>> Announcer: Live from San Francisco, it's theCUBE. Covering Google Cloud Next '19. Brought to you by Google Cloud and its ecosystem partners. >> Hey welcome back everyone. It's theCUBE's live coverage here in San Francisco in Moscone South. We're on the ground floor here at Google Next, Google's Cloud conference. I'm chatting with Stu Miniman; Dave Vellante's also hosting. He's out there getting stories. Our next two guests: Dana Berg, Chief Operating Officer of SADA and Chris Lehman, Head of Engineering for SADA. Guys, welcome to theCUBE. Thanks for joining us. We're here on the ground floor. >> Thank you. >> Thank you. >> This is exciting. I feel like a movie star right here. >> It's game day here. All the tech athletes are out, Dave. If you look at the show, look at the demographics, hardcore developers, lot of IT, leaders also here, cloud architects, a lot of people trying to figure it out. We heard the keynote. Google is bringing a lot to the table. So what's new with you guys? You guys recently sold your Microsoft business, going all-in on Google. Talk about that relationship. >> We are. This is a brand new day for SADA. The energy around this place, where we are in the market, and where we are with the expanded attendance here has actually reaffirmed our business strategy to go all-in with Google. I don't know if you are aware but SADA has been around for almost 20 years. Historically have always been leaders in bringing people to the cloud even before there was really much of a cloud. We were a you know a pilot partner within Microsoft and Google and had a great thriving Microsoft business but an even bigger Google business and you know, we looked at the tea leaves, we looked at where we wanted to be, and aligned with a company that shared our mission and values and it was a clear choice. We chose Google. We made a very specific and deliberate act to sell off our Microsoft business so that we could take the horsepower of all of our engineering staff and apply them to Google. >> It's interesting you know, we've been around for 10 years doing theCUBE, go to a lot of events, I mean Dave Vellante, Stu, and I have been around for 30 years covering the IT, you guys 20 years. You guys have seen many ways of innovation come and go. Now you're going all in on Google. What is it about this wave right now that made that decision? What do you guys see? You're seeing something early here. Expand on that. Give us some color commentary because there's a wave here, right? A lot of people try. It's a combination of things. I mean, we saw the client-server thing. We saw that movement. Also the internet, we saw the web, mobile, now it's cloud. What's the big wave? What are you guys riding? >> I think there's a couple of things and I think it's unique to, philosophically, how we think of our real special relationship with Google. There is a momentum, right, and not to quote like a Bernie Sanders, but, seems like there's a revolution going on here, right, and, you know, I think, you know, what we see when we look around and we hear conversations and even with our customers, the way that we're all winning together is because we're winning the hearts and minds of the people inside of our customer base that are actually the ones responsible for inventing and the ones responsible for building, so when we're in board rooms and we're selling and along with Google, we're talking with developers, we're talking with designers, we're talking about people that are actually driving the vision for these business applications. We're not always talking to the CIO down like some of our other competitors seems to have only been able to sell that way. We're talking about the people responsible for not only constructing it but maintaining it. So that revolution is there. These folks are bubbling that up and they're seeing the real value inside of Google and what is that value from our point of view, and why did we make such a bold statement just to stick with Google is, and we saw Thomas today echo this, I think there's very few cloud providers that are bold enough to actually lead with the fact that we want our customers to have full choice whether you're using GCP or not. We want to build, architect, and manufacture a product offering that allows you to keep your stuff in your data centers, move your stuff to AWS. That power of choice is really not like what we've never heard anywhere else. >> And then on top of that, too, you got an application renaissance, right? A whole new way of coding, infrastructure that's programmable and going away, I mean if you think about what that does to the existing infrastructures, they can now mix and match and rearchitect everything from scratch and accelerate the app movement. >> Well, that's absolutely true, and a lot of that has to do with the fact that there are managed services in the cloud which makes it dramatically easier to build applications of course, so there's no question about that. Some of the offerings on GCP are particularly attractive for our clients, particularly the managed Kubernetes service. That's where we're seeing perhaps most of the interest that we're seeing, like that's a very common theme. Also the ML stack is an area that our customers are very interested in. >> Chris, can you bring us in some of those customer environments, you know, one of the things you hear, you know, most customers, it's, "I've got my application portfolio." Modernizing that is pretty challenging. There are some things that are kind of easy, some things that take a lot more work, but, you know, migration is one of those things that makes most people that have been in IT a while cringe because there's always the devil in the details and something goes wrong once you've got 95 percent done. What are you