>>The cube presents HPE discover 2022 brought to you by HPE. >>Hey everyone. Welcome back to the Cube's day one coverage of HPE. Discover 22 live from Las Vegas. Lisa Martin, here with Dave Velante of a couple of guests from red hat. You may have seen some news yesterday. We're gonna be talking about that. Please. Welcome Ryan King, the senior director of hardware partner ecosystem, and Lori Fontine joins us as well. The senior director of global commercial partner ecosystem. Welcome to the program guys. >>Thanks for having us. Yeah, >>Thank you so great to be back in person and nobody word has summit was just last month or so. That's right. Ryan. Talk about hybrid cloud. It's all the buzz. We've been talking a lot about it in the last hour and a half alone. What are some of the trends that, that red hat is seen with respect to hybrid cloud? >>Well, I, I mean, hybrid cloud of red hat has been a trend for quite some time. In fact, we were very early in setting our course towards hybrid cloud with our products and platforms. And that's been a key part of our strategy in terms of the number of transformations have been happening in the enterprise. And with HPE, we're super excited about, you know, we're hitting our stride with OpenShift. I've been working with OpenShift for the better part of my 10 years here at 12 years at red hat, 10 years with OpenShift. And we're very excited about seeing the pattern of going where customers want to build their cloud. It's very important that where, where the market is going. So we're seeing trends from the public cloud now go into edge and telco and 5g and really exceed, see them expanding their infrastructure footprint out to those use cases. And again, we see REL everywhere. So re has continued to expand as well. And then Ansible automation platform has also been a great means of kind of bringing together community for that last mile of automating your entire infrastructure. >>Well, the Lin, the functionality of Linux continues to improve OpenShift is everywhere. I mean, I remember at the red hat summit, I mean, well, we, we, we coined this term super cloud, which is this layer that floats, you know, on-prem took across clouds out to the edge we had Verizon on. They were talking about, you know, 5g developers and how they're developing using, you know, a combination of, of, of OpenShift. So guys have been really crushing it with, with OpenShift. I remember, gosh, I mean, we've been covering, you know, red hat summits for a long time now. And just to see that evolution is actually quite amazing. >>Yeah. It's actually really neat to see our CEOs align too. Right. So the messaging that we've had around hybrid cloud from red hat, like you said, we were kind of the pioneers, honestly, this we've been talking about hybrid cloud from the very beginning. We always knew that it wasn't gonna be public cloud or private cloud. We had to have, you know, hybrid. And, and it's interesting to see that Antonio, you know, took that on and wanted to say, we're gonna do everything as a service right. A few years ago. And, and the whole theme was around hybrid cloud and giving customers that choice. Right? So it's exciting for us to see all of that come together. And I actually worked for HP for like 17 and a half years. So it's really fun for me to be on this side now with red hat and see the messaging come together, the vision come together and just really being able to align and move forward on >>This tremendous amount of transformation in the last few years >>Alone. Oh my gosh, we >>Talk about, you know, customers need choice. They want choice, but you also talked about, we have to meet customers where they are. That seems the last few years to have accelerated, there is no more option for companies. You've gotta meet the customers where they are. >>Exactly. Yeah. And it's all about choice, like you said, and it, everybody's got, you know, their own way to do everything as far as consumption goes and we have to be available and spot on with it, you know, and be able to move quickly with these trends that we're seeing. And so it's great to be aligned. And >>From a partnership standpoint, I mean, you, you mentioned H HP 17 years. I mean, it was, it was a hard to follow company. You had, you had PCs over here, you had services, the kind of the old EDS business. Now there's such a focus absolutely. On this mission, absolutely. Of as a service. And, you know, obviously a key part of that is having optionality and bringing open source tooling into that. I mean, we heard about this in, in spades, at, at red hat summit, which is really interesting this year. It was a smaller VIP event in Boston. And I, and I loved it, you know, cuz it was really manageable. We had all the execs on and customers and partners. It was awesome. What's new since red hat summit. >>Well, I mean, I would say that obviously GreenLake and what we've announced this week is a big new thing for us, but really like we're just continuing on our pattern. We are. Now, if you look at the Q1 report from IBM, you'll see that the growth of the customer base for OpenShift that they reported just continues to go up into the right. You'll see that now, like AMIA is saying that we're like 47.8% of the containers market for the enterprise. You'll see that like we're now in 65% of the fortune 500 with OpenShift, 90% with red hat in general. So we've established our footprint. And when you establish your footprint and customers start taking you out to the edge, we're going into these 5g use cases, we're, we've got an incredible amount happening in the AI space, all these emerging areas of where people are building their cloud, like we're now going to that next level of saying, how do they want to consume it? >>So what's really important to me about that is, is so Omni data around 50% of the market is, is open shift. A people may not realize a lot of people use, you know, do Kubernetes for free, you know, Hey, we're doing Kubernetes, but they don't have that application development framework and all the recovery and all the, the tooling around it. And the reason why I think that's so important, Laurie is ecosystems wanna monetize. So people are paying for things that becomes more interesting and it actually starts to attract people just naturally. >>Yeah, absolutely. And speaking of ecosystem, I mean, that's the beauty of what we're doing with GreenLake too. We're taking on a building block approach. So we're really, it's kind of ISV as a service if you will. And you know, personally, I, this was my baby for the past couple years, trying to make sure that we took into consideration every partner use case, every customer use case. So we created an agreement that would make sense to be able to scale, but also to meet all the demands of our customers. And so the, the what's