that Verizon has developed that's our network APIs. And a great example is our Edge Discovery Service. So think of this as like a service registry you've launched an application in all 17 edge zones. You would take that information, you would send it via API to the Edge Discovery Service so that for any mobile client say, you wake up one morning in Boston, you can ask the API or query, "Hey, what's the closest edge zone?" DNS isn't going to be able to figure it out. You need knowledge of the actual topology of the mobile network itself. So the API will answer. Let's say you take a little road trip 1,000 miles south to say Miami, Florida you ask that question again. It could change. So that's the workflow and how you would use the network API today. >> How'd you get into this? You're an engineer it's obvious how'd you stumble into this role? >> Well, yeah, I have a background in networks and distributed systems so I always knew I wanted to stay in the cloud somewhere. And there was a really unique opportunity at Verizon as the portfolio was being developed to really think about what this developer community looked like. And we built this all from scratch. If you look at say our Verizon 5G Edge Blog we launched it just along the timing of the actual GA of Wavelength. You look at our developer newsletter also around the time of the launch of Wavelength. So we've done a lot in such a short period and it's all been sort of organic, interacting with developers, working backwards from the customer. And so it's been a fairly new, but incredibly exciting journey. >> How will your data, architecture, data flow what will that look like in the future? How will that be different than it is sort of historically? >> When I think about customer workloads real time data architecture is an incredibly difficult thing to do. When you overlay the edge, admittedly, it gets more complicated. More places that produce the data, more places that consume data. How do you reconcile all of these environments? Maintain consistency? This is absolutely something we've been working on with the ecosystem at large. We're not going to solve this alone. We've looked at architecture patterns that we think are successful. And some of the things that we found that we believe are pretty cool this idea of taking that embedded mobile database, virtualizing it to the edge, even making it multi-tenant. And then you're producing data to one single source and simplifying how you organize and share data because all of the data being produced to that one location will be relevant to that topology. So Boston, as an example, Boston data being produced to that edge zone will only service Boston clients. So having a geo distributed footprint really does help data architectures, but at the core of all of this database, architectures, you need a compute environment that actually makes sense. That's performant, that's reliable. That's easy to use that you understand how to manage and that the edge doesn't make it any more difficult to manage. >> So are you building that? >> That's exactly what we're doing. So here at Red Hat Summit we've had the unique opportunity to continue to collaborate with our partners at Red Hat to think about how you actually use OpenShift in the context of hybrid MEC. So what have done is we've used OpenShift as is to extend what already exists to some of these new edge zones without adding in an additional layer of complexity that was unmanageable. >> So you use OpenShift so you don't have to cobble this together on your own as a full development environment and that's the role really that OpenShift plays here? >> That's exactly right. And we presented pieces of this at our re:Invent this past year and what we basically did is we said the edge needs to be inextricably linked with the cloud. And you want to be able to manage it from some seamless central pane of glass and using that OpenShift console is a great way. So what we did is we wanted to show a really geo-distributed footprint in action. We started with a Wavelength zone in Boston, the region in Northern Virginia, an outpost in the Texas area. We cobbled it all together in one cluster. So you had a whole compute mesh separated by thousands of miles all within a single cluster, single pane of glass. We take that and are starting to expand on the complexity of these architectures to overlay the network APIs we mentioned, to overlay multi-region support. So when we say you can use all 17 zones at once you actually can. >> So you've been talking about Wavelength and Outposts which are AWS products, but Microsoft and Google both have their distributed architectures as well. Where do you stand with those? Will you support those? Are you working with them? >> That's a great question. We have made announcements with Microsoft and Google but today I focus a lot on the work we do with AWS Wavelength and Outposts and continuing to work backwards from the customer and ultimately meet their needs. >> Yeah I mean, you got to start with an environment that the developers know that obviously a great developer community, you know, you see it at re:Invent. What was the reaction at re:Invent when you showed this from a developer community? >> Absolutely. Developers are excited and I think the best part is we're not the only ones talking about Wavelength not even AWS are the only ones talking about Wavelength. And to me from a developer ecosystem perspective that's when you know it's working. When you're not the one telling the best stories when others are evangelizing the power of your technology on your behalf that's when the ecosystem's starting to pick up. >> Speaking of making a bet on Outposts you know, it's somewhat limited today. I'll say it it's limited today in terms of we think it supports RDS and there's a few storage players. Is it your expectation that Outposts is going to be this essentially the cloud environment on your premises is that? >> That's a great question. I see it more as we want to expand customer choice more than ever and ultimately let the developers and architects decide. That's why I'm so bullish on this idea of hybrid MEC. Let's provide all of the options the most complicated geo distributed hybrid deployment you can imagine and automate it, make it easy. That way if you want to take away components of this architecture all you're doing is simplifying something that's already automated and fairly simple to begin with. So start with the largest problem to solve and then provide customers choice for what exactly meets their requirements their SLAs, their footprint, their network and work backwards from the customer. >> Exciting times ahead. Rob, thanks so much for coming on theCUBE. It's great to have you. >> Appreciate it, thanks for your time. >> Good luck. All right, thank you for watching. Keep it right there. This is Dave Vellante for Paul Gillin. We're live at Red Hat Summit 2022 from the Seaport in Boston. We'll be right back.

>>Machine intelligence is everywhere. AI is being embedded into our everyday lives, through applications, process automation, social media, ad tech, and it's permeating virtually every industry and touching everyone. Now, a major issue with machine learning and deep learning is trust in the outcome. That is the black box problem. What is that? Well, the black box issue arises when we can see the input and the output of the data, but we don't know what happens in the middle. Take a simple example of a picture of a cat or a hotdog for you. Silicon valley fans, the machine analyzes the picture and determines it's a cat, but we really don't know exactly how the machine determined that. Why is it a problem? Well, if it's a cat on social media, maybe it isn't so onerous, but what if it's a medical diagnosis facilitated by a machine? And what if that diagnosis is wrong? >>Or what if the machine is using deep learning to qualify an individual for a home loan and that person applying for the loan gets rejected. Was that decision based on bias? If the technology to produce that result is opaque. Well, you get the point. There are serious implications of not understanding how decisions are made with AI. So we're going to dig into the issue and the topic of how to make AI explainable and operationalize AI. And with me are two guests today, Shea speaky, who's the co-founder and COO of cognitive scale and long time friend of the cube and newly minted CEO of cognitive scale. Bob pitchy, Yano, gents. Welcome to the cube, Bob. Good to see you again. Welcome back on. >>Thanks for having us >>Say, let me start with you. Why did you start the company? I think you started the company in 2013. Give us a little history and the why behind cognitive scale. >>Sure. David. So, um, look, I spent some time, um, you know, through multiple startups, but I ended up at IBM, which is where I met Bob. And one of the things that we did was the commercialization of IBM Watson initially. And that led to, uh, uh, thinking about how do you operationalize this because of the, a lot of people thinking about data science and machine learning in isolation, building models, you know, trying to come up with better ways to deliver some kind of a prediction, but if you truly want to operationalize it, you need to think about scale that enterprises need. So, you know, we were in the early days, enamored by ways, I'm still in landed by ways. The application that takes me from point a to point B and our view is look as you go from point a to point B, but if you happen to be, um, let's say a patient or a financial services customer, imagine if you could have a raise like application giving you all the insights that you needed telling you at the right moment, you know, what was needed, the right explanation so that it could guide you through the journey. >>So that was really the sort of the thesis behind cognitive scale is how do you apply AI, uh, to solve problems like that in regulated industries like health care management services, but do it in a way that it's done at scale where you can get, bring the output of the data scientists, application developers, and then those insights that can be powered into those end applications like CRM systems, mobile applications, web applications, applications that consumers like us, whether it be in a healthcare setting or a financial services setting can get the benefit of those insights, but have the appropriate sort of evidence and transparency behind it. So that was the, that was the thesis for. >>Got it. Thank you for that. Now, Bob, I got to ask you, I knew you couldn't stay in the sidelines, my friend. So, uh, so what was it that you saw in the marketplace that Lord you back in to, to take on the CEO role? >>Yeah, so David is an exciting space and, uh, you're right. I couldn't stay on the sideline stuff. So look, I always felt that, uh, enterprise AI had a promise to keep. Um, and I don't think that many enterprises would say, you know, with their experience that yeah, we're getting the value that we wanted out of it. We're getting the scale that we wanted out of it. Um, and we're really satisfied with what it's delivered to us so far. So I felt there was a gap in keeping that promise and I saw cognitive scale as an important company and being able to fill that gap. And the reason that that gap exists is that, you know, enterprise AI, unlike AI, that relates to one particular conversational service or one particular small narrow domain application is really a team sport. You know, it involves all sorts of roles, um, and all sorts of aspects of a working enterprise. >>That's already scaled with systems of engagement, um, and, and systems of record. And we show up in the, with the ability to actually help put all of that together. It's a brown field, so to speak, not a Greenfield, um, and where Shea and Matt and Minosh and the team really focused was on what are the important last mile problems, uh, that an enterprise needs to address that aren't necessarily addressed with any one tool that might serve some members of that team? Because there are a lot of great tools out there in the space of AI or machine learning or deep learning, but they don't necessarily help come together to, to deliver the outcomes that an enterprise wants. So what are those important aspects? And then also, where do we apply AI inside of our platform and our capabilities to kind of take that operationalization to the next level, uh, with, you know, very specific insights and to take that journey and make it highly personalized while also making it more transparent and explainable. >>So what's the ICP, the ideal customer profile, is it, is it highly regulated industries? Is it, is it developers? Uh, maybe you could parse that a little bit. >>Yeah. So we do focus in healthcare and in financial services. And part of the reason for that is the problem is very difficult for them. You know, you're, you're working in a space where, you know, you have rules and regulations about when and how you need to engage with that client. So the bar for trust is very, very high and everything that we do is around trusted AI, which means, you know, thinking about using the data platforms and the model platforms in a way to create marketplaces, where being able to utilize that data is something that's provisioned in permission before we go out and do that assembly so that the target customer really is somebody who's driving digital transformation in those regulated industries. It might be a chief digital officer. It might be a chief client officer, customer officer, somebody who's really trying to understand. I have a very fragmented view of my member or of my patient or my client. And I want to be able to utilize AI to help that client get better outcomes or to make sure that they're not lost in the system by understanding and more holistically understanding them in a more personalized way, but while always maintaining, you know, that that chain of trust >>Got it. So can we get into the product like a little bit more about what the product is and maybe share, you can give us a census to kind of where you started and the evolution of the portfolio >>Look where we started there is, um, the application of AI, right? So look, the product and the platform was all being developed, but our biggest sort of view from the start had been, how do you get into the trenches and apply this to solve problems? And as well, pointed out, one of the areas we picked was healthcare because it is a tough industry. There's a lot of data, but there's a lot of regulation. And it's truly where you need the notion of being able to explain your decision at a really granular level, because those decisions have some serious consequences. So, you know, he started building a platform out and, um, a core product is called cortex. It's the, it's a software platform on top of this. These applications are built, but to our engagements over the last six, seven years, working with customers in healthcare, in financial services, some of the largest banks, the largest healthcare organizations, we have developed a software product to essentially help you scale enterprise AI, but it starts with how do you build these systems? >>Building the systems requires us to provide tooling that can help developers take models, data that exists within the enterprise, bring it together, rapidly, assemble this, orchestrate these different components, stand up. These systems, deploy these systems again in a very complex environment that includes, you know, on-prem systems as well as on the cloud, and then be able to done on APIs that can plug into an application. So we had to essentially think of this entire problem end to end, and that's poor cortex does, but extremely important part of cortex that didn't start off. Initially. We certainly had all the, you know, the, the makings of a trusted AI would be founded the industry wasn't quite ready over time. We've developed capabilities around explainability being able to detect bias. So not only are you building these end to end systems, assembling them and deploying them, you have as a first-class citizen built into this product, the notion of being able to understand bias, being able to detect whether there's the appropriate level of explainability to make a decision and all of that's embedded within the cortex platform. So that's what the platform does. And it's now in its sixth generation as we >>Speak. Yeah. So Dave, if you think about the platform, it really has three primary components. One is this, uh, uh, application development or assembly platform that fits between existing AI tools and models and data and systems of engagement. And that allows for those AI developers to rapidly visualize and orchestrate those aspects. And in that regard were tremendous partners with people like IBM, Microsoft H2O people that provide aspects that are helping develop the data platform, the data fabric, things like the, uh, data science tools to be able to then feed this platform. And then on the front end, really helping transform those systems of engagement into things that are more personalized with better recommendations in a more targeted space with explainable decisions. So that's one element that's called cortex fabric. There's another component called cortex certify. And that capability is largely around the model intelligence model introspection. >>It works, uh, across things that are of cost model driven, but other things that are based on deterministic algorithms, as well as rule-based algorithms to provide that explainability of decisions that are made upstream before they get to the black box model, because organizations are discovering that many times the data has, you know, aspects of dimensions to it and, and, and biases to it before it gets to the model. So they want to understand that entire chain of, of, uh, of decisioning before it gets there. And then there's the notion of some pew, preacher rated applications and blueprints to rapidly deliver outcomes in some key repeating areas like customer experience or like lead generation. Um, those elements where almost every customer we engage with, who is thinking about digital transformation wants to start by providing better client experience. They want to reduce costs. They want to have operational savings while driving up things like NPS and improving the outcomes for the people they're serving. So we have those sets of applications that we built over time that imagine that being that first use application, that starter set, that also trains the customer on how to you utilize this operational platform. And then they're off to the races building out those next use cases. So what we see as one typical insertion place play that returns value, and then they're scaling rapidly. Now I want to cover some secret sauce inside of the platform. >>Yeah. So before you do, I think, I just want to clarify, so the cortex fabric, cause that's really where I wanted to go next, but the cortex fabric, it seems like that's the way in which you're helping people operationalize inject use familiar tooling. It sounds like, am I correct? That the cortex certify is where you're kind of peeling the onion of that complicated, whether it's deep learning or neural networks, which is that's where the black box exists. Maybe you could tell us, you know, is that where the secret sauce lives, if not, where is it? And if >>It actually is in all places right though. So there's some really important, uh, introductions of capabilities, because like I mentioned, many times these, uh, regulated industries have been developed and highly fragmented pillars. Just think about the insurance companies between property casualty and personal lines. Um, many times they have grown through acquisition. So they have these systems of record that are, that are really delivering the operational aspects of the company's products, but the customers are sometimes lost in the scenes. And so they've built master data management capabilities and data warehouse capabilities to try to serve that. But they find that when they then go to apply AI across some of those curated data environments, it's still not sufficient. So we developed an element of being able to rapidly assemble what we call a profile of one. It's a very, very intimate profile around declared data sources, uh, that relate to a key business entity. >>In most cases, it's a person, it's a member, it's a patient, it's a client, but it can be a product for some of our clients. It's real estate. Uh, it's a listing. Um, you know, it can be someone who's enjoying a theme park. It can be someone who's a shopper in a grocery store. Um, it can be a region. So it's any key business entity. And one of the places where we applied our AI knowledge is by being able to extract key information out of these declared systems and then start to make longitudinal observations about those systems and to learn about them. And then line those up with prediction engines that both we supply as well as third parties and the customers themselves supply them. So in this theme of operationalization, they're constantly coming up with new innovations or a new model that they might want to interject into that engagement application. Our platform with this profile of one allows them to align that model directly into that profile, get the benefits of what we've already done, but then also continue to enhance, differentiate and provide even greater, uh, greater value to that client. IBM is providing aspects of those models that we can plug in. And many of our clients are that's really >>Well. That's interesting. So that profile of one is kind of the instantiation of that secret sauce, but you mentioned like master data management data warehouse, and, you know, as well as I do Bob we've we've we've decades of failures trying to get a 360 degree view for example of the customer. Uh, it's just, just not real time. It's not as current as we would want it to be. The quality is not necessarily there. It's a very asynchronous process. Things have changed the processing power. You and I have talked about this a lot. We have much more data now. So it's that, that, that profile one. So, but also you mentioned curated apps, customer experience, and lead gen. You mentioned those two, uh, and you've also talked about digital transformation. So it sounds like you're supporting, and maybe this is not necessarily the case, but I'm curious as to what's going on here, maybe supporting more revenue generation in the early phases than say privacy or compliance, or is it actually, do you have use cases for both? >>It's all, it's all of it. Um, and, and shake and, you know, really talk passionately about some of the things we've helped clients do, like for instance, uh, J money. Why don't you talk about the, the hospital, um, uh, uh, you know, discharge processes. >>Absolutely. So, so, you know, just to make this a bit more real, they, you know, when you talk about a profile on one, it's about understanding of patient, as I said earlier, but it's trying to bring this notion of not just the things that you know about the patient you call that declared information. You can find the system in, you can find this information in traditional EMR systems, right? But imagine bringing in, uh, observed information, things that you observed an interaction with the patient, uh, and then bring in inferences that you can then start drawing on top of that. So to bring this to a live example, imagine at the point of care, knowing when all the conditions are right for the patient to be discharged after surgery. And oftentimes as you know, those, if all the different evidence of the different elements that don't come together, you can make some really serious mistakes in terms of patient discharge, bad things can happen. >>Patient could be readmitted or even worse. That could be a serious outcome. Now, how do you bring that information at the point of care for the person making a decision, but not just looking at the information, you know, but also understanding not just the clinical information, but the social, the socioeconomic information, and then making sure that that decision has the appropriate evidence behind it. So then when you do make that decision, you have the appropriate sort of, uh, you know, the guidance behind it for audit reasons, but also for ensuring that you don't have a bad outcome. So that's the example Bob's talking about, where we have a flight this in real settings, in, in healthcare, but also in financial services and other industries where you can make these decisions based on the machine, telling you with a lot of detail behind it, whether this is the right decision to be made, we call this explainability and the evidence that's needed. >>You know, that's interesting. I, I, I'm imagining a use case in my mind where after a patient leaves, so often there's just a complete disconnect with the patient, unless that patient has problems and goes back, but that patient might have some problems, but they forget it's too much of a pain in the neck to go back, but, but the system can now track this and we could get much more accurate information and that could help in future diagnoses and, and also decision-making for a patient in terms of, of outcomes and probability of success. Um, question, what do you actually sell? So it's a middleware product. It's a, how do I license it? >>It's a, it's a, uh, it's a software platform. So we sell software, um, and it is deployed in the customer's cloud environment of choice. Uh, of course we support complete hybrid cloud capabilities. Um, we support native cloud deployments on top of Microsoft and Amazon and Google. And we support IBM's hybrid cloud initiative with red hat OpenShift as well, which also puts us in a position to both support those public cloud environments, as well as the customer's private cloud environments. So constructed with Kubernetes in that environment, um, which helps the customer also re you know, realize the value of that operational appar operationalization, because they can modify those applications and then redeploy them directly into their cloud environment and start to see those as struck to see those spaces. Now, I want to cover a couple of the other components of the secret sauce, if I could date to make sure that you've got a couple other elements where some real breakthroughs are occurring, uh, in these spaces. >>Um, so Dave, you and I, you know, we're passionate about the semiconductor industry, uh, and you know, we know what is, you know, happening with regard to innovation and broadening the people who are now siliconized their intellectual property and a lot of that's happening because those companies who have been able to figure out how to manufacture or how to design those semiconductors are operationalizing those platforms with our customers. So you have people like apple who are able to really break out of the scene and do things by utilizing utilities and macros their own knowledge about how things need to work. And it's just, it's very similar to what we're talking about doing here for enterprise AI, they're operationalizing that construction, but none of those companies would actually start creating the actual devices until they go through simulation and design. Correct. Well, when you think about most enterprises and how they develop software, they just immediately start to develop the code and they're going through AB testing, but they're all writing code. >>They're developing those assets. They're creating many, many models. You know, some organizations say 90% of the models they create. They never use some say 50, and they think that's good. But when you think about that in terms of, you know, the capital that's being deployed, both on the resources, as well as the infrastructure, that's potentially a lot of waste as well. So one of the breakthroughs is, uh, the creation of what we call synthetic data and simulations inside of our, of our operational platform. So cortex fabric allows someone to actually say, look, this is my data pattern. And because it's sensitive data, it might be, you know, PII. Um, we can help them by saying, okay, what is the pattern of that data? And then we can create synthetic data off of that pattern for someone to experiment with how a model might function or how that might work in the application context. >>And then to run that through a set of simulations, if they want to bring a new model into an application and say, what will the outcomes of this model be before I deployed into production, we allow them to drive simulations across millions or billions of interactions to understand what is that model going to be effective. Was it going to make a difference for that individual or for this application or for the cost savings goal and outcomes that I'm trying to drive? So just think about what that means in terms of that digital transformation officers, having the great idea, being in the