seeing, what's working, what's not working, how's the role of data changing, and all of that? >> I think migrations are usually more complex than they at first appear and so even with best intentions thinking that customers can just move their workloads seamlessly to the cloud have actually in practice been more challenging. So some of the areas that we find challenges are around data migration, especially in the context of zero downtime. That's always more difficult than with applications. So that's definitely an area that were we're spending a lot of time working with our customers to deliver. >> Just to add to that, I have to keep reminding myself of the name, but obviously the Anthos announcement today sounds incredibly intriguing as a lower barrier of effort to actually migrate. Our customers have been trying to really absorb and take a hold of Kubernetes and can it containerize methods for a long time. Some are having a harder time doing it than others. I think Anthos promises to make that endeavor much, much easier, and I think about as we leave here this week and we go back and we reeducate our own engineering teams as well as our customers, I think we might see some highly accelerated project timelines go from here down to here. >> And the demo that Jennifer Lynn did was pretty impressive. I mean, running inside of containers, whether it's VMs, and then having service patches on the horizon coming to the table is going to change the implementation delivery piece too in a massive way. I mean, you've got-- >> Oh, absolutely. >> Code, build, run on the cloud side, but this this kind of changes the equation on your end. Can you guys share the insight into that equation, because Google's clearly posturing to be partner friendly. You guys are a big partner now. You're going all-in. This is an interesting dynamic because you can focus on solving customers' problems. All this heavy lifting kind of goes away. Talk about the impact to you as a partner when you look at Anthem, Anthem migrate in particular, some of these migration challenges with containers and Kubernetes seems like it's a perfect storm right now to kind of jump in and do more, faster. >> Yeah. >> Well, it's certainly very interesting. Well, we'll want to take a really hard look at it. I mean, a very, very cool announcement. Moving to containers in the source prior to the migration obviously solves a lot of challenges so for that reason, it's definitely a move forward. >> And I think... You know, we always talk about, in this industry, the acceleration for consumption, but really that's a poor way of saying... Probably what we should be saying is an acceleration of value. So we're constantly in this battle to try and deliver value to our customers faster. That's what our customers want, right, and in essence we see Anthos as being potentially a big game-changer there so that, you know, our CIOs that we're talking with can show to their various stakeholders that they are making very good proactive moves into the cloud at lower-caught barriers of entry, right? >> Yeah. So, you brought up the the ML piece of Google. Wondering if you could help share a little bit on that. When I think back two years ago, you know, data was really at the core of what a lot of what Google was talking about. I was actually surprised not to hear a lot of it on the main stage this morning, but you know, AI, ML, what are you doing, what are your customers doing, does Google have leadership in the space? >> Google certainly has leadership in the space. Our customers, I think, relatively universally, think that their ML stack is the strongest among the competitors, but I think in practice what we're finding is there's a lot more urgency as far as just literal data migrations off of their data centers into the cloud, and I foresee a lot more AI and ML work as more move in. >> John: Yeah. >> So you might, in our booth here, not to give a plug, but we've got a booth down at the end with a full-fledged racing car, just to talk about the art of the possible with AI and ML. Our engineering teams in the race teams that we sponsor, they're there, the driver's there, you should go down and talk to 'em. We've taken all the race telemetry data for the last six months and all of his races and practices, we've aggregated that data all into GCP, run AI and ML algorithms on it to provide his racing team some very predictive ways that he can get better and that team can get better, and so I'd invite just anybody that wants to go there and take a look at, even if you're in banking, or if you're in retail, or if you're in health care, take a look at some of how that was done, because it's a very, very powerful way, to answer your question, head and shoulders down why Google is actually accelerating and exceeding in AI. >> And one of the things that Thomas Kurian showed onstage was the recent Hack-a-Thon they had with the college students with the NCAA data of the game that just finished, and throughout that experience, this is a core theme of GCP, and now Anthos, which is getting data in and using it easily, and scaling at a scale level that seems unprecedented. So this team seems to be the application... The new differentiator. >> I think it is. I think that announcement, obviously the big three takeaways for us, certainly, scale, unmatched. Certainly speed and migration with Anthos. If I could highlight one other, I was incredibly pleased with, well I've been pleased since Thomas' arrival in general by bringing an enterprise class strategy within sight of Google that I think are going to respond well to our enterprise customers, and part of enterprise class is also making sure that their partner community has amazing enhancement programs that really incentivize those partners that are actually in