really exciting about this is now we have a chance to take this building block approach, scale it out to all types of partner types, right throughout the entire ecosystem and build offerings together. That is really exciting for us. And that's where we see the real potential here with GreenLake and with red hat, >>What's actually available inside a GreenLake. >>So we are starting with OpenShift. So OpenShift will be available in Q3 that will follow in Q4 with re and then after that Ansible. So we're, we're moving very quickly to bring our platforms into it and it's really our strategic platforms, but it's all based on customer demand. We know we're seeing amazing transformation of customers moving to Kubernetes. You said, you know, OpenShift is Kubernetes with useful additions to it and an ecosystem around it, right? So that transformation is also happening at the bare metal layer. So we're seeing people move into Kubernetes bare metal, which is an amazing growth market for us. >>Explain those useful additions if you would. So why shouldn't I just go out and, and get the free version of Kubernete? Why should I engage red hat and, and OpenShift? What do I get? >>So you get all the day, two management stuff, you get, we have a whole set of additional stuff you can purchase around it, OpenShift platform. Plus you can get our ACM, our advanced cluster management. So you wanna manage multiple clusters, right? You get the ACS, the security side of it. You can also get ODF. So you get storage built into it as well. And we've done all these integrations. You can manage the whole thing as a cluster or as multiple clusters with the whole enterprise support and the long term support that we provide for these things up to 10 years. So >>When you look at the early days lease of, of Kubernetes, it was really, the focus was on simplicity. You had other platforms that were actually doing more sophisticated cluster management. And the, the committers that in Kubernetes said, you know, we're not gonna do that. We're gonna keep it simple. And so that leave some holes and gaps and you know, they're starting to fill those, but what if, if correct me if I'm wrong, but what red hat has done is said, okay, we're gonna accelerate, you know, the, the, the closing of those gaps and stay ahead and actually offer incremental value. And that's why you're winning in the marketplace. >>Well, we're an open company, so we're still doing everything upstream and open source as we do, of course, sticking with, you know, the APIs and APIs to make this all work, both, you know, in terms of what the community's trying to drive, what we're trying to drive for our customers on their behalf. And then just where things are going from a technology basis, make it a lot of investment, >>But you have to, you have to make a red hat, has to make a choice as to where it puts its commitments. You can't spread yourself too thin, so you gotta pick your spots. And you've, you've proven that you're pretty adept at doing that. >>That just comes back to customer centricity, right. And just knowing where our customers need to take the platform. That's, >>That's easy to say, but it's, it's an art form. And a little bit of science. >>Remember these customers have experts that are deep in this space. So it's like, you know, those experts trust us with where they needed to go. And they trust us to help shepherd that and deliver that as a platform to them. So it's not like anybody tell us what you want, right? Like it's really about like, knowing what's the best way to do it. And working with the people that can help you understand how to apply that to their use case >>And within the customer environment, who are you working with? Who is that key constituent or constituents that are guiding red hat in this direction? >>Well, it's certainly infrastructure folks. So it's your, it's your standard folks that are looking at the, how do we lay down our infrastructure? How do we manage it? How do we grow it? It goes out to the application developers. They're trying to deliver this in a cloud native way. And we have new personas, you know, coming in with the AI practitioners, right? So we've announced at before summit at Invidia's event, their new offering called Invidia AI enterprise. And so that's them bringing in enterprise support for GPU, for Kuda and for a software stack above that to start offering some more support there. So they're certifying OpenShift, we're both certifying the servers that run underneath it, and then they're offering support for their stuff on top of it. And that's a whole new use case for us. >>And, you know, I should also mention that even though this paper use with the GreenLake is new for us, and we just had this big announcement, we have done GreenLake deals though. We've done numerous GreenLake deals with our annual subs, right? So I, so even though this is new to us, as far as, you know, monthly utilization and being able to do this cloud consumption this isn't new to us as two companies coming together, we've been doing GreenLake deals for the past couple years. It's just, now we have this cloud consumption availability, which is really gonna make this thing launch. So, >>So what have been some of the customer benefits so far, you've been doing it for a couple years. The announcement was yesterday, but there's obviously feed on the street going on. What are some of the, the big outcomes that you're seeing customers actually bring to reality? >>I think speed and agility, right? That's the biggest thing with, with our products, being able to have it everything predictable and being able to have it consumed one way, instead of having this fragmented customer experience, which is, you know, what we've seen in the past. So I think that's the biggest thing is speed agility and just, you know, a really good customer experience at this point. >>Go get it, please. >>I would say the customer experience is critical. Yes. That's one of the things that we know that in terms of, of patients wearing thin the last couple of years, people expect to have a really strong consumer experience regardless of what you're doing, regardless of what industry and so attention and mind on that is a differentiator in my opinion. >>Absolutely. Yeah. And we've gotta constantly keep our eye on that. I mean, that's, that's our north star, if you will. Right. So, and Lori, >>I know you've saying you're, you've done GreenLake deals in the past, but what feels different to me now in that it's actually coalescing some of the things that Alma Russo announced this morning, the platform on which, you know, ISV is a service. I think you, you called it. Yeah. You, it, it now seems like, you know, look a couple years ago, HP said, okay, this is the direction that we're going. Yeah. They weren't there at that time. And