>> Presenter: From theCUBE Studios in Palo Alto and Boston, connecting with Dot leaders all around the world. This is a CUBE conversation. >> Everyone welcome to theCUBE studio here in Palo Alto. I'm John Furrier, host of theCUBE. We're here with our quarantine crew. We've been here for three months quarantining but we're getting the stories out. We're talking to all of our favorite guests and most important stories in technologies here remotely and we have a great conversation in store for you today with Amit Walia CEO of Informatica. Cube alumni, frequent guest of theCUBE, now, the CEO of Informatica. Amit, great to see you. Thank you for coming on this CUBE conversation. >> Good to see you John. It's different to be doing this like this versus being in the studio with you but I'm glad that we could leverage technology to still talk to each other. >> You're usually right here, right next to me, but I'm glad to get you remotely at least and I really appreciate you. You always have some great commentary and insights. And Amit, before we get into the real meaty stuff that I'd love around the data, I want to get your thoughts on this COVID-19 crisis. It's a new reality, it's highlighted as we've been reporting on SiliconANGLE for the past few months. The at scale problems that people are facing but it's also an opportunity. People are sheltered in place, there's a lot of anxiety on what their work environment is going to look like but the world still runs. Your thoughts on the current crisis and how you're looking at it, how you're navigating it as a leader. >> No doubt, it is a very unique situation we all live in. We've never all faced something like this. So I think first of all, I'll begin by expressing my prayers for anyone out there who has been impacted by it and of course, a huge round of thank you to all the heroes out there at the front lines. The healthcare workers, the doctors, the nurses (mumbles) so we can't forget that. These are very unique situations but as you said, let's not forget that this is a health crisis first and then it becomes an economic crisis. And then, as you said there is a tremendous amount of disruption and (mumbles) I think all of them will go through some phases and I think you can see already while there is disruption in front of us, you see the digital contents of organizations who are ready for that have definitely faced it lot better but as obviously the ones that have been somewhat in the previous generations, let's just say business models or technologies models are struggling through it. So there is a lot data chain. I think they're still learning. We're absolutely still learning and we will continue to learn til the end of this year and we'll come out very different for the next decade for sure. >> If anyone who's watching goes to YouTube on the SiliconANGLE CUBE and look at your videos over the years, we've been talking about big data and these transformational things. It's been an inside the industry kind of discussion. Board room for your clients and your business and Informatica but I think this is now showing the world this digital transformation. The future has been pulled forward faster than people have been expecting it and innovation strategy has been on paper, maybe some execution but now I think it's apparent to everyone that the innovation strategy needs to start now because of this business model impact, the economic crisis is exposed. The scale of opportunities and challenges, there will be winners and losers and projects still need to get done or reset or reinvented to come out of this with growth. So this is going to be the number one conversation. What are your thoughts around this? >> No, so I've talked to hundreds of customers across the globe and we see the same thing. In fact actually, in some ways as we went through this, something very profound dawned on me. We, John, talked about digital transformation for the last few years and clearly digital transformation will accelerate but as I was talking to customers, I came to this realization that we actually haven't digitally transformed. To be honest, what happened in the last three to four years is that it was more digital modernization. A few apps got tweaked, a few front-ends got tweaked but if you realize, it was more digital modernization, not transformation because in my opinion, there are four aspects to digital transformation. You think of new products and services, you think of new models of engaging with your customers, you think of absolutely new operating models and you think of fundamentally new business models. That's a whole rewrite of an organization, which is not just creating a new application out there, fundamental end to end transformation. My belief is, our belief is that, now starts a whole new era of transformation, digital transformation. We've just gone through digital modernization. >> Well, that's a great point and the business model impacts create... And in times of these inflection points, and again, you're a student of history in the tech industry, PC revolution, TCP IP. These are big points in time. They're not transitions. The big players tend to win the transitions. When you have a transformation, it's a Cambrian explosion of new kinds of capabilities. This is really, I would agree with your point but I think it's going to be a Cambrian explosion because the business model forcing function is there. How do you see it play, 'cause you're in the middle of all this, 'cause you guys are the control plane for data in the industry as a company. You enable these new apps. Could you