(upbeat music) >> Hello and welcome back to theCUBE. We're here in Palo Alto, California. I'm your host, John Furrier with a special guest here in the studio. As part of our Cloud Native SecurityCon Coverage we had an opportunity to bring in Jon Turow who is the partner at Madrona Venture Partners formerly with AWS and to talk about machine learning, foundational models, and how the future of AI is going to be impacted by some of the innovation around what's going on in the industry. ChatGPT has taken the world by storm. A million downloads, fastest to the million downloads there. Before some were saying it's just a gimmick. Others saying it's a game changer. Jon's here to break it down, and great to have you on. Thanks for coming in. >> Thanks John. Glad to be here. >> Thanks for coming on. So first of all, I'm glad you're here. First of all, because two things. One, you were formerly with AWS, got a lot of experience running projects at AWS. Now a partner at Madrona, a great firm doing great deals, and they had this future at modern application kind of thesis. Now you are putting out some content recently around foundational models. You're deep into computer vision. You were the IoT general manager at AWS among other things, Greengrass. So you know a lot about data. You know a lot about some of this automation, some of the edge stuff. You've been in the middle of all these kind of areas that now seem to be the next wave coming. So I wanted to ask you what your thoughts are of how the machine learning and this new automation wave is coming in, this AI tools are coming out. Is it a platform? Is it going to be smarter? What feeds AI? What's your take on this whole foundational big movement into AI? What's your general reaction to all this? >> So, thanks, Jon, again for having me here. Really excited to talk about these things. AI has been coming for a long time. It's been kind of the next big thing. Always just over the horizon for quite some time. And we've seen really compelling applications in generations before and until now. Amazon and AWS have introduced a lot of them. My firm, Madrona Venture Group has invested in some of those early players as well. But what we're seeing now is something categorically different. That's really exciting and feels like a durable change. And I can try and explain what that is. We have these really large models that are useful in a general way. They can be applied to a lot of different tasks beyond the specific task that the designers envisioned. That makes them more flexible, that makes them more useful for building applications than what we've seen before. And so that, we can talk about the depths of it, but in a nutshell, that's why I think people are really excited. >> And I think one of the things that you wrote about that jumped out at me is that this seems to be this moment where there's been a multiple decades of nerds and computer scientists and programmers and data thinkers around waiting for AI to blossom. And it's like they're scratching that itch. Every year is going to be, and it's like the bottleneck's always been compute power. And we've seen other areas, genome sequencing, all kinds of high computation things where required high forms computing. But now there's no real bottleneck to compute. You got cloud. And so you're starting to see the emergence of a massive acceleration of where AI's been and where it needs to be going. Now, it's almost like it's got a reboot. It's almost a renaissance in the AI community with a whole nother macro environmental things happening. Cloud, younger generation, applications proliferate from mobile to cloud native. It's the perfect storm for this kind of moment to switch over. Am I overreading that? Is that right? >> You're right. And it's been cooking for a cycle or two. And let me try and explain why that is. We have cloud and AWS launch in whatever it was, 2006, and offered more compute to more people than really was possible before. Initially that was about taking existing applications and running them more easily in a bigger scale. But in that period of time what's also become possible is new kinds of computation that really weren't practical or even possible without that vast amount of compute. And so one result that came of that is something called the transformer AI model architecture. And Google came out with that, published a paper in 2017. And what that says is, with a transformer model you can actually train an arbitrarily large amount of data into a model, and see what happens. That's what Google demonstrated in 2017. The what happens is the really exciting part because when you do that, what you start to see, when models exceed a certain size that we had never really seen before all of a sudden they get what we call emerging capabilities of complex reasoning and reasoning outside a domain and reasoning with data. The kinds of things that people describe as spooky when they play with something like ChatGPT. That's the underlying term. We don't as an industry quite know why it happens or how it happens, but we can measure that it does. So cloud enables new kinds of math and science. New kinds of math and science allow new kinds of experimentation. And that experimentation has led to this new generation of models. >> So one of the debates we had on theCUBE at our Supercloud event last month was, what's the barriers to entry for say OpenAI, for instance? Obviously, I weighed in aggressively and said, "The barriers for getting into cloud are high because all the CapEx." And Howie Xu formerly VMware, now at ZScaler, he's an AI machine learning guy. He was like, "Well, you can spend $100 million and replicate it." I saw a quote that set up for 180,000 I can get this other package. What's the barriers to entry? Is ChatGPT or OpenAI, does it have sustainability? Is it easy to get into? What is the market like for AI? I mean, because a lot of entrepreneurs are jumping in. I mean, I just read a story today. San Francisco's got more inbound migration because of the AI action happening, Seattle's booming, Boston with MIT's been working on neural networks for generations. That's what we've found the answer. Get off the neural network, Boston jump on the AI bus. So there's total excitement for this. People are enthusiastic around this area. >> You can think of an iPhone versus Android tension that's happening today. In the iPhone world, there are proprietary models from OpenAI who you might consider as the leader. There's Cohere, there's AI21, there's Anthropic, Google's going to have their own, and a few others. These are proprietary models that developers can build on top of, get started really quickly. They're measured to have the highest accuracy and the highest performance today. That's the proprietary side. On the other side, there is an open source part of the world. These are a proliferation of model architectures that developers and practitioners can take off the shelf and train themselves. Typically found in Hugging face. What people seem to think is that the accuracy and performance of the open source models is something like 18 to 20 months behind the accuracy and performance of the proprietary models. But on the other hand, there's infinite flexibility for teams that are capable enough. So you're going to see teams choose sides based on whether they want speed or flexibility. >> That's interesting. And that brings up a point I was talking to a startup and the debate was, do you abstract away from the hardware and be software-defined or software-led on the AI side and let the hardware side just extremely accelerate on its own, 'cause it's flywheel? So again, back to proprietary, that's with hardware kind of bundled in, bolted on. Is it accelerator or is it bolted on or is it part of it? So to me, I think that the big struggle in understanding this is that which one will end up being right. I mean, is it a beta max versus VHS kind of thing going on? Or iPhone, Android, I mean iPhone makes a lot of sense, but if you're Apple, but is there an Apple moment in the machine learning? >> In proprietary models, here does seem to be a jump ball. That there's going to be a virtuous flywheel that emerges that, for example, all these excitement about ChatGPT. What's really exciting about it is it's really easy to use. The technology isn't so different from what we've seen before even from OpenAI. You mentioned a million users in a short period of time, all providing training data for OpenAI that makes their underlying models, their next generation even better. So it's not unreasonable to guess that there's going to be power laws that emerge on the proprietary side. What I think history has shown is that iPhone, Android, Windows, Linux, there seems to be gravity towards this yin and yang. And my guess, and what other people seem to think is going to be the case is that we're going to continue to see these two poles of AI. >> So let's get into the relationship with data because I've been emerging myself with ChatGPT, fascinated by the ease of use, yes, but also the fidelity of how you query it. And I felt like when I was doing writing SQL back in the eighties and nineties where SQL was emerging. You had to be really a guru at the SQL to get the answers you wanted. It seems like the querying into ChatGPT is a good thing if you know how to talk to it. Labeling whether your input is and it does a great job if you feed it right. If you ask a generic questions like Google. It's like a Google search. It gives you great format, sounds credible, but the facts are kind of wrong. >> That's right. >> That's where general consensus is coming on. So what does that mean? That means people are on one hand saying, "Ah, it's bullshit 'cause it's wrong." But I look at, I'm like, "Wow, that's that's compelling." 'Cause if you feed it the right data, so now we're in the data modeling here, so the role of data's going to be critical. Is there a data operating system emerging? Because if this thing continues to go the way it's going you can almost imagine as you would look at companies to invest in. Who's going to be right on this? What's going to scale? What's sustainable? What could build a durable company? It might not look what like what people think it is. I mean, I remember when Google started everyone thought it was the worst search engine because it wasn't a portal. But it was the best organic search on the planet became successful. So I'm trying to figure out like, okay, how do you read this? How do you read the tea leaves? >> Yeah. There are a few different ways that companies can differentiate themselves. Teams with galactic capabilities to take an open source model and then change the architecture and retrain and go down to the silicon. They can do things that might not have been possible for other teams to do. There's a company that that we're proud to be investors in called RunwayML that provides video accelerated, sorry, AI accelerated video editing capabilities. They were used in everything, everywhere all at once and some others. In order to build RunwayML, they needed a vision of what the future was going to look like and they needed to make deep contributions to the science that was going to enable all that. But not every team has those capabilities, maybe nor should they. So as far as how other teams are going to differentiate there's a couple of things that they can do. One is called prompt engineering where they shape on behalf of their own users exactly how the prompt to get fed to the underlying model. It's not clear whether that's going to be a durable problem or whether like Google, we consumers are going to start to get more intuitive about this. That's one. The second is what's called information retrieval. How can I get information about the world outside, information from a database or a data store or whatever service into these models so they can reason about them. And the third is, this is going to sound funny, but attribution. Just like you would do in a news report or an academic paper. If you can state where your facts are coming from, the downstream consumer or the human being who has to use that information actually is going to be able to make better sense of it and rely better on it. So that's prompt engineering, that's retrieval, and that's attribution. >> So that brings me to my next point I want to dig in on is the foundational model stack that you published. And I'll start by saying that with ChatGPT, if you take out the naysayers who are like throwing cold water on it about being a gimmick or whatever, and then you got the other side, I would call the alpha nerds who are like they can see, "Wow, this is amazing." This is truly NextGen. This isn't yesterday's chatbot nonsense. They're like, they're all over it. It's that everybody's using it right now in every vertical. I heard someone using it for security logs. I heard a data center, hardware vendor using it for pushing out appsec review updates. I mean, I've heard corner cases. We're using it for theCUBE to put our metadata in. So there's a horizontal