(upbeat music) >> Hi, I'm Peter Burris, and welcome once again to another Cube Conversation from our studios here in beautiful Palo Alto, California. Today, we've got a really special guest. We're going to be talking about AI and some of the new technologies that are making that even more valuable to business. And we're speaking with Roy Kim, who's the lead for AI solutions at Pure Storage. Roy, welcome to theCUBE. >> Thank you for having me, very excited. >> Well, so let's start by just, how does one get to be a lead for AI solutions? Tell us a little bit about that. >> Well, first of all, there aren't that many AI anything in the world today. But I did spend eight years at Nvidia, helping build out their AI practice. I'm fairly new to Storage, I'm about 11 months into Pure Storage, so, that's how you get into it, you cut your teeth on real stuff, and start at Nvidia. >> Let's talk about some real stuff, I have a thesis, I (mumbles) it by you and see what you think about it. The thesis that I have: Wikibon has been at the vanguard of talking about the role that flash is going to play, flash memory, flash storage systems, are going to play in changes in the technology industry. We were one of the first to really talk about it. And well, we believe, I believe, very strongly that if you take a look at all the changes that are happening today with AI and the commercialization of AI and even big data and some other things that are happening, a lot of that can be traced back directly to the transition from memory, which had very very long lag times, millisecond speed lag times, to flash, which is microsecond speed. And, when you go to microsecond, you can just do so much more with data, and it just seems as though that transition from disk to flash has kind of catalyzed a lot of this change, would you agree with that? >> Yeah, that transition from disk to flash was the fundamental transition within the storage industry. So the fundamental thing is that data is now fueling this whole AI revolution, and I would argue that the big data revolution with Hadoop Spark and all that is really the essence underneath it is to use data get insight. And so, disks were really fundamentally designed to store data and not to deliver data. If you think about it, the way that it's designed, it's really just to store as much data as possible. Flash is the other way around, it's to deliver data as fast as possible. That transition is fundamentally the reason why this is happening today. >> Well, it's good to be right. (laughs) >> Yeah, you are definitely right. >> So, the second observation I would make is that we're seeing, and it makes perfect sense, a move to start, or trend to start, move more processing closer to the data, especially, as you said, on flash systems that are capable of delivering data so much faster. Is that also starting to happen, in you experience? >> That's right. So this idea that you take a lot of this data and move it to compute as fast as possible-- >> Peter: Or move the compute even closer to the data. >> And the reason for that, and AI really exposes that as much as possible because AI is this idea that you have these really powerful processors that need as much data as quickly as possible to turn that around into neural networks that give you insight. That actually leads to what I'll be talking about, but the thing that we built, this thing called AIRI, this idea that you pull compute, and storage, and networking all into this compact design so there is no bottleneck, that data lives close to compute, and delivers that fastest performance for your neural network training. >> Let's talk about that a little bit. If we combine your background at Nvidia, the fact that you're currently at Pure, the role that flash plays in delivering data faster, the need for that faster delivery in AI applications, and now the possibility of moving GPUs and related types of technology even closer to the data. You guys have created a partnership with Nvidia, what exactly, tell us a little bit more about AIRI. >> Right, so, this week we announced AIRI. AIRI is the industry's first AI complete platform for enterprises. >> Peter: AI Ready-- >> AI Ready Infrastructure for enterprises, that's where AIRI comes from. It really brought Nvidia and Pure together because we saw a lot of these trends within customers that are really cutting their teeth in building an infrastructure, and it was hard. There's a lot of intricate details that go into building AI infrastructure. And, we have lots of mutual customers at Nvidia, and we found is that there some best practices that we can pull into a single solution, whether it's hardware and software, so that the rest of the enterprises can just get up and running quickly. And that is represented in AIRI. >> We know it's hard because if it was easy it would've been done a long time ago. So tell us a little bit about, specifically about the types of technologies that are embedded within AIRI. How does it work? >> So, if you think about what's required to build deep learning and AI practice, you start from data scientists, and you go into frameworks like TensorFlow and PyTorch, you may have heard of them, then you go into the tools and then GPUs, InfiniBand typically is networking of choice, and then flash, right? >> So these are all the components, all these parts that you have access to. >> That's right, that's right. And so enterprises today, they have to build all of this together by hand to get their data centers ready for AI. What AIRI represents everything but data scientists, so start from the tools like TensorFlow all the way down to flash, all built and tuned into a single solution so that all, really, enterprises need to do is give it to a data scientist and to get up and running. >> So, we've done a fair amount of research on this at Wikibon, and we discovered that one of the reasons why big data and AI-related