share your-- >> So, we see a lot of that and I think the way to think about it, I think first of all, you said it right. This is a step function changing orbit. This is a whole new... You get to a new curve, you go to a different model. It's a whole new equation you're hiking for the curve you're going to be on. It's not just changing the gradient of the curve you've been on, this is going to be a whole journey. And when we think of the new world of digital transformation, there are four elements that are taught. First of all, it has to be strategic. It has to be Board, CEO, executive topped down, fundamentally across the whole organization, across every function of an organization. Second one you talked about scale. I believe this is all about innovating at scale. It's not about, hey, let me go put a new application in some far plans of my business. You've got to innovate at scale, end to end change does not happen in bits and pieces. Third one, this is cloud native, absolutely cloud native. If there was any minuscule of doubt, this is taking it away. Cloud nativity is the fundamental differentiator and the last but not the least is digital natives, which is where everybody wants to go become a digitally transformed company that are data-led. You got to make data-led decisions. So for competence, strategic mindset, innovation at scale cloud nativity and being data-led is going to define digital transformation. >> I think that encapsulates absolutely innovation strategy. I agree with you 100%, that's really insightful. I want to also get your thoughts on some things that you're talking about and you have always had some really kind of high level conversations around this and theCUBE has been a very social organization. We'd love to be that social construct between companies and audiences but you use a term, the digital transformation, the soul of digital transformation is data 4.0. This idea of having a soul is interesting because the apps all have personalization built in. You have CLAIRE, you've been doing CLAIRE AI for a while. So this idea of social organizations, a soul is kind of an interesting piece of metadata you're putting out in the messaging. What do you mean by that? How can digital transmission have a soul? >> I think we talked about it a lot and I think it just came to me that, look at the end of the day, any transformation is so fundamental to anything that anybody does and I think if you think about, you can go to a fundamental transformation that is just qualitative, it's qualitative and quantitative. It's about a human body, it's about a human body transforming itself and then something doesn't have a soul, John, it does not have life. It cannot truly move to the next paradigm. So I believe that, any transformation has to have a soul and the digital world is all about data. So obviously, we believe that we're walking into a data-for-data world where, as I said, the four pillars of digital transformation would be data-led and I believe data is the soul of that transformation and data itself is moving into a new paradigm. You've heard us talk about 1.0, 2.0, 3.0, and this is the new world of 4.0, a data 4.0 which basically is all about cloud nativity, intelligent automation, AI powered, focusing on data, trust in data ethics and operations and innovation at scale. When you bring these elements together, then that enables digital transformation to happen on the shoulders of data 4.0, which in my opinion, is the soul of digital transformation. >> All right, so just rewind on data 4.0 for a minute. Pretend I'm a CIO, I'm super busy. I don't have time to read up about it. Give me the bottom line, what is data 4.0? Describe it to me in basic terms, is it just an advancement, acceleration? What's the quick elevator pitch on 4.0, data 4.0? >> Very simple?. We're all walking into a world where we're going to be digital. Digital means that we're basically going to be creating tons of data. By the way, and data is everywhere. It's not just within the four walls of us. It's basically what I call transaction and interaction and with the scale and volume of data increasing, the complexity of it increasing. We want to make decisions. I say, tomorrow's decision, today and with data that is available to us yesterday, so I can be better at that decision. So we need intelligence, we need automation, we need flexibility, which is where AI comes in. These are all very fundamental rewrites of the technology stack to enable a fundamental business transformation. So in that world, data is front and center and you look at the amount of data we are going to collect, the whole concept of data ethics and data trust become very important, not just Goodwill governance, governance is important but data privacy, data trust becomes very important. Then we're going to do things like contact tracing, it's very important for the society but the ethics, trust and privacy of what you and I will give to the government is going to become very much important. So to me, that world that we go in, every enterprise has to think data first, data led, build an infrastructure to support the business in that context and then, as I said, then the soul, which is data will give life to digital transformation. >> That's awesome. Love the personalization and the soul angle on it. I always believe that you guys had that intelligent automation fabric and to me, you said earlier, cloud native is apparent to everyone now. I think out of all this crisis, I think the one thing that's not going to be debated anymore is that cloud native is the operating model. I