they're still not there. There's a lot of work to be, to be done. But now it's starting to form. You're seeing, you know, the pieces come together, the puzzle pieces that sort of substrate being laid out. And now you're hoping that we see the steep part of the S-curve and that's what customers I think are expecting. >>Right. And it's bringing that operating model to move to a monthly model so they can do pay as you go. Right. And that pairs up nicely with like the cloud native capabilities we're bringing to OpenShift and hybrid cloud in general. So it's, it just shows like we're already getting demand from customers. It's saying like, this is part of our model. Like we know a certain amount of infrastructure we wanna own, and we just wanna own it outright, but there's a lot that they want to have flexibility on. And so being able to add that portion to it is just, you know, gonna help us both. >>And you think about the critical aspects of, of the cloud operating model. It's obviously pay as you go it's, you know, massive scale it's ecosystem enablement, and also automation. I mean, that is, that is a key, what's your point of view on that? You guys with Ansible, you, you, you know, you go back to a couple years ago and it was, you know, there was this, there were a lot of other tooling, but now, I mean, Ansible is really taken off. Yeah. >>It's just, you know, Cinderella story, right? Like it really an amazing community driven thing where we just knew, we all know this, right. You have, when you get to the very last mile of doing infrastructure management, there's a variety of devices, there's variety, a variety of vendors. And then you have like the variety of skills of the people that have to figure out how to do automate all of this. And what Ansible did is it provided a common language across all of that. And so what we do with automation, our, an ible automation platform is we make it. So now teams can manage all of this together and they can share their playbooks and they can host that privately for all their enterprise stuff that they need to do. So it's just, you know, it fits our DNA so well to have something so community driven now with a really nice enterprise message wrapped around it. And it's playing out very well for where, you know, hybrid cloud. Right. Cause there's some more additional variety. You need to be able to manage, you know, across all of your different footprints, because really it's like, it's not just about flexibility and scale up scale down it's where do you need it to run at what time? Right. And that, that last leg Ansible plays a key role in that. >>And we actually, Ansible will be coming further down the, you know, the patch. I know we're gonna talk a little bit about what's available today versus what's available down the road, but yeah, we have that on the radar. So right outta the gate, we're working on OpenShift, obviously bare metal. And we see that happening in Q3 and then behind that as well in Q4 and then Ansible is gonna be right behind that. So that's kind of the order that, and there's other pieces, right? So our whole portfolio is basically available to HP right now. It's just making sure that we can operationalize everything and have the best experience >>All inside of GreenLake, >>All inside a GreenLake. Yeah. Pretty neat. Lori >>Question for you. You've been, you were with HP for a very long time. This is obviously the first discover in three years in person. Exactly. You know, three years ago, Antonio near stood on stage and said, we are going to buy 20, 22. And here we are deliver everything as a service, as a partner and as a former HP, what are you seeing at this discover 22? >>It's it's so interesting. I it's such a sea change if you will. Right. And having come from HPE, I actually led the software as a service organization for a while on the software side of things. And we thought that was like state of the art and cutting edge that was 10, 11, 12 years ago. Right. So to actually see this come to life, because we were all thinking really, everything is a service. How are you gonna do that? Like your entire portfolio is gonna be available. Like that is lofty. Right. And having worked at HP, I thought, wow, I don't, you know, I know things take time. And, but actually just even being around the showcase here and watching everything come to life is amazing. Cause I, I, you know, I, I was very positive about it, but at the same time, it's like that, that was a big goal three years. Right. And it's, I'm seeing it happen >>A big goal in two of those years during a pandemic. Right. So right. Talk about lofty. Oh my gosh. Quite a bit of accomplishments guys. Thank you so much for joining David me on the program talking about actually guys, this is great. What red hat and HPE are doing your power partnership, power ship. Is that a word? It is now your power. >>I like >>That with GreenLake. We appreciate that. We'll look forward to having you guys back on. >>Thank you so much, guys. >>All right. For our guests. I'm Lisa Martin. He's Dave ante. We are at HPE discover 22 live from the show floor in Las Vegas. This is just day one of our cupboards stick around. We'll be right back with our next guest.

>>Well, welcome back to the cubes coverage of AWS reinvent 2021. We're here joined by Ian buck, general manager and vice president of accelerated computing at Nvidia I'm. John Ford, your host of the QB. And thanks for coming on. So in video, obviously, great brand congratulates on all your continued success. Everyone who has does anything in graphics knows the GPU's are hot and you guys get great brand great success in the company, but AI and machine learning was seeing the trend significantly being powered by the GPU's and other systems. So it's a key part of everything. So what's the trends that you're seeing, uh, in ML and AI, that's accelerating computing to the cloud. Yeah, >>I mean, AI is kind of drape bragging breakthroughs innovations across so many segments, so many different use cases. We see it showing up with things like credit card, fraud prevention and product and content recommendations. Really it's the new engine behind search engines is AI. Uh, people are applying AI to things like, um, meeting transcriptions, uh, virtual calls like this using AI to actually capture what was said. Um, and that gets applied in person to person interactions. We also see it in intelligence systems assistance for a contact center, automation or chat bots, uh, medical imaging, um, and intelligence stores and warehouses and everywhere. It's really, it's really amazing what AI has been demonstrated, what it can do. And, uh, it's new use cases are showing up all the time. >>Yeah. I'd love to get your thoughts on, on how the world's evolved just in the past few years, along with cloud, and certainly