projects have not been as successful as they might have been, is precisely because so much time was spent trying to understand the underlying technologies in the infrastructure required to process it. And, even though it was often to procure this stuff, it took a long time to integrate, a long time to test, a long time to master before you could bring application orientations to bear on the problems. What you're saying is you're slicing all that off so that folks that are trying to do artificial intelligence related workloads can have a much better time-to-value. Have I got that right? >> That's right. So, think about, just within that stack, everything I just talked about InfiniBand. Enterprises are like, "What is InfiniBand?" GPU, a lot of people know what GPU is, but enterprises will say that they've never deployed GPUs. Think about TensorFlow or PyTorch, these are tools that are necessary to data scientists, but enterprises are like, "Oh, my goodness, what is that?" So, all of this is really foreign to enterprises, and they're spending months and months trying to figure out what it is, and how to deploy it, how to design it, and-- >> How to make it work together. >> How to make it work together. And so, what Nvidia and Pure decided to do is take all the learnings that we had from these pioneers, trailblazers within the enterprise industry, bring all those best practices into a single solution, so that enterprises don't have to worry about InfiniBand, or ethernet, or GPUs, or scale out flash, or TensorFlow. It just works. >> So, it sounds like it's a solution that's specifically designed and delivered to increase the productivity of data scientists as they try to do data science. So, tell us a little bit about some of those impacts. What kinds of early insights about more productivity with data science are you starting to see as a consequence of this approach. >> Yeah, you know, you'll be surprised that most data scientists doing AI today, when they kick off a job, it takes a month to finish. So think about that. When someone, I'm a data scientist, I come in on Monday, early February, I kick off a job, I go on vacation for four weeks, I come back and it's still running. >> What do you mean by "kicking off a job?" >> It means I start this workload that helps train neural nets, right? It requires GPUs to start computing, and the TensorFlow to work, and the data to get it consumed. >> You're talking about, it takes weeks to run a job that does relatively simple things in a data science sense, like train a model. >> Train a model, takes a month. And so, the scary thing about that is you really have 12 tries a year to get it right. Just imagine that. And that's not something that we want enterprises to suffer through. And so, what AIRI does, it cuts what used to take a month down to a week. Now, that's amazing, if you think about it. What used to, they only had 12 tries in a year, now they have 48 tries in a year. Transformative, right? The way that that worked is we, in AIRI, if you look at it there's actually four servers with FlashBlade. We figured out a way to have that job run across all four servers to give you 4X the throughput. Think that that's easy to do, but it actually is not. >> So you parallelized it. >> We parallelized it. >> And that is not necessarily easy to do. These are often not particularly simple jobs. >> But, that's why no one's doing it today. >> But, if you think about it, going back to your point, it's like the individual who takes performance-enhancement drugs so they can get one more workout than the competition and that lets them hit another 10, 15 home runs which leads to millions of extra dollars. You're kind of saying something similar. You used to be able to get only 12 workouts a year, now you can do 48 workouts, which business is going to be stronger and more successful as a result. >> That's a great analogy. Another way to look at it is, a typical data scientist probably makes about half a million dollars a year. What if you get 4X the productivity out of that person? So, you get the return of two million dollars in return, out of that $500,000 investment you make. That's another way of saying performance-enhancing drug for that data scientist. >> But I honestly think it's even more than that. Because, there's a lot of other support staff that are today, doing a lot of the data science grunt work, let's call it. Lining up the pipelines, building the, testing pipelines, making sure that they run, testing sources, testing sinks. And, this is reducing the need for infrastructure types of tasks. So, you're getting more productivity out of the data scientitists, but you're also getting more productivity out of all the people who heretofore were, you were spending on doing this type of stuff, when all they were doing was just taking care of the infrastructure. >> Yeah. >> Is that right? >> That's exactly right. We have a customer in the UK, one of the world's largest hedge fund companies that's publicly traded. And, what they told us is that, with FlashBlade, and not necessarily an AIRI customer at this time, but they're actually doing AI with FlashBlade today at Pure, from Pure. What they said is, with FlashBlade they actually got two engineers that were full time taking care of infrastructure, now they're doing data science. Right? To your point, that they don't have to worry about infrastructure anymore, because the simplicity of what we bring from Pure. And so now they're working on models to help them make more money. >> So the half a million dollars a year that you were spending on a data scientist and a couple of administrators, that you were getting two million dollars worth, that you're now getting two million dollars return, you can now take those administrators and have them start doing more data science, without necessarily paying them more. It's a little secret. But you're now getting four, five, six