the full managed services space from cradle to grave, lifetime customer value. So we're very excited about even further announcements this week that no doubt have been inspired by Thomas to try and really take advantage of their partner community that are in the business of cradle to grave support of customers. >> You feel comfortable with Thomas. He's taught a lot of customers, he knows the enterprise. >> We've had an opportunity to meet with him. We've had some shared customers that have had a great privilege of getting to know him and support us and collectively them. >> John: He knows the partner equation pretty well, and the enterprise. >> Without a doubt. >> It's about partnering, because there's a monetization, the shared go to markets together. Talk about the importance of that and what's it like to be a partner. >> Yeah, without a doubt, again, you know, his embrace of the open-source community that you saw today, really taking advantage of highlighting partner value is wonderful, but I think Thomas, above anything else, knows that Google needs to scale. They need to scale, and then they have to have breadth and they have to have depth, and, you know, to get to where Google needs to be over the course of the next two, three years, it's wonderful, it's refreshing, it's 100% accurate that Google knows and Thomas knows that the path to do that is via partners; partners that share in Google's vision, that are 100% aligned to the same things that Google is aligned with, and I think that's why I'm so thankful to be at SADA, large in part, because all of the things that we care about in terms of our customer success as well as Google's success, we all share that, so it's a great trifecta. >> It's a ground-floor opportunity. Congratulations. Guys, talk about your business. What's going on? You've got some new offices I heard you opened up. What's going on in the state of the business? Obviously the Google focus you're excited about obviously. >> Yeah, yeah, yeah. >> There, at the beginning, I called Google the dark horse. I think with the tech that they have and the renewed focus on the enterprise, building on what Diane Greene had put foundationally, Thomas is meeting with hundreds of customers. He's so busy he doesn't have time to come on theCUBE, but he'll come on soon, but he's focused. This is now a great opportunity. Talk about your business. What's the state of the union there? Give an update. >> I can take that one if you don't mind. >> Go ahead. >> You can add poetic color if you want. (laughing) Yeah, so as I said, we're entering a new journey for SADA in light of renewed focus, renewed conviction to Google. We are investing more than we ever have into the common belief that Google is the one to beat in terms of momentum, drive, and ultimately winning the hearts and the minds of who we've talked about. So, over the last four months, we've opened five new offices in New York, Austin, Chicago, Denver. Our headquarters is in Los Angeles, and just recently, we just opened a brand new office in Toronto, so we can really help our Canadian customers really see the the same type of white-glove treatment we provide those customers in the States and so that's why, well, I wasn't earlier, but I'm walking around with a Canadian flag. We're very excited about the presence that we're going to have in Canada >> Its "Toronno." I always blow and I call it "Toron-to," being the American that I am. It's "Toronno." >> Dana: Glad you said it right. Good. >> Now, on the engineering side, so you guys are on the front lines as also a sales, development, there's also customer relationship, engineering side, so I'm sure you guys are hiring. There's some hard problems to solve out there. Can you guys share some color commentary on the type of solutions you guys are doing? What's the heavy? What solutions are you solving, problems that you're solving for customers, what are the key things that you got going on? >> Yeah. >> Well, a lot of cloud migrations, a lot of web and application development, custom development, and data pipelines. I'd say those are really the three key focus areas that we're working on at the moment. >> One other thing, too: so... we believe that we want 100% customer retention, always, and that goes above and beyond an implementation. So the other big area of investments that we're making is in a whole revamped technical account management team, so for those of our GCP customers that have had the privilege, we've had the privilege of working with and for, we are building out a team of individuals that will, well beyond the project, stay with that customer, work with them weekly, monthly, quarterly, and try to always find ways to expand and move workloads into the cloud. We think that provides stickiness. We think that provides ultimate value to try and help our customers identify where else they can take full advantage of the cloud, and it's a fairly new program, and large in part I just want to thank Thomas and the partner team for new programs that are coming out to help us so that we can actually reinvest in things that go you know throughout the lifecycle of the customer. So, very, very good stuff. >> Dana, Chris, thanks for coming on. Appreciate it. We'll check out your booth, the car's there, with the data. Bring that data exhaust to the table, pun intended. >> Yes. >> Analyzing with Google Cloud, Anthos. Good commentary. Thanks for sharing. >> Really appreciate being on board. Thanks for having us. >> Alright, great. CUBE coverage here live on the floor in San Francisco. Google Next 2019. This is Google's cloud conference. Customers are here. A lot of developers. More action, live on the day one of three days of coverage after this short break. Stay with us. (theCUBE Theme)