the pandemics proven it. You had this whole kind of full stack mindset initially, and now you're seeing more of a horizontal scale, but yet enabling this vertical specialization in applications. I mean, you mentioned some of those apps, the new enablers, this kind of the horizontal play with enablement for specialization, with data, this is a huge shift that's going on. It's been happening. What's your reaction to that? >>Yeah, it's the innovations on two fronts. There's a horizontal front, which is basically the different kinds of neural networks or AIS as well as machine learning techniques that are, um, just being invented by researchers for, uh, and the community at large, including Amazon. Um, you know, it started with these convolutional neural networks, which are great for image processing, but as it expanded more recently into, uh, recurrent neural networks, transformer models, which are great for language and language and understanding, and then the new hot topic graph neural networks, where the actual graph now is trained as a, as a neural network, you have this underpinning of great AI technologies that are being adventure around the world in videos role is try to productize that and provide a platform for people to do that innovation and then take the next step and innovate vertically. Um, take it, take it and apply it to two particular field, um, like medical, like healthcare and medical imaging applying AI, so that radiologists can have an AI assistant with them and highlight different parts of the scan. >>Then maybe troublesome worrying, or requires more investigation, um, using it for robotics, building virtual worlds, where robots can be trained in a virtual environment, their AI being constantly trained, reinforced, and learn how to do certain activities and techniques. So that the first time it's ever downloaded into a real robot, it works right out of the box, um, to do, to activate that we co we are creating different vertical solutions, vertical stacks for products that talk the languages of those businesses, of those users, uh, in medical imaging, it's processing medical data, which is obviously a very complicated large format data, often three-dimensional boxes in robotics. It's building combining both our graphics and simulation technologies, along with the, you know, the AI training capabilities and different capabilities in order to run in real time. Those are, >>Yeah. I mean, it's just so cutting edge. It's so relevant. I mean, I think one of the things you mentioned about the neural networks, specifically, the graph neural networks, I mean, we saw, I mean, just to go back to the late two thousands, you know, how unstructured data or object store created, a lot of people realize that the value out of that now you've got graph graph value, you got graph network effect, you've got all kinds of new patterns. You guys have this notion of graph neural networks. Um, that's, that's, that's out there. What is, what is a graph neural network and what does it actually mean for deep learning and an AI perspective? >>Yeah, we have a graph is exactly what it sounds like. You have points that are connected to each other, that established relationships and the example of amazon.com. You might have buyers, distributors, sellers, um, and all of them are buying or recommending or selling different products. And they're represented in a graph if I buy something from you and from you, I'm connected to those end points and likewise more deeply across a supply chain or warehouse or other buyers and sellers across the network. What's new right now is that those connections now can be treated and trained like a neural network, understanding the relationship. How strong is that connection between that buyer and seller or that distributor and supplier, and then build up a network that figure out and understand patterns across them. For example, what products I may like. Cause I have this connection in my graph, what other products may meet those requirements, or also identifying things like fraud when, when patterns and buying patterns don't match, what a graph neural networks should say would be the typical kind of graph connectivity, the different kind of weights and connections between the two captured by the frequency half I buy things or how I rate them or give them stars as she used cases, uh, this application graph neural networks, which is basically capturing the connections of all things with all people, especially in the world of e-commerce, it's very exciting to a new application, but applying AI to optimizing business, to reducing fraud and letting us, you know, get access to the products that we want, the products that they have, our recommendations be things that, that excited us and want us to buy things >>Great setup for the real conversation that's going on here at re-invent, which is new kinds of workloads are changing. The game. People are refactoring their business with not just replatform, but actually using this to identify value and see cloud scale allows you to have the compute power to, you know, look at a note on an arc and actually code that. It's all, it's all science, all computer science, all at scale. So with that, that brings up the whole AWS relationship. Can you tell us how you're working with AWS before? >>Yeah. 80 of us has been a great partner and one of the first cloud providers to ever provide GPS the cloud, uh, we most more recently we've announced two new instances, uh, the instance, which is based on the RA 10 G GPU, which has it was supports the Nvidia RTX technology or rendering technology, uh, for real-time Ray tracing and graphics and game streaming is their highest performance graphics, enhanced replicate without allows for those high performance graphics applications to be directly hosted in the cloud. And of course runs everything else as well, including our AI has access to our AI technology runs all of our AI stacks. We also announced with AWS, the G 5g instance, this is exciting because it's the first, uh, graviton or ARM-based processor connected to a GPU and successful in the cloud. Um, this makes, uh, the focus here is Android gaming and machine learning and France. And we're excited to see the advancements that Amazon is making and AWS is making with arm and the cloud. And we're glad to be part of that journey. >>Well, congratulations. I remember I was just watching my interview with James Hamilton from AWS 2013 and 2014. He was getting, he was teasing this out, that they're going to build their own, get in there and build their own connections, take that latency down and do other things. This is kind of the harvest of all that. As you start looking at these new new interfaces and the new servers, new technology that you guys are doing, you're enabling applications. What does, what