million dollars in return as a consequence of this system. >> That's right. >> As we think about where AIRI is now, and you think about where it's going to go, give us a sense of, kind of, how this presages new approaches to thinking about problem solving as it relates to AI and other types of things. >> One of the beauty about AI is that it's always evolving. What used to be what they call CNNs as the most popular model, now is GANs, which-- >> CNN stands for? >> Convolution Neural Nets. Typically used for image processing. Now, people are using things like Generative Adversarial Networks, which is putting two networks against each other to-- >> See which one works and is more productive. >> And so, that happened in a matter of a couple of years. AI's always changing, always evolving, always getting better and so it really gives us an opportunity to think about how does AIRI evolve to keep up and bring the best, state of the art technology to the data scientist. There's actually boundless opportunities to-- >> Well, even if you talk about GANs, or Generative Adversarial Networks, the basic algorithms have been in place for 15, 20, maybe even longer, 30 years. But, the technology wouldn't allow it to work. And so, really what we're talking about is a combination of deep understanding of how some of these algorithms work, that's been around for a long time, and the practical ability to get business value out of them. And that's kind of why this is such an exploding thing, because there's been so much knowledge about how this stuff, or what this stuff could do, that now we can actually apply it to some of these complex business problems. >> That's exactly right. I tell people that the promise of big data has been around for a long time. People have been talking about big data for 10, 20 years. AI is really the first killer application of big data. Hadoop's been around for a really long time, but we know that people have struggled with Hadoop. Spark has been great but what AI does is it really taps into the big data platform and translates that into insight. And whatever the data is. Video, text, all kinds of data can, you can use AI on. That really is the reason why there's a lot of excitement around AI. It really is the first killer application for big data. >> I would say it's even more than that. It's an application, but it's also, we think there's a bifurcation, we think that we're seeing an increased convergence inside the infrastructure, which is offering up greater specialization in AI. So, AI as an application, but it also will be the combination of tooling, especially for data scientists, will be the new platform by which you build these new classes of applications. You won't even know you're using AI, you'll just build an application that has those capabilities, right? >> Right, that's right, I mean I think it's as technical as that or as simple as when you use your iPhone and you're talking to Siri, you don't know that you're talking to AI, it's just part of your daily life. >> Or, looking at having it recognize your face. I mean, that is processing, the algorithms have been in place for a long time, but it was only recently that we had the hardware that was capable of doing it. And Pure Storage is now bringing a lot of that to the enterprise through this relationship with Nvidia. >> That's right, so AIRI does represent all the best of AI infrastructure from all our customers, we pulled it into what AIRI is, and we're both really excited to give it to all our customers. >> So, I guess it's a good time to be the lead for AI solutions at Pure Storage, huh? >> (laughs) That's right. There's a ton of work, but a lot of excitement. You know, this is really the first time a storage company was spotlighted and became, and went on the grand stage of AI. There's always been Nvidia, there's always been Google, Facebook, and Hyperscalers, but when was the last time a storage company was highlighted on the grand stage of AI? >> Don't think it will be the last time, though. >> You know, it's to your point that this transition from disk to flash is that big transition in industry. And fate has it that Pure Storage has the best flash-based solution for deep learning. >> So, I got one more question for you. So, we've got a number of people that are watching the video, watching us talk, a lot of them very interested in AI, trying to do AI, you've got a fair amount of experience. What are the most interesting problems that you think we should be focusing on with AI? >> Wow, that's a good one. Well, there's so many-- >> Other than using storage better. >> (laughs) Yeah, I think there's so many applications just think about customer experience, just one of the most frustrating things for a lot of people is when they dial in and they have to go through five different prompts to get to the right person. That area alone could use a lot of intelligence in the system. I think, by the time they actually speak to a real live person, they're just frustrated and the customer experience is poor. So, that's one area I know that there's a lot of research in how does AI enhance that experience. In fact, one of our customers is Global Response, and they are a call center services company as well as an off-shoring company, and they're doing exactly that. They're using AI to understand the sentiment of the caller, and give a better experience. >> All that's predicated on the ability to do the delivery. So, I'd like to see AI be used to sell AI. (Roy laughs) Alright, so Roy Kim, who's the lead of AI solutions at Pure Storage. Roy, thank you very much for being on theCUBE and talking with us about AIRI and the evolving relationship between hardware, specifically storage, and new classes of business solutions powered by AI. >> Thank you for inviting me. >> And again, I'm Peter Burris, and once again, you've been watching theCUBE, talk to you soon. (upbeat music)