use case of value. So to me that tells me it's a market there. So when you have horizontal scalability in the use case you're going to have a stack. So you publish this stack and it has an application at the top, applications like Jasper out there. You're seeing ChatGPT. But you go after the bottom, you got silicon, cloud, foundational model operations, the foundational models themselves, tooling, sources, actions. Where'd you get this from? How'd you put this together? Did you just work backwards from the startups or was there a thesis behind this? Could you share your thoughts behind this foundational model stack? >> Sure. Well, I'm a recovering product manager and my job that I think about as a product manager is who is my customer and what problem he wants to solve. And so to put myself in the mindset of an application developer and a founder who is actually my customer as a partner at Madrona, I think about what technology and resources does she need to be really powerful, to be able to take a brilliant idea, and actually bring that to life. And if you spend time with that community, which I do and I've met with hundreds of founders now who are trying to do exactly this, you can see that the stack is emerging. In fact, we first drew it in, not in January 2023, but October 2022. And if you look at the difference between the October '22 and January '23 stacks you're going to see that holes in the stack that we identified in October around tooling and around foundation model ops and the rest are organically starting to get filled because of how much demand from the developers at the top of the stack. >> If you look at the young generation coming out and even some of the analysts, I was just reading an analyst report on who's following the whole data stacks area, Databricks, Snowflake, there's variety of analytics, realtime AI, data's hot. There's a lot of engineers coming out that were either data scientists or I would call data platform engineering folks are becoming very key resources in this area. What's the skillset emerging and what's the mindset of that entrepreneur that sees the opportunity? How does these startups come together? Is there a pattern in the formation? Is there a pattern in the competency or proficiency around the talent behind these ventures? >> Yes. I would say there's two groups. The first is a very distinct pattern, John. For the past 10 years or a little more we've seen a pattern of democratization of ML where more and more people had access to this powerful science and technology. And since about 2017, with the rise of the transformer architecture in these foundation models, that pattern has reversed. All of a sudden what has become broader access is now shrinking to a pretty small group of scientists who can actually train and manipulate the architectures of these models themselves. So that's one. And what that means is the teams who can do that have huge ability to make the future happen in ways that other people don't have access to yet. That's one. The second is there is a broader population of people who by definition has even more collective imagination 'cause there's even more people who sees what should be possible and can use things like the proprietary models, like the OpenAI models that are available off the shelf and try to create something that maybe nobody has seen before. And when they do that, Jasper AI is a great example of that. Jasper AI is a company that creates marketing copy automatically with generative models such as GPT-3. They do that and it's really useful and it's almost fun for a marketer to use that. But there are going to be questions of how they can defend that against someone else who has access to the same technology. It's a different population of founders who has to find other sources of differentiation without being able to go all the way down to the the silicon and the science. >> Yeah, and it's going to be also opportunity recognition is one thing. Building a viable venture product market fit. You got competition. And so when things get crowded you got to have some differentiation. I think that's going to be the key. And that's where I was trying to figure out and I think data with scale I think are big ones. Where's the vulnerability in the stack in terms of gaps? Where's the white space? I shouldn't say vulnerability. I should say where's the opportunity, where's the white space in the stack that you see opportunities for entrepreneurs to attack? >> I would say there's two. At the application level, there is almost infinite opportunity, John, because almost every kind of application is about to be reimagined or disrupted with a new generation that takes advantage of this really powerful new technology. And so if there is a kind of application in almost any vertical, it's hard to rule something out. Almost any vertical that a founder wishes she had created the original app in, well, now it's her time. So that's one. The second is, if you look at the tooling layer that we discussed, tooling is a really powerful way that you can provide more flexibility to app developers to get more differentiation for themselves. And the tooling layer is still forming. This is the interface between the models themselves and the applications. Tools that help bring in data, as you mentioned, connect to external actions, bring context across multiple calls, chain together multiple models. These kinds of things, there's huge opportunity there. >> Well, Jon, I really appreciate you coming in. I had a couple more questions, but I will take a minute to read some of your bios for the audience and we'll get into, I won't embarrass you, but I want to set the context. You said you were recovering product manager, 10 plus years at AWS. Obviously, recovering from AWS, which is a whole nother dimension of recovering. In all seriousness, I talked to Andy Jassy around that time and Dr. Matt Wood and it was about that time when AI was just getting on the radar when they started. So you guys started seeing the wave coming in early on. So I remember at that time as Amazon was starting to grow significantly and even just stock price and overall growth. From a tech perspective, it was pretty clear what was coming, so you were there when this tsunami hit. >> Jon: That's right. >> And you had a front row seat building tech, you were led the product teams for Computer Vision AI, Textract, AI intelligence for document processing, recognition for image and video analysis. You wrote the business product plan for AWS IoT and Greengrass, which we've covered a lot in theCUBE, which extends out to the whole edge thing. So you know a lot about AI/ML, edge computing, IOT, messaging, which I call the law of small numbers that scale become big. This is a big new thing. So as a former AWS leader who's been there and at Madrona, what's your investment thesis as you start to peruse the landscape and talk to entrepreneurs as you got the stack? What's the big picture? What are you looking for? What's the thesis? How do you see this next five years emerging? >> Five years is a really long time given some of this science is only six months out. I'll start with some, no pun intended, some foundational things. And we can talk about some implications of the technology. The basics are the same as they've always been. We want, what I like to call customers with their hair on fire. So they have problems, so urgent they'll buy half a product. The joke is if your hair is on fire you might want a bucket of cold water, but you'll take a tennis racket and you'll beat yourself over the head to put the fire out. You want those customers 'cause they'll meet you more than halfway. And when you find them, you can obsess about them and you can get better every day. So we want customers with their hair on fire. We want founders who have empathy for those customers, understand what is going to be required to serve them really well, and have what I like to call founder-market fit to be able to build the products that those customers are going to need. >> And because that's a good strategy from an emerging, not yet fully baked out requirements definition. >> Jon: That's right. >> Enough where directionally they're leaning in, more than in, they're part of the product development process. >> That's right. And when you're doing early stage development, which is where I personally spend a lot of my time at the seed and A and a little bit beyond that stage often that's going to be what you have to go on because the future is going to be so complex that you can't see the curves beyond it. But if you have customers with their hair on fire and talented founders who have the capability to serve those customers, that's got me interested. >> So if I'm an entrepreneur, I walk in and say, "I have customers that have their hair on fire." What kind of checks do you write? What's the kind of the average you're seeing for seed and series? Probably seed, seed rounds and series As. >> It can depend. I have seen seed rounds of double digit million dollars. I have seen seed rounds much smaller than that. It really depends on what is going to be the right thing for these founders to prove out the hypothesis that they're testing that says, "Look, we have this customer with her hair on fire. We think we can build at least a tennis racket that she can use to start beating herself over the head and put the fire out. And then we're going to have something really interesting that we can scale up from there and we can make the future happen. >> So it sounds like your advice to founders is go out and find some customers, show them a product, don't obsess over full completion, get some sort of vibe on fit and go from there. >> Yeah, and I think by the time founders come to me they may not have a product, they may not have a deck, but if they have a customer with her hair on fire, then I'm really interested. >> Well, I always love the professional services angle on these markets. You go in and you get some business and you understand it. Walk away if you don't like it, but you see the hair on fire, then you go in product mode. >> That's right. >> All Right, Jon, thank you for coming on theCUBE. Really appreciate you stopping by the studio and good luck on your investments. Great to see you. >> You too. >> Thanks for coming on. >> Thank you, Jon. >> CUBE coverage here at Palo Alto. I'm John Furrier, your host. More coverage with CUBE Conversations after this break. (upbeat music)

(upbeat music) >> Hello everyone, welcome to this CUBE Showcase, accelerating business transformation with VMware Cloud on AWS. It's a solution innovation conversation with two great guests, Fred Wurden, VP of Commercial Services at AWS and Narayan Bharadwaj, who's the VP and General Manager of Cloud Solutions at VMware. Gentlemen, thanks for joining me on the showcase. >> Great to be here. >> Great. Thanks for having us on. It's a great topic. >> We've been covering this VMware cloud on AWS since the launch going back and it's been amazing to watch the evolution from people saying, Oh, it's the worst thing I've ever seen. What's this mean? And the press were not really on board with the vision, but as it played out as you guys had announced together, it did work out great for VMware. It did work out great for AWS and it continues two years later and I want to just get an update from you guys on where you guys see this has been going. I'll see multiple years. Where is the evolution of the solution as we are right now coming off VMware explorer just recently and going in to re:Invent, which is only a couple weeks away Feels like tomorrow. But as we prepare, a lot going on. Where are we with the evolution of the solution? >> I mean, first thing I want to say is October 2016 was a seminal moment in the history of IT. When Pat Gelsinger and Andy Jassy came together to announce this. And I think John, you were there at the time I was there. It was a great, great moment. We launched the solution in 2017 year after that at VMworld, back when we called it VMworld. I think we have gone from strength to strength. One of the things that has really mattered to us is we've learned from AWS also in the processes, this notion of working backwards. So we really, really focused on customer feedback as we built a service offering now five years old. Pretty remarkable journey. In the first years we tried to get across all the regions, that was a big focus because there was so much demand for it. In the second year, we started going really on enterprise great features. We invented this pretty awesome feature called Stretched Clusters, where you could stretch a vSphere cluster using vSAN and NSX-T across to AZs in the same region. Pretty phenomenal four nines of availability that applications started to get with that particular feature. And we kept moving forward, all kinds of integration with AWS Direct Connect, Transit Gateways with our own advanced networking capabilities. Along the way, Disaster Recovery, we punched out two new services just focused on that. And then more recently we