>>Welcome back everybody to AWS reinvent. You're watching the cube, the leader in high tech coverage. My name is Dave Volante with my co-host David Nicholson. We're here all week. We got two sets, 20 plus thousand people here live at AWS reinvent. 21 of course last year was virtual. We got a hybrid event running. We had two studios running before the show running. A lot of pre-records really excited to have ninja Bardelli Walla, who is the chief product officer at data robot. Really interesting AI company. We're going to talk about insights with machine intelligence and then shout. It's great to see you again. It's been awhile. >>Great to see you as well. And I'm so happy to be on the cube. I think eight years since I first came on. >>When you launched the company that you founded back then Peck Sada on the cube, that was part >>Of the inner robot >>Family part of data, robot family. And of course, friend of the cube. Chris Lynch is the executive chairman of data robot. So a lot of connections, I always joke a hundred people in our industry, 99 seats, but tell us about data robot. What's the, what's the scoop these days. >>Thanks. Thanks very much for the opportunity to speak with both of you. Uh, I think we're seeing some very interesting trends. Uh, we've all been in the industry long enough to recognize, uh, that hype cycles they're cycles. They go in waves and, uh, the level of interest in AI has never been higher. Uh, every company in the world is looking for the opportunity to take advantage of AI, to improve their business processes, whether it's to improve their revenue it's to lower their cost profile or it's to lower their risk. What we're seeing that's most interesting is that, uh, we spend a lot of time working with companies on what we consider applied AI. That is how do we solve real business problems, uh, with the technology and not just run a bunch of experiments. You know, it's very tempting for a lot of us, Dave and David, uh, to, to do, uh, you know, spin up a spark cluster with 10,000 nodes and slosh a bunch of data through it. >>But the question we always ask at data robot is what is the business value of doing this? Why are we using these AI techniques and in order to solve what problem? So the biggest trend we see a data robot and one that we feel we're very well positioned to solve is that companies are coming out of that experimental phase. There's still a lot of experimentation going on and they're saying, okay, we, we stood up a cluster. Uh, we got a bunch of Python notebooks running around here, but we haven't really seen a return on our investment yet data robot, can you help us actually make AI real and concrete in terms of achieving a specific business outcome for us? >>Well, and I want to test something on your niche. That's something we've talked about a lot on the cube is a change in the way in which companies are architecting their data. When we first, it was like, okay, create a Hadoop cluster. And that spark came along to make that easier, but it was still this highly technical, highly centralized, hyper specialized roles where the business, people who have a really good understanding of the outcome had to kind of beg to get what they wanted because it was so technical and the success was defined as, Hey, it worked or we ran the experiment and it looks like it has promise. So now it seems like with companies like data robot, you're democratizing AI, allowing organizations to inject AI into their business processes, their applications. And it seems to be more business led. One of you could comment on that. >>I think that is a various dude observation. Uh, we launched this concept a little bit earlier this year of AI cloud. And the idea behind AI cloud is if you want to democratize AI, which is in fact has been DataRobot's vision since 2012, we were the first company on the cloud. The first AI cloud that ever existed was data robots in 2014. And the entire idea was that we knew that data scientists would always play a very important role in an organization, but yet the demand for AI would vastly outstrip the supply. And so in order to solve that challenge, we built AI cloud. We've actually spent over a million engineering hours in building this technology over the, over the last decade and put this together in a way where all of the different personas and the organizations, you have people who create AI applications. >>Those are the folks we usually think about, but those are the data scientists. Those are the analysts, those are the data engineers, but then you actually have to put it into production. You've got to run the system. So you also have to democratize this capability for the folks who are going to operate the system for the folks in risk and compliance. We're actually going to, uh, ensure that the system is operating in accordance with your policies and compliance regimes. And then the third wave of democratization, which we've just embarked on is then how do you bring AI into the hands of the actual business people? How do you put on a mobile device or a web browser, or in context, in an application with the decision, the ability for AI to drive a decision in your organization, which leads to an action, which helps drive you towards the outcome you're trying to optimize for. >>So AI cloud is about this pervasive tapestry, bringing together the creators, the consumers, the individuals who operate these systems into a single system that can lower the barrier to entry for people who don't have the skills, but allow you to plug in and go deep underneath the covers and modify whatever you need to, if you have that level of technical skill and that ability for us to kind of slide, slide the slider in one direction or the other, I could slide it to the right and say, I want all automation, something