C-suite and saying, I want to do this with my business. Oftentimes they have to turn around to the CIO or the chief data officer and say, when can you get me that data? And we all know the answer to that question. They go like this, like the, yeah, I've got a couple other things on the plate and I'll get to that as soon as I can. >>Now we're able to liberate that. Now we're able to say, look, you know, what's the concept that you're trying to develop. Let's create the synthetic data off of that environment. We have a Corpus of data that we have collected through various client directions that many times gets that bootstrapped and then drive that through simulation. So we're able to drive from imagination of what could be the outcome to really getting high confidence that this initiative is going to have a meaningful value for the enterprise. And then that stimulates the right kind of following and the right kind of endorsement, uh, throughout really driving that change to the enterprise and that aspect of the simulations, the ability to plan out what that looks like and develop those synthetic aspects is another important element that the secret sauce inside of cortex fabric, >>Back to the semiconductor innovation, I can do that very cheaply. I think, I think I I'm thinking AWS cloud, I could experiment using graviton or maybe do a little bit of training with some, you know, new processors and, and then containerize it, bring it back to my on-premise state and apply it. Uh, and so, uh, just a as you say, a much more agile environment, um, yeah, >>Speed efficiency, um, and the ability to validate the hypothesis that, that started the process. >>Guys, think about the Tam, the total available market. Can we have that discussion? How big is that? >>I mean, if you think about the spend across, uh, the healthcare space and financial services, we're talking about hundreds of billions, uh, in that, in terms of what the enterprise AI opportunity, as in just those spaces. And remember financial services is a broad spectrum. So one of the things that we're actually starting to roll out today in fact, is a SAS service that we developed. That's based on top of our offerings called trust star trust star.ai, and trust star is a set of personalized insights that get delivered directly to the loan officer inside of, uh, an institution who's trying to, uh, really match, uh, lending to someone who wants to buy a property. Um, and when you think about many of those organizations, they have very, very high demand. They've got a lot of information, they've got a lot of regulation they need to adhere to. >>But many times they're very analytically challenged in terms of the tools they have to be able to serve those needs. So what's happening with new listings, what's happening with my competitors, what's happening. As people move from high tax states, where they want to potentially leave into new, more attractive toxin and opportunity-based environments where they're not known to those lending institutions that maybe, you know, they're, they're trying to be married up with. So we've developed a set of insights that are, is, this is a subscription service trust r.ai, um, which goes directly to the loan officer. And then we use our platform behind the scenes to use things like the home disclosure act, data, MLS data, other data that is typically Isagenix to those sources and providing very customized insights to help that buyer journey. And of course, along the way, we can identify things like are some of the decisions more difficult to explain, are there potential biases that might be involved in that environment as people are applying for mortgages, and we can really drive growth through inclusion for those lending institutions, because they might just not understand that potential client well enough, that we can identify the kind of things that they can do to know them better. >>And the benefit is really to hold there, right? And shale, I'll let you jump in, but to me, it's twofold. There. One is, you know, you want to have accurate decisions. You want to have low risk decisions. And if you want to be able to explain that to an individual that may get rejected, here's why, um, and, and it wasn't because of bias. It was because of XYZ and you need to work on these things, but go ahead shape. >>Now, this is going to add that point here, Dave, which is a double-faced point on the dam. One of the things that, and the reason why, you know, industries like healthcare, financial services spending billions, it's not because they look at AI in isolation, they actually looking at the existing processes. So, you know, established disciplines like CRM or supply chain procurement, whether it is contact center and so on. And the examples that we gave you earlier, it's about infusing AI into those existing applications, existing systems. And that's, what's creating the left because what's been missing so far is the silos of data and you traditional traditional transaction systems, but this notion of intelligence that can be infused into the systems and that's, what's creating this massive market opportunity for us. >>Yeah. And I think, um, I think a lot of people just misunderstood in the, or in the early, early days of the AI, you know, new AI when we came out of the AI winter, if you will, people thought, okay, the incumbents are in big trouble now because they are not, they're not AI developers, but really what you guys are showing is it's not about building your own AI. It's about applying AI and having the tools to do so. The incumbents actually have a huge advantage because they've got the systems in place. They can, if they, if they're smart, they can infuse AI and then extract value out of that for their customers. >>And that's why, you know, companies like, uh, like IBM are an investor in a great partner in this space. Anthem is an investor, uh, you know, of the company, but also, you know, someone who can utilize the capabilities, Microsoft, uh, Intel, um, you know, we've been, we've been, uh, you know, really blessed with a great backing Norwest venture partners, um, obviously is, uh, an investor in us as well. So, you know, we've seen the ability to really help those organizations think about, um, you know, where that future lies. But one of the things that is also, you know, one of the gaps in the promises when a C-suite executive like a digital transformation officer, chief digital chief customer officer, they're having their idea, they want to be accountable to that idea. They're having that idea in the boardroom. And they're saying, look, I think I can improve my customer satisfaction and, uh, by 20 points and decrease the cost of my call center by 20 or 30 or 50 points. >>Um, but they need to be able to measure that. So one of the other things that, uh, we've done a cognitive scale is help them understand the progress that they're making across those business goals. Um, now when you think about this people like Andrew Nang, or just really talking about this aspect of goal oriented AI, don't start with the problem, start with what your business goal is, start with, what outcome you're trying to drive, and then think about how AI helps you along that goal. We're delivering this now in our product, our version six product. So while some people are saying, yeah, this is really the right way to potentially do it. We have those capabilities in the product. And what we do is we identify this notion of the campaign, an AI campaign. So when the case that I just gave you where the chief digital officer is saying, I want to drive customer satisfaction up. >>I want to have more explainable decisions, and I want to drive cost down. Maybe I want to drive, call avoidance. Um, you know, and I want to be able to reduce a handling time, um, to drive those costs down, that is a campaign. And then underneath that campaign, there's all sorts of missions that support that campaign. Some of them are very long running. Some of them are very ephemeral. Some of them are cyclical, and we have this notion of the campaign and then admission planner that supports the goals of that campaign, showing that a leader, how they're doing against that goal by measuring the outcomes of every interaction against that mission and all the missions against the campaign. So, you know, we think accountability is an important part of that process as well. And we've never engaged an executive that says, I want to do this, but I don't want to be accountable to the result, but they're having a hard time identifying I'm spending this money. >>How do I ensure that I'm getting the return? And so we've put our, you know, our secret sauce into that space as well. And that includes, you know, the information around the trustworthiness of those, uh, capabilities. Um, and I should mention as well, you know, when we think about that aspect of the responsible AI capabilities, it's really important. The partnerships that we're driving across that space, no one company is going to have the perfect model intelligence tool to be able to address an enterprise's needs. It's much like cybersecurity, right? People thought initially, well, I'll do it myself. I'll just turn up my firewall. You know, I'll make my applications, you know, uh, you know, roll access much more granular. I'll turn down the permissions on the database and I'll be safe from cybersecurity. And then they realized, no, that's not how it was going to work. >>And by the way, the threats already inside and there's, long-term persistent code running, and you have to be able to scan it, have intelligence around it. And there are different capabilities that are specialized for different components of that problem. The same is going to be turnaround responsible and trustworthy AI. So we're partnered with people like IBM, people like Microsoft and others to really understand how we take the best of what it is that they're doing partner with the best, uh, that they're doing and make those outcomes better for clients. And then there's also leaders like the responsible AI Institute, which is a non-profit independent organization who were thinking about a new rating systems for, um, the space of responsible and trusted AI, thinking about things like certifications for professionals that really drive that notion of education, which is an important component of addressing the problem. And we're providing the integration of our tools directly with those assessments and those certifications. So if someone gets started with our platform, they're already using an ecosystem that includes independent thinkers from across the entire industry, um, including public sector, as well as the private sector, to be able to be on the cutting edge of what it's going to take to really step up to the challenge in that space. >>Yeah. You guys got a lot going on. I mean, you're eight years in now and you've got now an executive to really drive the next scale. You mentioned Bob, some of your investors, uh, Anthem, IBM Norwest, uh, I it's Crunchbase, right? It says you've raised 40 million. Is that the right number? Where are you in fundraising? What can you tell? >>Um, they're a little behind where we are, but, uh, you know, we're staged B and, uh, you know, we're looking forward to now really driving that growth. We're past that startup phase, and now we're into the growth phase. Um, and we're seeing, you know, the focus that we've applied in the industries, um, really starting to pay off, you know, initially it would be a couple of months as a customer was starting to understand what to be able to do with our capabilities to address their challenges. Now we're seeing that happen in weeks. So now is the right time to be able to drive that scalability. So we'll be, you know, looking in the market of how we assemble that, uh, you know, necessary capability to grow. Um, Shay and I have worked, uh, in the past year of, uh, with the board support of building out our go to market around that space. >>Um, and in the first hundred days, it's all about alignment because when you're going to go through that growth phase growth phase, you really have to make sure that things were pointed in the right direction and pointed together in the right direction, simplifying what it is that we're doing for the market. So people could really understand, you know, how unique we are in this space, um, and what they can expect out of an engagement with us. Um, and then, you know, really driving that aspect of designing to go to market. Um, and then scaling that. >>Yeah, I think I, it sounds like you've got, you got, if you're, if you're in down to days or weeks in terms of the ROI, it sounds like you've got product market fit nailed. Now it's about sort of the next phase is you really driving your go to market and the science behind how your dimension and your, your sales productivity, and you can now codify what you've learned in that first phase. I like the approach. A lot of, a lot of times you see companies, of course, this comes out of the west coast, east coast guy, but you see the double, double, triple, triple grow, grow, grow, grow, grow, and then, and then churn becomes that silent killer of the S the software company. I think you guys, it sounds you've, you've taken a much, much more adult-like approach, and now you're ready to really drive that scale. I think it's the new formula really for success for hitting escape velocity. Guys, we got to go, but thanks so much. Uh, uh, Bob, I'll give you the last word, w w w what you mentioned some of your a hundred day priorities. Maybe you can summarize that and what should we be looking for as Martin? >>I mean, I, I think, I think the, you know, the, our measures of success are our clients measure success and the same for our partners. So we're not doing this alone, we're doing it with system integrator partners, and we're doing it with a great technology partners in the market as well. So this is a part about keeping that promise for enterprise AI. And one of the things that I'll say just in the last couple of minutes is, you know, this is not just a company with a great vision and great engineers to develop out this great portfolio, but it's a company with great values, great commitments to its employees and the marketplace and the communities we serve. So I was attracted to the culture of this company, as well as I was, uh, to the, uh, innovation and what they mean to the, to the space of a, >>And I said, I said, I'll give you last word. Actually, I got a question for Shea you Austin based, is that correct? >>But we have a global presence, obviously I'm operating out of Austin, other parts of the U S but, uh, offices in, in, uh, in the UK, as well as in India, >>You're not moving to tax-free Texas. Like everybody else. >>I've got to, I've got an important home, uh, and life in Connecticut cell. I'll be traveling back and forth between Connecticut and Austin, but keeping my home there. >>Thanks for coming on and best of luck, we want to follow your progress and really appreciate your time today. Good luck. >>Thank you, Dave. All right. >>Thank you for watching this cube conversation. This is Dave Volante. We'll see you next time.

(upbeat music) >> Welcome to theCUBE's coverage of HPE Discover 2021. I'm Lisa Martin. Robert Christiansen joins me, one of our alumni the VP of Strategy in the Office of the CTO at HPE. Robert, it's great to see you, welcome back to the program. >> It's nice being here, Lisa. Thank you so much for having me. >> So here we are still in this virtual world. Things are opening up a little bit, which is nice but one of the things I'm excited to talk to you about today is Edge to Cloud from the customer's perspective. Obviously, that's why HPE does what it does for its customers. So let's talk about some of the things that you see from your perspective, with respect to data. We can't have a Cube conversation without talking about data, there's more and more of it, value but getting access to it quickly, getting access to it in real-time and often cases to make data-driven decisions is a challenging thing to do. Talk to me about what you see from the customer's lens. >> Well, the customer at a very highest level from the board level on down they're saying, "Hey, what is our data strategy? How are we going to put the value of data in place? Are we going to have it manifest its value in an internal fashion where it makes us run better as an organization? Can we get cost improvements? Can we move quicker with that? And then can we monetize that data if it's like very specific to an industry like healthcare or pharma or something like that? Can we expose that data to the rest of the world and give them access into what we call like data sets?" And there's a lot of that going on right now too. So we're seeing these two different angles about how they're going to manage and control that data. And you were talking about, and you mentioned it, you know the Edge related focus around that. You know, the Edges where business is done is where people actually do the transaction whether it's in a healthcare like in a hospital or a manufacturing facility et cetera. And then, but that data that they're using at that location is really important to make a decision at that location. They can't send it back to a Cloud. They can't send it back to someplace, wait for a decision to happen and then shoot it back again and say, "Hey, stop the production line because we found a defect." You need to act at that moment which the clients are saying, "Hey, can you improve my reliability? Can you give me better SLS? Can you improve the quality of my products? Can you improve healthcare in a hospital by immediate decisions?" And that is a data problem. And that requires the movement of compute and networking and storage and fundamentally the core piece of HPE's world. But in addition to that, the software necessary to take the action on that data when they detect that there's some action that needs to be taken. >> And I mentioned a minute ago, you know real-time and we've learned in the last 15 months plus. One of the things we learned is for a lot of cases, access to real-time data is no longer a nice to have. It's really going to be something, an element that separates those that succeed versus those that aren't as competitive. But I want to talk about data from a consumption perspective consumers, producers, obviously, meeting to ensure that the data consumers have what they need, what is it? What is your thought when you talk with customers, the consumers versus the producers? >> Yeah, that's a great question, Lisa. One of the key fundamental areas that HPE and the Office of the CTO has really been focused on over the last six months is something that we call data spaces and that is putting in place a platform, a set of services that connect data consumers with data producers. And when you think about that, that really isn't nothing new. I mean, you could go all the way back, if you've been around for a while remember the company called TRW and they used to have credit reporting, and they used to sell that stuff. And then it moved into Experian and those things. But you've got Bloomberg and next LexisNexis and all these companies that sell data. And they've been doing it, but it's very siloed. And so the explosion of data, the valuableness the value of the data for the consumers of it has put the producers in a position where they can't readily be discovered. And whether it be a private source of data like an IoT device and an industrial control, or a set of data that might say, "Hey, here's credit card for our data on a certain geography." Those sets need to be discovered, curated, and be made available to those who would want that. You know, for example, the folks that want to know how IoT device is working inside an industrial control or a company who's trying to lower their fraud rates on credit card transactions, like in stadiums or something like that. And so this discoverability in this space, or what you just talked about is such a core piece of what we're working on right now. And we haven't, our strategy is not only to just work on what HPE has to bring that and manifest that to the marketplace. But more importantly, how are we working with our partners to really bridge that gap and bring that next generation of services to those clients that can make those connections. >> So connecting and facilitating collaboration, absolutely key, as well as that seamless flow of data sharing without constraints. How are customers working with HPE and some of your partners to be able to create a data strategy, launch it, and start gleaning value from data faster than they can before? (Robert chuckles) >> This is the big question because it's a maturity curve. Organizations are in various states of what we call data maturity or data management maturity. They can be in very early stages. You know what we consider, you know, they just more worried about just maintaining the lights on DR strategies and make sure that data doesn't go away versus all the way through a whole cycle where they're actually governing it and putting it into what I call those discoverable buckets that are made available. And there's a whole life cycle about that. And so we see a big opportunity here for our A&PS and other professional services organizations to help people get up that maturity curve. But they also have to have the foundational tools necessary to make that happen. This is really where the Ezmeral product line or software applications really shines being able to give that undercarriage that's necessary to help that data maturity and the growth of that client to meet those data needs. And we see the data fabric being a key element to that, for that distributed model, allowing people to get access and availability to have a highly redundant, highly durable data fabric and then to build applications specifically as data-intensive applications on top of that with the Ezmeral platform all the way into our GreenLake solutions. So it's quite a journey here, Lisa. I want to just, point to the fact that HPE has done a really, really good job of positioning itself for the explosion of all of these data-intensive AI/ML workloads that are making their way into every single conversation every single enterprise to this day that wants to take advantage of the value of the data they have and to augment that data through other sources. >> One, when you think about data-intensive applications the first one that pops into my mind is Uber. And it's one of those applications that we just expect. We kind of think of as a taxi service when really it's logistics and transportation, but all of the data on the backend that it is organizing to find the ride for me at my location to take me where I'm going. The explosion of data-intensive applications is great but there's also so much more demand from consumers whether we're in business or we're consuming in our personal lives. >> It's so true and that's a very popular example. And you know, you think about the real-time necessity of what's the traffic patterns at the time I order my thing. Is it going to route me the right way? That's a very real consumer facing one, but if we click into our clients and where HPE very much is like the backbone of the global economy. We provide probably one third of the compute for the global economy and it's a staggering stat if you really think about it. Our clients, I was just talking with a client here earlier, very, very large financial services company. And they have 1200 data sets that have been selling to their clients globally. And a lot of these clients want to augment that data with their existing real-time data to come up with a solution. And so they merge it and they can determine some value through a model, an AI model. And so we're working hand-in-hand with them right now to give them that backbone so that they can deliver data sets into these other systems and then make sure they get controlled and secured. So that the company we're working with, our client has a deep sense of security that that data set is not going to find itself out into the wild somewhere. And uncontrolled for a number of reasons, from security and governance mind. But the number of use cases, Lisa are as infinite as the number of opportunities for people see value in business today. >> When you're talking about 1200 data sets that a company is selling, and of course there are many, many data sets that many types of companies consume. How do you work with them to ensure that they don't just proliferate silos, but that they get more of a unified data repository that they can act on? >> Yeah, that's a great question. A key tenant of the strategy at HPE is Open-source. So we believe in a hybrid, multi-Cloud environment meaning that as long as we all agree that we are going to standardize on Open-source technologies and APIs, we will be able to write and build applications that can natively run on any abstract platform. So for example, it's very important that we containerize, for example, and we use storage and data tools that adhere to Open standards. So if you think about that, if you write a Spark application you want that Spark application potentially to run on any of the hyperscalers, the Amazon's or the Microsoft to GCPS, or you want it to run on-premises and specifically like on HPE equipment. But the idea here is I consider one of our clients right now. I mean, think about that. One of our clients specifically ask that question that you just said. They said, "Hey, we are building out this platform, this next generation platform. And we don't want the lock-in. We want to be, we want to create that environment where that data and the data framework." So they use very specific Open -source data frameworks and they open, they use very specific application frameworks the software from the Open-source community. We were able to meet that through the Ezmeral platform. Give them a very high availability, five nines high availability, redundant, redundant geographically to geographic data centers to give them that security that they're looking for. And because of that, it's opened so many other doors for us to walk in with a Cloud strategy that is an alternative, not just the one bet to public Cloud but you haven't other opportunity to bring a Cloud strategy on-premises that is compatible with Cloud-native activities that are going on in the public Cloud. And this is at the heart of HPE strategy. I think it's just, it's been paying off. It continues to pay off. We just keep investing and keep moving down that path. I think we're going to be doing really well. >> It sounds to me that the strategy that HP is developing is highly collaborative and synergistic with your customers. Talk to me a little bit about that, especially in the last year, as we've seen a massive acceleration in digital transformation about the rapid pivot to work from home, the necessity to collaborate electronically. Talk to me a little bit about that yin and yang with HPE and its customers in terms of your strategy. >> Yeah, well, I think when COVID hit one of the very first things that just took off with VDI. Rohit Dixon and I were talking on a podcast we had earlier around the work from home strategy that was implemented almost immediately. Well, we had it already in the can, we already were doing it for many clients already but it went from like a three priority to a 12, 10 being the max. Super, super charged up on how do we get work from home secured, work from home applications and stuff in the hands of people doing, you know, when data sensitivity is super important, VDI kicks in that's on that side. But then if you start looking at the digital transformation that has to happen in the supply chain that's going on right now. The opening up of our economies it's been various starts and stops if you look around the globe. The supply chains have absolutely gone under a huge amount of pressure, because, unlike in the United States, everybody just wants everything now because things are starting to open up. I was talking to a meat packing company and a restaurant business a little while ago. And they said, "Everybody wants to order the barbecue. Now we can't get the meat for the barbecues 'cause everybody's going to the barbecues." And so the supply, this is a multi-billion dollar industry supplying meat to all of the rest of the countries and stuff like that. And so they don't have optics into that supply chain today. So they're immediately having to go through a digitization process, the transformation in something as what you would call as low tech as delivering meat. So no industry is immune, none anywhere in this whole process. And it will continue to evolve as we exit and change how we live our life going into these next couple of years. I think it's going to be phenomenal just to watch. >> Yeah, it's one of the things I call a COVID catalyst some of the silver linings that have come out of this 'cause I wouldn't have thought of the meatpacking industry as a technology field as well, but now thanks to you, I will. Last question for you. When customers in this dynamic world in which we're still living talk about Edge to Cloud are they working with you to develop a Cloud initiatives, Cloud mandates, Cloud everywhere? And if so, how do you help them start? >> Yeah, that's a great question. So again, it's like back into the data model, everybody has a different degree or a starting point that they will engage us with a strategy but specifically with what you're talking about. Almost everybody already has a Cloud strategy. So they may be at different maturity levels with that Cloud strategy. And there's almost always a Cloud group. Now, historically HPE has not had much of a foot in the Cloud group because they never really historically looked at us says that HPE is a Cloud company. But what's happened over the last couple of years with the acceleration of the acceptance of Cloud on-premises and GreenLake, specifically, and the introduction of Ezmeral and the Cloud-native infrastructure services and past layer stuff that's coming up through the Ezmeral product into our clients. It's immediately opened the door for conversations around Cloud that is available for what is staying on-premises which is in excess of 70% of the applications today. Now, if you were to take that now and extend that into the Edge conversation, what if you were able to take a smaller form factor of a GreenLake Cloud and push it more closer to an Edge location while still giving the similar capabilities, Cloud-native functions that you had before? When we're provocative with clients in that sense they suddenly open up and see the art of the possible. And so this is where we are really, really breaking down a set of paradigms of what's possible by introducing, you know, not just from the Silicon all the way up but the set of services all the way to the top of stack to the actual application that they're going to be running. And we say, "Hey, we can offer it to you in as a pay as you go model, we can get you the consumption models that are necessary, that lets you buy at the same way as the Cloud offers it. But more importantly, we'll be able to run it for you and provide you an abstraction out of that model. So you don't have to send your people out into the field to do these things. We have the software, the tools, and the systems necessary to manage it for you." But the last part is I want to be really really focused on when clients are writing that application for the Edge that matters. They are putting it into new Cloud-native architectures containers, microservices, they're using solid pipelines development pipelines, they've implemented what they call their DevOps or their DataOps practices in field, in country, if you would say. That's where we shine. And so we had a really, really good conversation start there. And so how we start that is we arrive with a set of blueprints to help them establish what that roadmap looks like. And then our professional services staff, or A&PS groups around the globe are really really set up well to help them take that trip. >> Wow, that's outstanding, Robert. We could have a whole conversation on HPE's transformation. Internet itself that was my first job in tech was at Hewlett Packard back in the day. But this has been really interesting, really getting it your vision of the customer's experience and the customer's perspective from the Office of the CTO. Great to talk to you, Robert. Thank you for sharing all that you did. This could have been a Part 2 conversation. >> Well, I'm hopeful then that we'll do Part 3 and 4 here as the months go by. So I look forward to seeing you again, Lisa. >> Deal, that's a deal. All right. >> All right. >> For Robert Christiansen, I'm Lisa Martin. You're watching theCUBE's coverage of HPE Discover 2021. (upbeat music)