launched our Outposts partnership. We were up on stage at re:Invent, again, with Pat and Andy announcing AWS Outposts and the VMware flavor of that, VMware Cloud and AWS Outposts. I think it's been significant growth in our federal sector as well with our federal and high certification more recently. So all in all, we are super excited. We're five years old. The customer momentum is really, really strong and we are scaling the service massively across all geos and industries. >> That's great, great update. And I think one of the things that you mentioned was how the advantages you guys got from that relationship. And this has been the theme for AWS, man, since I can remember from day one, Fred. You guys do the heavy lifting as you always say for the customers. Here, VMware comes on board. Takes advantage of the AWS and just doesn't miss a beat. Continues to move their workloads that everyone's using, vSphere, and these are big workloads on AWS. What's the AWS perspective on this? How do you see it? >> Yeah, it's pretty fascinating to watch how fast customers can actually transform and move when you take the skill set that they're familiar with and the advanced capabilities that they've been using on-prem and then overlay it on top of the AWS infrastructure that's evolving quickly and building out new hardware and new instances we'll talk about. But that combined experience between both of us on a jointly engineered solution to bring the best security and the best features that really matter for those workloads drive a lot of efficiency and speed for the customers. So it's been well received and the partnership is stronger than ever from an engineering standpoint, from a business standpoint. And obviously it's been very interesting to look at just how we stay day one in terms of looking at new features and work and responding to what customers want. So pretty excited about just seeing the transformation and the speed that which customers can move to while at VMC. >> That's a great value proposition. We've been talking about that in context to anyone building on top of the cloud. They can have their own supercloud, as we call it, if you take advantage of all the CapEx and investment Amazon's made and AWS has made and continues to make in performance IaaS and PaaS, all great stuff. I have to ask you guys both as you guys see this going to the next level, what are some of the differentiations you see around the service compared to other options in the market? What makes it different? What's the combination? You mentioned jointly engineered. What are some of the key differentiators of the service compared to others? >> Yeah. I think one of the key things Fred talked about is this jointly engineered notion. Right from day one we were the early adopters of the AWS Nitro platform. The reinvention of EC2 back five years ago. And so we have been having a very, very strong engineering partnership at that level. I think from a VMware customer standpoint, you get the full software-defined data center, compute storage networking on EC2, bare metal across all regions. You can scale that elastically up and down. It's pretty phenomenal just having that consistency globally on AWS EC2 global regions. Now the other thing that's a real differentiator for us, what customers tell us about is this whole notion of a managed service. And this was somewhat new to VMware. But we took away the pain of this undifferentiated heavy lifting where customers had to provision rack stack hardware, configure the software on top, and then upgrade the software and the security patches on top. So we took away all of that pain as customers transitioned to VMware cloud in AWS. In fact, my favorite story from last year when we were all going through the Log4j debacle. Industry was just going through that. Favorite proof point from customers was before they could even race this issue to us, we sent them a notification saying, we already patched all of your systems, no action from you. The customers were super thrilled. I mean, these are large banks. Many other customers around the world were super thrilled they had to take no action, but a pretty incredible industry challenge that we were all facing. >> Narayan, that's a great point. The whole managed service piece brings up the security. You kind of teasing at it, but there's always vulnerabilities that emerge when you are doing complex logic. And as you grow your solutions, there's more bits. Fred, we were commenting before we came on camera more bits than ever before and at the physics layer too, as well as the software. So you never know when there's going to be a zero-day vulnerability out there. It happens. We saw one with Fortinet this week. This came out of the woodwork. But moving fast on those patches, it's huge. This brings up the whole support angle. I wanted to ask you about how you guys are doing that as well, because to me, we see the value when we talk to customers on theCUBE about this. It was a real easy understanding of what the cloud means to them with VMware now with the AWS. But the question that comes up that we want to get more clarity on is how do you guys handle support together? >> Well, what's interesting about this is that it's done mutually. We have dedicated support teams on both sides that work together pretty seamlessly to make sure that whether there's a issue at any layer, including all the way up into the app layer, as you think about some of the other workloads like SAP, we'll go end-to-end and make sure that we support the customer regardless of where the particular issue might be for them. And on top of that, we look at where we're improving reliability in as a first order of principle between both companies. So from availability and reliability standpoint, it's top of mind and no matter where the particular item might land, we're going to go help the customer resolve that. It works really well. >> On the VMware side, what's been the feedback there? What are some of the updates? >> Yeah, I think, look, I mean, VMware owns and operates the service, but we work phenomenal backend relationship with AWS. Customers call VMware for the service or any issues. And then we have a awesome relationship with AWS on the backend for support issues or any hardware issues. The key management that we jointly do. All of the hard problems that customers don't have to worry about. I think on the front end, we also have a really good group of solution architects across the companies that help to really explain the solution, do complex things like cloud migration, which is much, much easier with the VMware Cloud in AWS. We're presenting that easy button to the public cloud in many ways. And so we have a whole technical audience across the two companies that are working with customers every single day. >> You had mentioned, I've got list here of some of the innovations. You mentioned the stretch clustering, getting the geos working, advanced network, Disaster Recovery, FedRAMP, public sector certifications, Outposts. All good, you guys are checking the boxes every year. You got a good accomplishments list there on the VMware AWS side here in this relationship. The question that I'm interested in is what's next? What recent innovations are you doing? Are you making investments in? What's on the list this year? What items will be next year? How do you see the new things, the list of accomplishments? People want to know what's next. They don't want to see stagnant growth here. They want to see more action as cloud continues to scale and modern applications cloud native. You're seeing more and more containers, more and more CI/CD pipelining with modern apps, put more pressure on the system. What's new? What's the new innovations? >> Absolutely. And I think as a five year old service offering, innovation is top of mind for us every single day. So just to call out a few recent innovations that we announced in San Francisco at VMware Explore. First of all, our new platform i4i.metal. It's isolate based. It's pretty awesome. It's the latest and greatest, all the speeds and feeds that we would expect from VMware and AWS at this point in our relationship. We announced two different storage options. This notion of working from customer feedback, allowing customers even more price reductions, really take off that storage and park it externally and separate that from compute. So two different storage offerings there. One is with AWS FSx with NetApp ONTAP, which brings in our NetApp partnership as well into the equation and really get that NetApp based really excited about this offering as well. And the second storage offering called VMware Cloud Flex Storage. VMware's own managed storage offering. Beyond that, we have done a lot of other innovations as well. I really wanted to talk about VMware Cloud Flex Compute where previously customers could only scale by hosts and a host is 36 to 48 cores, give or take. But with VMware Cloud Flex Compute, we are now allowing this notion of a resource defined compute model where customers can just get exactly the vCPU memory and storage that maps to the applications, however small they might be. So this notion of granularity is really a big innovation that we are launching in the market this year. And then last but not least, top of ransomware. Of course it's a hot topic in the industry. We are seeing many, many customers ask for this. We are happy to announce a new ransomware recovery with our VMware Cloud DR solution. A lot of innovation there and the way we are able to do machine learning and make sure the workloads that are covered from snapshots and backups are actually safe to use. So there's a lot of differentiation on that front as well. A lot of networking innovations with Project Northstar. Our ability to have layer four through layer seven, new SaaS services in that area as well. Keep in mind that the service already supports managed Kubernetes for containers. It's built in to the same clusters that have virtual machines. And so this notion of a single service with a great TCO for VMs and containers is sort at the heart of our (faintly speaking). >> The networking side certainly is a hot area to keep innovating on. Every year it's the same, same conversation, get better faster, networking more options there. The Flex Compute is interesting. If you don't mind me getting a quick clarification, could you explain the resource-defined versus hardware-defined? Because this is what we had saw at Explore coming out, that notion of resource-defined versus hardware-defined. What does that mean? >> Yeah, I mean I think we have been super successful in this hardware-defined notion. We we're scaling by the hardware unit that we present as software-defined data centers. And so that's been super successful. But customers wanted more, especially customers in different parts of the world wanted to start even smaller and grow even more incrementally. Lower the cost even more. And so this is the part where resource-defined starts to be very, very interesting as a way to think about, here's my bag of resources exactly based on what the customers request before fiber machines, five containers. It's size exactly for that. And then as utilization grows, we elastically behind the scenes, we're able to grow it through policies. So that's a whole different dimension. That's a whole different service offering that adds value and customers are comfortable. They can go from one to the other. They can go back to that host based model if they so choose to. And there's a jump off point across these two different economic models. >> It's cloud flexibility right there. I like the name. Fred, let's get into some of the examples of customers, if you don't mind, let's get into some of the, we have some time. I want to unpack a little bit of what's going on with the customer deployments. One of the things we've heard again on theCUBE is from customers is they like the clarity of the relationship, they love the cloud positioning of it. And then what happens is they lift and shift the workloads and it's like feels great. It's just like we're running VMware on AWS and then they start consuming higher level services. That adoption next level happens and because it's in the cloud. So can you guys take us through some recent examples of customer wins or deployments where they're using VMware cloud on AWS on getting started and then how do they progress once they're there? How does it evolve? Can you just walk us through a couple use cases? >> Sure. Well, there's a couple. One, it's pretty interesting that like you said, as there's more and more bits, you need better and better hardware and networking. And we're super excited about the i4 and the capabilities there in terms of doubling and or tripling what we're doing around lower variability on latency and just improving all the speeds. But what customers are doing with it, like the college in New Jersey, they're accelerating their deployment on onboarding over like 7,400 students over a six to eight month period. And they've really realized a ton of savings. But what's interesting is where and how they can actually grow onto additional native services too. So connectivity to any other services is available as they start to move and migrate into this. The options there obviously are tied to all the innovation that we have across any services, whether it's containerized and with what they're doing with Tanzu or with any other container and or services within AWS. So there's some pretty interesting scenarios where that data and or the processing, which is moved quickly with full compliance, whether it's in like healthcare or regulatory business is allowed to then consume and use things, for example, with Textract or any other really cool service that has monthly and quarterly innovations. So there's things that you just could not do before that are coming out and saving customers money and building innovative applications on top of their current app base in a rapid fashion. So pretty excited about it. There's a lot of examples. I think I probably don't have time to go into too many here. But that's actually the best part is listening to customers and seeing how many net new services and new applications are they actually building on top of this platform. >> Narayan, what's your perspective from the VMware side? 