data robot has pioneered and is absolutely the leader in, but we can also, especially in these last couple of years, say, I want to be able to use as much code as I want to bring in. And the beauty of the model is that customers can choose how much they want to let the machine drive or how much they want to let the human being drive. David. I love that, >>That idea of a slider, because now you're talking about generalists getting access to really powerful tools. >>Yeah, no, exactly. And I, I'm curious, what's your view on where we are culturally with AI at this point? And what I mean by culturally is the idea that, okay, that's great. You put powerful tools in the hands of business users. Um, do most of us still need to have a lot of visibility under the covers to understand the inner workings so that we trust what we're being told? You know, I'm fine pulling a lever and having a little biscuit come out of SWOT as long as I've gotten a tour of the kitchen at some point in time. Yes. I mean, where are we with that? Where where's the level of >>Absolutely fantastic question and it's one that's, it's actually pervasive to the way data robot operates. So trust gets, uh, engendered by multiple different capabilities that you build throughout the platform. The first one is around, uh, explainability. So when you get a prediction from a system, just like you mentioned, you know, if, if the stakes are not very high, you know, you, uh, we're here in Las Vegas, of course I'm thinking of slot machines. If you get a biscuit at the end of it and it tastes pretty good. Hey, great. Right? When you're making a mission critical business decision, you don't want to be in the position where you don't understand why the system is making the decision. It does. So we have historically invested an enormous amount of effort in explainability tools, having the system actually at a prediction level, explain to you, why is it making the recommendation it's making? >>For example, the system says this customer has a high likelihood of churn. Why? Because their account balance has been declining over the last five months. Uh, number two, because their credit score has been going down. And what gives you the trust is actually the machine and the human able to communicate in the same language and same vernacular about the business value. So that's one part of it. The second part is about transparency, right? So one of the things that the automated machine learning movement, that data robot pioneered, uh, has been, I'd say rightfully criticized for frankly, is that it's too much of a black box. It's too much magic. I load my dataset. I press the start button and data robot does everything else for me. Well, that's not very satisfying when you have a 10 or a hundred million dollar decision coming on the other side, even if the technology is actually doing the job correctly, which data robot usually does. >>So where we've morphed and evolved our position in the market and where I have driven our technology portfolio at data robot is to say, you know what? There is a very important aspect of trust that needs to be brought to bear here, which is that if somebody wants to see code, let them see code. And in fact, the beauty of AI cloud is that on the same platform, the people who don't like code, but are, are very good at understanding the business domain con uh, the business domain knowledge and the context. They now have the ability to do that. But when they're at the stage before they're going to deploy anything to production. Now you can raise your hand at data robot and actually use our workflow and say, I need a coder to review this. I want the professional data scientist who has all this knowledge who understands and has read up on the latest advances in hyper parameter tuning to look at the model and tell me that this is going to be okay. And so we allow both the less technical folks and the very deep technical data scientists, the ability to collaborate on the same environment, which allows you to build trust in terms of the human side of, Hey, I don't want to just let anybody throw a model into production. I like, >>I mean, I see those, the transparency and the explainability is almost two sides of the same coin, right? Because you know, if you're gonna be accused of gender bias, you can say, no, here's how the system may, it's not like, you know, you think about the internet. It tells you it's a cat, but you don't really know how the machine determined that you're breaking apart, blowing away that black box. And the other thing I like what you said was you have data producers and data consumers, and you also talked about context because a lot of times the data producers, they don't necessarily care about the context or the PI data pipeline. People necessarily care about the context. So, okay. So now we're at the point where you're democratizing data, you're doing some great work. What are some of the blockers that you see today that you're obliterating with data robot? Maybe you could talk about that a little bit. Sure. >>So, so I think, uh, you know, one very important concept is that, uh, in a democracy, we talked about democratization. You still have rules, you still have governance. It's not a free for all the free for all version of that is called NRG. That's not what any company wants, right? So we have to blend the freedom and flexibility that we want businesses to have with the compliance and regulatory observability that we need in order to be successful. So what we're seeing in, in our, in our customer base and what companies are coming to data robot to discuss is, okay, we've tried these experiments. Now we want to actually get to real business value. And one of the things that's really unique about data robot is that we have