(upbeat music) >> Welcome to theCUBE's coverage of HPE Discover 2021. I'm Lisa Martin. Robert Christiansen joins me, one of our alumni the VP of Strategy in the Office of the CTO at HPE. Robert, it's great to see you, welcome back to the program. >> It's nice being here, Lisa. Thank you so much for having me. >> So here we are still in this virtual world. Things are opening up a little bit, which is nice but one of the things I'm excited to talk to you about today is Edge to Cloud from the customer's perspective. Obviously, that's why HPE does what it does for its customers. So let's talk about some of the things that you see from your perspective, with respect to data. We can't have a Cube conversation without talking about data, there's more and more of it, value but getting access to it quickly, getting access to it in real-time and often cases to make data-driven decisions is a challenging thing to do. Talk to me about what you see from the customer's lens. >> Well, the customer at a very highest level from the board level on down they're saying, "Hey, what is our data strategy? How are we going to put the value of data in place? Are we going to have it manifest its value in an internal fashion where it makes us run better as an organization? Can we get cost improvements? Can we move quicker with that? And then can we monetize that data if it's like very specific to an industry like healthcare or pharma or something like that? Can we expose that data to the rest of the world and give them access into what we call like data sets?" And there's a lot of that going on right now too. So we're seeing these two different angles about how they're going to manage and control that data. And you were talking about, and you mentioned it, you know the Edge related focus around that. You know, the Edges where business is done is where people actually do the transaction whether it's in a healthcare like in a hospital or a manufacturing facility et cetera. And then, but that data that they're using at that location is really important to make a decision at that location. They can't send it back to a Cloud. They can't send it back to someplace, wait for a decision to happen and then shoot it back again and say, "Hey, stop the production line because we found a defect." You need to act at that moment which the clients are saying, "Hey, can you improve my reliability? Can you give me better SLS? Can you improve the quality of my products? Can you improve healthcare in a hospital by immediate decisions?" And that is a data problem. And that requires the movement of compute and networking and storage and fundamentally the core piece of HPE's world. But in addition to that, the software necessary to take the action on that data when they detect that there's some action that needs to be taken. >> And I mentioned a minute ago, you know real-time and we've learned in the last 15 months plus. One of the things we learned is for a lot of cases, access to real-time data is no longer a nice to have. It's really going to be something, an element that separates those that succeed versus those that aren't as competitive. But I want to talk about data from a consumption perspective consumers, producers, obviously, meeting to ensure that the data consumers have what they need, what is it? What is your thought when you talk with customers, the consumers versus the producers? >> Yeah, that's a great question, Lisa. One of the key fundamental areas that HPE and the Office of the CTO has really been focused on over the last six months is something that we call data spaces and that is putting in place a platform, a set of services that connect data consumers with data producers. And when you think about that, that really isn't nothing new. I mean, you could go all the way back, if you've been around for a while remember the company called TRW and they used to have credit reporting, and they used to sell that stuff. And then it moved into Experian and those things. But you've got Bloomberg and next LexisNexis and all these companies that sell data. And they've been doing it, but it's very siloed. And so the explosion of data, the valuableness the value of the data for the consumers of it has put the producers in a position where they can't readily be discovered. And whether it be a private source of data like an IoT device and an industrial control, or a set of data that might say, "Hey, here's credit card for our data on a certain geography." Those sets need to be discovered, curated, and be made available to those who would want that. You know, for example, the folks that want to know how IoT device is working inside an industrial control or a company who's trying to lower their fraud rates on credit card transactions, like in stadiums or something like that. And so this discoverability in this space, or what you just talked about is such a core piece of what we're working on right now. And we haven't, our strategy is not only to just work on what HPE has to bring that and manifest that to the marketplace. But more importantly, how are we working with our partners to really bridge that gap and bring that next generation of services to those clients that can make those connections. >> So connecting and facilitating collaboration, absolutely key, as well as that seamless flow of data sharing without constraints. How are customers working with HPE and some of your partners to be able to create a data strategy, launch it, and start gleaning value from data faster than they can before? (Robert chuckles) >> This is the big question because it's a maturity curve. Organizations are in various states of what we call data maturity or data management maturity. They can be in very early stages. You know what we consider, you know, they just more worried about just maintaining the lights on DR strategies and make sure that data doesn't go away versus all the way through a whole cycle where they're actually governing it and putting it into what I call those discoverable buckets that are made available. And there's a whole life cycle about that. And so we see a big opportunity here for our A&PS and other professional services organizations to help people get up that maturity curve. But they also have to have the foundational tools necessary to make that happen. This is really where the Ezmeral product line or software applications really shines being able to give that undercarriage that's necessary to help that data maturity and the growth of that client to meet those data needs. And we see the data fabric being a key element to that, for that distributed model, allowing people to get access and availability to have a highly redundant, highly durable data fabric and then to build applications specifically as data-intensive applications on top of that with the Ezmeral platform all the way into our GreenLake solutions. So it's quite a journey here, Lisa. I want to just, point to the fact that HPE has done a really, really good job of positioning itself for the explosion of all of these data-intensive AI/ML workloads that are making their way into every single conversation every single enterprise to this day that wants to take advantage of the value of the data they have and to augment that data through other sources. >> One, when you think about data-intensive applications the first one that pops into my mind is Uber. And it's one of those applications that we just expect. We kind of think of as a taxi service when really it's logistics and transportation, but all of the data on the backend that it is organizing to find the ride for me at my location to take me where I'm going. The explosion of data-intensive applications is great but there's also so much more demand from consumers whether we're in business or we're consuming in our personal lives. >> It's so true and that's a very popular example. And you know, you think about the real-time necessity of what's the traffic patterns at the time I order my thing. Is it going to route me the right way? That's a very real consumer facing one, but if we click into our clients and where HPE very much is like the backbone of the global economy. We provide probably one third of the compute for the global economy and it's a staggering stat if you really think about it. Our clients, I was just talking with a client here earlier, very, very large financial services company. And they have 1200 data sets that have been selling to their clients globally. And a lot of these clients want to augment that data with their existing real-time data to come up with a solution. And so they merge it and they can determine some value through a model, an AI model. And so we're working hand-in-hand with them right now to give them that backbone so that they can deliver data sets into these other systems and then make sure they get controlled and secured. So that the company we're working with, our client has a deep sense of security that that data set is not going to find itself out into the wild somewhere. And uncontrolled for a number of reasons, from security and governance mind. But the number of use cases, Lisa are as infinite as the number of opportunities for people see value in business today. >> When you're talking about 1200 data sets that a company is selling, and of course there are many, many data sets that many types of companies consume. How do you work with them to ensure that they don't just proliferate silos, but that they get more of a unified data repository that they can act on? >> Yeah, that's a great question. A key tenant of the strategy at HPE is Open-source. So we believe in a hybrid, multi-Cloud environment meaning that as long as we all agree that we are going to standardize on Open-source technologies and APIs, we will be able to write and build applications that can natively run on any abstract platform. So for example, it's very important that we containerize, for example, and we use storage and data tools that adhere to Open standards. So if you think about that, if you write a Spark application you want that Spark application potentially to run on any of the hyperscalers, the Amazon's or the Microsoft to GCPS, or you want it to run on-premises and specifically like on HPE equipment. But the idea here is I consider one of our clients right now. I mean, think about that. One of our clients specifically ask that question that you just said. They said, "Hey, we are building out this platform, this next generation platform. And we don't want the lock-in. We want to be, we want to create that environment where that data and the data framework." So they use very specific Open -source data frameworks and they open, they use very specific application frameworks the software from the Open-source community. We were able to meet that through the Ezmeral platform. Give them a very high availability, five nines high availability, redundant, redundant geographically to geographic data centers to give them that security that they're looking for. And because of that, it's opened so many other doors for us to walk in with a Cloud strategy that is an alternative, not just the one bet to public Cloud but you haven't other opportunity to bring a Cloud strategy on-premises that is compatible with Cloud-native activities that are going on in the public Cloud. And this is at the heart of HPE strategy. I think it's just, it's been paying off. It continues to pay off. We just keep investing and keep moving down that path. I think we're going to be doing really well. >> It sounds to me that the strategy that HP is developing is highly collaborative and synergistic with your customers. Talk to me a little bit about that, especially in the last year, as we've seen a massive acceleration in digital transformation about the rapid pivot to work from home, the necessity to collaborate electronically. Talk to me a little bit about that yin and yang with HPE and its customers in terms of your strategy. >> Yeah, well, I think when COVID hit one of the very first things that just took off with VDI. Rohit Dixon and I were talking on a podcast we had earlier around the work from home strategy that was implemented almost immediately. Well, we had it already in the can, we already were doing it for many clients already but it went from like a three priority to a 12, 10 being the max. Super, super charged up on how do we get work from home secured, work from home applications and stuff in the hands of people doing, you know, when data sensitivity is super important, VDI kicks in that's on that side. But then if you start looking at the digital transformation that has to happen in the supply chain that's going on right now. The opening up of our economies it's been various starts and stops if you look around the globe. The supply chains have absolutely gone under a huge amount of pressure, because, unlike in the United States, everybody just wants everything now because things are starting to open up. I was talking to a meat packing company and a restaurant business a little while ago. And they said, "Everybody wants to order the barbecue. Now we can't get the meat for the barbecues 'cause everybody's going to the barbecues." And so the supply, this is a multi-billion dollar industry supplying meat to all of the rest of the countries and stuff like that. And so they don't have optics into that supply chain today. So they're immediately having to go through a digitization process, the transformation in something as what you would call as low tech as delivering meat. So no industry is immune, none anywhere in this whole process. And it will continue to evolve as we exit and change how we live our life going into these next couple of years. I think it's going to be phenomenal just to watch. >> Yeah, it's one of the things I call a COVID catalyst some of the silver linings that have come out of this 'cause I wouldn't have thought of the meatpacking industry as a technology field as well, but now thanks to you, I will. Last question for you. When customers in this dynamic world in which we're still living talk about Edge to Cloud are they working with you to develop a Cloud initiatives, Cloud mandates, Cloud everywhere? And if so, how do you help them start? >> Yeah, that's a great question. So again, it's like back into the data model, everybody has a different degree or a starting point that they will engage us with a strategy but specifically with what you're talking about. Almost everybody already has a Cloud strategy. So they may be at different maturity levels with that Cloud strategy. And there's almost always a Cloud group. Now, historically HPE has not had much of a foot in the Cloud group because they never really historically looked at us says that HPE is a Cloud company. But what's happened over the last couple of years with the acceleration of the acceptance of Cloud on-premises and GreenLake, specifically, and the introduction of Ezmeral and the Cloud-native infrastructure services and past layer stuff that's coming up through the Ezmeral product into our clients. It's immediately opened the door for conversations around Cloud that is available for what is staying on-premises which is in excess of 70% of the applications today. Now, if you were to take that now and extend that into the Edge conversation, what if you were able to take a smaller form factor of a GreenLake Cloud and push it more closer to an Edge location while still giving the similar capabilities, Cloud-native functions that you had before? When we're provocative with clients in that sense they suddenly open up and see the art of the possible. And so this is where we are really, really breaking down a set of paradigms of what's possible by introducing, you know, not just from the Silicon all the way up but the set of services all the way to the top of stack to the actual application that they're going to be running. And we say, "Hey, we can offer it to you in as a pay as you go model, we can get you the consumption models that are necessary, that lets you buy at the same way as the Cloud offers it. But more importantly, we'll be able to run it for you and provide you an abstraction out of that model. So you don't have to send your people out into the field to do these things. We have the software, the tools, and the systems necessary to manage it for you." But the last part is I want to be really really focused on when clients are writing that application for the Edge that matters. They are putting it into new Cloud-native architectures containers, microservices, they're using solid pipelines development pipelines, they've implemented what they call their DevOps or their DataOps practices in field, in country, if you would say. That's where we shine. And so we had a really, really good conversation start there. And so how we start that is we arrive with a set of blueprints to help them establish what that roadmap looks like. And then our professional services staff, or A&PS groups around the globe are really really set up well to help them take that trip. >> Wow, that's outstanding, Robert. We could have a whole conversation on HPE's transformation. Internet itself that was my first job in tech was at Hewlett Packard back in the day. But this has been really interesting, really getting it your vision of the customer's experience and the customer's perspective from the Office of the CTO. Great to talk to you, Robert. Thank you for sharing all that you did. This could have been a Part 2 conversation. >> Well, I'm hopeful then that we'll do Part 3 and 4 here as the months go by. So I look forward to seeing you again, Lisa. >> Deal, that's a deal. All right. >> All right. >> For Robert Christiansen, I'm Lisa Martin. You're watching theCUBE's coverage of HPE Discover 2021. (upbeat music)

(upbeat music) >> Welcome to today's session of the AWS Startup Showcase, featuring Dremio. I'm your host, Lisa Martin. And today we're joined by Robert Maybin, Principal Architect at Dremio. Robert is going to talk to us about democratizing your data by eliminating data copies. Robert, welcome. It's great to have you in today's session. >> Great. Thank you, Lisa. It's great to be here. >> So talk to me a little bit about why data copies, as Dremio says, are the key obstacle to data democratization? >> Oh, sure. Sure. Well, I think when you think about data democratization and really what that means, what people mean when they talk about data democratization, what they're really speaking to is kind of the desire for people in the organization to be able to, you know, work with the enterprises data, discover data, really, in a more self-service way. And you know, when you think about democratization, you might say, "Well, what's wrong with copies? What could be more democratic than giving everybody their own copy of the data?" But I think when you really think about that and how that ties into, you know, traditional architectures and environments, there are a lot of problems that come with copies, and those are real impediments. And so, you know, traditionally, in the data warehousing world, what often happens is that there are numerous sources of data that are coming in in all different formats, all different structures. These things, typically, for people that query them, have got to be, you know, loaded into some sort of a data warehousing tool. You know, maybe they land in cloud storage, but before they can be queried, you know, somebody has to go in and basically reformat those data sets, transform them in ways that make them more useful and make them more performant. And so this is very, very common. Like I think many, many organizations do this, and it makes a lot of sense to do it, because, you know, traditionally, the formats of the data is sourced in is pretty hard to work with and it's very slow to query. So copies is kind of a natural thing to do, but it comes at a real cost, right? There's a tremendous complexity that can come about, and having to do all these transformations. There's a real dollar cost, and there's a lot of time involved too. So, you know, if you could kind of take all of these middle steps out, where you're copying and transforming, and then transforming again, and then, potentially, persisting very high-performance structures for fast BI queries, you can reduce a lot of those impediments. >> So talk to me about... Oh, I'm sorry. Go ahead. >> Go ahead. >> I was just going to say, you know, of the things that is even in more demand now is the need for real time data access. I think real-time is no longer a nice-to-have. And I think what we've been through the last year has really shown that. So given the legacy architectures and some of the challenges with copies being an obstacle to that true democratization, how can data teams actually get in there and solve this challenge? >> Yeah, so, you know, I think going back a little bit to the prior question, and I can fill out a little bit more of the detail, and that'll lead us to your point, that one of the things that is also really born as a cost, when you have to go through and make multiple copies, is that, you know, typically you need experts in the organization, who are the ones who are going to, you know, write the ETL scripts, or, you know, kind of do the data architecture and design the structures that have to be performant for real-time BI queries, right? So typically these take the form of things like, you know, OLAP cubes, or, you know, big flattened data structures with all of the attributes joined in, or there's a lot of different ways that you can get query performance. Typically that's not available directly against the source data. So, you know, one of the things that data teams can do, and, you know, there's really two ways to go about this, right? One is you can really go all in on the data copy approach, and kind of home grow or build yourself a lot of the automation and tooling, and, you know, parts that it would take to basically transform the data. You can build UIs for people to go in, and kind of request data, and you can automate this whole process. And we found that a number of large organizations have actually gone this route. And they've kind of been at these projects for, in some cases, years, and they're still not completely there. And so I wouldn't really recommend that approach. I think that the real approach, and this is really available today with kind of the the rise of cloud technologies, is that we can shift our thinking a bit, right? And so we can think about how do we take some of these, you know, features and capabilities that one would expect in a data warehousing environment, and how can we bring that directly to the data? So, you know, with the shift in thinking, it requires kind of new technology to do this, right? So if you could imagine a lot of these traditional data warehousing features, like interactive speed, and, you know, the ability to kind of build structures, or, you know, views or things on top of your data, but do that directly on the data itself without having to transform and copy, transform and copy. So that's really something that we kind of call the next generation data lake architecture, is bringing those capabilities directly to the data that's on the lake. >> So leaving the data where it is, next generation is a term like future-ready, that's used a lot. Let's unpack that and dig into why what you're talking about is the next generation data lake architecture. >> Sure, sure. And I think to talk about that, the first thing that we really have to discuss is, really, a fundamental shift in technologies that's come about really in the last few years. So, you know, as really cloud services, like AWS, who've have risen to prominence, there are some capabilities that are available to us now that just weren't, you know, three, four or five years ago. And so what we can do now is that we have the ability to truly separate compute and storage, connected together with really fast networking. And we can, you know, provision storage, and we can provision compute. And from the perspective of the user, those two things can basically be scaled infinitely, right? And if you contrast that with what used to have to happen, or what we used to have to do in platforms like Hadoop or in scale-out MPP data warehouses, is that we didn't have, not only the the flexibility to scale compute and storage independently, but we didn't have the kind of networking that we have today. And so it was a requirement to take, you know, basically the compute, and push it as close to the data as we could, which is what you would get in a large Hadoop cluster. You've got, you know, nodes, which have compute right next to the storage, and you try to push as much work as you can onto each node before you start to transfer the data to other nodes for further processing. And now what we've got with some of the new cloud technology is the ability to, basically, do away with that requirement, right? So now we can have very, very large provision pools of data that can grow and grow and