do you see this enabling as this, as this new capability comes out, new speed, more, more performance, but also now it's enabling more capabilities so that new workloads can be realized. What would you say to folks who want to ask that question? >>Well, so first off I think arm is here to stay and you can see the growth and explosion of my arm, uh, led of course, by grab a tiny to be. I spend many others, uh, and by bringing all of NVIDIA's rendering graphics, machine learning and AI technologies to arm, we can help bring that innovation. That arm allows that open innovation because there's an open architecture to the entire ecosystem. Uh, we can help bring it forward, uh, to the state of the art in AI machine learning, the graphics. Um, we all have our software that we released is both supportive, both on x86 and an army equally, um, and including all of our AI stacks. So most notably for inference the deployment of AI models. We have our, the Nvidia Triton inference server. Uh, this is the, our inference serving software where after he was trained to model, he wanted to play it at scale on any CPU or GPU instance, um, for that matter. So we support both CPS and GPS with Triton. Um, it's natively integrated with SageMaker and provides the benefit of all those performance optimizations all the time. Uh, things like, uh, features like dynamic batching. It supports all the different AI frameworks from PI torch to TensorFlow, even a generalized Python code. Um, we're activating how activating the arm ecosystem as well as bringing all those AI new AI use cases and all those different performance levels, uh, with our partnership with AWS and all the different clouds. >>And you got to making it really easy for people to use, use the technology that brings up the next kind of question I want to ask you. I mean, a lot of people are really going in jumping in the big time into this. They're adopting AI. Either they're moving in from prototype to production. There's always some gaps, whether it's knowledge, skills, gaps, or whatever, but people are accelerating into the AI and leaning into it hard. What advancements have is Nvidia made to make it more accessible, um, for people to move faster through the, through the system, through the process? >>Yeah, it's one of the biggest challenges. The other promise of AI, all the publications that are coming all the way research now, how can you make it more accessible or easier to use by more people rather than just being an AI researcher, which is, uh, uh, obviously a very challenging and interesting field, but not one that's directly in the business. Nvidia is trying to write a full stack approach to AI. So as we make, uh, discover or see these AI technologies come available, we produce SDKs to help activate them or connect them with developers around the world. Uh, we have over 150 different STKs at this point, certain industries from gaming to design, to life sciences, to earth scientist. We even have stuff to help simulate quantum computing. Um, and of course all the, all the work we're doing with AI, 5g and robotics. So, uh, we actually just introduced about 65 new updates just this past month on all those SDKs. Uh, some of the newer stuff that's really exciting is the large language models. Uh, people are building some amazing AI. That's capable of understanding the Corpus of like human understanding, these language models that are trained on literally the continent of the internet to provide general purpose or open domain chatbots. So the customer is going to have a new kind of experience with a computer or the cloud. Uh, we're offering large language, uh, those large language models, as well as AI frameworks to help companies take advantage of this new kind of technology. >>You know, each and every time I do an interview with Nvidia or talk about Nvidia my kids and their friends, they first thing they said, you get me a good graphics card. Hey, I want the best thing in their rig. Obviously the gaming market's hot and known for that, but I mean, but there's a huge software team behind Nvidia. This is a well-known your CEO is always talking about on his keynotes, you're in the software business. And then you had, do have hardware. You were integrating with graviton and other things. So, but it's a software practices, software. This is all about software. Could you share kind of more about how Nvidia culture and their cloud culture and specifically around the scale? I mean, you, you hit every, every use case. So what's the software culture there at Nvidia, >>And it is actually a bigger, we have more software people than hardware people, people don't often realize this. Uh, and in fact that it's because of we create, uh, the, the, it just starts with the chip, obviously building great Silicon is necessary to provide that level of innovation, but as it expanded dramatically from then, from there, uh, not just the Silicon and the GPU, but the server designs themselves, we actually do entire server designs ourselves to help build out this infrastructure. We consume it and use it ourselves and build our own supercomputers to use AI, to improve our products. And then all that software that we build on top, we make it available. As I mentioned before, uh, as containers on our, uh, NGC container store container registry, which is accessible for me to bus, um, to connect to those vertical markets, instead of just opening up the hardware and none of the ecosystem in develop on it, they can with a low-level and programmatic stacks that we provide with Kuda. We believe that those vertical stacks are the ways we can help accelerate and advance AI. And that's why we make as well, >>Ram a little software is so much easier. I want to get that plug for, I think it's worth noting that you guys are, are heavy hardcore, especially on the AI side. And it's worth calling out, uh, getting back to the customers who are bridging that gap and getting out there, what are the metrics they should consider as they're deploying AI? What are success metrics? What does success look like? Can you share any insight into what they should be thinking about and looking at how they're doing? >>Yeah. Um, for training, it's all about time to solution. Um, it's not the hardware that that's the cost, it's the opportunity that AI can provide your business and many, and the productivity of those data scientists, which are developing, which are not easy to come by. So, uh, what we hear from customers is they need a fast time to solution to allow people to prototype very quickly, to train a model to convergence, to get into production quickly, and of course, move on to the next or continue to refine it often. So in training is time to solution for inference. It's about our, your ability to deploy at scale. Often people need to have real time