put, uh, we have, we've worked in our system on over 1 million projects, training models, inside data robot. >>We have seen every type of use case across different industries, whether it's healthcare or manufacturing, uh, or, or retail, uh, we have the ability to understand those different data sets and actually to come up with models. So we have that breadth of information there if you aggregate that over time, right? So again, we did not come to AI. This is not a fad for us. We didn't start as one kind of company than slap the AI label on and say, Hey, we're an AI company now, right? We have been AI native since day one. And in that process, what we have found is working on these, this million plus projects on these data sets across these industries, we have a very good sense of which projects will actually deliver value and which don't. And that gets to a previous point that you were making, which is that you have to know and partner with an organization who it's not just about the technology. So we have fantastic people who we call our customer facing data scientists who will tell the customer, look, I know you think this is a really high value use case, but we've tried it at other customers. And unfortunately it didn't work very well. Let's steer you, cause you need with a, with a technology that is largely at the early stage and the maturity that organizations have with it, you need to help them in order to deliver success. And no vendor has delivered more successful production deployment of AI than data road. >>No, don't go down that path. It's a dead end as a cul-de-sac. So just avoid it. So we talked about transparency, explainability governance. Can you get that to the point where it's self-serve as you, as you put data in the hands of business, people where the context lives, the domain experts, can you get to self-serve and federate that governance? Yes. >>So you can, uh, that's one of the key principles of what we, what we do at data robot. And it comes back to a concept that I learned, uh, you, you both will remember. We were in the Sarbanes-Oxley crazy world of, I dunno, was that 15 years of saved data warehousing. >>Everybody wanted to talk about socks. You know, my wife would hear me on the phone. She'd be like, what is your sudden obsession with socks? I'm like, no, no, it's not what you fit. And so, um, but what came from Sarbanes Oxley are, are these, uh, longstanding principles around the segregation of duties and segregation of responsibilities. You can have democracy democratization with governance, if you have the right segregation of duties. So for example, I have somebody who can generate lots of different models, right? But I don't allow them to, to, uh, in a self-service way, just deploy into production. I actually have a workflow system which will go through multiple rigorous approvals and say, these three people have signed off, they've done an audit, uh, an, an audit assessment of this model. It's good to go, let's go and drop it into production. So the way that you get to self-service with governance is to have the right controls and policies and frameworks that surround the self-service model with the right checks and balances that implement the segregation of duties I'm talking >>And you get that right. And then you can automate it and then you can really scale, right? You gotta have your back because it's such a great topic. We, we barely scratched the surface. It was great to see you again, congratulations on all the success. And, uh, as I say any time, let's do this again. Fantastic. Thank >>You so much. All right, you're welcome. And thank you for watching you watching the cubes coverage of AWS reinvent 2021, Dave Volante for David Nicholson. Keep it right there. You're watching the cube, the leader in high-tech coverage.

>>Live from San Francisco. It's the cube covering my groin. Insight 2019 brought to you by micron. >>Welcome back to pier 27 in San Francisco. I'm your host Dave Vellante with my cohost David foyer and this is the cube, the leader in live tech coverage. This is our live coverage of micron insight 2019 we were here last year talking about some of the big picture trends. Derek ticker is here, he's the general manager and vice president of the storage business unit at micro and great to see you again. Thank you so much for having me here. Welcome. So you know we talk about the super powers a lot, you know, cloud data, AI and these new workloads that are coming in. And this, this, I was talking to David earlier in our kickoff like how real is AI? And it feels like it's real. It's not just a bunch of vendor industry hype and it comes in a lot of different forms. Derek, what are you seeing in terms of the new workloads and the big trends in artificial intelligence? >>I think just on the, on the front end, you guys are absolutely right. The, the role of artificial intelligence in the world is, uh, is absolutely transformational. I was sitting in a meeting in the last couple of days and somebody was walking through a storyline that I have to share with you. That's a perfect example of why this is becoming mainstream. In Southern California at a children's hospital, there were a set of parents that had a few days old baby and this baby was going through seizures and no one could figure out what it was. And during the periods of time of the seizure, the child's brain activity was zero. There was no brain activity whatsoever. And what they did is they performed a CT scan, found nothing, check for infections, found nothing. And can you imagine a parent just sitting there dealing with their child and that situation, you feel hopeless. >>This particular institution is so much on the bleeding edge. They've been investing in personalized medicine and essentially what they