grow, really, without the limitations of nodes of hardware. And we can spin up and down compute process that. And the thing that we need, though, is a way of processing it, a query processing engine that's built for those dynamics, right? That's built, so that it performs really, really well when compute and storage are decoupled. So I think that that's really the trick, is that once we really, you know, come into the fact that we've got this new paradigm with separate compute, separate storage, very fast networking, if we start to look for technologies that can scale out and back, and do really performance query in that environment, then that's really what we're talking about. Now, I think the very last piece, and what I would call kind of next gen data lake architecture, is very common even today for organizations to have a data lake, right? That contains a tremendous amount of data, but in order to do actual BI queries at that interactive speed that people expect, they still have to take portions of the data from the lake and go load it into a warehouse, right? And then probably from there build, you know, OLAP cubes, or, you know, extracts into a BI tool. So the last piece, really, in the next gen data lake architecture puzzle, is once you've got that fast query engine foundation, how do you then move those interactive workloads into that platform, so they don't have to be in a data warehouse, right? How do you take some of those data warehousing expectations and put those into a platform that can query data directly? So that that's really what the next generation means to us. >> So let's talk about Dremio now. I see that just in January of 2021, Series D funding of $135 million. And then I saw that Datanami actually coined Dremio as a unicorn, as it's reached a $1 billion valuation. Talk to us about what Dremio is, and how you're part of this modern data architecture. >> Absolutely. Yeah. So, you know, you can think about Dremio as a... You know, in the technology context, really, is solving that problem that I just laid out, which is we're in the business of, you know, building technology that allows users to query very large data sets in a scale-out, very performant way, you know, directly on the data where it lives. So there's no real need for data movement. And in fact, we can also not only query one source of data, but we can query multiple sources of data, and, you know, join those things together in the context of the same query. So, you know, you may have most of your data in a data lake, but then you may have some relational sources. So there's a potent story there, in that you don't have to consolidate all of your data into one place. You don't have to load all of your data into, you know, a data warehouse or a cloud data warehouse. You can query it where it is. That's the first piece. I think the next piece that the Dremio provides is kind of, as we mentioned before, we're giving almost a data warehouse-like user experience in terms of very, very fast response times for things like BI dashboards, right? So really interactive queries. And the ability to do things, like you would normally expect to do inside a warehouse. So you can, you know, create schemas, for instance, you can create layers of views and accelerations, and effectively allow users to build out virtually in the form of views, what they would have done before with all of their various ETL pipelines, to, you know, scrub and prepare and transform the data to get it in shape to query. And at the very end, what we can do is selectively, kind of in an internally managed way, we can accelerate certain query patterns by creating something that we call reflections, which is an internally managed, you know, persistence of data that accelerates certain queries, but it's entirely internally managed by Dremio. The user doesn't have to worry with anything to do with setup, or configuration, or clean up, or maintenance, or any of that stuff. >> So does reflections really provide a differentiator for Dremio, if you look in the market and you see competitors, like Snowflake, SingleStore, for example, is this really kind of that competitive differentiator? >> I think it's one of them. I think the ability to create reflections is it's certainly a differentiator, because what it allows is it allows you to basically accelerate different kinds of query patterns against the same underlying source data, right? So rather than have to go build a transformation for a user, that, you know, potentially aggregates data a certain way, and persist that somewhere, and have to build all the machinery to do that and maintain it, in Dremio, literally, it's a button click. You can, you know, go in and look at the dataset, identify those dimensions that you need to, say, aggregate by, the measures that you want to compute, and Dremio will just manage that for you, and any query that comes in, that may be going after this massive detail table with a trillion rows, that has a GROUP BY in it, for instance, will just match that reflection and use it. And that query can respond in less than a second, where typically the work that would have to happen on the backend engine might take a minute to process that query. So really that's the edge piece that gives us that BI acceleration without having to use additional tools or in any additional complexity for the user. >> And I assume you're talking about like millisecond response times, right? You said under a second, but I'm sure milliseconds? >> Hundreds of milliseconds, typically. So we're not really in the one to two millisecond range. That's pretty, pretty rare (chuckles), but certainly sub-second response times is very, very common with very, very large backend data sets when you use reflections, mm-hmm. >> Got it, and that speed and performance is absolutely table stakes today for organizations to succeed and thrive. So is what Dremio delivers a no-copy data strategy? Is that what you consider it? >> It's that, and it's actually much more than that, right? So I think, you know, when you talk to, really, users of the platform, there are a number of layers of Dremio, and, you know, we often get asked, I get asked, you know, who are our direct competitors, right? And I think that when you think about that question, it's really interesting, because we're not just the backend high-performance query engine. We aren't just the acceleration layer, right? We also have a very rich, fully-featured UI environment, that allows users to actually log in, find data, curate data, you know, reflect data, build their own views, et cetera. So there's really a whole suite of services that are built in to the Dremio platform, that make it very, very easy to install Dremio on, you know... You know, install it on AWS, get started right away, and be querying data, kind of building these virtual views, adding accelerations. All this can happen within minutes. And so it's really interesting that there's kind of a wide spectrum of services that allow us to really power a data lake in its entirety, really, without too many other technologies that have to be involved there. >> What are some of key use cases that you've seen, especially in the last year, as we've seen this rapid acceleration of digital transformation, this adoption of SaaS applications, more and more and more data, some of those key use cases that Dremio is helping customers solve? >> Sure. Yeah. I think there's a number of verticals, and there's some that I'm very familiar with, because I've worked very closely with customers, and in financial services is a large one, you know, and that would include, you know, banking, insurance, investment, you know, a lot of the large fortune 500 companies that maybe in manufacturing, or, you know, transportation, shipping, et cetera. You know, I think lately I'm most familiar with some of the transformation that's going on in the financial services space, and what's happening there, you know, companies have typically started with very, very large data warehouses, and often for the last four or five years, maybe a little longer, they've been in this transition to building kind of an in-house data lake, typically on a Hadoop platform of some flavor, with a lot of additional services that they've created to try to enable this data democratization. But these are huge efforts. And, you know, typically these are on-prem, and, you know, lots of engineers working on these things, really, full-time, to build out this full spectrum of capabilities. The way that Dremio really impacts that is, you know, we can come in and actually take the place of a lot of parts of that puzzle. And we give a really rich experience to the user, you know, allow customers to kind of retire some of these acceleration layers that they've put in to try to make BI queries fast, get rid of a lot of the transformations, like the ETL jobs or ELT processes that have to run. So, you know, there's a really wide swath of that puzzle that we can solve. And then when you look at the cloud, because all of these organizations, they've got a toe in the water, or they're halfway down the path, of really exploring how do we take all of this on-prem data and processing and everything else, and get it into AWS, you know, put it in the cloud? What does that architecture look like? And we're ideally positioned for that story. You know, we've got an offering that runs, you know, natively on AWS, and takes full advantage of kind of the decoupling of compute and storage. So we give organizations a really good path to solve some of their on-prem problems today, and then give them a clear path as they migrate into cloud. >> Can you walk me through a customer example that you think really underscores what you just described as what Dremio delivers, and helping customers with this migration, and to be able to take advantage and find value in volumes and volumes of data? >> Yeah, absolutely. Unfortunately, I can't mention their name, but I have worked very, very closely with a large customer, as I mentioned in financial services. And one of the things that they're very keenly interested in is, you know, they've had a pretty large deployment that traditionally has been both Hadoop-based, and they've got a large, several large on-prem relational data warehouses as well. And Dremio has been able to come in and actually provide that BI performance piece, basically, you know, the very, very fast, you know, second, two second, three-second performance that people would expect from the data warehouse, but we're able to do that directly on, you know, the files and tables that are in their Hadoop cluster. And that project's been going on for quite some time, and we've had success there. I think that where it really starts to get exciting though, and this is just beginning, is this customer also is, you know, investigating and actually prototyping and building out a lot of these functions in the AWS cloud. And so, you know, the nice thing that we're able to offer is, really, a consistent technology stack, consistent interfaces you know, consistent look and feel of the UI, both on-prem and in the cloud. And so we can really, once they start that move, now they've got kind of the familiar place to connect to for their data and to run their queries. And that's a nice seamless transition as they migrate. >> What about other verticals? Like, I can imagine healthcare and government services, are you seeing traction in those segments as well? >> Yeah, absolutely. We are. There are a number of companies in the healthcare space. I think that one of the larger ones in the government space, which I have some exposure to, is CMS, which is one that we had done some work through a partner to implement Dremio there. And, you know, this was a project, I think, that was undertaken about a year ago. They implemented our technology as part of a larger data lake architecture, and had a good bit of success there. So what's been interesting, when you talk about the funding and the valuation, and the kind of the buzz that's going on around Dremio is that we really have customers in so many different verticals, right? So we've got certainly financials and healthcare, and, you know, insurance, and, you know, big commercials, like in manufacturing, et cetera. So we're seeing a lot of interest across a number of different verticals, and customers are are buying and implementing the product in all those verticals, yeah. >> All right, so take us out with where customers can go, and prospects that are interested, and even investors, in finding out more about this next generation data engine that is Dremio. >> Absolutely. So I think the first thing that people can do is they can go to our website, which is dremio.com, and they can go to dremio.com/labs. And from there they can launch a self-guided product tour. I think that's probably a very quick way to get an overview of the product, and who we are, what we do, what we offer. And then there's also a free trial that's actually on the AWS marketplace. So if you want to actually try Dremio out, and, you know, spin up an instance, you can get us on the marketplace. >> Do most of your customers do that, like doing a trial with a proof of concept, for example, to see really how, from an architecture perspective, how these technologies are synergistic? >> Absolutely. Yeah. I think that probably every large enterprise, you know, there's a number of ways that customers find us. And so, you know, often customers may just try the trial on the marketplace. But, you know, customers may also, you know, reach out to our sales team, et cetera, but it's very, very common for us to do a proof of concept, that's not just architecture, but it would cover, you know, performance requirements and things like that. So I think pretty much all of our very largest enterprise customers would go through some sort of a proof of concept, and that would be done with the support of our field teams. >> Excellent, well, Robert, thanks for joining me today, and sharing all about Dremio with our audience. We appreciate your time. >> Great. Thank you, Lisa. It was a pleasure. >> Likewise, for Robert Maybin, I'm Lisa Martin. Thanks for watching. (upbeat music)

>> Okay. Now we're going to dig deeper into HPE Ezmeral and try to better understand how it's going to impact customers. And with me to do that are Robert Christiansen, who is the Vice President of Strategy in the office of the CTO and Kumar Sreekanti, who is the Chief Technology Officer and Head of Software, both of course, with Hewlett Packard Enterprise. Gentlemen, welcome to the program. Thanks for coming on. >> Good seeing you, Dave. Thanks for having us. >> It's always good to see you guys. >> Thanks for having us. >> So, Ezmeral, kind of an interesting name, catchy name, but Kumar, what exactly is HPE Ezmeral? >> It's indeed a catchy name. Our branding team has done fantastic job. I believe it's actually derived from Esmeralda, is the Spanish for emarald. Often it's supposed some very mythical bars, and they derived Ezmeral from there. And we all initially when we heard, it was interesting. So, Ezmeral was our effort to take all the software, the platform tools that HPE has and provide this modern operating platform to the customers and put it under one brand. So, it has a modern container platform, it does persistent storage with the data fabric and it doesn't include as many of our customers from that. So, think of it as a modern container platform for modernization and digitazation for the customers. >> Yeah, it's an interesting, you talk about platform, so it's not, you know, a lot of times people say product, but you're positioning it as a platform so that has a broader implication. >> That's very true. So, as the customers are thinking of this digitazation, modernization containers and Microsoft, as you know, there is, has become the stable all. So, it's actually a container orchestration platform with golfers open source going into this as well as the persistence already. >> So, by the way, Ezmeral, I think Emerald in Spanish, I think in the culture, it also has immunity powers as well. So immunity from lock-in, (Robert and Kumar laughing) and all those other terrible diseases, maybe it helps us with COVID too. Robert, when you talk to customers, what problems do you probe for that Ezmeral can do a good job solving? >> Yeah, that's a really great question because a lot of times they don't even know what it is that they're trying to solve for other than just a very narrow use case. But the idea here is to give them a platform by which they can bridge both the public and private environment for what they do, the application development, specifically in the data side. So, when yo're looking to bring containerization, which originally got started on the public cloud and it has moved its way, I should say it become popular in the public cloud and it moved its way on premises now, Ezmeral really opens the door to three fundamental things, but, you know, how do I maintain an open architecture like you're referring to, to some low or no lock-in of my applications. Number two, how do I gain a data fabric or a data consistency of accessing the data so I don't have to rewrite those applications when I do move them around. And then lastly, where everybody's heading, the real value is in the AI ML initiatives that companies are really bringing and that value of their data and locking that data at where the data is being generated and stored. And so the Ezmeral platform is those multiple pieces that Kumar was talking about stacked together to deliver the solutions for the client. >> So Kumar, how does it work? What's the sort of IP or the secret source behind it all? What makes HPE different? >> Yeah. Continuing on (indistinct) it's a modern glass form of optimizing the data and workloads. But I think I would say there are three unique characteristics of this platform. Number one is that it actually provides you both an ability to run statefull and stateless as workloads under the same platform. And number two is, as we were thinking about, unlike another Kubernete is open source, it actually add, use you all open-source Kurbenates as well as an orchestration behind them so you can actually, you can provide this hybrid thing that Robert was talking about. And then actually we built the workflows into it, for example, they'll actually announced along with it Ezmeral, ML expert on the customers can actually do the workflow management around specific data woakload. So, the magic is if you want to see the secrets out of all the efforts that has been going into some of the IP acquisitions that HPE has done over the years, we said we BlueData, MAPR, and the Nimble, all these pieces are coming together and providing a modern digitization platform for the customers. >> So these pieces, they all have a little bit of a machine intelligence in them, you have people, who used to think of AI as this sort of separate thing, I mean the same thing with containers, right? But now it's getting embedded into the stack. What is the role of machine intelligence or machine learning in Ezmeral? >> I would take a step back and say, you know, there's very well the customers, the amount of data that is being generated and 95% or 98% of the data is machine generated. And it does a series of a window gravity, and it is sitting at the edge and we were the only one that had edge to the cloud data fabric that's built to it. So, the number one is that we are bringing computer or a cloud to the data that taking the data to the cloud, right, if you will. It's a cloud like experience that provides the customer. AI is not much value to us if we don't harness the data. So, I said this in one of the blog was we have gone from collecting the data, to the finding the insights into the data, right. So, that people have used all sorts of analysis that we are to find data is the new oil. So, the AI and the data. And then now your applications have to be modernized and nobody wants write an application in a non microservices fashion because you wanted to build the modernization. So, if you bring these three things, I want to have a data gravity with lots of data, I have built an AI applications and I want to have those three things I think we bring to the customer. >> So, Robert let's stay on customers for a minute. I mean, I want to understand the business impact, the business case, I mean, why should all the cloud developers have all the fun, you've mentioned it, you're bridging the cloud and on-prem, they talk about when you talk to customers and what they are seeing is the business impact, what's the real drivers for that? >> That's a great question cause at the end of the day, I think the recent survey that was that cost and performance are still the number one requirement for this, just real close second is agility, the speed at which they want to move and so those two are the top of mind every time. But the thing we find Ezmeral, which is so impactful is that nobody brings together the Silicon, the hardware, the platform, and all of that stack together work and combine like Ezmeral does with the platforms that we have and specifically, we start getting 90, 92, 93% utilization out of AI ML workloads on very expensive hardware, it really, really is a competitive advantage over a public cloud offering, which does not offer those kinds of services and the cost models are so significantly different. So, we do that by collapsing the stack, we take out as much intellectual property, excuse me, as much software pieces that are necessary so we are closest to the Silicon, closest to the applications, bring it to the hardware itself, meaning that we can interleave the applications, meaning that you can get to true multitenancy on a particular platform that allows you to deliver a cost optimized solution. So, when you talk about the money side, absolutely, there's just nothing out there and then on the second side, which is agility. One of the things that we know is today is that applications need to be built in pipelines, right, this is something that's been established now for quite some time. Now, that's really making its way on premises and what Kumar was talking about with, how do we modernize? How do we do that? Well, there's going to be some that you want to break into microservices containers, and there's some that you don't. Now, the ones that they're going to do that they're going to get that speed and motion, et cetera, out of the gate and they can put that on premises, which is relatively new these days to the on-premises world. So, we think both won't be the advantage. >> Okay. I want to unpack that a little bit. So, the cost is clearly really 90 plus percent utilization. >> Yes. >> I mean, Kumar, you know, even pre virtualization, we know that it was like, even with virtualization, you never really got that high. I mean, people would talk about it, but are you really able to sustain that in real world workloads? >> Yeah. I think when you make your exchangeable cut up into smaller pieces, you can insert them into many areas. We have one customer was running 18 containers on a single server and each of those containers, as you know, early days of new data, you actually modernize what we consider week run containers or microbiome. So, if you actually build these microservices, and you all and you have versioning all correctly, you can pack these things extremely well. And we have seen this, again, it's not a guarantee, it all depends on your application and your, I mean, as an engineer, we want to always understand all of these caveats work, but it is a very modern utilization of the platform with the data and once you know where the data is, and then it becomes very easy to match those two. >> Now, the other piece of the value proposition that I heard Robert is it's basically an integrated stack. So I don't have to cobble together a bunch of open source components, there's legal implications, there's obviously performance implications. I would imagine that resonates and particularly with the enterprise buyer because they don't have the time to do all this integration. >> That's a very good point. So there is an interesting question that enterprises, they want to have an open source so there is no lock-in, but they also need help to implement and deploy and manage it because they don't have the expertise. And we all know that the IKEA desk has actually brought that API, the past layer standardization. So what we have done is we have given the open source and you arrive to the Kubernetes API, but at the same time orchestration, persistent stories, the data fabric, the AI algorithms, all of them are bolted into it and on the top of that, it's available both as a licensed software on-prem, and the same software runs on the GreenLake. So you can actually pay as you go and then we run it for them in a colo or, or in their own data center. >> Oh, good. That was one of my latter questions. So, I can get this as a service pay by the drink, essentially I don't have to install a bunch of stuff on-prem and pay it perpetualized... >> There is a lot of containers and is the reason and the lapse of service in the last discover and knowledge gone production. So both Ezmeral is available, you can run it on-prem, on the cloud as well, a congenital platform, or you can run instead on GreenLake. >> Robert, are there any specific use case patterns that you see emerging amongst customers? >> Yeah, absolutely. So there's a couple of them. So we have a, a really nice relationship that we see with any of the Splunk operators that were out there today, right? So Splunk containerized, their operator, that operator is the number one operator, for example, for Splunk in the IT operation side or notifications as well as on the security operations side. So we've found that that runs highly effective on top of Ezmeral, on top of our platforms so we just talked about, that Kumar just talked about, but I want to also give a little bit of backgrounds to that same operator platform. The way that the Ezmeral platform has done is that we've been able to make it highly active, active with HA availability at nine, it's going to be at five nines for that same Splunk operator on premises, on the Kubernetes open source, which is as far as I'm concerned, a very, very high end computer science work. You understand how difficult that is, that's number one. Number two is you'll see just a spark workloads as a whole. All right. Nobody handles spark workloads like we do. So we put a container around them and we put them inside the pipeline of moving people through that basic, ML AI pipeline of getting a model through its system, through its trained, and then actually deployed to our ML ops pipeline. This is a key fundamental for delivering value in the data space as well. And then lastly, this is, this is really important when you think about the data fabric that we offer, the data fabric itself doesn't necessarily have to be bolted with the container platform, the container, the actual data fabric itself, can be deployed underneath a number of our, you know, for competitive platforms who don't handle data well. We know that, we know that they don't handle it very well at all. And we get lots and lots of calls for people saying, "Hey, can you take your Ezmeral data fabric "and solve my large scale, "highly challenging data problems?" And we say, "yeah, "and then when you're ready for a real world, "full time enterprise ready container platform, "we'd be happy to prove that too." >> So you're saying you're, if I'm inferring correctly, you're one of the values as you're simplifying that whole data pipeline and the whole data science, science project pun intended, I guess. (Robert and Kumar laughing) >> That's true. >> Absolutely. >> So, where does a customer start? I mean, what, what are the engagements like? What's the starting point? >> It's means we're probably one of the most trusted and robust supplier for many, many years and we have a phenomenal workforce of both the (indistinct), world leading support organization, there are many places to start with. One is obviously all these salaries that are available on the GreenLake, as we just talked about, and they can start on a pay as you go basis. There are many customers that actually some of them are from the early days of BlueData and MAPR, and then already running and they actually improvise on when, as they move into their next version more of a message. You can start with simple as well as container platform or system with the store, a computer's operation and can implement as an analyst to start working. And then finally as a big company like HPE as an everybody's company, that finance it's services, it's very easy for the customers to be able to get that support on day to day operations. >> Thank you for watching everybody. It's Dave Vellante for theCUBE. Keep it right there for more great content from Ezmeral.