'Cause you guys have now a lot of headroom to offer customers with Amazon's higher level services and or whatever's homegrown where it's being rolled out 'cause you now have a lot of hybrid too. So what's your take on what's happening in with customers? >> I mean, it's been phenomenal. The customer adoption of this and banks and many other highly sensitive verticals are running production-grade applications, tier one applications on the service over the last five years. And so I have a couple of really good examples. S&P Global is one of my favorite examples. Large bank, they merge with IHS Markit, big conglomeration now. Both customers were using VMware Cloud and AWS in different ways. And with the use case, one of their use cases was how do I just respond to these global opportunities without having to invest in physical data centers? And then how do I migrate and consolidate all my data centers across the global, which there were many. And so one specific example for this company was how they migrated 1000 workloads to VMware Cloud and AWS in just six weeks. Pretty phenomenal if you think about everything that goes into a cloud migration process, people process technology. And the beauty of the technology going from VMware point A to VMware point B. The lowest cost, lowest risk approach to adopting VMware Cloud and AWS. So that's one of my favorite examples. There are many other examples across other verticals that we continue to see. The good thing is we are seeing rapid expansion across the globe, but constantly entering new markets with a limited number of regions and progressing our roadmap. >> It's great to see. I mean, the data center migrations go from months, many, many months to weeks. It's interesting to see some of those success stories. Congratulations. >> One of the other interesting fascinating benefits is the sustainability improvement in terms of being green. So the efficiency gains that we have both in current generation and new generation processors and everything that we're doing to make sure that when a customer can be elastic, they're also saving power, which is really critical in a lot of regions worldwide at this point in time. They're seeing those benefits. If you're running really inefficiently in your own data center, that is not a great use of power. So the actual calculators and the benefits to these workloads are pretty phenomenal just in being more green, which I like. We just all need to do our part there and this is a big part of it here. >> It's a huge point about the sustainability. Fred, I'm glad you called that out. The other one I would say is supply chain issue is another one. You see that constraints. I can't buy hardware. And the third one is really obvious, but no one really talks about it. It's security. I mean, I remember interviewing Steven Schmidt with that AWS and many years ago, this is like 2013 and at that time people were saying, the cloud's not secure. And he's like, listen, it's more secure in the cloud on-premise. And if you look at the security breaches, it's all about the on-premise data center vulnerabilities, not so much hardware. So there's a lot, the stay current on the isolation there is hard. So I think the security and supply chain, Fred, is another one. Do you agree? >> I absolutely agree. It's hard to manage supply chain nowadays. We put a lot of effort into that and I think we have a great ability to forecast and make sure that we can lean in and have the resources that are available and run them more efficiently. And then like you said on the security point, security is job one. It is the only P1. And if you think of how we build our infrastructure from Nitro all the way up and how we respond and work with our partners and our customers, there's nothing more important. >> And Narayan, your point earlier about the managed service patching and being on top of things is really going to get better. All right, final question. I really want to thank you for your time on this showcase. It's really been a great conversation. Fred, you had made a comment earlier. I want to end with a curve ball and put you eyes on the spot. We're talking about a new modern shift. We're seeing another inflection point. We've been documenting it. It's almost like cloud hitting another inflection point with application and open source growth significantly at the app layer. Continue to put a lot of pressure and innovation in the infrastructure side. So the question is for you guys each to answer is, what's the same and what's different in today's market? So it's like we want more of the same here, but also things have changed radically and better here. What's changed for the better and what's still the same thing hanging around that people are focused on? Can you share your perspective? >> I'll tackle it. Businesses are complex and they're often unique, that's the same. What's changed is how fast you can innovate. The ability to combine managed services and new innovative services and build new applications is so much faster today. Leveraging world class hardware that you don't have to worry about, that's elastic. You could not do that even five, 10 years ago to the degree you can today, especially with innovation. So innovation is accelerating at a rate that most people can't even comprehend and understand the set of services that are available to them. It's really fascinating to see what a one pizza team of engineers can go actually develop in a week. It is phenomenal. So super excited about this space and it's only going to continue to accelerate that. That's my take, Narayan. >> You got a lot of platform to compete on. With Amazon, you got a lot to build on. Narayan, your side. What's your answer to that question? >> I think we are seeing a lot of innovation with new applications that customers are constantly (faintly speaking). I think what we see is this whole notion of how do you go from desktop to production to the secure supply chain and how can we truly build on the agility that developers desire and build all the security and the pipelines to energize that production quickly and efficiently. I think we are seeing, we are at the very start of that sort of journey. Of course, we have invested in Kubernetes, the means to an end, but we're so much more beyond that's happening in industry and I think we're at the very, very beginning of this transformations, enterprise transformation that many of our customers are going through and we are inherently part of it. >> Well, gentlemen, I really appreciate that we're seeing the same thing. It's more the same here on solving these complexities with distractions, whether it's higher level services with large scale infrastructure. At your fingertips, infrastructure as code, infrastructure to be provisioned, serverless, all the good stuff happen and Fred with AWS on your side. And we're seeing customers resonate with this idea of being an operator again, being a cloud operator and developer. So the developer ops is kind of, DevOps is changing too. So all for the better. Thank you for spending the time and we're seeing again that traction with the VMware customer base and AWS getting along great together. So thanks for sharing your perspectives. >> We appreciate it. Thank you so much. >> Thank you John. >> This is theCUBE and AWS VMware showcase accelerating business transformation, VMware Cloud on AWS. Jointly engineered solution bringing innovation to the VMware customer base, going to the cloud and beyond. I'm John Furrier, your host. Thanks for watching. (gentle music)

(upbeat music) >> Hey everyone, welcome back to theCUBE's continuous coverage of AWS reinvent 2021, I'm Lisa Martin. This is day four for theCUBE. We have two live sets, I'm here with Dave Nicholson, Dave two live sets, 100 guests on theCUBE for AWS Re:Invent 2021. >> Not all at the same time. >> Not all-- That's a good, he brings up a good point, not all at the same time, we are pleased to welcome Gretchen Peri who's going to be sitting down and chatting with Dave with me next. She is from Slalom, at the US State Local and Education, SLED leader. We're going to be talking about Slalom and AWS digital innovation in the public sector. Gretchen, it's a pleasure to have you on the program. >> Thank you for having me. >> For the audience that might not be familiar with Slalom before we dig into AWS and SLED in particular, talk to us about Slalom and what it is that you guys do. >> I'd love to. So Slalom's a modern business and technology consulting firm. We're headquartered in Seattle Washington, we have about 11,000 employees across 40 markets globally. And what's different about Slalom is we're local model firm, so our consultants live and work in the same locale, which means we're personally invested in our client's outcomes because they impact us directly in the communities in which we live. >> And you've been in a leader in SLED for a long time, talk to us about what's going on on SLED these days. Obviously the last 18, 22 months have been quite dynamic, but what's going on in the market? >> Absolutely. What we're seeing is an extra emphasis on data data data, obviously, data is king and data is queen right now, right? So when the pandemic hit, we saw a ton of digital innovation, as our SLED clients needed to get their services online. That had been going on for a long time but it absolutely accelerated when then pandemic it and then it was a public health hazard, to ask people to come into the location. So what we saw was for constituents, we saw just absolute blast of omni-channel service delivery, so we saw the advent of SMS and chat bots and the more tech services, right? Leveraging AWS Lex and Transcribe and other services of AWS really helped our SLED clients react to the pandemic and respond to make sure that their constituents were receiving the digital services they needed, and their employees were able to be productive at home. >> Well, that was one of the keys the employee productivity, the student productivity, when everything's went remote overnight, one of the most challenging things was the demand for collaboration tools. Then of course, there's security challenges, there was concerns there, but talk to us about, and we've seen so much innovation out of AWS in the last, I mean always, but even what they announced the last couple of days, the innovation flywheel of AWS is probably stronger than ever enabling organizations like SLED, FED, private sector, public sector to be data-driven. >> Absolutely. One thing that's really exciting right now is to see the evolution of how our SLED customers are thinking about data. So we've been working on like integrated visions in SLED for a long time, integrated justice, integrated health care, integrated eligibility, how do we bring all this information together so that we can supply the right information to the right people at the right time to deliver the right outcomes? And AWS has been a huge part of that. It's not the journey to get to the cloud, it's the destination once you get there, right? Because then you can leverage all their AIML tools, IOT, edge, container, blockchain. And so our customers, who have already made that switch to AWS, they're able to take advantage of that. It's not what you can do in the cloud anymore is what you can't do without it really, right? So we're seeing tons of advances, intelligent document processing is one area I'm really excited about for our SLED clients, and working very closely with AWS to make sure that we see our clients adopt that and achieve the value out of it. >> AWS is dominating the IT space, although what five to 15% of IT is in the cloud, which means the vast majority is still on premises. So there's a huge potential for growth. In this sort of wild, wild west that we're in, there are all sorts of different kinds of services and consultancy partners, that are seeking to bridge the gap between the technology that AWS delivers and the outcomes that customers desire. >> Right. >> Now I've had a couple of experiences actually with Slalom folks, that were very, very positive. And what I saw was that the Slalom people were embedded in a way that you don't see some other consultancies embedded. You mentioned that something that piqued my interest, you talked about the local nature, is that your superpower? Because it sure seemed to be powerful to see this person where some of these very, very large global companies had no idea who Slalom was, until they realized that Sally was the one who had the best relationship with the customer. So Sally's a fictitious name that I just came up with, but I want to hear a little more about Slalom and your superpower and your differentiation. 