were able to do was extract a sample of blood from that sample of blood within a matter of minutes. They were able to run an algorithm that could sift through 5 million genetic variants to go find a potential match for a genetic variant that existed within this child. They found one that was 0.01% of the population found a tiny, tiny, call it a less than a needle in the haystack. And what they were able to do is translate that actual insight into a treatment. And that treatment wasn't invasive. It didn't involve surgery. It involves supplements and providing this shower, just the nutrients that he needed to combat this genetic variant. But all of this was enabled through technology and through artificial intelligence in general. And a big part of the show that we're here at today is to talk about the industry coming together and discussing what are the great advances that are happening in that domain. >>It's just, it's super exciting to see something that touches that close to our life. I love that story and that's, that's why I love this event. I mean, well, obviously micron memories, you know, DRAM, NAND, et cetera, et cetera. But this event is all about connecting to the impacts on our lives. You take, you take that, I used to ask this question a lot of when will machines be able to make better diagnoses than, than doctors. And I think, you know, a lot people say, well they already can, but the real answer is it's really about the augmentation. Yeah. You know, machines helping doctors get to that, you know, very, you know, uh, a small probability 0.1001% yes. And it'd be able to act on it. That's really how AI is affecting our lives every day. >> Wholeheartedly agree. And actually that's a, that's a big part of our mission. >>Our mission is to transform how the world uses information to enrich life. That's the heart and soul of what you just described. Yeah. And we're actually, we're super excited about what we see happening in storage as a result of this. Um, one of the, one of the things that we've noticed as we've gotten engaged with a broad host of customers in the industry is that there's a lot of focus on artificial intelligence workloads being handled based on memory and memory bandwidth and larger amounts of memory being required. If you look at systems of today versus systems of tomorrow, based on the types of workloads that are evolving from machine learning, the need for DRAM is growing dramatically. Multiple factors, we see that, but what nobody ever talks about or rarely talks about is what's going on in the storage subsystem and one of the biggest issues that we've found over time or challenges that exist is as you look at the AI workloads going back to 2014 the storage bandwidth required was a few megabytes per second and called tens of, but if you just look every year, over time we're exceeding at gigabyte, two gigabytes of bandwidth required out of the storage subsystem. >>Forget the memory. The storage is being used as a cash in it flushes, but once you get into a case where you actually want to do more work on a given asset, which of course everybody wants to do from a TCO perspective, you need super high performance and capability. One of the things that that we uncovered was by delivering an SSD. This is our 9,300 drive. We actually balanced both the read IOPS and the ride IOPS at three gigs per second. And what we allow to have happened is not just what you can imagine as almost sequential work. You load up a bunch of data into a, into a training machine, the machine goes and processes on it, comes back with a result, load more data in by actually having a balanced read and write a model. Your ingest times go faster. So while you're working on a sequence, you can actually ingest more data into the system and it creates this overall efficiency. And it's these types of things that I think provided a great opportunity for innovation in the storage domain for these types of that's working >> requiring new architectures in storage, right? I mean, yeah, >>I mean, th th so one of the things that's happened in, in bringing SSDs in is that the old protocols were very slow, etc. And now we all the new protocols within in Vme and potentially even more new protocols coming in, uh, into this area. What's micron? What, how is micron making this thing happen? This speed that's gonna provide these insights? >>It's a fan fan. Fantastic question and you're absolutely right. The, the world of standards is something that we found over the course of time. If you can get a group of industry players wrapped around a given set of standards, you can create a large enough market and then people can innovate on top of that. And for us in the, in the storage domain, the big transitions had been in Sada and NBME. You see that happening today when we talked a little bit about maybe a teaser for what's coming a little later at, at our event, um, in some of the broader areas in the market, we're talking about how fabrics attach storage and infrastructure. And interestingly enough, where people are innovating quite a bit right now is around using the NBME infrastructure over fabrics themselves, which allows for shared storage across a network as opposed to just within a given server there. >>There's some fantastic companies that are out there that are actually delivering both software stacks and hardware accelerators to take advantage of existing NBME SSDs. But the protocol itself gets preserved. But then they can share these SSDs over a network, which takes a scenario where before you were locked with your storage stranded within a server and now you can actually distribute more broad. It's amazing difference, isn't it at that potential of looking at data over as broad an area as you want to. Absolutely. And being able