>>Welcome to this IBM Watson health client conversation here. We're probing the dynamics of the relationship between IBM and its clients. And we're looking back, we're going to explore the present. We're going to discuss the future state of healthcare. My name is Dave Volante from the Cuban with me are Robert Stell horn. Who's associate director, H E O R at sukha, otherwise known as pharmaceuticals, America and Rena Felton. Who's with of course, IBM Watson health. Welcome folks. Great to have you. Hi, so like strong relationships, as we know, they're the foundation of any partnership. And of course over the past year, we've had to rely on both personal and professional relationships to get us through some of the most challenging times, if not the most challenging times of our lives. So let me start with you, Robert, how has the partnership with IBM helped you in 2020? >>I think it was just a continuation of the excellent relationship we have with Rena and IBM. Um, starting in March, we had really a shift to an all remote, uh, workplace environment. And I think that constant communication with Rina and IBM helped that situation because she kept us up to date with, uh, additional products and offerings. And basically we came up with some additional solutions towards the end of the year. So we're gonna watch >>Pick it up from here. Let's go, let's go a little bit deeper and maybe you can talk about some of the things that you've done with Robert and his team and, and maybe some of the accomplishments that you're most proud of in 2020. >>No, absolutely. And I have to kind of echo what you first said about the foundation and our partnerships being the foundation, um, of our past present and future. So I do want to take the opportunity to thank Rob again for joining us today. It is, um, I know, you know, with his kids home and remote learning, um, it's a lot, uh, to, to ask in addition to, you know, your day to day work. So, so thank you, Rob. Um, I guess the question that I have for you is what would be the greatest accomplishment, um, that Watseka and IBM Watson had in 2020? >>I would say it was the addition of the linked claims EMR data, the LDCD product that we were able to license in-house, uh, thanks to your attention and to show the advantages and the strengths of that data. We are able to license that in to our, uh, set up assets we have internally. And what that's gonna allow us to do is really find out more information about the patients. Uh, we're existing users of the Mark IBM, uh, market scan data. Um, this is going to allow us to tie into those same patients and find out more about them. Um, in particular, uh, a lot of our products are in the mental health space and a lot about standing questions we have are why are the patients getting different products? And with the notes are available in that link data. We're going to now be able to tap into more information about what is happening with the patient. >>Okay. Can I ask a question on that? Um, if you guys don't mind, I mean, you know, when you, when you hear about, you know, uh, EMR, uh, in the early days, it was a lot about meaningful use and getting paid. It sounds like you guys are taking it much deeper and as a, as a, you know, as an individual, right, you're, you're really happy to hear that this information is now going to be used to really improve, uh, healthcare is, do I have that right? Is that, you know, kind of the nature of where you guys are headed? >>Well, I think ultimately it's the, the, the, the main goal is to help the patients and provide the products that can really, um, help them in their daily lives. So, um, really with this data, now, we're going to be able to tap into more of the why, um, exist in claims data. We cannot really get that information, why VC information, about what diagnoses they incurred during their treatment history. And we also can see, uh, different prescriptions that are given to them, but now we're going to be able to tie that together and get more understanding to really see more focused treatment pattern for them. >>So, Reno, w w you sit down with Rob, do you have like a, sort of a planning session for 2021? Why don't you sort of bring us up to, uh, to what your thinking is there and how you guys are working together this year? >>Yeah, no, absolutely. Um, actually, before we get to that, I wanted to kind of add onto what Rob was saying as well. It's interesting given, you know, the pandemic in 2020 and what the LCD data is going to do, um, to really be able to look back. And as Rob mentioned, looking specifically at mental health, the ability to look back and start looking at the patients and what it's really done to our community and what it's really done to our country, um, and looking at patients, you know, looking back at, at sort of their, their patient journey and where we are today. Um, but Rob and I talk all the time, we talk all the time, we probably talk three or four times a day sometimes. So I would say, um, we, we text, uh, we do talk and have a lot of our strategic, um, sessions, uh, our outlook for 2021 and what the data strategy is for Otsuka. Um, in addition, additional data assets to acquire from IBM, as well as how can we sort of leverage brander IBM, um, assets like our red hat, our OpenShift, our cloud-based solutions. So, you know, Rob and I are constantly talking and we are, um, looking for new ways to bring in new solutions into Otsuka. Um, and you know, yeah, we, we, we talk a lot. What do you think, Rob? >>I think we have an excellent partnership. Uh, basically, um, I think their relationship there is excellent. Um, we have excellent communication and, you know, I find when there's situations where I may be a bind Reno's is able to help out instantly. Um, so it's, it's really a two way street and it's an excellent partnership. >>I wonder if I could double click on that. I mean, relative to maybe some of your, I mean, I'm sure you have lots of relationships with lots of different companies, but, but what makes it excellent specifically with regard to IBM? Is there, is there anything unique Rob, that stands out to you? >>It would be the follow-up, um, really, it's not just about, uh, delivering the data and say, okay, here you have your, your product work with it in basically the, the, the vendor disappears, it's the constant followup to make sure that it's being used in any way they can help and provide more information to really extract the full value out of it. >>So I'm gonna forget to ask you guys, maybe each of you, you know, both personally and professionally, I feel like, you know, 20, 20 never ended it just sort of blended in, uh, and, and, but some things have changed. We all talk about, geez, what's going to be permanent. How have you each been affected? Um, how has it helped you position for, for what's coming in in the years ahead, maybe Reena, you could start and then pick it up with, with Rob. >>Oh man. Um, you know, 2020 was definitely challenging and I think it was really challenging given the circumstances and in my position where I'm very much used to meeting with our customers and having lunch and really just kind of walking down the hallways and bumping into familiar faces and really seeing, you know, how we can provide value with our solutions. And so, you know, that was all stripped in 2020. Uh, so it's been, it's been quite challenging. I will say, working with Rob, working with some of my other customers, um, I've had, uh, I've had to learn the resilience and to be a little bit more relentless with phone calls and follow ups and, and being more agile in my communications with the customers and what their needs are, and be flexible with calendars because there's again, remote learning and, and, um, and the like, so I think, you know, positioned for 2021 really well. Um, I am excited to hopefully get back out there and start visiting our customers. But if not, I certainly learned a lot and just, um, the follow-up and again, the relentless phone calls and calling and checking up on our customers, even if it's just to say, hi, see how everyone's doing a mental check sometimes. So I think that's, that's become, um, you know, what 2020 was, and, and hopefully, you know, what, 2021 will be better and, uh, kind of continue on that, that relentless path. >>What do you think, Rob? Hi, how are you doing? >>I would echo a lot of Rina's thoughts and the fact of, yeah, definitely miss the in-person interaction. In fact, I will say that I remember the last time I was physically in the office that Scott, it was to meet with Rina. So I distinctively remember that they remember the date was March, I believe, March 9th. So it just shows how this year as has been sort of a blur, but at the same time, you remember certain milestones. And I think it's because of that relationship, um, we've developed with IBM that I can remember those distinctive milestones and events that took place. >>So Rob, I probably should have asked you upfront, maybe tell us a little bit about Alaska, uh, maybe, maybe give us the sort of quick soundbite on where you guys are mostly focused. Sure. >>Oh, it's guys, uh, a Japanese pharmaceutical company. The focus is in mental health and nephrology, really the two main business areas. Um, my role at guys to do the internal research and data analytics within the health economics and outcomes research group. Um, currently we are transitioned to a, uh, name, which is global value and real world evidence. Um, fact that transition is already happened. Um, so we're going to have more of a global presence going forward. Um, but my role is really to, uh, do the internal research across all the brands within the company. >>So, so Rena, I wonder this, thank you for that, Robert. I wonder if you could think, thinking about what you know about Scott and your relationship with Robin, your knowledge of, of the industry. Uh, there's so much that IBM can bring to the table. Rob was talking about data earlier, talking about EMR, you were talking about, you know, red hat and cloud and this big portfolio you have. So I wonder if you could sort of start a conversation for our audience just around how you guys see all those assets that you have and all the knowledge, all that data. How do you see the partnership evolving in the future to affect, uh, the industry and the, in the future of healthcare? >>Well, I would love to see, um, the entire, uh, uh, platform, um, shift to, to the IBM cloud, um, and certainly, you know, leverage the cloud pack and analytics that, that we have to offer, um, baby steps most definitely. Um, but I do think that there is, uh, the opportunity to really move, um, and transform the business into something a lot more than, than what it is. >>Rob has the pandemic effected sort of how you think about, um, you know, remote services and cloud services and the, like, were you already on the path headed there? Did accelerate things, have you, you know, have you not had time because things have been so busy or maybe you could comment? >>Yeah, I think it's really a combination. And so I think you hit on a, a fair point there, just the time, uh, aspect. Um, it's definitely been a challenge and your, um, I have two children and remote learning has definitely been a challenge from that perspective. So time has definitely been, uh, on the short side. Um, I do see that there are going to in the future be more and more users of the data. So I think that shift to a potential cloud environment is where things are headed. >>So we, I have a bunch more questions, but I want to step back for a second and see if there's anything that you'd like to ask Rob before I go onto my next section. Okay. So I wonder if you could think about, um, maybe both of you, the, the, when you think back on, on 2020 and all the, you know, what's transpired, what, what transitions did you guys have to make? Uh, maybe as a team together IBM and Alaska. Um, and, and, and what do you see as sort of permanent or semi-permanent is work from home? We're gonna going to continue at a higher rate, uh, are there new practice? I mean, I know just today I made an online appointment it's for a remote visit with my doctor, which never could have happened before the pandemic. Right. But are there things specific to your business and your relationship that you see as a transition that could be permanent or semi-permanent? >>Well, I, I think it's there, there's definitely a shift that's happened that will is here to stay, but I don't know if it's full, it's going to be a combination in the future. I think that in-person interactions, especially what Rena mentioned about having that face-to-face interaction is still going to be one things are in the right place and safe they're going to happen again. But I think the ability to show that work can happen in a virtual or a full remote workplace, that's going to just allow that to continue and really give the flex of people. The flexibility I know for myself, flexibility is key. Like I mentioned, with two small children, um, that, that, that becomes such a valuable addition to your work, your life and your work life in general, that I think that's here to stay. >>Okay. Um, so let me ask you this, uh, w one of the themes of this event is relentless re-invention. So what I'm hearing from you Rob, is that it kind of a hybrid model going forward, if you will, uh, maybe the option to work from home, but that face to face interaction, especially when you're creating things like you are in the pharmaceutical business and the deep R and D that collaborative aspect, you know, you, it's harder when you're, when, when, when you're remote. Um, but maybe you could talk about, you know, some of those key areas that you're, you're going to be focused on in 2021 and, and really where you would look for IBM to help. >>I think in 2021, the team I'm part of it, part of is, is growing. So I think there's going to be additional demand for internal research, uh, uh, capabilities for analysis done within the company. So I think I'm going to be looking to Rena to, uh, see what new data offerings are available and all what new products are going to be available. But beyond that, um, I think it's the potential that, you know, there's so much, uh, projects, um, that are going to be coming to the table. We may need to outsource some of that projects and IBM could be potentially be a partner there to do some of the analysis on to help out there. >>Anything you'd add. >>Uh, no, I think that, that sounds good. >>How would you grade IBM and your relationship with IBM Rob? >>Well, I have to be nice to Rina cause she's been very nice to me. I would say an a, an a plus >>My kids, I got kids in college. Several, they get A's, I'm happy. Oh, that's good. You know, you should be proud. So, congratulations. Um, anything else Reno, you give you, I'll give you a last word here before we wrap, >>You know, 2020 was, was a challenging. And, you know, we talked a little bit about, you know, what time in 2020, you know, Rob and I have always had a really good relationship. I think 2020, we got closer, um, with just both professionally and really diving in to key business challenges that they have, and really working with him to understand what the customer needs are and how we can help, not only from, you know, an HR perspective, but also how can we help Otsuka, um, as a company in, in totality. So, you know, we've been able to do that, but personally, I would say that I really appreciated the relationship. I mean, we can go from talking about work to talking about children, to talking about family, um, all in the same five minute conversation or 10 minute conversation, sometimes our conversation. So, you know, thank you, Rob 2020 was definitely super challenging. >>I know for you on so many levels. Um, but I have to say you've been really great at just showing up every time picked up the phone, asked questions. If I needed something I can call you, I knew you were going to pick up, I had an offering and be like, do you have 10 minutes? Can I share this with you? And you would pick up the phone, no problem, and entertain a call or set up a call with all your internal colleagues. And I, I appreciate that so much. And, you know, I appreciate our relationship. I appreciate the business and I, I do hope that we can continue on in 2021, we will continue on in 2021. Uh, but, um, but yeah, I thank you so much. >>Rain has been extremely helpful. I don't want to thank you for all the help. Um, just to add to that one point there, you know, we have, uh, also another product, which I forgot to mention that we licensed in from IBM, it's the treatment pathways, um, tool, which is an online tool. Um, and we have users throughout the globe. So there's been times where I've needed a new user added very quickly for someone in the home office in Japan. And Rena has been extremely helpful in getting things done quickly and very proactively. >>Well, guys, it's really clear that the depth of your relationship I'm interested that you actually got closer in 2020. Uh, the fact that you communicate, you know, several times a day is I think Testament to that relationship. Uh, I'm really pleased to hear what you're doing and the potential with the EMR data for patient outcomes. Uh, as I say in the early days, I used to hear all about how well you have to do that to get paid. And it's really great to see a partnership that's, that's really focused on, on, on patient health and, and changing our lives. So, and mental health is such an important area that for so many years was so misunderstood and the, and the data that we now have, and of course, IBM's heritage and data is key. Uh, the relationship and the follow-up and also the flexibility is, is something I think we all learned in 2020, we have to, we've kind of redefined, you know, resilience in our organizations and, uh, glad to see you guys are growing. Congratulations on the relationship. And thanks so much for spending some time with me. >>Thank you. Thank you, Dave. Thank you, Raina >>For watching this client conversation with IBM Watson health.

>>from around the globe. It's the Cube covering HP. Discover Virtual Experience Brought to you by HP >>Everyone welcome to the Cube studios here in Palo Alto, California We here for remote conversation. Where for HP Discover virtual experience. 2020. We would Kumar, Sri Context, chief technology officer and head of Software Cube alumni. We've been following Kumar since he started Blue Data. Now he's heading up the software team and CTO at HP and Robert Christensen, VP of Strategy of Office of the CTO Robert Both Cube alumni's Robert, formerly with CTP, now part of the team that's bringing the modernization efforts around enterprises in this fast changing world that's impacting the operating models for businesses. We're seeing that playing out in real time with Covert 19 as customers are modernizing the efforts. Guys, thanks for coming on. Taking the time. >>You're welcome, John. Good to be back here, >>Kumar. First I have to ask you, I have to ask you your new role at HP sent it up to CTO but also head of the software. How >>do you >>describe that role Because you're CTO and also heading up? This offers a general manager. Could you take him in to explain this new role and why It's important. >>Thank you. Thank you, John. And so good to be back. You get two for one with me and Robert didn't. Yeah, it's very exciting to be here as the CTO of HB. And as Antonio described in in his announcement, we consider software will be very key, essential part of the our people as a service. And, uh, we want we see that it's an opportunity for not only layer division but help drive the execution of that reason. Both organic them in our. So we we see we want to have a different change of software that helps the customers, too, to get us to the workloads optimized, or are there specific solutions? >>You guys were both on the Cube in November, Pre cove it with the minimum John Troyer talking about the container platform news, leveraging the acquisitions you guys have done at HP Kumar, your company Blue Data map, our CTP, Robert, the group. You're there really talking about the strategies around running these kinds of workloads. And if you think about Cove in 19 this transformation, it's really changing work. Workforces, workplaces, workloads, work flows everything to do with work and people are at home. That's an extension of the on premise environment. VPN provisions were under provisional hearing all these stories, exposing all the things that need to be worked on because no one ever saw this kind of direction. It highlights the modern efforts that a lot of your customers are going through rubber. Can you explain? And Kumar talk about this digital transformation in this cove it and then when we come out of it, the growth strategies that need to be put in place and the projects take a minute to explain. >>Go ahead. Robert Cover has been spending a lot of time with our customers, and I would like to go ahead. >>Yeah, thank you so much. It's Ah, uh, accelerators. What's happened? Many of our clients have been forced into the conversation about how do I engage our customers, and how do we engage our broad constituents, including our employees and colleagues, in a more rapid and easier way? And many of the systems that were targeted to make their way to a public cloud digital transformation process did not get the attention just because of their size and breadth and depth effort. So that's really put an accelerator down on what are we gonna do? So we have to be able to bring a platform into our clients organizations that have the same behavior characteristics or what we call you know, the same cloud experiences that people are expecting public. Bring it close to our client's data and their applications without having that you don't have a platform by which you can have an accelerated digital transformation because it's historically a public cloud. But the only path to get that done, what we're really considering, what we introduced a while ago was platform near our clients applications. That data that gives them that ability to move quicker and respond to these industries, situations and specifically, what's happened with company really pushes it harder for real solutions Now that they can act on >>Kumar, your thoughts on this pre coded >>Yeah, yeah, this is the piece of acceleration for the digital transformation is just is a longer dynamically multiplied the code. But I think as you pointed out, John the remote working and the VPN is the security. We were as an edge to the Cloud platform company we were already in that space, so it's actually very, very. As Robert pointed out, it's actually nice to see that transformation is his transition or rapidly getting into the digitization. But one thing that is very interesting to note here is you can you can lift and shift of data has gravity. And you actually saw we actually see the war. All the distributor cloud. We see that we're glad to see what we've seen we've been talking about prior to the Kool Aid. And recently even the industry analysts are talking about we believe there is a computer can happen where the data is on. But this is actually an interesting point for me to say. This is why we have actually announced our new software platform, which we as well, which is our our key differentiator pillar for our as a service people that companies are facing. >>Could you talk about what this platform is? You guys are announcing the capabilities and what customers can expect from this. Is that a repackaging? Is there something new here? What's is it something different, Making something better? What? Can you just give us a quick taste of what this is and what it means. >>Good love alive. >>Yeah, so yeah, that's a great question. Is it repackage There's actually something. Well, I'm happy to say. It's a combination of a lot of existing assets that come together in the ecosystem, I think a platform that is super unique. You know, you look at what the Blue data container Onda adoption of communities holistically is a control plane as well as our data fabric of motion to the market with Matt Bahr and you combine that with our network experiences and our other platform very specific platform solutions and your clients data that all comes together in intellectual property that we have that we packed together and make it work together. So there's a lot of new stuff in there, But more importantly, we have a number of other close partners that we've brought together to form out our as moral platform. We have a new, really interesting combination of security and authentication. Piece is through our site L organization that came underneath with us a few months back and are aggressive motion towards bringing in strong networking service that complexity as well. So these all come together and I'm sure leaving a few out there are specifically with info site software to continue to build out a Dr solution on premises that provides that world class of services that John >>Sorry, Johnny, was the question at the beginning is, what is that? Why the software role is This is exactly what I was waiting for that that that moment where Robert pointed out, our goal is we have a lots of good assets. In addition to a lot of good partnerships, we believe the market is the customers want outcome based solutions. Best motion not. I want peace meal. So we have an opportunity to provide the customers the solution from the top to the bottom we were announced, or the Discover ML ops as a service which is actually total top to the bottom and grow, and customers can build ml solutions on the top of the Green lake. This is built on HP is moral, so it's not. I wouldn't use the word repackaging, but it is actually a lot of the inorganic organic technologies that have come together that building the solution. >>You know, I don't think it's ah, negative package something up in >>Toto. So I wouldn't >>I didn't think >>negative, but I was just saying that it is. It's Ah, it's a lot of new stuff, but also, as Robert said included, or you built a very powerful container platform. As you said, you just mentioned it that you've gone. We announced the well. >>One of the things I liked about your talk on November was that the company is kind of getting in the weeds, but stateless versus State. Full data's a big part >>of >>it, but you don't get the cloud and public cloud and horizontal scalability. No one wants Peace meal, that word you guys just mentioned or these siloed tools and about the workforce workplace transformation with Cove it it's exposing the edge, everybody. It's not just a nightie conversation. You need to have software that traverses the environment. So you now looking at not so much point solutions best to breed but you guys have had in the past, but saying Okay, I got to look at this holistically and say, How do I make sure I make sure security, which is the new perimeter, is the home right or wherever is no perimeter anymore is everywhere, So >>this is now >>just a architectural concept. Not so much a point solution, right? I mean, is that kind of how you're thinking about it? >>That's correct. In fact, as you said, the data is generated at the edge and you take the compute and it's been edge to the cloud platform. What we have, actually what we are actually demonstrating is we want to give a complete solution no matter where the processing needs are. And with HP, you have no that cloud like experience both as UNP prime as well as what we call a hybrid. I think let's be honest, the world is going to be hybrid and you can actually see the changes that is happening even from the public cloud vendors. They're trying to come on pram. So HP is being established player in this, and with this technology I think provides that solution, you can process where the data is. >>Yeah, I would agree it's hybrid. I would say Multi cloud is also, you know, code word for multi environment, right? And Robert, I want todo as you mentioned in your talk with stew minimum in November, consistency across environments. So when you talk to customers. Robert. What are they saying? Because I can imagine them in zoom meetings right now or teleconferencing saying, Look it, we have to have an operating model that spans public on premise. Multiple environments, whether it's edge or clouds. I don't wanna have different environments and being managed separately and different data modeling. I won't have a control plane, and this is architectural. I mean, it's kind of complex, but customers are dealing with this right now. What are you hearing from customers? How are they handling and they doubling down on certain projects? Are they