think that's pretty much a done deal at this point. So having this horizontally scalable data, you know I've been on this rant for years. I think that's the killer app. I think having horizontally scalable data is going to enable a lot, souls and more life. So I got to ask you the real, the billion dollar question. I'm a customer of yours or prospect or a large enterprise. I'm seeing what's happening at scale, provisioning of VPNs for 100% employees at home, except for the most needed workers. I now see all the things I need to either process, I need to cancel and projects that double down on. I still got to go out and build my competitive advantage. I still have to run my business. So I need to really start deploying right out of the gate data centric, data first, virtual first, whatever you want to call it, the new reality first, this inflection point. What do I do? What is the things that you see as projects or playbook recipes that people could implement? >> First of all, we see a very fundamental reevaluation of the entire business model. In fact, we have this term that we're using now that we have to think of business has a business 360 and if I think about it in this new world, that the businesses that stood the test is that had basically what I call, a digital supply chain or in a very digital scalable way of interacting with their customers, being able to engage with their customers. A digital fabric often making sure that they can bring their product and services to the customers very quickly or in some cases, if they were creating new products and services, they had the ability for a whole new supply chain to reach that end customer. And of course, a business model that is flexible so they dont obviously, they can cater to the needs of their customers. So in all of these worlds, customers are a building digital, scalable data platforms and when I say platforms, it's not about some monolithic platform. These are, as you and I have talked about, very modular microservices based platform that reside on what we call metadata. Data has to be the soul of the digital enterprise. Metadata is the nervous system, that makes it all work. That's the left brain, right brain, that makes it all work, which is where we put AI on top. AI that works for the customers and then they leverage it but AI applied to that metadata allows them to be very flexible, nimble and make these decisions very rapidly, whether they are doing analytics for tomorrow's offering to be brought in front of a customer or understanding the customer better to give them something that appeals to them in changing times or to protect the customer's data or to provide governance on top of it. Anything that you would like to do has to ride on top of what I call a, AI led metadata driven platform that can scale horizontally. >> Okay, so I got to go to the next level on this, which is, okay, you got me on that. I hear what you're saying, I agree, great. But I got to put my developers to work and I got insight, I got analytics teams, I got competencies but Amit, my complexities don't go away. I still got compliance at scale, I got governance at scale but I also got, now my developers not just to get analytical insight, there's great dashboards and there's great analytic data out there, you guys do a good job there. I got to get my developers coding so I can get that agility of the data into the apps for visualization in the app or having a key ingredient of the software. How do I do that? What's your answer to that one? >> So, that's a critic use case. If you think about it, for a developer, one of the biggest challenge for analytics project is how do I bring all the data that is in sites across the enterprise so then I can put it in any kind of visualization analytics tool and things are happening at scale. An enterprise is spread across the globe. It's so many different data sources available everywhere. Again, what we've done is that as a part of the data platform when you focus upon the metadata, that allows you to go to one place where you can have full access to all of the data assets that are available across (mumbles). Do you remember at theCUBE years ago, we unveiled the launch of our enterprise data catalog, which as I said, was the Google for enterprise data through metadata. Now, developers don't have to go start wasting their time, trying to find whether data has (mumbles), through the catalog that CLAIRE is in-built, they have access to it. They can start putting that to work and figuring out how do I take different kinds of data? How do I put it in some data times tool? Through which we have the in-built integrations. Do what I call the valuable last mile work, which is where the intelligence is needed from them versus spend their energy trying to figure out where good data, clean data, all kinds of data sets. We have eliminated all of that complexity with the help of metadata data platform, CLAIRE, to let the developers do what I call value-added productive work. >> Amit, final question for you. I know you talk to customers a lot, you're always on the road, you got a great product background, that's where you came from, good mix understanding of the business but now your customers and prospects are trynna put the fires out. The big room that... No one's going to talk about their kitchen appliances when the house is burning down and in some cases on the business model side or if it's a growth strategy, they're going to put all their energies where the action is. So getting mind share with them is going to be very difficult. How are you as a leader and how is Informatica getting in front of these folks and saying, "Look, I know things are tough "but we're an important supplier for you." How do you differentiate? How are you going to get that mind share? What are some of those conversations? 