'Cause it's a crowded space, you've got global systems integrators, you got all kinds of people. What makes you special? >> It's really the breadth of professional services that we provide, combined with AWS's cloud technologies and services. What we do I think a little bit differently is whereas AWS works back from the customer, we work back from our customer's vision. And so what we do with our, especially with our SLED clients, but with all of our commercial clients, is we say, what is your business strategy and your business vision, and how do we design the technology solutions, working back from that. So you're able to answer the business questions through data-driven tech technology, that's really important to you. And when we look at that, it's not just generating data to create information to then garner insights, but let's go one step further. And how do we create knowledge and how do we create wisdom this space, right? Where we understand situational awareness, common operating pictures, that's really what we want to do. When we talk about criminal justice and public safety, I love how we're thinking about joining data in new and different ways. It's not necessarily applications anymore, right? How do we create data as a service? How do we create documents as a service? Where we're pulling out the exact information that we need from semi-structured, structured and unstructured data and providing it to the right people to make the right decisions. >> Talk to us about intelligent document processing, a lot of buzz going on with that. What is it? Where are public sector agencies in terms of embracing it, adopting it and having it be part of that vision? >> Yeah, the promise is huge for IDP. What IDP is basically is leveraging AWS AI services to create intelligent automation solutions that help extract information from printed documents, digital documents, paper documents, right? So leveraging AWS services like Amazon Textract, Comprehend, Augmented AI, things like, and Kendra. What that does in combination, is it helps our clients unlock the data from, you can imagine government, it's heavy, heavy documents, and in criminal justice and public safety in particular, these documents represent key milestones and processes, right? So we're never going to get rid of documents in SLED, they're going to be used in perpetuity, it's important for accessibility and practicality and everything else. But what this does is it lets us unlock the data from those kind of stale documents and create it into usable formats for so that people can make decisions. >> That's critical because there's, I mean, we talk about in Amazon, AWS been this week have been talking about it and Dave, we have too. Every company, public sector, private sector, it needs to be a data-driven company, but they need to be able to extract that value from the data and the data isn't just digital. And that's something that, to your point, that's going to be persistent within SLED, they have to be able to extract the value from it quickly. >> Yes. >> To be able to see what new products and services can we deliver? What directions should we be going? And what outcomes should we be driving based on that visibility? And that visibility is critical. >> Exactly. And right now we absolutely have to support our communities. And we have a lot of our slide clients who are talking about this is a time where we don't just respond in a way that helps people kind of navigate this pandemic, we have to build resiliency as well in our communities and we do that through helping people through these hard times and making sure that we're moving our services to places where people can access them, in any language from wherever they are, right? We're having to actually go into people's homes on their couches, to deliver government services. Where we used to bring them into a single location. >> Right. >> Typically public sector has often been seen as lagging behind the private sector in some ways, the pandemic, as I'm sure ignited a fire with, especially with federal acknowledgement of things that need to happen, budgets flowing, are you seeing even more of an awakening from a cloud perspective within public sector? >> We are, we are and we're seeing really interesting initiatives pop up like, behavioral health initiatives, that are meant to address some really serious concerns in our country like nationwide 988 suicide prevention projects, right? And the federal government is providing a lot of funding to states and local governments so that they can help take care of our communities and also make sure that we're moving our services online so everyone can access them. >> I'm curious about that point, the funding. >> Yeah. >> Do you find yourself almost in the position of prize patrol? Where were some of the state local governments aren't necessarily as aware as Slalom might be of programs that are coming down immediately. Is that part of the conversation? >> It is part of the conv-- That's a great point because what we do is we look at what's coming down from the federal government, how is it going to flow to the states? How is it going to land ultimately, and then helping governments come up with a strategy for how to spend that money in the right way is really important, right? And we saw with some of the funding that come out, that there were delays on getting like eviction prevention funding out to folks. And so making sure that we have the technology to support those outcomes. >> It's all about outcomes. >> Yes. >> Speaking of outcomes, something I want to congratulate Slalom on is winning the first ever National Essay Partner of the Year for the US. >> Yes. >> Nice. >> That's awesome, congratulations. >> What does that mean for Slalom and what direction can we expect the Slalom and AWS partnership to go? >> Up and up. >> To the right? >> Yes. For us it's about validating the relationship that we have, right? It's really, when we walk into a client conversation, what we want to do is develop trust that our clients know we're looking for their best interest and their best outcomes. We're not trying to sell them something we're trying to solve their problems together. And it validates that for us, our partnership with AWS obviously is so important. And what we're doing in terms of making sure that we have a strong bench full of certifications and we can go to market together in the right way for our clients. This is a huge award and the recognition is very powerful for us. >> Well, congratulations. And so last question, you mentioned AWS and we always talk about when we talk with them at their event, we talk about their customer obsession, right? They work backwards, as you said, from the customer. And you guys from customer vision. Talk to me about when you go in jointly together, work with the customer, what does that alignment look like? >> Absolutely. So what we typically do is, Slalom will focus on what is the business outcome that we want to generate? And we will help design, how are we going to go about solving that problem? And how is AWS going to help support us with enabling technology? And so we will go into client conversations together, say, what is the outcome we want from this initiative together? And how are both partners going to get aligned to support the client in that conversation, in that product. >> That alignment is (indistinct). Gretchen, thank you for joining Dave and me today, talking about Slalom, what you guys are doing, how you're really helping organizations in SLED transform and not just survive challenging time but really thrive and be data-driven. We appreciate your insights and congratulations again on the National Essay Partner of The Year. >> Thank you so much. >> All right. For Dave Nicholson, I'm Lisa Martin. You're watching theCUBE, the global leader in live tech coverage. (lively music)

(bright upbeat music) >> Okay, welcome back everyone to the live CUBE coverage here in Las Vegas for in-person AWS re:Invent 2021. I'm John Furrier host of theCUBE two sets, live wall to wall coverage, all scopes of the hybrid events. Well, great stuff online. That was too much information to consume, but ultimately as usual, great show of new innovation for startups and for large enterprises. We've got a great guest, Paul Duffy head of startups Solutions Architecture for North America for Amazon Web Services. Paul, thanks for coming on. Appreciate it. >> Hi John, good to be here. >> So we saw you last night, we were chatting kind of about the show in general, but also about start ups. Everyone knows I'm a big startup fan and big founder myself, and we talk, I'm pro startups, everyone loves startups. Amazon, the first real customers were developers doing startups. And we know the big unicorns out there now all started on AWS. So Amazon was like a dream for the startup because before Amazon, you had to provision the server, you put in the Colo, you need a system administrator, welcome to EC2. Goodness is there, the rest is history. >> Yeah. >> The legacy and the startups is pretty deep. >> Yeah, you made the right point. I've done it myself. I co-founded a startup in about 2007, 2008. And before we even knew whether we had any kind of product market fit, we were racking the servers and doing all that kind of stuff. So yeah, completely changed it. >> And it's hard too with the new technology now finding someone to actually, I remember when we stood with our first Hadoop and we ran a solar search engine. I couldn't even find anyone to manage it. Because if you knew Hadoop back then, you were working at Facebook or Hyperscaler. So you guys have all this technology coming out, so provisioning and doing the heavy lifting for start is a huge win. That's kind of known, everyone knows that. So that's cool. What are you guys doing now because now you've got large enterprises trying to beat like startups. You got startups coming in with huge white spaces out there in the market. Jerry Chen from Greylock, and it was only yesterday we talked extensively about the net new opportunities in the Cloud that are out there. And now you see companies like Goldman Sachs have super cloud. So there's tons of growth. >> Paul: Yeah. >> Take us through the white space. How do you guys see startups taking advantage of AWS to a whole another level. >> And I think it's very interesting when you look at how things have changed in those kind of 15 years. The old world's horrible, you had to do all this provisioning. And then with AWS, Adam Szalecki was talking in his keynote on the first day of the event where people used to think it was just good for startups. Now for startups, it was this kind of obvious thing because they didn't have any legacy, they didn't have any data centers, they didn't have necessarily a large team and be able to do this thing with no commitment. Spin up a server with an API call was really the revolutionary thing. In that time, 15 years later, startups still have the same kind of urgency. They're constrained by time, they're constrained by money, they're constrained by the engineering talent they have. When you hear some of the announcements this week, or you look what is kind of the building blocks available to those startups. That I think is where it's become revolutionary. So you take a startup in 2011, 2012, and they were trying to build something maybe they were trying to do image recognition on forms for example, and they could build that. But they had to build the whole thing in the cloud. We had infrastructure, we had database stuff, but they would have to do all of the kind of the stuff on top of that. Now you look at some of the kind of the AIML services we have things like Textract, and they could just take that service off the shelf. We've got one startup in Canada called Chisel AI. They're trying to disrupt the insurance industry, and they could just use these services like text extracts to just accelerate them getting into that product market fit instead of having to do this undifferentiated (indistinct). >> Paul, we talk about, I remember back in the day when Web Services and service oriented architecture, building blocks, decoupling