reshaping some of their investments? I mean, what's the mindset of the customer >>right now? The mindset is that the customers, under extreme pressure to control costs and improve automation and governance across all their platforms, the business, the businesses that we deal with have established themselves in a public cloud, at least to some extent, with what they call their systems of engagement. Those are all the lot of the elastic systems, the hype ones that the hyper scale very well, and then they have all of their existing on premises, stuff that you typically heavily focused on. A VM based mindset which is being more more viewed as legacy, actually, and so they're looking for that next decade of operating. While that spans both the public and the private cloud on Premises World and what's risen up, that operating model is the open source kubernetes orchestration based operating model, where they gives them the potential of walking into another operating model that's holistic across both public and private but more importantly, as a way for their existing platforms to move into this new operating model. That's what you're talking about, using state full applications that are more legacy minded, monolithic but still can run in the container based platform and move to a new ballistic operating model. Nobody's under the impression, by the way, that the existing operating model we have today on premises is compatible with the cloud operating model. Those two are not compatible in any shape. Before we have to get to an operating model that holistic in nature. We see that, >>and that's a great tee up for the software question Robert, I want to go to. Come on, I want to get thoughts because I know you personally and I've been following your career. Certainly you know. Well, well, well, deep in computer science and software. So I think it's a good role for you. But if you look at what the future is, this is the conversation we're having with CIOs and customers on the Cube is when I get back to work postcode. But I've gotta have a growth strategy. I need to reset, reinvent and have growth strategy. And all the conversations come back to the APS that they have to redevelop or modernize, right? So workloads or whatever. So what that means is they really want true agility, not just as a punch line or cliche. They gotta move security into the Dev Ops pipeline ing. They got to make the application environment. Dev Ops and Dev Ops was kind of a fringe industry thing for about a decade. And now that's implement. That's influencing I T ops, security ops and network ops. These are operational systems, not just, you know, Hey, let's sling some kubernetes and service meshes around. This is like really nuts and bolts business operations. So, you know, I t Ops has impacted SEC ops isn't impacted. They're working us not for the faint of Heart Dev Ops I get that now it's coming everywhere. What's your thoughts on that? What's your reaction? >>We see those things coming together, John. So again, going back to the Israel were the world we believe this innovative software is. It can run on any infrastructure to start with, whether it's HP hardware knowledge we are with. It's called Hybrid. And as we said we talked about, it is it is, um it's whether it is an edge already where the processing is. We also committed to providing integrated, optimized, secure, elastic and automate our solutions. Right. This is, I think, your question of are it's not just appealing to the one segment of the organization. I think there's going to be a I cannot just say I'm only giving you the devil ops solution, but it has to have a security built into. This is why we are actually committed to making our solutions more elastic, more scalable. We're investing in building a complete runtime stack and making sure it has the all the fleet compose. It's not only optimized for the work solution which we call the work runtime stack, it's also has this is our Green Lake solution that that brings these two pieces together. Robert? Yeah. Sorry. Go ahead. >>Robert, you mentioned automation earlier. This is where the automation dream comes in. The Mission ml ops service. What you're really getting at is program ability for the developer across the board, right? Is that kind of what you're thinking? Or? >>Well, there's two parts of that. This is really important. The developer community is looking for a set of tools that they could be very creative and movement right. They don't want to have to be worried about provisioning managing, maintaining any kind of infrastructure. And so there's this bridge between that automation and the actual getting things done. So that's number one. But more importantly, I think this is hugely important, as you look about pushing into the on premises world for for H, P E or anybody else to succeed in that space, you have to have a high degree of automation that takes care of potential problems that humans would otherwise have to get involved with. And that's when they cost. So you have to drive in a commercial. I'm gonna fleet controls of Fleet management services that automate their behavior and give them an S L A that are custom to public cloud. So you've got two sets of automation that you really have to be dealing with. Not only are you talking about Dev ops, the second stage you just talked about, but you gotta have a corresponding automation bake back into drive. A higher user experience at both levels >>and Esmeraldas platforms is cool. I get that. I hear that. So the question next question on that Kumar is platforms have to enable value. What are you guys enabling for the value when you talk to customers? Because who everyone sees the platform play as the as the architecture, but it has to create disruptive, enabling value. What do you >>Yeah, that I'll go on as a starter, I think way pointed out to you. This is the when we announced the container platform, it's off, the very unique. It's not only it's open source Cuban it is. It has a persistent one of the best underlying persistent stories integrated the original map or a file system, as I pointed out, drones one of the world's largest databases, and we can actually allow the customers to run both both state full and stateless workloads. And as I said a few minutes ago, we are committed to having the run times off they run and both which we are. We're not a hardware, so the customers have the choice on. In addition to all of that, I think we're in a very unique solutions. We're offering is ML ops as we talked about and this is only beginning, and we have lots of other examples of Robert is working on a solution. Hopefully, we'll announce sometime soon, which is similar to that. Some of the key elements that we're seeing in the marketplace, the various solutions that goes from the top of the bar >>Robert to you on the same question. What's in it for me in the customer? Bottom line. What's the what's in it for me? >>Well, so I think, just the ease of simplicity. What we are ultimately want to provide for a client is one opportunity to solve a bunch of problems that otherwise have to stitch together myself. It's really about value and speed to value. If I have to solve the same computer vision problem in manufacturing facility and I need a solution and I don't have the resource of the wherewithal stacks like that, but I got to bring a bigger solution. I want a company that knows how to deliver a computer vision solution there or within an airport or wherever, where I don't need to build out sophisticated infrastructure or people are technologies necessary, is point on my own or have some third party product that doesn't have a vested interest in the whole stack. H P E is purposely have focused on delivering that experience with one organization from both hardware and software up to the stack, including the applications that we believe with the highest value to the client We want to be. That organization will be an organization on premises. >>I think that's great, consistent with what we're hearing if you can help take the heavy lifting away and have them focus on their business and the creativity. And I think the application renaissance and transformation is going to be a big focus both on the infrastructure side but also just straight up application developers. That's gonna be really critical path for a lot of these companies to come out of this. So congratulations on that love that love the formula final conclusion question for both you guys. This is something that a lot of people might be asking at HP. Discover virtual experience, or in general, as they have to plan and get back to work and reset, reinvent and grow their organizations. Where is HP heading? How do you see HP heading? How would you answer that question? If the customers like Kumar Robert, where's HP heading? How would you answer that? >>Go ahead, Robert. And then I can >>Yeah, yeah. Uh huh, Uh huh. I see us heading into the true distributed hybrid platform play where that they would look to HP of handling and providing all of their resource is and solutions needs as they relate to technology further and further into what their specific edge locations would look like. So edge is different for everybody. And what HP is providing is a holistic view of compute and our storage and our solutions all the way up through whether they be very close to the edge. Locations are all the way through the data center and including the integration with our public cloud partners out there. So I see HP is actually solving real value business problems in a way that's turnkey and define it for our clients. Really value >>John. I think I'll start with the word Antonio shared. We are edge to the cloud, everything as a service company and I think the we're actually sending is HPE is Valley Legend, and it's actually honored to be part of the such a great company. I think what we have to change with the market transformation the customer needs and what we're doing is we're probably in the customers that innovative solution that you don't have to. You don't have to take your data where the computers, as opposed to you, can take the compute where the data is and we provide you the simplified, automated, secure solutions no matter where you very rare execution needs are. And that is through the significant innovation of the software, both for as Model and the Green Lake. >>That's awesome. And, you know, for all of us, have been through multiple ways of innovation. We've seen this movie before. It's essentially distributive computing, re imagine and re architected with capability is the new scale. I mean, it's almost back to the old days of network operating systems and networking and Os is and it's a you know, >>I that's a very, very good point. And I will come through the following way, right? I mean, it is, It's Ah, two plus two is four no matter what university, Gordo. But you have to change with the market forces. I think the market is what is happening in the marketplace. As you pointed out, there was a shadow I t There's a devil Ops and his idea off the network ops and six years. So now I think we see that all coming together I call this kubernetes is the best equalizer of the past platform. The reason why it became popular is because it's provided that abstraction layer on. I think what we're trying to do is okay, if that is where the customers want and we provide a solution that helps you to build that very quickly without having to lock into any specific platform. >>I think you've got a good strategy there. I would agree with you. I would call that I call it the old TCP I p. What that did networking back in the day. Kubernetes is a unifying, disruptive enabler, and I think it enables things like a runtime stack. Things that you're mentioning. These are the new realities. I think Covad 19 has exposed this new architectures of the world. >>Yeah, one last year, we were saying >>once, if not having something in place >>started. So the last thing I would say is it we're not bolting coolness to anything. Old technologies. It's a fresh it's built in. It's an open source. And it is as a salaries, it can run on any platform that you choose to run. Now. >>Well, next time we get together, we'll refund, observe ability and security and all that good stuff, because that's what's coming next. All the basic in guys. Thank you so much, Kumar. Robert. Thanks for spending the time. Really appreciate it here for the HP Discover Virtual Spirits Cube conversation. Thanks for Thanks for joining me today. >>Thank you very much. >>I'm John Furrier with Silicon Angle. The Cube. We're here in our remote studios getting all the top conversations for HP Discover virtual experience. Thanks for watching. Yeah, >>yeah, yeah.

(uptempo music) >> From theCUBE studios in Palo Alto in Boston, connecting with thought leaders all around the world. This is theCUBE conversation. >> Hi everybody. This is Dave Vellante and welcome back to my ongoing coverage of CXOs in the mix of this pandemic, Robert Youngjohns this year. He's now the chairman of ABBYY. Robert, great to see you again. >> Thank you. Great to see you too. >> Hey, last time I think we talked when you were on theCUBE, you were kind of in the middle of going through quite an unwinding of HP. So, and you've been quite busy since then, but I've got to ask you, kind of thinking about back a few years ago, what did you learn through that whole process? >> What did I learn through the whole process of coming out of HP or in HP? I thoughT-- >> Coming out. The unwinding. >> The unwinding. I think it was something that we talked about the last time we were together actually, which is that the software industry is an incredible industry to be in, its always being it churn. New companies have been created and new ideas are coming to the fore, old ideas of dropping out of the one side and you have to make choices or either in the investment business going to participate in that tremendous upswing or you're going to say, "I'm a legacy software company," you seem to make as much money as possible out of the software you had. And I guess what I really learned in that transition was learned a very very distinct place. >> Which kind of brings me to this new role. ABBYY, not a lot of people may not know about them, but they're large company, billion dollars growing very nicely. What attracted you to come to ABBYY? >> Well, after i left HP, I spent a little bit of time working with McKinsey and then I did some consulting with a company called Automation Anywhere, which is in the robotic process automation space. And I've been very enthralled about that space. The automation of business processes, the concept of digital labor, digital workforce and so on, and then I came across ABBYY and then I... ABBYY is really specializing in some pieces of the robotic process automation puzzle. I don't think it have been fully cracked yet. One is identification of the process that you need to automate, it sounds obvious, but a lot of companies are still struggling over that. And secondly, the provision of the data that actually feed these robotic processes. Often that comes from unstructured data sources. It comes from documents, scans, PDFs, screen scrapes and so on. And ABBYY fitted extremely well into that part of the sort of RPA value proposition. So I'm really enthused about them. They are a Russian company that relocate to the U S which I think will be very helpful to them. And they asked me to help in that relocation and help them make sure they participate fully in the growth of the robotic process automation space. >> Well, it's a hot space and I think you're right, I think a lot of people are, I sometimes joke, they're sort of paving the cow path. They're taking mundane tasks, they're sort of automating it without really necessarily thinking through, an entire transformation. But at the same time you talk to customers, they're saving a lot of money. So what's your vision for sort of the automation future generally and then specifically for ABBYY? >> Well, I think it's fair to say that a lot of the early automation was about very, very simplistic tasks. And actually it's almost frightening how many of those exist, how many people spend their days essentially shuffling data from one source to another and doing well, mundane tasks that are wide open to automation. But I think automation gets more sophisticated. The one thing that enterprises are going to absolutely need to do is get very, very clear on what are their high value processes. How do they actually operate in practice as opposed to how they would design and then how do they go about automating that. And I think to do that, we need tuning up the front end, getting process identification tooling, and then you've got to crack the biggest single problem of RPA, which is making sure all the data that processes need to work is available to the process. And as I said before, that could come from documents, scans, screens scrapes, screen scrape a PDF, but it's got to be gotten in an intelligent way that recognizes the context in which the document existed and then can therefore extract the appropriate document and feed it into the right parts of the automation sequence. And I think those two front ends of automating critical and many of the existing vendors I think would be relatively slow to get into that space. And I think that's where ABBYY has differentiation ABBYY has value. >> So you guys, you talk about digital intelligence company, is that what you mean by digital intelligence? Not just being able to sort of read forums and PDFs and documents, but actually putting them into context. Can you add some color to that? >> Well, it gets broader than that. Obviously that's a critical part of it and we sort of underestimate that because a lot of data and enterprise is not fully digital. People still submit orders over faxes or scans. And there's a lot of unstructured data out there, but I think the real digital intelligence comes in the ability to understand the way business processes really happen within your enterprise, apply a new analytic lens to that. Then one step, maybe simply to automate those processes, but a more interesting step maybe to actually look in detail of how those processes operate and find new way of linking processes together, which is a much more efficient for the enterprise. Historically what companies have been doing with the classic ELP suite is they be saying, "Well, "someone else's has cracked this, "there's no value in creating new business process. "I'm just going to take whatever it is that SAP, Oracle, "Financials, whatever defines, and I'm going to build "my business around it." I think in this new world, if you could really understand how processes actually operate within your enterprise and you can create analytics around that, you'd have the capability to innovate around the processes and that innovation can produce real competitive advantage and it's enabled by a combination of things. It's enabled by, but its the tools to actually find out what's really happening. Its the ability to extract all that unstructured data and make sense of it, and then the ability to apply analytics to that to say, "Could we do this smarter?" If you have traditionally done A goes to B goes to C, but your analytics showed that in practice A always goes to C, well that should be your new concept. That's trivial, but it actually encapsulates what I think we can do, around all this activity we're engaged in. That's I think what we think the digital enterprise looks like. >> So what's the sweet spot for ABBYY? If you had to sort of look at the ideal customer profile, where's their wheelhouse? >> I think it is process rich organizations at the front end of those processes is high amounts of unstructured data and unstructured data could be, as I said, anything from PDF to a scan to a screen scrape, all the things that, for example, your orders are coming in through, your insurance payments are coming in through. Its a paperwork, heavy organization and we use the word paper there in a very general and expansive sense. Those are the companies I think that most benefit from automation and at the same time would both benefit from what ABBYY has to offer. >> Robert, you mentioned earlier that sometimes companies and they struggled to even understand what processes should be automated. How do you do that? Is that where machine intelligence or AI or machine learning comes in? >> Yeah, I think that's the biggest single problem of the automation market. A lot of processes are sort of obvious and it's not surprising that the first people into automation were the business process outsourcers because they had to define processes in order to be able to make bids to clients to take over those functions. But as we move through that and we try to find out what are the high value processes, then it really is about analytics. Now a lot of the traditional tools, whether it's SAP or Oracle or many of these, they just throw out huge amounts of information, and you can actually track the way a process actually maps out in an enterprise. The ability to apply analytics to that is where the real value comes. And ABBYY has a product called Timeline PI, does exactly that. Provides intelligence on the actual flow, an analyst may say, A goes to B goes to C, goes to D, but you're often finding that it's looping round from the C to B, for example, because there's an error in the data coming into it. The ability to apply analytics to that and from that be able to say, okay, these are the processes we really should automate because these are the ones that will got real value. And these are the ones where frankly are either so obvious or, have got relatively limited value because they're not that extensive in the enterprise. I don't need to bother. Or they can go to the back of the automation, I think it's a critical front end and it's enabled, I think we talked from the very last time we were together about, we're not short of data. Data has been spun out of everything that happened. Whether you're using an ERP, whether you're placing an order, the number of data sets that just arise from a simple internet transaction, we're not short of data. It's having the tools that allow you to analyze that data, reduce them down to the business flows that drive them, and then identify where you make improvements through automation that the real value comes. >> Robert, one of the things we also talked about was disruption. It's a topic of conversation always in theCUBE but , a number of industries thus far haven't been disrupted. We've talked about the ones that have, but, think about financial services, for example, healthcare. A lot of parts of government, particularly defense has not been disrupted. Do you think the COVID-19 pandemic is going to change that? >> Oh, I think absolutely. I think one of the things that in sports, I'll give you a very trivial example. It's forced us to use remote technologies for meetings. And I think with some of us beginning to realize that it's actually a massive productivity boost. I'll give you an example. A couple of weeks ago I was due to go visit, a client company of a private equity company. With which I'm working and I was going to fly to Chicago, spend a day and a half in Chicago and then come back, three days probably in total. Together with a certain amount of time the time zone changes and so on. The truth is we did that in six hours over Zoom and I think the output was every bit as good. In fact, you could argue the output was better because everyone was prepared, everyone was thoughtful, there were no random interruptions and so on, I'm running board meeting I've seen the same thing happen. Board meetings are running in to time. They're not getting distracted. They're not running about tangents. I think the productivity lift that's coming out of those things is very substantial and I think you can go and apply that to almost any industry. My wife yesterday I did a remote consultation with her doctor. Again, it would much more efficient of everybody's time than getting in a car, driving to the hospital, waiting in line, for an appointment was bound to overrun. The appointment was on time, it was to the point and it just happened. And I think almost every industry is going to see that. And it may never go back to what it was. It may never go back to that idea that if you want to meet with someone, you jumped on a plane, you crossed three times zones, you spend seven hours on a plane going to New York or wherever, when you can just do it quickly, efficiently and with an amazing productivity remotely. >> Your telehealth example's a good one, especially these days. You feel a lot safer and doing it from your home. And I'm sure it can be very productive. Over your career you've got quite an observation space, large companies, small companies, you're doing some investments now. Let me start with sort of the smaller companies, maybe the VC funded startups, that you might be working with or observing. What do you see going on there? What are you advising them? What kind of companies do you think will emerge from this pandemic as a strong ones? What do they look like? >> Well, I think there's two sets of answers. Firstly, the companies that are succeeding I think will be those that are in the remote working automation space. I think that's going to get a master boost from what's happening right now. In terms of practical short term advice. I think there two factors. One is survival and every company I'm working with is in cash preservation mode. Just making sure that even against the incredibly pessimistic outlook for the rest of the year, they can still be in business at the end of the year. And that's, meant some tough decisions. It's meant, reducing staff in some cases, reducing costs, across the board but all with that, if it's the worst case, then how do we make sure we're still in business at the end of the year. But much more importantly, getting people to focus on where are they, when they come out of. Have they built competitive advantage between now and say the end of the year or whenever the state starts to get back to normality. Are they reaching out to customers right now that are struggling and acknowledging that they're struggling and putting deals on the table that are going to win them and win their loyalty, over a five to 10 year period even at the expense of short term revenue issues. I think that's critically important and keeping yourself in that mindset of where do you want to be when this is all over? How we increased our competitiveness? How have we improved our customer intimacy? How have we used remote technologies to make calls on customers that perhaps we wouldn't have made in the past? I was talking to a senior executive at Microsoft as it happens. So when you used to work for me, just a couple of days ago, amazing example of remote technology, he's in Yehud, Israel, and I'm in the Bay Area, and he was chatting about his personal efficiency in terms of customer interaction and saying that, three months ago, the way he would meet with customers, he'd get on a plane, you fly to a city, work on the assumption that his local team had put together some meetings, which were often highly dependent not the agendas they could locate with and then fly out. Right now, he's able to put meetings on ad hoc basis, 10 minutes, 15 minutes at a time, across multiple times zones, multiple geographies without going through that sort of impediment. And it's making him much more intimate with his customers, getting to know those customers much better. And conversely, they getting to know him much better. And getting to understand what he does and the value he add much better. And again, that's a massive difference from where he was, three months ago. >> We have to say we love not being on planes on Sunday night. The guys in the studio I'm sure are laughing about that, but so we've seen a real bifurcation in IT spending as a result of COVID-19. Upstarts these days have, half a billion dollars in the bank. But, guys like Snowflake, you mentioned Automation Anywhere security companies like CrowdStrike and Okta. These are companies that, clearly, this has been a tailwind for them. On the flip side, you're seeing some, large companies that, but again, even within these big portfolios, take like a Cisco for instance. Some of the traditional networking stuff might be under fire, but then the WebEx stuff is rocking to the new highs. So it's a complicated situation for a lot of folks. If you're running a large company right now, where would you be focused? How would you be steering the ship? >> I actually don't think it's that different for