to address it directly and having it done with standards and then having it done with low enough latency such that you aren't feeling severely disadvantaged, taking that SSD out of a box and making it available across a broad network. So you guys have a huge observation space. Uh, you sell storage to the enterprise, you sell storage to the cloud everywhere. >>I want to ask you about the macro because when you look at the traditional storage suppliers, you know, some of them are struggling right now. There aren't many guys that are really growing and gaining share because the cloud is eating away at that. You guys sell to the cloud. So that's fine. Moving, you know, arms dealer, whoever wins it may the best man win. Um, but, but at the same time, customers have ingested so much all flash. It's giving them head room and so they're like, Hey, I'm good for awhile. I used to have this spinning disc. I'd throw spinning disc at it at the problem till I said, give me performance headroom. That has changed. Now we certainly expect a couple of things that that will catch up and there'll be another step function. But there's also elasticity. Yes. Uh, you saw for instance, pure storage last quarter said, wow, hit the price dropped so fast, it actually hurt our revenues. >>And you'd say, well, wait a minute. If the price drops, we want people to buy more. There's no question that they will. It just didn't happen fast enough from the quarter. All of these interesting rip currents going on. I wonder what you're seeing in terms of the overall macro. Yeah. It's actually a fantastic question. If you go back in time and you look at the number of sequential quarters, when we had ASP decreases across the industry, it was more than six. And the duration from peak to trough on the spot markets was high double digit percentages. Not many markets go through that type of a transition. But as you suggested, there's this notion of elasticity that exists, which is once the price gets below a certain threshold, all of a sudden new markets open up. And we're seeing that happen today. We're seeing that happen in the client space. >>So, so these devices actually, they're going through this transition where companies are actually saying, you know what, we're going to design out the hard drive cages for all platforms across our portfolio going into the future. That's happening now. And it's happening largely because these price points are enabling that, that situation and the enterprise a similar nature in terms of average capacities and drives being deployed over time. So it's, I told you, I think the last time we saw John, I told just one of the most exciting times to be in the memory and storage industry. I'll hold true to that today. I, I'm super excited about it, but I just bought a new laptop and, and you know, I have, you know, a half a half a terabyte today and they said for 200 bucks you can get a terabyte. Yes. And so I said, Oh wow, I could take everything from 1983 and bring it, bring it over. >>Yeah. Interestingly, it was back ordered, you know, so I think, wow, it am I the only one, but this is going to happen. I mean, everybody's going to have, you know, make the price lower. Boom. They'll buy more. We, we, we believe that to be the case for the foreseeable future. Okay. Do you see yourself going in more into the capacity market as well with SSTs and I mean, this, this, this drop, let's do big opportunity or, yeah. Actually, you know, one of the areas that we feel particularly privileged to be able to, to engage in is the, the use of QLC technology, right. You know, quad level solar for bits per cell technology. We've integrated this into a family of, uh, of SSDs for the enterprise, or interestingly enough, we have an opportunity to displace hard drives at an even faster rate because the core capability of the products are more power efficient. >>They've got equal to, or better performance than existing hard drives. And when you look at the TCO across a Reed intensive workloads, it's actually, it's a no brainer to go replace those HDD workloads in the client space. There's segments of the market where we're seeing QLC to play today for higher, higher capacity value segments. And then there's another segment for performance. So it's actually each segment is opening up in a more dramatic way. So the last question, I know you got some announcements today. They haven't hit the wire yet, but what, can you show us a little leg, Derrick? What can you tell us? So I, I'll, I'll give you this much. The, um, the market today, if you go look in the enterprise segment is essentially NBME and SATA and SAS. And if you look at MDME in 20 2019 essential wearing crossover on a gigabyte basis, right? >>And it's gonna grow. It's gonna continue to grow. I mentioned earlier the 9,300 product that we use for machine learning, AI workloads, super high performance. There's a segment of the market that we haven't announced products in today that is a, a a mainstream portion of that market that looks very, very interesting to us. In addition, we can never forget that transitions in the enterprise take a really long time, right, and Sada is going to be around for a long time. It may be 15% of the market and 10% out a few years, but our customers are being very clear. We're going to continue to ship Satta for an extended period of time. The beautiful thing about about micron is we have wonderful 96 layer technology. There's a need in the market and both of the segments I described, and that's about as much as I can give you, I don't bet against data. Derek, thanks very much for coming on. Thank you guys so much. You're welcome. There's a lot of facts. Keep it right there, buddy. We'll be back at micron insight 2019 from San Francisco. You're watching the cube.