APIs, all that's now so real and so excellent, but you brought up a great point, Glue layers had to be built. Now you have with the scale of Amazon Web Services, things we're learning from other companies. It reminds me of the open source vibe where you stand on the shoulders of others to get success. And there's a lot of new things coming out that startups don't have to do because startup before then did. This is like a new, cool thing. It's a whole nother level. >> Yeah, and I think it's a real standing on the shoulders of giants kind of thing. And if you just unpick, like in Verna's announcement this morning, his key to this one, he was talking about the Amplify Studio kind of stuff. And if you think about the before and after for that, front-end developers have had to do this stuff for a long period of time. And in the before version, they would have to do all that kind of integration work, which isn't really what they want to spend that time doing. And now they've kind of got that headstart. Andy Jassy famously would say, when he talked about building AWS, that there is no compression algorithm for experience. I like to kind of misuse that phrase for what we try to do for startups is provide these compression algorithms. So instead of having say, hire a larger engineering team to just do this kind of crafty stuff, they can just take the thing and kind of get from naught to 60 (indistinct). >> Gives some examples today of where this is playing out in real time. What kinds of new compression algorithms can startups leverage that they couldn't get before what's new that's available? >> I think you see it across all parts of the stack. I mean, you could just take it out of a database thing, like in the old days, if you wanted to start, and you had the dream that every startup has, of getting to kind of hyper scale where things bursting that seems is the problem. If you wanted to do that in the database layer back in the day, you would probably have to provision most of that database stuff yourself. And then when you get to some kind of limiting factor, you've got to do that work where all you're really wanting to do is try and add more features to your application. Or whether you've got services like Aurora where that will do all of that kind of scaling from a storage point of view. And it gives that startup the way to stand on the shoulders of giants, all the same kind of thing. You want to do some kind of identity, say you're doing a kind of a dog walking marketplace or something like that. So one of the things that you need to do for the kind of the payments thing is some kind of identity verification. In the old days, you would have to have gone pulled all those premises together to do the stuff that would look at people's ID and so on. Now, people can take things like Textracts for example, to look at those forms and do that kind of stuff. And you can kind of pick that story in all of these different stream lines whether it's compute stuff, whether it's database, whether it's high-level AIML stuff, whether it's stuff like amplify, which just massively compresses that timeframe for the startup. >> So, first of all, I'm totally loving this 'cause this is just an example of how evolution works. But if I'm a startup, one of the big things I would think about, and you're a founder, you know this, opportunity recognition is one thing, opportunity capture is another. So moving fast is what nimble startups do. Maybe there's a little bit of technical debt. There maybe a little bit of model debt, but they can get beach head quickly. Startups can move fast, that's the benefit. So where do I learn if I'm a startup founder about where all these pieces are? Is there a place that you guys are providing? Is there use cases where founders can just come in and get the best of the best composable cloud? How do I stand up something quickly to get going that I could regain and refactor later, but not take on too much technical debt or just actually have new building blocks. Where are all these tools? >> I'm really glad you asked that one. So, I mean, first startups is the core of what everyone in my team does. And most of the people we hire, well, they all have a passion for startups. Some have been former founders, some have been former CTOs, some have come to the passion from a different kind of thing. And they understand the needs of startups. And when you started to talk about technical debt, one of the balances that startups have always got to get right, is you're not building for 10 years down the line. You're building to get yourself often to the next milestone to get the next set of customers, for example. And so we're not trying to do the sort of the perfect anonymity of good things. >> I (indistinct) conception of startups. You don't need that, you just got to get the marketplace. >> Yeah, and how we try to do that is we've got a program called Activate and Activate gives startup founders either things like AWS credits up to a hundred thousand dollars in credits. It gives them other technical capabilities as well. So we have a part of the console, the management console called the Activate Console people can go there. And again, if you're trying to build a backend API, there is something that is built on AWS capability to be launched recently that basically says here's some templatized stuff for you to go from kind of naught to 60 and that kind of thing. So you don't have to spend time searching the web. And for us, we're taking that because we've been there before with a bunch of other startups, so we're trying to help. >> Okay, so how do you guys, I mean, a zillion startups, I mean, you and I could be in a coffee shop somewhere, hey, let's do a startup. Do I get access, does everyone gets access to this program that you have? Or is it an elite thing? Is there a criteria? Is it just, you guys are just out there fostering and evangelizing brilliant tools. Is there a program? How do you guys- >> It's a program. >> How do you guys vet startup's, is there? >> It's a program. It has different levels in terms of benefits. So at the core of it it's open to anybody. So if you were a bootstrap startup tomorrow, or today, you can go to the Activate website and you can sign up for that self-starting tier. What we also do is we have an extensive set of connections with the community, so T1 accelerators and incubators, venture capital firms, the kind of places where startups are going to build and via the relationships with those folks. If you're in one, if you've kind of got investment from a top tier VC firm for example, you may be eligible for a hundred thousand dollars of credit. So some of it depends on where the stock is up, but the overall program is open to all. And a chunk of the stuff we talked about like the guidance that's there for everybody. >> It's free, that's free and that's cool. That's good learning, so yeah. And then they get the free training. What's the coolest thing that you're doing right now that startups should know about around obviously the passionate start ups. I know for a fact at 80%, I can say that I've heard Andy and Adam both say that it's not just enterprising, well, they still love the startups. That's their bread and butter too. >> Yeah, well, (indistinct) I think it's amazing that someone, we were talking about the keynote you see some of these large customers in Adam's keynote to people like United Airlines, very, very large successful enterprise. And if you just look around this show, there's a lot of startups just on this expert floor that we are now. And when I look at these announcements, to me, the thing that just gets me excited and keeps me staying doing this job is all of these little capabilities make it in the environment right now with a good funding environment and all of these technical building blocks that instead of having to take a few, your basic compute and storage, once you have all of these higher and higher levels things, you know the serverless stuff that was announced in Adam's keynotes early, which is just making it easy. Because if you're a founder, you have an idea, you know the thing that you want to disrupt. And we're letting people do that in different ways. I'll pick one start up that I find really exciting to talk to. It's called Study. It's run by a guy called Zack Kansa. And he started that start up relatively recently. Now, if you started 15 years ago, you were going to use EC2 instances building on the cloud, but you were still using compute instances. Zack is really opinionated and a kind of a technology visionary in this sense that he takes this serverless approach. And when you talk to him about how he's building, it's almost this attitude of, if I've had to spin up a server, I've kind of failed in some way, or it's not the right kind of thing. Why would we do that? Because we can build with these completely different kinds of architectures. What was revolutionary 15 years ago, and it's like, okay, you can launch it and serve with an API, and you're going to pay by the hour. But now when you look at how Zack's building, you're not even launching a server and you're paying by the millions. >> So this is a huge history lesson slash important point. Back 15 years ago, you had your alternative to Amazon was provisioning, which is expensive, time consuming, lagging, and probably causes people to give up, frankly. Now you get that in the cloud either you're on your own custom domain. I remember EC2 before they had custom domains. It was so early. But now it's about infrastructures code. Okay, so again, evolution, great time to market, buy what you need in the cloud. And Adam talked about that. Now it's true infrastructure is code. So the smart savvy architects are saying, Hey, I'm just going to program. If I'm spinning up servers, that means that's a low level primitive that should be automated. >> Right. >> That's the new mindset. >> Yeah, that's why the fun thing about being in this industry is in just in the time that I've worked at AWS, since about 2011, this stuff has changed so much. And what was state of the art then? And if you take, it's funny, when you look at some of the startups that have grown with AWS, like whether it's Airbnb, Stripe, Slack and so on. If you look at how they built in 2011, because sometimes new startups will say, oh, we want to go and talk to this kind of unicorn and see how they built. And if you actually talked to the unicorn, some of them would say, we wouldn't build it this way anymore. We would do the kind of stuff that Zack and the folks studied are doing right now, because it's totally different (indistinct). >> And the one thing that's consistent from then to now is only one thing, it has nothing to do with the tech, it's speed. Remember rails front end with some backend Mongo, you're up on EC2, you've got an app, in a week, hackathon. Weekend- >> I'm not tying that time thing, that just goes, it gets smaller and smaller. Like the amplify thing that Verna was talking about this morning. You could've gone back 15 years, it's like, okay, this is how much work the developer would have to do. You could go back a couple of years and it's like, they still have this much work to do. And now this morning, it's like, they've just accelerated them to that kind of thing. >> We'll end on giving Jerry Chan a plug in our chat yesterday. We put the playbook out there for startups. You got to raise your focus on the beach head and solve the problem you got in front of you, and then sequence two adjacent positions, refactor in the cloud. Take that approach. You don't have to boil the ocean over right away. You get in the market, get in and get automating kind of the new playbook. It's just, make everything work for you. Not use the modern. >> Yeah, and the thing for me, that one line, I can't remember it was Paul Gray, or somehow that I stole it from, but he's just encouraging these startups to be appropriately lazy. Like let us do the hard work. Let us do the undifferentiated heavy lifting so people can come up with these super cool ideas. >> Yeah, just plugging the talent, plugging the developer. You got a modern application. Paul, thank you for coming on theCUBE, I appreciate it. >> Thank you. >> Head of Startup Solution Architecture North America, Amazon Web Services is going to continue to birth more startups that will be unicorns and decacorns now. Don't forget the decacorns. Okay, we're here at theCUBE bringing you all the action. I'm John Furrier, theCUBE. You're watching the Leader in Global Tech Coverage. We'll be right back. (bright upbeat music)