a small company. I think the first thing you've got to do is just make sure you're using this as an opportunity to get your cash under control. Getting your spending under control and unpleasant though it is, it's actually a really good catalyst for asking yourself, all that spending that I committed to over the last couple of years in this point in time did I really need to do it. This is the time to go clean house, make sure we're running efficiently. I think that's a good starting point. But again, just like I said about the smaller companies, it's also focused on, getting out of this at the other end in a much more competitive position than you went into it. Using it creating customer intimacy, using it to create loyalty, by doing things that acknowledge the difficulty that many of your clients are facing. I think in things like that, it's not actually that different. You may have more of an offer and then may be a more inertia in the system, which you're actually less likely to get the same sense of urgency that a startup has. But nevertheless, it's pretty important but as much the same, unless otherwise. >> Do you see as a software executive, I'm interested if you're seeing sort of new pricing models emerge, maybe as a result of the pandemic or maybe just sort of another wave of disruption? And the reason I bring this up is, a lot of so-called cloud companies and SaaS companies, they'll charge you, a one year term or a two year term or three year term. You're starting to see some emergence of companies that are saying, " No, let's do a real club paid "by the drink." We're going to drive that intimacy that you were sort of referring to before. Do you see that changing or is it going to be sort of the more "traditional", SaaS models? >> I'm actually seeing a little bit differently. I think what's happening is that, the subscription model clearly is the right model. I look at companies that have subscription software revenues, they're doing a little better. They're feeling a lot better about life. Than those are all running on perpetual licenses. And having to close that deal every single quarter. But what I'm also seeing is companies responding to clients by saying, "Look, I understand you may have cash flow "issues now, i understand that for example, "within the hospitality industry, your business is gone away "and you're not going to go buy this stuff "for the next three months. "So rather than force my subscription contract down your "throat, I'm actually going to give you a payment holiday. "But in return I'd like something back, and I'd like "something back and it could be as trivial as I want you "to act as a reference, I want more access to executives, "but it also could be, I'm going to extend the terms on "which this contract exists. "So want a yearly term?" "I would like a three yearly term." I'm seeing a lot of that going to that going on. And it's a way of responding to the immediacy of the pressures that your clients face, but at the same time acknowledging that it's a two-way street, but nothing you won't back from, as is said it could be as trivial as a reference or executive contact or it could be as significant as signing up for a five year term, be a renewal rather than an annual renewal or a month renewal. >> I think in general, my observation as in speaking to CEOs and other practitioners that are buyers, this time around this pandemic, the vendor community seems to be doing, I think a better job than 2008, 2009 you're coming out of that, you saw some audits in the light, whereas here, maybe it's because we're all in this together. It's a global pandemic. There's been maybe more sensitivity, but I think generally with all this ted talk of breaking up big tech and bad tech, but there's a lot of tech for good. So I just kind of want to throw that out. Last question, Robert, what should we be paying attention to with regard to your tenure at ABBYY? What are you trying to accomplish in this sort of near-term, mid-term? What are those things that we should be watching as milestones or indicators? >> Well, I think the key is that we want ABBYY to be participating in the growth of the RPA market. We think we had unique value to add. And I think we can bring that to market in unique ways. The ABBYY team is incredibly talented. |We do development out of Moscow. Very talented development team. We have, great product managers and great product executives, and massive experience, Matt, processed management over many company. And I think if we can bring those things together, , with sort of analytic layers and so on, it can make a real term for the perceived value of RPA amongst all our clients. And if we do that RPA itself, it's a virtuous circle cross. If we do that, the alternate market doesn't get into that, our partner referred to as the silent despair, after initial burst everybody goes, "Well, "this really wasn't as valuable as we thought." We can get through that and into what I think really matters, which is the longterm sustainable growth of the sector. And we want to be part of that. And I believe we can be. >> It's definitely a hot sector. It's one of the highest spending momentum areas we've seen. And even last fall we were saying, in a downturn, many were predicting sort of an economic downturn, not because of a pandemic, but we had said at the time, automation is just, that's a tailwind for automation. So Robert, congratulations on the new role. Really appreciate you coming back in the queue. It was great to see you again. >> Thank you. Thanks for you, for inviting me. I really appreciate it. >> All right ,thank you for watching everybody. This is Dave Vellante and we will see you next time on our CXO series. (bright music)

>> Announcer: Live from Barcelona, Spain it's theCUBE covering Cisco Live 2020, brought to you by Cisco and its ecosystem partners. >> Okay, welcome back everyone. It's theCUBE's live coverage here in Barcelona, Spain for Cisco Live 2020, I'm John Furrier host of theCUBE, My co Stu Miniman. We've been talking about the value of data for many, many years and privacy and today's Data Privacy Day, and super important we are here every year past couple years, and the routine at Cisco has some answers for us. Our next guest Robert Waitman Director, Data Privacy and Economic Security and Trust Organization at Cisco, Robert Cube alumni, welcome back, good to see you. Thanks for coming back on. >> Thank you. Great to see you again. >> John: So you know we've had great chats in the past. You knows my favorite topic, the value of data, the role of data, we all believe data driven organizations. You guys just put out your annual report, which is privacy to profit. We asked question here on theCUBE, what's the value of data? That's the Holy Grail. But you guys actually got some progress on this, and narrowly defining around privacy, what's it worth with privacy if you invest in privacy, there is an ROI. We've seen similar reports on diversity, investing in these areas that look like impact mission based items actually has economic value. You guys have new data on a return on investment of privacy, share us the results. >> Happy to do so. And we've been on this journey for three years to try to understand where the value is coming from from privacy, putting protections in place. We first saw that it was showing up in terms of better sales motions, we're having fewer sales delays because organizations put privacy in place. Last year, we started looking at some of the security benefits that those organizations that invested in privacy we're seeing fewer and less costly breaches for example, and less records exfiltrated. So the idea of getting your data house in order is translating into business value. This year, we've not only validated those results from the past two years, but we've now taken it to the next step to have an actually a return on investment on those privacy investments. So our survey this year, which we put out yesterday, was based on 2800 companies, 2500 of which knew about privacy at their organizations. And we asked them about their investments, we ask them about the benefits that they thought they were getting, some in tangible ways and also some intangible ways, like competitive advantage, or operational efficiency, things that are hard to quantify. Overall results on that the average organization spends $100 on privacy, they're getting $270 back, it is a great investment. I don't know how many investments they have to have that kind of return. But in this environment, and this is where we're seeing, the customers who want these kinds of protections, it's a great investment. >> It's an omni directional kind of forcing function if you think about it. I wanted to ask you, how do you see some of the categories because I can certainly see the benefit of just, people who are afraid of their privacy and their data. You see a lot of train wrecks in the industry, from Facebook to other things where users are in control, right? They want to be in control. That's the trend. So I can see the halo effect of saying, well, this company's got good privacy, I like that company. >> Robert: Right? >> It's almost a modern kind of table stakes, like going green or something like that. Is there areas that pop out in the survey, where the ROI was a must have, in terms of privacy or sort of categorical? >> Well, the this idea of building your loyalty and trust of your customers, is something that we had explored. If like, there's a companion piece that we just put out a few weeks ago, exactly on this issue of the consumer interest in having that available to them. And I would say, wouldn't take it for granted. Until recently, most people have said, privacy is dead, and I don't know who has access to my data, and I don't know what's controlling it. But the combination of GDPR, which swung the pendulum a little bit back so that users again had the ability to know what data companies had about them, and in some cases to modify or delete it, started that tread, the CCPA in California, carries out a little bit further. And what we saw in this companion survey around individuals was fascinating because we saw people that are more active. They're saying not only do I care about privacy, which most people will say that I will spend time and money, which many people may say that, but the real test was here that they've made a change, that they've changed a provider or someone that they work with over their data practices or data policies. And what that saying to us is, there's an active community, we're calling them privacy actives. It's a third of the population today, who are standing up to say, I now know that I have some control over how my data is used. Therefore, think about the companies and how they relate to that their customers are saying to them, I'm not going to work with you. And I'm not going to do business with you. And I want to only work with companies who I know how the data is being used. It's now become an important priority. It's part of the brand. It's part of the overall customer experience. So customers aren't going to-- >> John: I think you are understanding the numbers too. I think you I believe what you just said, is only going to be amplified because with social networking and what we've seen with virality and even just with fake news and disinformation. There's also information that could go viral, like, hey, this company, the buyer swing, the influence that these groups could have could be a force multiplier on impact negatively and positively. >> Robert: Right. And I think that actually, we would bear that out as well. So even though I described the third of people who already have made that change, there's another 30 plus percent, who said the first two, they just haven't made that change yet, maybe they aren't comfortable with doing it yet or they haven't had the opportunity. So again, this is something that all companies A need to pay attention to, and B it's going to be fundamentally part of the overall experience. If you don't have the privacy right, you're like not in business. And again, I think that's a positive trend, getting to the creating the conditions in the world that I think we all want to live in where, where when I share my information with somebody who uses it well, I'm happy with that. If I share it with somebody who misuses it, I don't want anything to do with them. And that's, I think, what we all think how it should work. >> Yeah, that's really fascinating and I love what you're saying about how the consumers are getting involved. I was a little bit concerned that things like GDPR and CCPA were going to be like the old, software accept it to use it. Nobody reads it, nobody pays any attention to it, I just opt in to anything. So, what advice do you have to users, how do you make sure that you're working with companies that are going to be using your data correctly and get involved, if they're not? >> Robert: Well, first thing they should do is be aware of the regulations and the rights that they have. I mean, the awareness even if GDPR in Europe runs under two thirds, right? So it's not something to take for granted that everybody knows about what they can do. So the first thing is know what you can do ask for the data if you're not sure. And ask the questions about how your data is being used. If the company is not completely upfront and transparent with how the data is being used, and I don't mean a 20 page consent document, which you can't figure out what they're doing, then you should be either not doing it or asking those questions and you should have comfort that there are a lot of other consumers out there that are doing the same. So make sure you're doing that. Cisco tries to work very hard to share with our customers exactly how data is using in all of our products that's why it published the data privacy maps and the data privacy sheets to kind of make that easy on our customers. But in any business, that's something that, a consumer should be asking, a customers should be asking and the company should explain, simply and transparency. The one number one complaint that individuals still have today is they don't understand what companies are doing with their data. >> Yeah. >> I mean, it's just mind boggling and that's, I think, again, the advice I give them is, you've got to get that right. >> How does Cisco do? What do you guys do? What do you offer people? I mean, let's just say people want to check. What was the mechanism that you guys are putting in place? Because I have no idea of WebEx my video is going to be facial recognition or my packets being routed through Cisco routers are being sniffed out, how do you guys put that transparency out there? >> Robert: Well, you like many customers ask those questions. And so we started creating and publishing these privacy data sheets, which were relatively streamlined, fairly short documents that you could go through and say, okay, I understand where the data is going. And we've done that on a whole bunch of the most requested products. We've taken another step to make them now very visual. I think we talked, we just launched that a year ago, where we tried to make them look like subway maps. Where you have sort of color coded ways the data flows through the system. And those are available. Anybody can come get them from trust.Cisco.com on the website, publicly available for customers who are interested in a product, don't have to go down the road and say well, it's just going to be my needs, they can get almost all of their questions answered through that. Yes, there may be some additional questions we want to answer later, like through the lawyers and through the conversations, but we least have a mechanism for giving the most of that information up front. >> Stu: Yeah, I love that trust was something that was front and center in the keynote this morning. I'm curious, Robert, with Cisco's position in the marketplace, the ecosystem you have is either something Cisco can lead or their industry considering to have kind of like a better business bureau. I shouldn't be able to go there and say, is this a reputable company? Am I okay, doing business with them? From a privacy standpoint, are there any initiatives in the works or is that something you might foresee going forward that I know oh, hey, this is somebody that it makes sense for me to work with. >> It's an interesting idea of, that could be created around that. I mean, I think where we are today is there's still a huge value of the government playing a role. I mean, the idea of GDPR and other regulations, if you have too much of it may not be helpful. But in today's environment, because the consumer can't always trust the company to do what they say they're going to do, you may not even be able to figure it out from the policy to begin with. But the government's role is to make sure that they're doing what they say they're going to do and therefore, consumers want government involved in that. So that again, there's a role to see fines and see penalties means that some of the guys are at least being-- >> Stu: Well, I wonder even you look at some of the fragmentation of the internet today, is there something that government or intergovernmental, kind of like the organization that runs the internet today, if there's there would be some room for them to be involved in something like that? I know it's a big audacious thing, but it is something that the general public companies, they don't trust most corporations with their information. >> Right. And it's a nice idea, especially in an environment where we want to avoid 50 different state legislative environments that companies are going to have to comply with. I mean, so far we go back to our study, we see this very positive return on privacy investment. If we get 50 more state laws that people have to comply with, that's very quickly going to get negative, right? So as although consumers are demanding more, it's more part of the brand. If we have too much regulation will start to see that around. So you're getting your idea of consolidation, having a single way is a very positive idea. >> Stu: In your report, I saw that GDPR and CCPA, oh, China's doing something, Brazil's doing something, it's going to be well it's from it's going to become onerous on the supplier and the consumer side if there isn't some commonality between them. >> Robert: Fully agree. That's right. >> John: Well, I got the report here folks, check it out. It is an amazing report. Every year, the team does an amazing job. This year it's about privacy ROI. This proves that good hiring works. Privacy, hiring practice, diversity inclusion, inclusion and diversity has pay off and this is the new modern era. I want to switch gears well on that note because Robert, we always love to talk about your role you're a data privacy and economics. So privacy economic, ROI, get that security and trust organization. The economic value is a big part of your study here. I think it's just scratching the surface. And I want to give you an example and I want you to react to it. Was having a conversation with a big time venture capitalist who just changed this job to start a fund for impact investing for profit. And one of his focus area is economics around self governing communities around policing some of these regulation issues. So there's so much regulation, business could get stunted. There's a trend going on now, Stu kind of lead it into it where communities are going to start to govern the brands as a check based on buyer behavior. So there's real signals that users are reacting to companies, policies, with data or whatever whether its environment whatever people are making purchasing decisions and organizing, that's going to change the economics, which is the top line impact, not just so much a cost structure to have certain regulatory policies. This is a venture capitalist. What's your reaction? He's investing in this direction. He thinks it's going to be big, your thoughts. >> Well, I think there's evidence for that, again, it's the idea that a company is more valuable because of some of the things that we're talking about was also we actually asked that question, did respondents think that their company was more valuable because they had progressed along the privacy dimension. Because you think about the loyalty and trust they built with their customers, aside from the operational benefits, and maybe the compliance benefits as well. So I would say, evidence for companies thinking that they themselves are further along, and those companies that have gone more than just the minimum that it's sort of becoming a little more mature, a little more accountable from their privacy programs are getting the best returns. We talked about that $270 on 100. If you're investing a little more and going up that curve, it's $310 on that 100. So again, better return on your investment, more loyalty and value and you see your company as being more valuable. So I think there's strong evidence for that happening again, you know how that actually works operationally another question, but there's something there. >> John: Stu and I were talking about how advertising and how social network and medium is all changing. And one of the things is you're driving at is that advertising used to be an attention game, get on TV, spread the message around, while you're teasing out and what Stu was talking about earlier in our other session is that influence and reputation is a new benchmark. So it's not so much, know my brand, my key rating or brand impression, its reputation, you're getting at something that's really interesting around reputation, which is swinging buying behavior. This is a new dynamic. >> Robert: Yeah, I think that reputational aspect is such an important part of the brand and even doing business and why this whole issue. I mean, the idea of privacy becoming a central tenet of the company and the brand and the overall experience is kind of what we're seeing as that pendulum swings back to the consumer and the ability to make those choices. It's becoming more and more important for the companies to get that right and have that be part of it. That's the value of the company, again, the value of the overall relationship. So I think that's a positive direction. >> John: We really appreciate you coming on. I want to get your thoughts, last question. What's your vision of where we are today in the world? You look around and you'll be happy with some of the things. You look at things like Facebook's going through a change, Jeff Bezos' phone was hacked via video on WhatsApp. You got the political environment, you have this entire trust equation. And it's just a dynamic time, your vision of how trust and data privacy and the economics and all your role. What do you see happening? What's your vision? >> I'm very optimistic about where it's going. I mean, I think we see ups and downs and we see setbacks. We see millions of records get exposed on users, and they get concerned about things. But I think we're trying to put the right processes and controls in place so that those controls and so the right things do happen with data, all trying to create that world that we all want to live in. That when our data is shared, it's used appropriately. So it's not going to be a smooth upward curve, but again, I think that idea of not only the legislative process where our governments are seeing that consumers need these protections that we can't go it alone, we need help with the companies that we work with, and the idea that they're willing to take it more into themselves. I mean, the fact that governments and companies who are concerned about the regulations and individuals themselves, would share the responsibility for creating all of those protections is, I mean, that makes me very positive about where it's going. >> John: And as politicians from all around the world, whether it's United States or other countries have to figure out how much regulation to put on the tech companies, this is a flashpoint of where industry could do their part and be part of the solution, not just be regulated, hopefully. Too much regulation kills entrepreneurship, in my opinion, but that's my opinion. >> Robert: It would kill our ROI right? >> ROI. >> Down the toilet. >> Okay, theCUBE comes bringing all the great conversations here at Barcelona, Data Privacy Day, this is a big part of our society now, and there's now evidence that it's worth investing in privacy thanks to Cisco's report. Good ROI. Of course great ROI of you stay with theCUBE for more action after this short break