'Cause this is really the psychology of the marketplace right now, the buyer and the customer. >> Well, first of all, obviously we had to adapt to reach our customers in a different way because, virtually based just like you and I are chatting right now and to be candid, our teams were fantastic in being able to do it. We've actually already had multiple pretty big sides of it. In fact, the first week before we started (mumbles), we had set up the MDM and Data Governance Summit up in New York and we expected thousands of customers to come there, ask them (mumbles) virtual and we did it virtually and we had three times more people attend the virtual event. It was much easier for people who attended from the confines of their living room. So we'd gone 100% virtual and good news is, that our customers are heavily engaged. We've actually had more participation of customers coming and attending our events. We've had obviously our customers speaking, talking about how they've created value. In light of that next week, we have the big event which we're calling, CLAIREview named after ClAIRE AI engine. It's basically a beautiful net-filled tech experience. We'll have a keynote, we'll have seasons and episodes, people can do bite-sized viewing at their own leisure. We'll talk about all kinds of transformation. In fact, we have Scott Guthrie who runs all of Azure and Cloud at Microsoft as a part of my Keynote. We have two great customers, CDO at XXL and a CEO of GDR nonprofit that does (mumbles) on diabetes work talk about the data journeys. We have Martin Byer from Gardner. So we've been able to pivot and our customers are heavily engaged because data is a P-zero or a P-one activity for them to invest in. So we haven't seen any drop-off in customer engagement with us and we've been very blessed that we have a very loyal and a very high retention rate customer base. >> Well, I would expect that being the center of the value proposition, where we've always said data has been. One more final question since this just popped in my head. You and I have been talking about the edge for years. Certainly now the edge is exposed, we all know what the edge is, it's working at home. It's the human, it's me, it's my IOT devices. More than ever, the edge is now the new perimeter. It's the edge and now the edges is there. There's something that you've been talking a while. This is another part of data fabric that's important. Your view on this new edge that's now visualized by everybody, realized this immersion. What's your thoughts on the edge? >> Oh, I think the edge is real now. You and me chatted about that almost four years ago and I (mumbles). Look, think of it this way. Think of how security is going to change. There's no more data center to which we route our traffic anymore. It's sitting over there somewhere where no human beings is going to have access. People are connecting to all kinds of cloud application directly from their offices or living rooms or their cultures and the world of security has to change in that context. And people are more going to be more, enterprise (mumbles) are more worried about, hey, how do I make sure that that data centric, privacy and security is there in my device and that connects to the third party cloud vendors versus I can't transfer traffic to mine, everything to my VPN. So the edge is going to become a lot more compute intensive as well as it will require a lot of the elements that are, to be honest, used to be data center centric. We have to lighten them and bring them to the edge so enterprises can feel assured and working because at the end of the day, they have to run a business by the standards that an enterprise is held to. So you will see a ton of innovation, by the way, robotics. Robotics is going to make edge even more interesting in live view. So I see the next couple of years, heavy IOP edge computing, just like the clients that are modeled to mainframe that the PC became like a mainframe in terms of compute capacity. I guarantee at the desktop, compute capacity will go down to the edge and we're going to see that happen in the next five years or so. >> The edge is the new data centers. I always say, it's the land is the way, the way is the land. Amit, great to see you and thanks for sharing and I'm sorry, we can't do it in person but this has been like a fireside chat meets CUBE interview, remote. Thanks for spending the time and sharing your insights and we've always had great interviews at your events, virtual again, this year. We're going to spread it out over time, good call. Thanks for coming on, I appreciate it. >> Thanks, John, take care. >> Okay, Amit, CEO of Informatica, always great to get the conversation updates from him on the industry and what Informatica, as at the center of the value proposition data 4.0. This is really the new transformation, not transition, data science, data, data engineering, all happening. theCUBE with our remote interviews, bringing you all the coverage here from our Palo Alto studios, I'm John Furrier. Thanks for watching. (gentle music)