>> Narrator: From theCUBE Studios in Palo Alto and Boston, connecting with thought leaders all around the world, this is a CUBE conversation. >> Hello everyone, welcome to this special CUBE interview. We are here at theCUBE Virtual covering AWS Summit Virtual Online. This is Amazon's Summits that they normally do all around the world. They're doing them now virtually. We are here in the Palo Alto COVID-19 quarantine crew getting all the interviews here with a special guest, Vice President of Machine Learning, we have Swami, CUBE Alumni, who's been involved in not only the machine learning, but all of the major activity around AWS around how machine learning's evolved, and all the services around machine learning workflows from transcribe, recognition, you name it. Swami, you've been at the helm for many years, and we've also chatted about that before. Welcome to the virtual CUBE covering AWS Summit. >> Hey, pleasure to be here, John. >> Great to see you. I know times are tough. Everything okay at Amazon? You guys are certainly cloud scaled, not too unfamiliar of working remotely. You do a lot of travel, but what's it like now for you guys right now? >> We're actually doing well. We have been I mean, this many of, we are working hard to make sure we continue to serve our customers. Even from their site, we have done, yeah, we had taken measures to prepare, and we are confident that we will be able to meet customer demands per capacity during this time. So we're also helping customers to react quickly and nimbly, current challenges, yeah. Various examples from amazing startups working in this area to reorganize themselves to serve customer. We can talk about that common layer. >> Large scale, you guys have done a great job and fun watching and chronicling the journey of AWS, as it now goes to a whole 'nother level with the post pandemic were expecting even more surge in everything from VPNs, workspaces, you name it, and all these workloads are going to be under a lot of pressure to do more and more value. You've been at the heart of one of the key areas, which is the tooling, and the scale around machine learning workflows. And this is where customers are really trying to figure out what are the adequate tools? How do my teams effectively deploy machine learning? Because now, more than ever, the data is going to start flowing in as virtualization, if you will, of life, is happening. We're going to be in a hybrid world with life. We're going to be online most of the time. And I think COVID-19 has proven that this new trajectory of virtualization, virtual work, applications are going to have to flex, and adjust, and scale, and be reinvented. This is a key thing. What's going on with machine learning, what's new? Tell us what are you guys doing right now. >> Yeah, I see now, in AWS, we offer broadest-- (poor audio capture obscures speech) All the way from like expert practitioners, we offer our frameworks and infrastructure layer support for all popular frameworks from like TensorFlow, Apache MXNet, and PyTorch, PowerShell, (poor audio capture obscures speech) custom chips like inference share. And then, for aspiring ML developers, who want to build their own custom machine learning models, we're actually building, we offer SageMaker, which is our end-to-end machine learning service that makes it easy for customers to be able to build, train, tune, and debug machine learning models, and it is one of our fastest growing machine learning services, and many startups and enterprises are starting to standardize their machine learning building on it. And then, the final tier is geared towards actually application developers, who did not want to go into model-building, just want an easy API to build capabilities to transcribe, run voice recognition, and so forth. And I wanted to talk about one of the new capabilities we are about to launch, enterprise search called Kendra, and-- >> So actually, so just from a news standpoint, that's GA now, that's being announced at the Summit. >> Yeah. >> That was a big hit at re:Invent, Kendra. >> Yeah. >> A lot of buzz! It's available. >> Yep, so I'm excited to say that Kendra is our new machine learning powered, highly accurate enterprise search service that has been made generally available. And if you look at what Kendra is, we have actually reimagined the traditional enterprise search service, which has historically been an underserved market segment, so to speak. If you look at it, on the public search, on the web search front, it is a relatively well-served area, whereas the enterprise search has been an area where data in enterprise, there are a huge amount of data silos, that is spread in file systems, SharePoint, or Salesforce, or various other areas. And deploying a traditional search index has always that even simple persons like when there's an ID desk open or when what is the security policy, or so forth. These kind of things have been historically, people have to find within an enterprise, let alone if I'm actually in a material science company or so forth like what 3M was trying to do. Enable collaboration of researchers spread across the world, to search their experiment archives and so forth. It has been super hard for them to be able to things, and this is one of those areas where Kendra has enabled the new, of course, where Kendra is a deep learning powered search service for enterprises, which breaks down data silos, and collects actually data across various things all the way from S3, or file system, or SharePoint, and various other data sources, and uses state-of-art NLP techniques to be able to actually index them, and then, you can query using natural language queries such as like when there's my ID desk-scoping, and the answer, it won't just give you a bunch of random, right? It'll tell you it opens at 8:30 a.m. in the morning. >> Yeah. >> Or what is the credit card cashback returns for my corporate credit card? It won't give you like a long list of links related to it. Instead it'll give you answer to be 2%. So it's that much highly accurate. (poor audio capture obscures speech) >> People who have been in the enterprise search or data business know how hard this is. And it is super, it's been a super hard problem, the old in the old guard models because databases were limiting to schemas and whatnot. Now, you have a data-driven world, and this becomes interesting. I think the big takeaway I took away from Kendra was not only the new kind of discovery navigation that's possible, in terms of low latency, getting relevant content, but it's really the under-the-covers impact, and I think I'd like to get your perspective on this because this has been an active conversation inside the community, in cloud scale, which is data silos have been a problem. People have had built these data silos, and they really talk about breaking them down but it's really again hard, there's legacy problems, and well, applications that are tied to them. How do I break my silos down? Or how do I leverage either silos? So I think you guys really solve a problem here around data silos and scale. >> Yeah. >> So talk about the data silos. And then, I'm going to follow up and get your take on the kind of size of of data, megabytes, petabytes, I mean, talk about data silos, and the scale behind it. >> Perfect, so if you look at actually how to set up something like a Kendra search cluster, even as simple as from your Management Console in the AWS, you'll be able to point Kendra to various data sources, such as Amazon S3, or SharePoint, and Salesforce, and various others. And say, these are kind of data I want to index. And Kendra automatically pulls in this data, index these using its deep learning and NLP models, and then, automatically builds a corpus. Then, I, as in user of the search index, can actually start querying it using natural language, and don't have to worry where it comes from, and Kendra takes care of things like access control, and it uses finely-tuned machine learning algorithms under the hood to understand the context of natural language query and return the most relevant. I'll give a real-world example of some of the field customers who are using Kendra. For instance, if you take a look at 3M, 3M is using Kendra to support search, support its material science R&D by enabling natural language search of their expansive repositories of past research documents that may be relevant to a new product. Imagine what this does to a company like 3M. Instead of researchers who are spread around the world, repeating the same experiments on material research over and over again, now, their engineers and researchers will allow everybody to quickly search through documents. And they can innovate faster instead of trying to literally reinvent the wheel all the time. So it is better acceleration to the market. Even we are in this situation, one of the interesting work that you might be interested in is the Semantic Scholar team at Allen Institute for AI, recently opened up what is a repository of scientific research called COVID-19 Open Research Dataset. These are expert research articles. (poor audio capture obscures speech) And now, the index is using Kendra, and it helps scientists, academics, and technologists to quickly find information in a sea of scientific literature. So you can even ask questions like, "Hey, how different is convalescent plasma "treatment compared to a vaccine?" And various in that question and Kendra automatically understand the context, and gets the summary answer to these questions for the customers, so. And this is one of the things where when we talk about breaking the data silos, it takes care of getting back the data, and putting it in a central location. Understanding the context behind each of these documents, and then, being able to also then, quickly answer the queries of customers using simple query natural language as well. >> So what's the scale? Talk about the scale behind this. What's the scale numbers? What are you guys seeing? I see you guys always do a good job, I've run a great announcement, and then following up with general availability, which means I know you've got some customers using it. What are we talking about in terms of scales? Petabytes, can you give some insight into the kind of data scale you're talking about here? >> So the nice thing about Kendra is it is easily linearly scalable. So I, as a developer, I can keep adding more and more data, and that is it linearly scales to whatever scale our customers want. So and that is one of the underpinnings of Kendra search engine. So this is where even if you see like customers like PricewaterhouseCoopers is using Kendra to power its regulatory application to help customers search through regulatory information quickly and easily. So instead of sifting through hundreds of pages of documents manually to answer certain questions, now, Kendra allows them to answer natural language question. I'll give another example, which is speaks to the scale. One is Baker Tilly, a leading advisory, tax, and assurance firm, is using Kendra to index documents. Compared to a traditional SharePoint-based full-text search, now, they are using Kendra to quickly search product manuals and so forth. And they're able to get answers up to 10x faster. Look at that kind of impact what Kendra has, being able to index vast amount of data, with in a linearly scalable fashion, keep adding in the order of terabytes, and keep going, and being able to search 10x faster than traditional, I mean traditional keyword search based algorithm is actually a big deal for these customers. They're very excited. >> So what is the main problem that you're solving with Kendra? What's the use case? If I'm the customer, what's my problem that you're solving? Is it just response to data, whether it's a call center, or support, or is it an app? I mean, what's the main focus that you guys came out? What was the vector of problem that you're solving here? >> So when we talked to customers before we started building Kendra, one of the things that constantly came back for us was that they wanted the same ease of use and the ability to search the world wide web, and customers like us to search within an enterprise. So it can be in the form of like an internal search to search within like the HR documents or internal wiki pages and so forth, or it can be to search like internal technical documentation or the public documentation to help the contact centers or is it the external search in terms of customer support and so forth, or to enable collaboration by sharing knowledge base and so forth. So each of these is really dissected. Why is this a problem? Why is it not being solved by traditional search techniques? One of the things that became obvious was that unlike the external world where the web pages are linked that easily with very well-defined structure, internal world is very messy within an enterprise. The documents are put in a SharePoint, or in a file system, or in a storage service like S3, or on naturally, tell-stores or Box, or various other things. And what really customers wanted was a system which knows how to actually pull the data from various these data silos, still understand the access control behind this, and enforce them in the search. And then, understand the real data behind it, and not just do simple keyword search, so that we can build remarkable search service that really answers queries in a natural language. And this has been the theme, premise of Kendra, and this is what had started