>>Live from San Diego, California. It's the cube covering to clock in cloud native con brought to you by red hat, the cloud native computing foundation and its ecosystem Marsh. >>Welcome back. This is the cubes coverage of coupon, cloud-native con 2019 here in San Diego. I'm Stu Miniman co-hosting for three days with John Troyer to my left and happy to welcome back to the program. Two of our cube alumni to my right is Robert Christiansen who is the vice president of strategy and office of the CTO with the IP group to see you. And sitting next to him is Kumar Sri Conti, SVP and CTO of that hybrid it group at HPE Kumar. Great to see you. Thank you very much. Thank you John. Good to be back here. Yes, hot off the presses. HP had a big announcement today. Uh, really unveiling it. Full container platform. Uh, Kumar, maybe it help us frame and understand, uh, what that is and why that wants here at at the show. Thank you. Is too good, too good to see John and it's very nice to be back on the cube. >>Yeah, we are very excited. We made an announcement, a HV container platform as we sat in the presser lays and various conversations. This is built on a proven technologies. HP has acquired a few companies in the past which includes my company blue data map. Our blue data has been in the container technology for more than five years. We have containers running specifically for the spa workloads like big data and AML and we brought those technologies together to give the customers the choice of 100% coupon. It has to run both stateful and stateless workloads under the same pane of glass and we are very excited about this opportunity and we have actually talked to a lot of customers and the most important in addition to all of that is the, we also integrated the map, our technology, which is one of the very so robust and sophisticated data store that gives you a persistency for the containers. >>Kumar, John and I were coming out of the keynote and saying, if you're brand new in this environment, Oh my gosh, there's just so many projects and so many pieces. You know, when I think back, you know, who helped me along the way, uh, one of the pieces you picked up with CTP, cloud technology partner and you're talking about specific applications. So you know, really building those bridges to where customers are and helping them give us, if you could some of those key use cases where you're finding that that cloud native philosophy and where customers are, are looking for HPS help. Robert and I spend a lot of time over the last few months internally and talking to the customers. Our thesis is the, all the low hanging fruit applications have mode. It's actually the most difficult applications, both stateful and stateless applications. So customers are asking and say, we want to standardize, we want to have a abstract platform and Gouverneur does is it? And, but we wanted to have a platform that gives us the board hybrid opportunity. I wanted to be able to run the on prem >>when necessary, also on the public cloud. And I wanted to be able to have a same platform to run both stateful answered as application. Yeah. And that's, that's a really interesting point because what Kumar's really, really looking at is that the only way that an enterprise has been using the path that modernization has been been a public cloud, uh, trajectory. Okay. And they really haven't had anything on premises that gave them the set of services necessary to get parody between the two. And what we're finding and you know, been been involved with public cloud since 2010 right? So hundreds and hundreds of engagements, the portion that they thought they were going to move to cloud is substantially dropped the actual number of applications versus now those are going to stay on prem. And we were looking at each other and we're saying, Hey, this is a trifecta of opportunities with the containers coming in and the normalization of Kubernetes as the unified pass platform, the abstraction of bullying all the way down to bare metal, right? >>And giving those clients that true native architectures where they are not having to pay what we consider excessive prices to be putting in that, that world right there and then allowing that monetization practice to happen. So you've got to start with that platform, that, that container platform, and to do it in the way that the motion is going right now in the world today that's consistent with the public cloud. This is really important that you have to have consistency in your development environments, whether they're public or private. And that's where we believe is important. So Robert, you're seeing enterprises develop that. It sounds like you're seeing enterprises develop that operational experience and operational expertise, process development, independent of where their workloads are running. Well, that's the goal. Okay. Yeah, yeah. Well, right now they're siloed. Right? Okay. You've got a public operating model and you've got a private operating model. >>Right? And there's some people that tried to stitch this stuff together, but it's really difficult. What we're looking to do is given consistent plain across, all right? And when you have a consistent plane, a control point across all, no matter where you put your clusters and a management frame around it, now you have the ability to build an operating model that's consistent to go forward. Okay. So you know, we've been at the show for four years. I interviewed Joe beta, uh, and, and Joe says, he said, look, you know, Kubernetes, it's not a magic layer. It does not all of a sudden say add Coobernetti's in it and everything works every hair there. No, it's a very thin layer. I'm glad he said that. Washing my car from that happened on top. Right. If flip problem just rubbed Coobernetti's on it and get better. So Kumar, help us understand kind of the HPE stack if you work and what you put together and therefore it will be an enabler for customers in your application. Thank you. That's a very, very well said and I joke that Gouverneur does, we'll wash your car and post to read and babysit. And um, so I think he enjoys the ride, a lot of wisdom there. So what we found is, uh, content has an ensemble persistence always problem per se. So if I want, if >>I have a database running and my container goes away, we also notice that you want to make sure your endpoints are well secured and you want to expose only things what you want in the thing. We also found out that customers are more interested in applications and are giving me just the engine and the tires. I need to go from point a to point B. What blue data has done is actually it actually automates all your deployments of applications. We announced that product in September, so what what what this continent platform does is bring all these pieces together so the customers to be able to move to the deploy man and not worry about whether I have tires or I have an engine or not. In addition, I would like to find out that, I think Antonio talked about it the hour Sammo we want to come to the customers and it's the best possible lowest cost workload per application. >>This is why we think better metals are very, very, very important. Running containers on bare metal will Remos techs and and there is an, and we've been running better minerals in on bare metal containers in the blue data for almost five years. One of the things I think I wanted to add to that because I, you, you were guys saying, Hey, deploying Kubernetes and just add a little bit on top of that and it's all fine, right? I thought that was a great comment. Um, a lot of our clients are literally talking about container sprawl, right? It don't take anything to go to cncf.org and pull down could the Kubernetes distro launch it out there? And I've got a bunch of stuff running. They're popping up faster than all the shadow it did when the cloud, the public cloud started coming up, right? So you have this, this, um, motion that's uncontrolled, and if you're an enterprise and you're and governance and you're trying to put your arms around a global infrastructure that you want to be able to put your arms around that, more importantly, you may have one group running 1.15. >>You may have another group 0.1, 1.8. You may have two other groups that have an older version that's into production right now, and you have them all independently running. And then you need to maintain a multitenancy across all of that and then separate those. Okay. You have to have a system that does that. And so the container HP container platform does that. This is a huge differentiating with consistent data layer underneath and that, that abstraction between the two and that governance around it is so much bigger than what we consider just Kubernetes on its own and that world comfort zone. Right, right. >>Well, I, I to play on that, right. Uh, we used to say, talk about paths a lot, right? And then a lot of words were spilled. I, I, what I love about some of the work here is that it comes from actual use, you know, proven in production use cases, years of work, you, the rough edges, the, the, the sharp, the, the cuts on your hands. Um, so that's actually great. All open source also and, and, and contributed back to the community. Also. Interesting. There is a, um, you know, but as so as folks, and there's many ways of getting Kubernetes raw, Kubernetes, Kubernetes with pieces, uh, in this room right here. So, you know, an interesting set of technologies that you've put together that with, for ease of use and for, for governance and you know, at the, from the business, from the ops layer, from the, from the dev layer. >>Um, but there is a difference of speed sometimes of uh, of uh, you know, the, what the enterprise wants to move Kubernetes these releases every quarter. And you know, I and you know, the other projects released at their own pace. So in this open source philosophy, uh, and the HPE as a partner with the, you know, point next and, and you know, support is your middle name kind of, uh, you know, how do you, how do you marry the, the, the speed of the cloud native technologies and all of the open source, uh, collaboration with, with kind of the enterprise on the enterprise side and help them? >>Yeah, very good question. I think Robert Weiner, there's one other focus for us is we didn't want to provide, I think before the injury you are talking about the curator Cuban or that we are supporting a hundred percent covenant is open source. So Robert says, I am a developer. I want 1.19 and Stu says I want do I have a 1.17 because I'm stable on that. You can have both the clusters along with the blue data, Epic controller clusters in the same pane of glass. Now you can run big data applications, you can run your cloud narrative, you can run your cloud narrative because you are on 1.19 so that is our goal. So when the CNCF releases newer versions obviously that we will support it. And then as you pointed out, HP support is the middle lame. We have a point next organization we have a CDP. So we will help the customers and we will obviously support certain versions and make sure when somebody gives a call and help the customers. And so we want to give that flexibility so that the developers can deploy whatever the native new versions that are coming up under the umbrella of HP container. It's this Epic layer that's providing some of the multitenancy and governance and controls. >>Exactly right. So this, you know, if you look at the, the, the CNCF, uh, roadmap, they're their grid, right? And you see where Coobernetti's lands in that one piece. There's all these surrounding pieces like that. There's lots and lots of vendors here that have pieces of it, right? But it takes a system, right? And you know that, and then it takes an operating model around that. Then it takes a deployment and governance model around that, right? And then you have, so there's so much more that the enterprise world acquires to make this a legitimate platform that can be scaled. >>One thing that I would like to add it, I don't want to underplay the, the, the value of a persistent proven data layer that has been there for 10 years with the map, our map around some of the best and largest databases in the world. And we are now bringing those two together. It's a, it's a very, very profound and very, very useful for the, for the enterprises. You know, Robert, you were emphasizing the consistency that needs to happen, uh, explained to how that fits in with your partnerships with all the public clouds. Uh, because you know, you hear a very different Coobernetti's message if you go to the Google show versus the Azure versus AWS. And I see HPE know at all of them. >>That's absolutely true. So, you know, I was the CTO with cloud technology partners, right? So I joined in 2013 and it was, um, our, our whole world was how do we work with the three hyperscalers to bring some consistency across them, right? You know, and you have operating models that are different for all three. I mean, what runs on AWS in a certain way is going to run differently on Azure. What's going to be running differently in GCP, right? So the tooling, all that, all the pieces are different. You go pull that back on prem. Now you have a whole different conversation as well. So what we know is that you have to have a unification of behavioral control systems in place before, wherever you deploy your clusters, wherever those are going to be like that. So what we know is is that the tagging nomenclature, the tagging is key to all of this operational models. >>All your tools are gonna be using tagging. And when you go into existing environments, taggy will be inconsistent between, even with inside AWS will be consistent, inconsistent with an Azure. So you have to have a mapping. So what we have as part of our GreenLake offering that would come in together with this is we have a unification tagging layer that bridges that gap and unifies that into a consistent nomenclature and control plane that gives you a basis to have an operating model. This is a, this only gets exposed until you start having 2050 102 hundred clusters out there. And everybody goes, how do I put my arms around this? So it's very important that that, that's just one piece of it. But operating model, operating model, operating model, I keep going back to this every time. There's a bunch of people here can spin up manage clusters all day long and some of them doing better than others, but unless you surround it and you surround it with the stuff that he's talking about is a consistent data layer, persistent and a consistent management system of all these people's behaviors, you're going to get just an unbelievable out of control platform. >>Yup. Kumar, I'd love your viewpoint as to just the overall maturity of this ecosystem and where does HPE see their role as to, you know, we talked about, you know, data and you know, everything that's changing. I heard a lot in the keynote this morning about, >>uh, some of the progress that's being made, but I'd love your viewpoint there. HP is a legend in the Valley as you know. I mean, they've done every, we, all engineering calculator starts with HV calculator. HP recognize they missed a couple of transitions in the industry. And I think there's a new leadership with, uh, with our, with the Robert and me and other other key leaders recognizes this is a great opportunity for us. We see this window to help the customers. Make the modern digitalization transition the applications, taking the monolithic applications, doing microservices. You can. In fact, Robert and I was talking to a bank and they told us they have 6,000 applications built so far. They have micro service, four of them and, and, and we have actually what, what, what we believe with this application is you can actually run your monolithic applications in a container platform while you are figuring it right. So what we see is helping the customers make the digital transition and making sure that they have, they make, they go down this journey. That's what we see. Kumar, Robert, thank you so much for the updates. Congratulations on the launch. I look forward to seeing your presence. Thanks for having and cube. I allow Q. yeah. Thanks Jeff. Again, look for next time. Okay. All right. Bye. Thanks so much for John Troyer. I'm Stu Miniman. Lots more in our three days wall to wall coverage here at cube colon cloud native con 2019 thanks for watching. Fuck you..

>>live from Atlanta, Georgia. It's the Q covering answerable best 2019. Brought to you by Red hat. >>Hey, welcome back to the Cuban Live coverage in simple fest. Two days of coverage. Day one, wrapping up. I'm John forwards. Accused Too many men. My guest co host today, our next two guests at his van. Van Velzen. Okay, welcome to the Cube. You're an engineer at I n G Bank and Robert de Bock, product owner, engineer I n g. Bank. Hey, guys, Thanks for coming on. Thank you. Have the practitioner on. Well, first of all, we have a lot of great feedback from the practitioners here. And also people in deploying answerable and other other cool Dev ops Tools on automation is at the top of the list. Yes, More efficient. Getting things done. Focus. You got satisfaction in job because things go awaiting time savings. I'm saving security drives a conversation and re skilling opportunities. Love. These are cutting edge. Things you got to do is take a minute to explain what you guys do. What a night. What a night. Angie bank. >>Yeah. I work in a team that provides redhead images for other teams. in 90 to consume to use two insane she ate way. Also live from playbooks, amendable code and rolls to manage those things. And he's very scattered, which sort of decentralized, which is a good thing. In my opinion, it's ready for scaling. In that case, I used to work with Dennis are lots in the tower team, so take it away. >>Okay, so I still work at the answer, built our squad What we do, it's ah, We make sure that the instable tower service keeps running 24 7 and we also ensure that we, uh, provide updates next to all this. We also have unanswerable community where we basically support our end users, which are their love. So, uh, from some numbers, I heard we have 1200 applications teams that are using our service. Um, and they all have, like, answerable playbook, sensible rolls, questions, difficulties with, uh, with anything. And we're basically there to support them as well. >>So 1200 teams are using answerable, Yes, inside the bank. Yes. Yeah, like >>it's set up very decentralized. And I think what I hear from instable fest that is not very common. I still think it's very good thing to do. We try to basically give these teams all the tools they need to do their stuff on. What I hear hear mostly is that there's essential team off administrators pushing the buttons for them. Towers. Great answer was great in that case, I think, for our case is really it's a perfect fit. >>E guess help Explain. Is this do you provide? You know, he said it's not centralized, but is this you know, here's best practices here. Some play boat out. How do you end? You support them? Because they're a little bit those relationships. >>Okay. Okay. Um so what we do is we basically all the rules and get ah ah, good lap. So it's an own premise. Get environment. You can search in this. Get for rules. Uh, not like all rules are easily to be found when searching for them. So that's why there are these communities to share what you have made. Um, >>plus these teams, they can themselves pick and choose. Some will try to rewrite everything That's fine. Others can can benefit from existing coat, so it's just a good trick. Thio enable these team to participate on it really different. Some people make it all themselves another >>next to this. So we basically have these 12 on the teams do their own thing. But next to this, we also have a self service portal where they can choose, like from, uh, generic finks like us. But your machine at new disc. So New capacity Cp use memory. That's all being done through a portal s so they don't need to do anything on their own for this they can, but most of them choose the easy way off using this portal. This portal basically doesn't a vehicle to instable tower, which executes a sensible playbook and some other stuffs. Maybe some AP eyes. And this is one of the things you guys create A manage these books. So, um >>and if you go back in time so the alternative way, which we happily got rid off, is to do it ourselves. I think it was before we we work together. Way had batch weekends, for example, and it >>was no very different. No life. Oh, that's working on weekends, >>weekends and, for example, he used to patch machine some 10,000 or so, and we were not aware what was important. What? Not so you you'd stop the whole pitch. Oh, this machine has a problem. Let's stop everything in focus and that's >>not important. Was like a complete order. >>And the other way around Also this machine. I guess it's not that important. Let's just >>continue this >>Sunday morning. Oh, my God. Everything's broken. >>Can you give us a little flavor of kind of the spectrum of solutions that you leverage answerable on >>tap? Yeah. We, uh I think what we see Moses for Lennox machines, eso fetching is a big one. We got a second operation, so there's a few of them. The deployment also depends on and small. So if you order a new machine, answer was involved somewhere to do to make it happen on network on board and the Windows teams are very interested. I'm not sure if we notice on board yet. To >>be honest, I know we did some book in the boss so a couple of months ago, using wind around when you needed set on policies there, But you can see that the networking teams were getting more momentum. Uh, five. There's some suffer suffer to find switches Bob. I don't know. The, uh Never mind the name, but ah, you can see some momentum in the in the networking. Uh, it's not Morgan departments >>configuration network networking with the activists. So that's where the action is in the >>network. Um, there were some cool talks also here on five workshops. So you can see there is, um, that there is some attention on these modules and integrations as well. >>What's your guy's goal here for the show? What brought you here? I'll see Big user. >>Yeah. So what do you think was like sharing our own thing? We did. They talk this morning. Ah, regarding and programming A really cool we wanted to share. It is this behavioral thing, and and >>we'll talk about take a minute and programming. >>So, um, basically, it's, ah programming with the whole team and making sure that you get something done with all the knowledge in the team. So you don't have to align off the words or if some other if you're Kulik says from basically session, you can do better using this staying. It's all, um it's It's all done during the decision >>as basically a good way to get a team up to speed. So in a team that's probably a few few people that are very quick and understand the concept and few starters or so So >>you guys decentralized, which makes sense for scale. I get that. So this sounds like you can operate decentralized, but where danceable. You can still have that common a book Switch >>teams, for example. So it used to be very specific. H team would have their own type of coat. Now that more answers used people can switch a little easier to to another product of surface because the languages have lied, shared, steal it, steal. It's quite >>well happy with this, right? I am. I really, really have to work on the weekend. That's good. I think >>the good thing is that you have one generic way of working. So his playbook is readable by all engineers. And if you want to learn this thing, you just do the inevitable course. So you know what this thing is? A mosque and roll, and it's all like >>way. We do see horrible >>koto. Come on, don't throw your college under the bus. But here's the international tough question can see is what we have been here. I want you guys to test this. We hear that there's a lot of time savings involved. Yes, with answer. True or false. That's true order of magnitude. What? What kind of saving way talking about? I >>think it depends on the thing because we saw a huge I don't know, except numbers. But this this os patching that Really? Really Uh, >>yes. Now, especially waas. Two people working a full time basically collecting, who needs to do what? The win. And then for a weekend, 10 15 people or so. So, uh, that's reduced now to sort of nothing. Yes, some maintenance to that playbook and roll. But I mean, yeah, it's difficult to express what message? So >>no one's getting phone call? Hey, come in on the weekend. So 15 people on the weekend jam and then to Fulton will just managing it all Go away. >>Yeah, not needed, but not needed. But they basically they can do something else, so those people are still there. But now they're not doing Os patching and doing all the excel sheets and keeping order off. The systems are important, and this shall be the first, and then they because way are basically doing the thing they know better. This application team knows their dependency, so they know they. But first I need to patch the database machine and then there during the front end or Andi. It's difficult to do this so they do it themselves. >>That's Dev Ops. That's that's the way it's supposed to be, right? >>So you've matured this thes deployments over time. As you look back, What key learnings do you have that maybe you'd recommend to your peers toe? You know how things could run a little bit smoother >>next time, a good amount of time. So they're stools. That's not the problem, So answer is great, but there's others to their great Give it time to sink in with the people. So you start something and you have to have a pretty strong team to do the long the long stretch with it and give it some time, maybe a year or so before everyone's on board it. In our case, in the beginning, we spend lots of time on this community model where we basically organized small meet ups or get together, too, show things or to hear problems and try to express them. That really helped a lot. And by now it's starting to get normal, more normal. So all the teams do sensible, basically. And problem starts slowly disappearing. Also. So So >>one of the things, um, that will be better. Probably in our scenario. Housekeeping metrics. So what are the improvements over time? I don't know how to measure this. No, no, no aspect. But it will be better if you had, like, better numbers like we did hair Very good. Or this is something like, what did the community thing bring way indirectly what the results are Because the engineers are doing things really, really things. They're really patching the replication. And they're really, um, restarting their own machines, for example, when there is something wrong. Whatever. Um, but our days related to our community thing or all that's really related to Sensible Tower >>last. I think we we are very technical focus. So So we like it as a nerd, so to say, to do things but what the business value is, for example, I'm not so interested or less interested so way typically, like the technology, so it could be good to have some someone onboard and your team that says, Yeah, but this is the problem. It's crossed. This amount of money and that solved now are improved. >>Well, they assume the applications are doing a good job. So you guys helped those guys out. They get to do their own thing. They do the heavy lifting. They're doing the coding anyway for those guys that were coming in managing full time on the 15 or so on the weekend. What are they doing now? >>Most are spread across. All the application teams go back. But the other side there is now it's our team that was not there s. So that's the price you have to pay. And that's a serious team. I mean, it's far six people now 86 people and 100 machines or so. So it is a serious amount of time, but it makes it at least much more constant. So people are not surprised by machines being patched, and Monday they come back into the half broken or so. So it's a lot more control now, so I don't know if you can express it in price, but at least it's more stable >>more consistent. >>Well, one of the things that we hear here and I want to get your thoughts as we wrap up is as you go forward, you got answerable 1200 teams using it. You got a lot of collaboration. The work cultures change. Sounds like a shower. Team steps service everything else. So some scale building out what's next? Because as it becomes a platform. Okay, you have to enable something. There has value there. Okay, technical nerd value and then business value >>scaling, uh, because we continuously see this thing growing like more application teams are adapting answerable, invincible tower. So, um, right now we have, like, a cluster. We have different clusters running. Go into much detail, but we can see that the load is getting higher and higher, so we need to skill. Um, and this is sort of difficult, but red. That is really supporting in this because they're going to change some things at the application level two to allow scaling even better. Um, >>plus, also, for most teams, they're starting their configuration. Everything is coat process. They're not there yet. As soon as they discover the power of it, I'm sure that's being used a lot. A lot more. And plus, there's other countries that are going to be connected. So you have a lot of work >>because your engineering doing some getting down and dirty with the code, automating everything. >>Yeah. Yeah. So, um, what else do we >>Oh, what's the coolest thing you've done that you've automated? >>Uh >>uh, Pick your favorite. >>So but the child during Encircle Tower and with answerable, um, let me think about this. >>I I really like the patching that saved us so much work. And, uh, I think also one of the next goes to make much more simpler. So we as a company, we're complex and the people also like complexity. That's wrong. We should change >>that. Patching up our >>offense, Melissa Simplicity. So we should really use that. >>You don't want any open holes in the network housely and assistance >>about your previous question. Like I have sort of a finger and all these small things. So it's sort of what I did. It's more like an A team thing. We created the OS patch playbooks, the configure stuff, the second day offs. So we did this as a team >>like sports but the playbooks together run the play. Some defense on security >>and programming. So you're doing >>this as a team, which is very cool. Has a scoreboard look good? Winning? >>Yeah, Yeah, yeah, yeah. We're looking at the graphite. Uh, it's girl. >>Final question. How you enjoying the show here? Having a good time? What's the vibe here? What's it like here? Share for the people who aren't here. What's going on? What's the vibe with >>a conversation? It's great. We went to some sessions yesterday really technical stuff with developers. And this was really amazing because you heard details that that are not in the India in the talks today and tomorrow. Um, yeah, it's great. It's great community. It's just I really I really enjoy it because you can. It's You can have, like one on one conversations go into depth. I was showing something I created, and this guy's we'll hold. This is really great in the It's cool. It's just if you it's really great. It's really >>cool. Really? Yeah, for me also, it feels like coming home, So I know these people and I think the first day, the collaboration day, what's it called and I'm not sure you community, that's it's great because it's been a bit rough and unpolished in today's more polished and more presented and prepared to, uh, both are great. >>Good. Give the hard feedback. >>Yeah, you meet all the people. So, for example, I used instable a lot, and then I'm getting up. I see all these names. Like, who would that be there walking here and shake hands like, Oh, that's >>why guys like your code looking good. Yeah. Looks good. A contributor. Summit contributed. Okay. Sorry. After it for >>anyone that goes to visit that day, too. That's just great. >>It's great to see people face to face that, you know, online for their digital identity or the code >>you can You can't complain about stuff out on. Do you know that you don't hurt them or something with just commenting on get like after this issue and this issue and this issue. Then you can see them in person. And then you >>him a high five assault, you know? Hey, >>it's really very cool. >>Guys. Great conversations were coming on cue. Thanks, Dennis. Appreciate Robert. Thanks for coming on. Skew coverage here Day one of two days of live coverage here inside the Cube here in Atlanta, Georgia for Ansel Fest is the cute I'm John 1st 2 minute. Thanks for watching.