>>live from San Francisco. It's the Q covering Micron Insight 2019 to You by Micron. >>Welcome back, everybody. We hear a Pier 27 in San Francisco. Beautiful day. David Floor is my co host on Day Volante, and this is Micron Inside. 2019. Jeff Baylor is here. He's the corporate vice president of the embedded business unit at Micron. Jeff, great to see you again. >>Thank you. Nice to be here >>so love to talk about autos. I o. T Edge. Use cases to talk about the focus of your team. Let's start there. Yeah, >>sure. So the embedded business to point. It's absolutely focused on the automotive industry's way. Call industrial markets. So factory automation, surveillance and stolen a swell as a consumer electronics businesses on we're really in across all those sort of focused on how connectivity and compute is changing inside of those. And, of course, how that drives memory. >>I mean, yeah, memory and storage. They hide in places that we use every day. You don't see them, but if they weren't there, you wouldn't be able to use all these devices. They wouldn't be as life changing as they are. So you know you mentioned some of the consumer stuff. You know what the big trends that are driving your business? Well, I do >>think it is absolutely. That's sort of the ubiquity of connectivity. First of all, and then, sort of the ubiquity of compute has enabled all of these what used to be sort of isolated applications to now be connected and doing a whole lot more analytics inside that machine. Do you think about intelligence in your thermostat on the wall? You think about intelligence, obviously, in the automotive business, where safety features and so on are using so much more electron ICs and a I machine learning. And that's happening really in every application, whether it's the smart speakers at home, voice control on your TV and so on and so forth. All of those drive more intelligence, more connectivity and then more memory and storage behind that. >>When people talk about automotive, of course, everybody wants to talk about autonomous vehicles. I love to talk about autonomous vehicles, but there's so much action going on in today's vehicles dozens and dozens of microprocessors throwing off all kinds of data. So give us the update on the automotive industry. >>Yeah, you're exactly right. I mean, autonomous gets the headlines and it will for several more years just be headlines more or less right? And the real story is what we call eight ass or advanced driver assistant system. So things like lane departure warning, lane departure, keeping things like auto emergency braking those those sort of much simpler, easier problems to solve are still very compute intensive on. So are driving a huge growth and electronics on memory of storage inside the car. The other major part of the car market in the automotive market is what we call infotainment, sort of the center console. More and more large screens going into that more high function capabilities being integrated in that whether it's navigation or streaming media service is and all of those air driving again a much richer mix that's required >>for those applications. I was at the arm conference and they were talking about automotive and some of the challenges, one of the most fascinating areas they were talking about. How do you make something that will last for 20 years in the car on make it such that if it does go wrong that it that it could recover seamless less. Can you talk about some of the technologies that >>are sort of two parts to that? Unpack a little bit? First through? What does it take to succeed in automotive? First of all, it's all about quality. Yeah, right. It is quality, quality, quality location, location, location. It's quality. It's it's reducing and eliminating defense fundamentally at the end of the day and so inside of our process. Design inside of our technology designed our product designs. Our product manufacturing flows are all designed to sort of fundamentally improve and continue to improve the quality level because at the end of the day, that is what what makes or breaks you in the car. As soon as you solve that, you know, small problem. Next problem is longevity and stability of that solution, because the design cycle itself is shortening and automotive. But it's a very long design cycle, and then the life cycle in automotive is still very, very long. I mean, the average car on the road in the U. S. Is 12 or 15 years old, right, and that needs to both continue to be viable but also often need toe continue shipping that product. It's gonna shipment volumes or have spares and replace. So So we have a strategy that sort of focused on both bringing those leading edge technologies that Micron has into automotive as soon as possible and that timeline is shrinking. But then also having a very long life manufacturing strategy to continue to provide those for so long. >>So you're certainly a leader in automotive. You might even be the leader. I'm not sure I have the data, but what is it you mentioned? You know, quality and those other factors. What is it that's allowing you to do so well in automotive? >>So So we are the beater for sure. We're about 40% market share, which is a little more than three times as big as the nearest competitors, right, So leader by far, really an automotive. And it's been a very long time that we're in this industry and very focused on. So it is. It is about the product mix and bringing in particular lately leading edge technology into that story. You know, we are at the very beginnings of LP five, the low power GDR five generation, where the very beginnings of that rolling out into mobile applications, its primary markets at the same time, almost literally the same time. Way air sampling and providing that into our automotive customers and our automotive partners to start beginning building their