requirements. They want to run in a certain amount of latency, a certain amount of time. And typically most companies don't have a single AI model. They have a collection of them. They want, they want to run for a single service or across multiple services. That's where you can aggregate some of your infrastructure leveraging the trading infant server. I mentioned before can actually run multiple models on a single GPU saving costs, optimizing for efficiency yet still meeting the requirements for latency and the real time experience so that your customers have a good, a good interaction with the AI. >>Awesome. Great. Let's get into, uh, the customer examples. You guys have obviously great customers. Can you share some of the use cases, examples with customers, notable customers? >>Yeah. I want one great part about working in videos as a technology company. You see, you get to engage with such amazing customers across many verticals. Uh, some of the ones that are pretty exciting right now, Netflix is using the G4 instances to CLA um, to do a video effects and animation content. And, you know, from anywhere in the world, in the cloud, uh, as a cloud creation content platform, uh, we work in the energy field that Siemens energy is actually using AI combined with, um, uh, simulation to do predictive maintenance on their energy plants, um, and, and, uh, doing preventing or optimizing onsite inspection activities and eliminating downtime, which is saving a lot of money for the engine industry. Uh, we have worked with Oxford university, uh, which is Oxford university actually has over two, over 20 million artifacts and specimens and collections across its gardens and museums and libraries. They're actually using convenient GPS and Amazon to do enhance image recognition, to classify all these things, which would take literally years with, um, uh, going through manually each of these artifacts using AI, we can click and quickly catalog all of them and connect them with their users. Um, great stories across graphics, about cross industries across research that, uh, it's just so exciting to see what people are doing with our technology together with, >>And thank you so much for coming on the cube. I really appreciate Greg, a lot of great content there. We probably going to go another hour, all the great stuff going on in the video, any closing remarks you want to share as we wrap this last minute up >>Now, the, um, really what Nvidia is about as accelerating cloud computing, whether it be AI, machine learning, graphics, or headphones, community simulation, and AWS was one of the first with this in the beginning, and they continue to bring out great instances to help connect, uh, the cloud and accelerated computing with all the different opportunities integrations with with SageMaker really Ks and ECS. Uh, the new instances with G five and G 5g, very excited to see all the work that we're doing together. >>Ian buck, general manager, and vice president of accelerated computing. I mean, how can you not love that title? We want more, more power, more faster, come on. More computing. No, one's going to complain with more computing know, thanks for coming on. Thank you. Appreciate it. I'm John Farrell hosted the cube. You're watching Amazon coverage reinvent 2021. Thanks for watching.

>>Live from San Diego, California at the cube covering to clock in cloud native con brought to you by red hat, the cloud native computing foundation and its ecosystem Marsh. >>Welcome back. This is the cubes coverage of CubeCon cloud date of con 2019 in San Diego, 12,000 in attendance. I'm just two minute and my cohost is John trier. And welcome back to the program. A multi-time cube alumni. You're on Aviv, who is the CTO and cofounder of a Gwoza. We've had quite a lot of, you know, founders, CTOs, you know, their big brains at this show, your own. So you know, let, let, let's start, you know, there's, there's really a gathering, uh, there's a lot of effort building out, you know, a very complicated ecosystem. Give us first, kind of your overall impressions of the show in this ecosystem. Yeah, so we're very early on on Desecco system. We were one of the first in the first batch of CNCF members when there were a few dozens of those. Not like a thousand of those. Uh, so I've been, I've been to all those shows. >>Uh, we're part of the CNCF committees for different things. And any initiating, I think this has become much more mainstream. I told you before, it's sort of the new van world. You know, I lot a lot more, uh, all day infrastructure vendors along with middleware and application vendor are coming here. All right, so, so one of the things we like having you on the program you're on is you don't pull any punches. So we've seen certain waves of technology come with big promise and fall short, you know, big data was going to allow us to leverage everything and you know, large percentage of, uh, solutions, you know, had to stop or be pulled back. Um, give us, what's the cautionary tale that we should learn and make sure that we don't repeat, you know, so I've been a CTO for many years in different companies and, and what everyone used to say about it, I'm always right. >>I'm only one year off usually. I'm usually a little more optimistic. So, you know, we've been talking about Cloudera and Hadoop world sort of going down and Kubernetes and cloud services, essentially replacing them. We were talking about it four years ago and what do you see that's actually happening? You know, with the collapse of my par and whore, then we're going to Cloudera things are going down, customer now Denon guys, we need equivalent solution for Kubernetes. We're not going to maintain two clusters. So I think in general we've been, uh, picking on many of those friends. We've, we've invented serverless before it was even called serverless with, with nuclear and now we're expanding it further and now we see the new emerging trends really around machine learning and AI. That's sort of the big thing. I'm surprised, you know, that's our space where essentially you're doing a data science platform as a service fully automated around serverless constructs so people can, can develop things really, really quickly. >>And what I see that, you know, third of the people I talk to are, have some relations to machine learning and AI. Yeah. Maybe explain that for our audience a little bit. Because when, you know, Kubernetes first started very much an infrastructure discussion, but the last year or two, uh, very much application specific, we hear many people talking about those data use cases, AI and ML early days. But you know how, how does that fit into the overall? It's simple. You know there, if you're moving to the cloud are two workloads. There is lift and shift workloads and there are new workloads. Okay, lift