to resonate with our customers. I talked with some of the other examples even in areas like contact centers. For instance, Magellan Health is using Kendra for its contact centers. So they are able to seamlessly tie like member, provider, or client specific information with other inside information about health care to its agents so that they can quickly resolve the call. Or it can be on internally to do things like external search as well. So very satisfied client. >> So you guys took the basic concept of discovery navigation, which is the consumer web, find what you're looking for as fast as possible, but also took advantage of building intelligence around understanding all the nuances and configuration, schemas, access, under the covers and allowing things to be discovered in a new way. So you basically makes data be discoverable, and then, provide an interface. >> Yeah. >> For discovery and navigation. So it's a broad use cat, then. >> Right, yeah that's sounds somewhat right except we did one thing more. We actually understood not just, we didn't just do discovery and also made it easy for people to find the information but they are sifting through like terabytes or hundreds of terabytes of internal documentation. Sometimes, one other things that happens is throwing a bunch of hundreds of links to these documents is not good enough. For instance, if I'm actually trying to find out for instance, what is the ALS marker in an health care setting, and for a particular research project, then, I don't want to actually sift through like thousands of links. Instead, I want to be able to correctly pinpoint which document contains answer to it. So that is the final element, which is to really understand the context behind each and every document using natural language processing techniques so that you not only find discover the information that is relevant but you also get like highly accurate possible precise answers to some of your questions. >> Well, that's great stuff, big fan. I was really liking the announcement of Kendra. Congratulations on the GA of that. We'll make some room on our CUBE Virtual site for your team to put more Kendra information up. I think it's fascinating. I think that's going to be the beginning of how the world changes, where this, this certainly with the voice activation and API-based applications integrating this in. I just see a ton of activity that this is going to have a lot of headroom. So appreciate that. The other thing I want to get to while I have you here is the news around the augmented artificial intelligence has been brought out as well. >> Yeah. >> So the GA of that is out. You guys are GA-ing everything, which is right on track with your cadence of AWS laws, I'd say. What is this about? Give us the headline story. What's the main thing to pay attention to of the GA? What have you learned? What's the learning curve, what's the results? >> So augmented artificial intelligence service, I called it A2I but Amazon A2I service, we made it generally available. And it is a very unique service that makes it easy for developers to augment human intelligence with machine learning predictions. And this is historically, has been a very challenging problem. We look at, so let me take a step back and explain the general idea behind it. You look at any developer building a machine learning application, there are use cases where even actually in 99% accuracy in machine learning is not going to be good enough to directly use that result as the response to back to the customer. Instead, you want to be able to augment that with human intelligence to make sure, hey, if my machine learning model is returning, saying hey, my confidence interval for this prediction is less than 70%, I would like it to be augmented with human intelligence. Then, A2I makes it super easy for customers to be, developers to use actually, a human reviewer workflow that comes in between. So then, I can actually send it either to the public pool using Mechanical Turk, where we have more than 500,000 Turkers, or I can use a private workflow as a vendor workflow. So now, A2I seamlessly integrates with our Textract, Rekognition, or SageMaker custom models. So now, for instance, NHS is integrated A2I with Textract, so that, and they are building these document processing workflows. The areas where the machine learning model confidence load is not as high, they will be able augment that with their human reviewer workflows so that they can actually build in highly accurate document processing workflow as well. So this, we think is a powerful capability. >> So this really kind of gets to what I've been feeling in some of the stuff we worked with you guys on our machine learning piece. It's hard for companies to hire machine learning people. This has been a real challenge. So I like this idea of human augmentation because humans and machines have to have that relationship, and if you build good abstraction layers, and you abstract away the complexity, which is what you guys do, and that's the vision of cloud, then, you're going to need to have that relationship solidified. So at what point do you think we're going to be ready for theCUBE team, or any customer that doesn't have the or can't find a machine learning person? Or may not want to pay the wages that's required? I mean it's hard to find a machine learning engineer, and when does the data science piece come in with visualization, the spectrum of pure computer science, math, machine learning guru to full end user productivity? Machine learning is where you guys are doing a lot of work. Can you just share your opinion on that evolution of where we are on that? Because people want to get to the point where they don't have to hire machine learning folks. >> Yeah. >> And have that kind support too. >> If you look at the history of technology, I actually always believe that many of these highly disruptive technology started as a way that it is available only to experts, and then, they quickly go through the cycles, where it becomes almost common place. I'll give an example with something totally outside the IT space. Let's take photography. I think more than probably 150 years ago, the first professional camera was invented, and built like three to four years still actually take a really good picture. And there were only very few expert photographers in the world. And then, fast forward to time where we are now, now, even my five-year-old daughter takes actually very good portraits, and actually gives it as a gift to her mom for Mother's Day. So now, if you look at Instagram, everyone is a professional photographer. I kind of think the same thing is about to, it will happen in machine learning too. Compared to 2012, where there were very few deep learning experts, who can really build these amazing applications, now, we are starting to see like tens of thousands of actually customers using machine learning in production in AWS, not just proof of concepts but in production. And this number is rapidly growing. I'll give one example. Internally, if you see Amazon, to aid our entire company to transform and make machine learning as a natural part of the business, six years ago, we started a Machine Learning University. And since then, we have been training all our engineers to take machine learning courses in this ML University, and a year ago, we actually made these coursework available through our Training and Certification platform in AWS, and within 48 hours, more than 100,000 people registered. Think about it, that's like a big all-time record. That's why I always like to believe that developers are always eager to learn, they're very hungry to pick up new technology, and I wouldn't be surprised if four or five years from now, machine learning is kind of becomes a normal feature of the app, the same with databases are, and that becomes less special. If that day happens, then, I would see it as my job is done, so. >> Well, you've got a lot more work to do because I know from the conversations I've been having around this COVID-19 pandemic is it's that there's general consensus and validation that the future got pulled forward, and what used to be an inside industry conversation that we used to have around machine learning and some of the visions that you're talking about has been accelerated on the pace of the new cloud scale, but now that people now recognize that virtual and experiencing it firsthand globally, everyone, there are now going to be an acceleration of applications. So we believe there's going to be a Cambrian explosion of new applications that got to reimagine and reinvent some of the plumbing or abstractions in cloud to deliver new experiences, because the expectations have changed. And I think one of the things we're seeing is that machine learning combined with cloud scale will create a whole new trajectory of a Cambrian explosion of applications. So this has kind of been validated. What's your reaction to that? I mean do you see something similar? What are some of the things that you're seeing as we come into this world, this virtualization of our lives, it's every vertical, it's not one vertical anymore that's maybe moving faster. I think everyone sees the impact. They see where the gaps are in this new reality here. What's your thoughts? >> Yeah, if you see the history from machine learning specifically around deep learning, while the technology is really not new, especially because the early deep learning paper was probably written like almost 30 years ago. And why didn't we see deep learning take us sooner? It is because historically, deep learning technologies have been hungry for computer resources, and hungry for like huge amount of data. And then, the abstractions were not easy enough. As you rightfully pointed out that cloud has come in made it super easy to get like access to huge amount of compute and huge amount of data, and you can literally pay by the hour or by the minute. And with new tools being made available to developers like SageMaker and all the AI services, we are talking about now, there is an explosion of options available that are easy to use for developers that we are starting to see, almost like a huge amount of like innovations starting to pop up. And unlike traditional disruptive technologies, which you usually see crashing in like one or two industry segments, and then, it crosses the chasm, and then goes mainstream, but machine learning, we are starting to see traction almost in like every industry segment, all the way from like in financial sector, where fintech companies like Intuit is using it to forecast its call center volume and then, personalization. In the health care sector, companies like Aidoc are using computer vision to assist radiologists. And then, we are seeing in areas like public sector. NASA has partnered with AWS to use machine learning to do anomaly detection, algorithms to detect solar flares in the space. And yeah, examples are plenty. It is because now, machine learning has become such common place that and almost every industry segment and every CIO is actually already looking at how can they reimagine, and reinvent, and make their customer experience better covered by machine learning. In the same way, Amazon actually asked itself, like eight or 10 years ago, so very exciting. >> Well, you guys continue to do the work, and I agree it's not just machine learning by itself, it's the integration and the perfect storm of elements that have come together at this time. Although pretty disastrous, but I think ultimately, it's going to come out, we're going to come out of this on a whole 'nother trajectory. It's going to be creativity will be emerged. You're going to start seeing really those builders thinking, "Okay hey, I got to get out there. "I can deliver, solve the gaps we are exposed. "Solve the problems, "pre-create new expectations, new experience." I think it's going to be great for software developers. I think it's going to change the computer science field, and it's really bringing the lifestyle aspect of things. Applications have to have a recognition of this convergence, this virtualization of life. >> Yeah. >> The applications are going to have to have that. So and remember virtualization helped Amazon formed the cloud. Maybe, we'll get some new kinds of virtualization, Swami. (laughs) Thanks for coming on, really appreciate it. Always great to see you. Thanks for taking the time. >> Okay, great to see you, John, also. Thank you, thanks again. >> We're with Swami, the Vice President of Machine Learning at AWS. Been on before theCUBE Alumni. Really sharing his insights around what we see around this virtualization, this online event at the Amazon Summit, we're covering with the Virtual CUBE. But as we go forward, more important than ever, the data is going to be important, searching it, finding it, and more importantly, having the humans use it building an application. So theCUBE coverage continues, for AWS Summit Virtual Online, I'm John Furrier, thanks for watching. (enlightening music)