systems around L P. Five. So that time to adopt leading edge technology is rowing is shrinking very rapidly. And so we're able to provide that leading edge Tech started, coupled with that long life solution and then one of the areas, when you think about being in a 40% market share position, way air investing tremendously in sort of partnering with the customers around, essentially defining and driving the innovation that they need to deliver So way have a number of labs that we've established customer facing labs that were able to bring customers and even our customers customers. So the Auto am is directly into those labs to start looking at usage models and architectural sort of feasibility and optimization kinds of things that we could then plan into our road map to follow two or three years later. After that, >>a lot of domain expertise there, so tremendous I said the Derrick Dicker that Micron has a very large observation space. You sell to a lot of different channels and I want to ask you about industrial I ot David night. We spent a lot of time in the Enterprise and we see a lot of I t company saying, Hey, here's a box. We're gonna throw it over. We're gonna go dominate the edge anywhere you talkto operations, technology, professions there like No, we're talking about machines and equipment and it's like this whole different parlance and language. So what are you seeing? Just in terms of the ecosystem, how it's developing the sort of analog going to digital And that whole explosion? Yeah, >>again, Industrial is extremely broad market, and it means a 1,000,000,000 things toe people. Right? So So, one of the first things we have to do is sort of narrow the field a little bit, at least into specific verticals and specific areas. Way have the right product mix and opportunity, right? So, for example, in the in the space of factory automation, it's a little bit what you're just saying the operational technology guys are trying to figure out how they're gonna drive efficiency, drive productivity inside a factory on, and that is often a question of instrument ing, and putting in my crown is doing a lot of this sort of smart manufacturing deployment. Putting this sensor network multiple cameras, multiple high resolution cameras, audio sensors, accelerometers, sort of sensors and capturing all of that sensor data to Dr Things like better predictive maintenance, better sort of yield detection or excursion detection kind of capability. So you could tell this machine, you know, seven days, five days out of the week Sounds like this. But last night at 10 o'clock, it started sounding different way. Don't know what it means necessarily, but we can detect that. And that's where all of the A I and Machine Learning is now being applied to say. And that means it's due for a P M. About this particular portion of >>what about security at the edge, obviously a hot topic in the Enterprise on every C. I ose mind what's happening with security in Io ti industrial out in the edge. Yeah, I think >>to some extent, security in the I. O. T. I think is, is why I ot is where it is in the hype cycles. Maybe it's sort of still at the bottom of one of these types cycles, meaning solving that increasing security problem, that cyber security problem that the edge is really a big problem. You saw you know the hacks a few years back of the Jeep charity. You saw the hack two years back on surveillance cameras. All these cameras moving toe i p surveillance cameras means they're now connected and open to the world. Dispersed. He just announced last week in a report that basically showed I ot specific hacks up seven fold or seven fold this year after being up tenfold last year. So it's absolutely a growing problem for people thinking about deploying again. Connectivity is a great tool in a great weapon, Depending. And I was so so. One of my crown is doing is is way. >>Have a >>solution called authentic, which is essentially a cybersecurity, is a secure element built into the non volatile memory that goes in each one of these systems. So today, security is not a one chip problem. It is a full and and system problem. And so what we're tryingto build with that is the capability at a very sort of lowest level in the system right where the code is right where the four part of the system is to protect that in the memory itself and sort of a test that that is safe and secure. And then the system can build out about around that. And that sort of simple boot device, in the case of a nor device or Anand device is in every embedded application >>right in the world, >>right? I mean, you think about you go back a long way, Stuxnet. You know, 10 plus years ago with a seaman's controller, which was the and now you think about fast forward, how much Maur infrastructure is out there? How much more complicated it is, It's ah, it's a scary situation is Oh, it is so that we think that's a >>big opportunity. And we're making the announcement later, uh, later in the show today, on an extension of what we're doing already in that space. >>I know you're working with other vendors. People like >>me are worry with Yes, >>it is really >>an end to end. >>This is really an end to an an ecosystem >>activity, for sure, because again, arm is a great example. You know, all of the S o. C. Vendors. You know, everybody in this industry has some slice of the of the rules. Let's say to figure out how they're going to secure this system and we're tryingto build a basic building block that they can then build on >>that when we started this morning was really quiet. But the crowd is rolling in. Now there's a buzz that you can hear, hear. The key was excited to be here, Jeff. Thanks very much for coming on. The king here to see you again. >>Very much nicer here. >>All right. Keep it right to everybody. We're gonna be taking a short break. We'll be back. Day long coverage wall to Wall of Micron inside. 2019. You're watching the cube.