and ship. Why? Why bother moving them to Kubernetes? Okay, so you end up with new workloads. Everyone is trying to be cloud native server, elastic services and all that. Everyone has to feed data and machine learning into those new applications. This is why you see those trends that talk about old data integration, various frameworks and all that in that space. >>So I don't think it's by coincidence. I think it's, that's because new applications incorporate the intelligence. That's why you hear a lot of the talk about those things. What I loved about the architecture, what you just said is like people don't want to run into another cluster. I don't want to run two versions of Kubernetes, you know, if I'm moving there you, because you, but you're still built on that, that kind of infrastructure framework and, and knowledge of, of how to do serverless and how to make more nodes and fewer nodes and persistent storage and all that sort of good stuff and uh, and, and run TensorFlow and run, you know, all these, all these big data apps. But you can, um, you can talk about that just as a, as a, the advantage to your customer cause you could, it seems like you could, you could run it on top of GKE. >>You could run it on prem. I could run my own Coobernetti's you could, you could just give me a, uh, so >> we, we say Kubernetes is not interesting. I didn't know. I don't want anyone to get offended. Okay. But Kubernetes is not the big deal. The big deal is organizations want to be competitive in this sort of digital world. They need to build new applications. Old ones are sort of in sort of a maintenance mode. And the big point is about delivering new application with elastic scaling because your, your customers may, may be a million people behind some sort of, uh, you know, uh, app. Okay. Um, so that's the key thing and Kubernetes is a way to deliver those microservices. But what we figured out, it's still very complicated for people. Okay. Especially in, in the data science work. Uh, he takes him a few weeks to deliver a model on a Jupiter notebook, whatever. >>And then productizing it is about the year. That's something we've seen between six months to a year to productize things that are relatively simple. Okay. And that's because people think about the container, the TensorFlow, the Kuda driver, whatever, how to scale it, how to make it perform, et cetera. So let's, we came up with is traditionally there's a notion of serverless, which is abstraction with very slow performance, very limited set of use cases. We sell services about elastic scaling paper, use, full automation around dev ops and all that. Okay. Why cannot apply to other use cases are really high concurrency, high-speed batch, no distributed training, distributed workload. Because we're coming, if you know my background, you know, been beeping in Mellanox and other high-performance companies. So where I have a, we have a high performance DNA so we don't know how to build things are extremely slow. >>It sort of irritates me. So the point is that how can we apply this notion of abstraction and scaling and all that to variety of workloads and this is essentially what it was. It is a combination of high speed data technology for like, you know, moving data around on between those function and extremely high speed set though functions that work on the different domains of data collection and ingestion, data analytics, you know, machine learning, training and CIN learning model serving. So a customer can come on on our platform and we have testimonials around that, that you know, things that they thought about building on Amazon or even on prem for months and months. They'd built in our platform in few weeks with fewer people because the focus is on building the application. The focus is not about joining your Kubernetes. Now we go to customers, some of them are large banks, et cetera. >>They say, Alrighty, likes Kubernetes, we have our own Kubernetes. So you know what, we don't butter. Initially we, we used to bring our own Kubernetes, but then you know, I don't mind, you know, we do struggle sometimes because our level of expertise in Coobernetti's is way more sophisticated than what they have to say. Okay, we've installed Kubernetes and we come with our software stack. No you didn't, you know, you didn't configure the security, they didn't configure ingress, et cetera. So sometimes it's easier for us to bring, but we don't want him to get into this sort of tension with it. Our focus is to accelerate development on the new application that are intelligent, you know, move applications from, if you think of the traditional data analytics and data science, it's about reporting and what people want to do. And some applications we've announced this week and application around real time cyber collection, it's being used in some different governments is that you can collect a lot of information, SMS, telephony, video, et cetera. >>And in real time you could detect terrorists. Okay. So those application requires high concurrency always on rolling upgrades, things that weren't there in the traditional BI, Oracle, you know, kind of reporting. So you have this wave of putting intelligence into more highly concurrent online application. It requires all the dev ops sort of aspects, but all the data analytics and machine learning aspects to to come to come along. Alright. So speaking of those workloads for, for machine learning, uh, cube flow is a project, uh, moving the, moving in that space along it. Give us the update there. Yeah. So, so there is sort of a rising star in the Kubernetes community around how to automate machine learning workflows. That's cube flow. Uh, I'm personally, I one of the committers and killed flow and what we've done, because it's very complicated cause Google developed the cube cube flow as one of the services on, on a GKE. >>Okay. And the tweaked everything. It works great in GK, even that it's relatively new technology and people want to move around it in a more generic. So one of the things in our platform is a managed cube flow that works natively with all the rest of the solutions. And other thing that we've done is we make it, we made it fully. So instead of queue flow approach is very con, you know, Kubernetes oriented containers, the ammos, all that. Uh, in our flavor of Coupa we can just create function and you just like chain functions and you click and it runs. Just, you've mentioned a couple of times, uh, how does serverless, as you defined it, fit in with, uh, Coobernetti's? Is that working together just functions on top or I'm just trying to make here, >> you'll, you'll hear different things. I think when most people say serverless, they mean sort of front end application things that are served low concurrency, a Terra, you know, uh, when we mean serverless, it's, we have eight different engines that each one is very good in, in different, uh, domain like distributed deep learning, you know, distributed machine learning, et cetera. >>And we know how to fit the thing into any workloads. So for me, uh, we deliver the elastic scaling, the paper use and the ease of use of sort of no dev ops across all the eight workloads that we're addressing. For most people it's like a single Dreek phony. And I think really that the future is, is moving to that. And if you think about serverless, there's another aspect here which is very important for machine learning and Israel's ability. I'm not going to develop any algorithm in the world. Okay. There are a bunch of companies or users or developers that can develop an algorithm and I can just consume it. So the future in data science but not just data science is essentially to have like marketplaces of algorithms premade or analytic tools or maybe even vendors licensing their technology through sort of prepackaged solution. >>So we're a great believer of forget about the infrastructure, focus on the business components and Daisy chain them in to a pipeline like UFO pipeline and run them. And that will allow you most reusability that, you know, lowest amount of cost, best performance, et cetera. That's great. I just want to double click on the serverless idea one more time, but, so you're, you're developing, it's an architectural pattern, uh, and you're developing these concepts yourself. You're not actually, sometimes the concept gets confused with the implementations of other people's serverless frameworks or things like that. Is that, is that correct? I think there are confusion. I'm getting asked a lot of times. How do you compare your technology compared to let's say a? You've heard the term gay native is just a technology or open FAS or, yeah. Hold on. Pfizer's a CGIs or Alito. An open community is very nice for hobbies, but if you're an enterprise and it's security, Eldep integration, authentication for anything, you need DUIs, you need CLI, you need all of those things. >>So Amazon provides that with Lambda. Can you compare Lambda to K native? No. Okay. Native is, I need to go from get and build and all that. Serverless is about taking a function and clicking and deploying. It's not about building. And the problem is that this conference is about people, it people in crowd for people who like to build. So they, they don't like to get something that work. They want to get the build the Lego building blocks so they can play. So in our view, serverless is not open FAS or K native. Okay. It's something that you click and it works and have all the enterprise set of features. We've extended it to different levels of magnitude of performance. I'll give you an anecdote. I did a comparison for our customer asking me the same question, not about Canadian, but this time Lambda. How do you guys compare with London? >>Know Nokia is extremely high performance. You know we are doing up to 400,000 events on a single process and the customer said, you know what, I have a use case. I need like 5,000 events per second. How do you guys compare a total across all my functions? How do you compare against Lambda? We went into, you know the price calculator, 5,000 events per second on Lambda. That's $50,000 okay. $50,000 we do about, let's say even in simple function, 60,000 per process, $500 VM on Amazon, $500 VM on Amazon with our technology stick, 2000 transactions per second, 5,000 events per second on Lambda. That's 50,000. Okay. 100 times more expensive. So it depends on the design point. We designed our solution to be extremely efficient, high concurrency. If you just need something to do a web hook, use Lambda, you know, if you are trying to build a high concurrency application efficient, you know, an enterprise application on it, on a serverless architecture construct come to us. >>Yeah. So, so just a, I'll pause at this for you because a, it reminds me what you were talking about about the builders here in the early days of VMware to get it to work the way I wanted to. People need to participate and build it and there's the Ikea effect. If I actually helped build it a little bit, I like it more to get to the vast majority, uh, to uh, adopt those things. It needs to become simplified and I can't have, you know, all the applications move over to this environment if I have to constantly tweak that. Everything. So that's the trend we've been really seeing this year is some of that simplification needs to get there. There's focus on, you know, the operators, the day two operations, the applications so that anybody can get there without having to build themselves. So we know there's still work to be done. >>Um, but if we've crossed the chasm and we want the majority to now adopt this, it can't be that I have to customize it. It needs to be more turnkey. Yeah. And I think it's a friendly and attitude between what you'll see in Amazon reinvent in couple of weeks. And then what you see here, because there is those, the focus of we're building application a what kind of tools and the Jess is gonna just launch today on the, on the floor. Okay. So we can just consume it and build our new application. They're not thinking, how did Andy just, he built his tools. Okay. And I think that's the opposite here is like how can you know Ali's is still working inside underneath dude who cares about his team. You know, you care about having connectivity between two points and and all that. How do you implement it that, you know, let someone else take care of it and then you can apply your few people that you have on solving your business problem, not on infrastructure. >>You know, I just met a guy, came to our booth, we've seen our demo. Pretty impressive how we rise people function and need scales and does everything automatically said we want to build something like you're doing, you know, not really like only 10% of what you just showed me. And we have about six people and for three months where it just like scratching our head. I said, okay, you can use our platform, pay us some software license and now you'll get, you know, 10 times more functionality and your six people can do something more useful. Says right, let's do a POC. So, so that's our intention and I think people are starting to get it because Kubernetes is not easy. Again, people tell me we installed Kubernete is now installed your stack and then they haven't installed like 20% of all the things that you need to stop so well your own have Eve always pleasure to catch up with you. Thanks for the all the updates and I know we'll catch up with you again soon. Sure. All right. For John Troyer, I'm Stu Miniman. We'll be back with more coverage here from CubeCon cloud date of con in San Diego. Thanks for watching the cube.