>> Live from San Francisco, it's theCUBE, covering Micron Insight 2018. Brought to you by Micron. >> Welcome back to San Francisco everybody. You're watching theCUBE the leader in live tech coverage. I'm Dave Vellante. He's David Floyer, and we're covering Micro Insight'18. It's all about bringing together artificial intelligence and the memory and storage requirements. We're here on the embarcadero. We've got treasure island that way. We've got the financial district over there. We've got Golden Gate bridge behind us. Tom Eby is here as senior vice president and GM of Micron's booming compute and networking business unit. Good to see you Tom. >> Great to be here. >> And Permal Savla is here. He's the director of deep learning at NVIDIA. Welcome. >> Thank you. >> So obviously some of these new emerging work loads require collaboration between folks like Micron and folks like NVIDIA. But Tom why don't you kick it off. What are some of the big trends that you're seeing in some of these alternative work loads that's driving this collaboration? >> Well a lot of what we're talking about here today is the drive of AI and machine learning work loads, and the implications for memory. Certainly there's a host of them, natural language processing, photo and image recognition, applications in medical research, applications in optimizing manufacturing like we're doing in our fabs, and there's many many more. And of course what's exciting for us is that to support those in an optimized way really does require the mating of the optimal processing architecture, things like GPUs. With the right high band width with low latency memory and storage solutions. That's what leads to great partner ships between partnerships like Micron and NVIDIA. >> David was explaining at our open the intensity of the work loads that you guys are serving, and how much more resources that requires to actually deliver the type of performance. Maybe you could talk about some of the things that you're seeing in terms of these emerging work loads. >> Yes, so at NVIDIA, we build systems for X rated computing. AI and deep learning is a very quickly expanding field at this point which needs a lot of CP horse power. What we are seeing is that different applications like you said there's image processing, whether it's video, whether it's natural language processing the amount of data that is there, that is required to do deep learning and AI around it, we break it up into two work flows. One is the training where you actually train the software, and make it intelligent enough to then go and do inference later on. So that you can go and get you results out of it at the end of it. We concentrate on this entire workflow. That's where when we are looking at it from a training perspective, the GPU gives it the processing power. But at the same time all the other components around it perform at the peak. That's where the memory comes in. That's where the storage comes in, and we need to process that data very quickly. >> Yeah, so we know from system's design that you got to have a balanced system or else you're just going to push the bottle necks around. We've learned that over the years, but so it's more than just slapping on a bunch of storage and a bunch of memory. You're doing some other deeper integration, is that correct and what is that integration? >> Yeah, I think the two companies have had a great relationship, just to talk about a couple examples. We essentially co-defined a technology called GEDR 5X, which greatly enhanced the speed of graphics technology. We gently introduced that to the marketplace with NVIDIA about 18 months ago. And then worked with them again very closely on a technology called GDDR six, which is the next generation of even faster technology. We were their launch and ran partner for their recently announced G-force RTX line of cards. It's a very deeply engaged early in the process, define the process, define the standards, jointly develop the solution. Very intimate sharing in the supply chain area. It's a great relationship for us. We're excited about how we can continue to expand and extend that relationship by going forward. >> So obviously there's the two parts of it. You said the learning part of it, and the inference part of the computing. What do you think is the difference between the two? I mean obviously at the end of the day, the inference part is critical. That's got to be the fastest response time. You have to have that in real time. Can you talk a little bit about what you're doing to really speed that up, to make that micro seconds as opposed to milliseconds? >> So from an NVIDIA perspective we build the entire end to end tools steps for training and inferencing. We have a set of libraries that we have made it openly available for all of our customers, all our partners, and all users. So that they can go download it, and do the training so they can use the different frameworks and libraries to accelerate the work that they're doing. And then transform it onto the inference spot. We have something called denser RT, which is basically denser real time. That gives the capability to get these answers very quickly. So on our D4 of the tuning, Chip said that we just announced. We can get a very high performance for our image. So any kind of image recognition or image processing that we need to do, we can do that on the systems very quickly. And we can meet, rebuild entire architectures. So it's not just about one piece. It's about the whole end to end architecture of the system. >> So we heard earlier today in the analyst briefing, the press briefing that Micron certainly in the last 40 years has changed. We're seeing a lot more diversity. Usually it'd be all about PCs. Now there's just so many alternative work loads emerging. Clearly NVIDIA is playing there as well with alternative processing capabilities. What do you guys see as some of the more exciting, emerging work loads that are going to require continued collaboration and innovation? >> Yeah, well I think to build a little bit on some of the other comments about the need for real time inference, one of the things in the area of diversity that we've found interesting. The relationship between Micron and NVIDIA in high performance memory really started around their graphics business. But we are seeing in other markets closer to the edge, in automotive, in networking and in other areas where there's a need for that real time performance. Yet there's also a need for a degree of cost effectiveness. Perhaps a little more so than in the data center. That we're seeing technologies like GDR six being applied to a much broader range of applications like automotive, like networking, like Edge AI, to provide the performance to get that real time response but in a form factor and at a cost point that's affordable for the application. >> Anything you'd add to that Permal? >> So I would also add you talked about applications, different applications that are changing right? Today we announced a new set of libraries and tools for the analytic space. That's again a big work load in the enterprise data centers, that we are trying to optimize and accelerate with machine learning. So we announced a whole set of tools which take in these large data sets that are coming in, and applying it in the data centers and using it to get answers very quickly. So that's what NVIDIA is also doing is expanding on these capabilities as we go in. And as these components and as these technologies get better it just gets our answers much more quickly. >> As exacts in the space and you guys both, you're component manufacturers, and so you sell to people who sell to end consumers. How do you get your information in that sort of pull through? Obviously you work with your customers very closely. >> Mm-hm. >> How do you get visibility to their customers? Just going to go to shows, you go do joint sales calls, how does that all work? >> Certainly some of that is in discussions with our customers and their marketing groups about what they're seeing from a customer point of view. But certainly there's other paths. One of the reasons behind the hundred million dollar venture fund that we announced today, is one of the best ways to get that advanced insight, is to be working with some of the most innovative start ups that understand what some of those end users needs might be and are developing some unique technologies. So there's a range. Working with our customers through eventually finding others, but it's important that we understand those needs because the lead time to developing the solutions both memory and processing architectures is quite well. >> Of course everybody wants to work with NVIDIA, you guys have an inundated like come on oh no we're the most. We're tied up now. Of course there's not a lot of choices here when you're talking about the levels of components that you're selling. But what's life like at NVIDIA? I mean they've been knocking down your doors to do partnerships. >> I think we've grown from being just the component to now being a complete system and an architecture. We don't only just build just a chip that the GPU was. We also build full SLCs. We also build the libraries, software, and the tools that are required to make this complete end to end solutions. We also do a lot of open source technologies because we want our customers and our end cast partners to build and take what we have and go beyond what it's capable of. That's where we end value at the end of the day. Yes, it's all of us together. We need to work together to make that much more faster as we go. >> The tuning is incredibly important. This is complicated stuff. It doesn't just work out of the box, right? So you need an ecosystem as well. >> Yes. >> Yes. >> That's what you guys have been out building. Tom, well give your final thoughts. >> Yeah well I guess to build a little bit. Certainly NVIDIA is moving up the stack in terms of the ecosystem, the software, the complete solution and I think Micron does as well. Like you commented, traditionally it was a component play. And increasingly, we're going to be building subsystems in memory and storage that occurs today on the storage side. I think we'll increasingly see that in memory, and with some of the future, very promising technologies like 30 Cross Point. >> Yeah it's the dawn of the days where everybody just gets piece parts and put them all together. They need you you guys to do more integration, and more out of the box like you say subsystems. So guys thanks very much for coming on theCUBE. Really appreciate it. >> Thank you. >> Thank you. >> Alright you're welcome, keep it right there everybody. We'll be back in San Francisco, you're watching theCUBE from Micron Insight 2018, accelerate intelligence. We'll be right back after this short break. (music)