>>Well, hello everybody at John Wallace here on the Cube, and glad to have you along here for day two of our coverage here at AWS Reinvent 22. We're up in the global startup program, which is part of AWS's Startup Showcase, and I've got Kevin Farley with me. He is the director of Strategic Alliances with Maria Day db. And Kevin, good to see you this morning. Good to see you, John. Thanks for joining us. Thank >>You. >>Appreciate it. Yeah. First off, tell us about Maria db. Sure. Obviously data's your thing. Yep. But to share that with some folks at home who might not be familiar with your offering. >>Yeah. So Maria DB's been around as a corporate entity for 10 plus years, and we have a massive customer base. You know, there's a billion downloads from Docker Hub, 75% of the Fortune 500. We have an enormous sea of really happy users. But what we realize is that all of these users are really thinking about what do we, what does it mean to transform it? What does cloud modernization mean? And how do we build a strategy on something we really love to drive it into the cloud and take it to the future. So what we launched about two years ago, two and a half years ago, is Skye. It's our database as a service. It leverages all the best elements, what we provide on the enterprise platform. It marries to the AWS cloud, and it really provides the best of both worlds for our >>Customers. So in your thought then, what, what problem is that solving? >>I think what you see in the overall database market is that many people have been using what we would call legacy technology. There's been lots of sort of stratification and mixes of different database solutions. All of them come with some promise, and all of 'em come with a lot of compromise. So I think what the market is really looking for is something that can take what they know and love, can bring it to the cloud and can survive the port drive the performance and scale. That completely changes the landscape, especially as you think about what modern data needs look like, right? What people did 10 years ago with the exponential scale of data no longer works. And what they need is something that not only can really deliver against their core business values and their core business deliverables, but gets 'em to the future. How do we drive something new? How do we innovate? How do we change the game? And I think what we built with AWS really delivers what we call cloud scale. It's taking something that is the best technology, and I as a V can build, marrying it to, you know, Kubernetes layer, marrying it to global availability, thinking about having true global high availability across all of your environments and really delivering that to customers through an integrated partnership. >>Could we see this coming? I mean, because you know data, right? I mean, yeah, we, we, everybody talked about the tsunami of growth, you know, >>Back 10 >>Sure. 11 years ago. But, but maybe the headlights didn't go far enough or, or, but, but you could see that there was going to be crunch time. >>There's no doubt. And I think that this has been a, there's, there's been these sort of pocket solutions, right? So if you think at the entire no sequel world, right? People said, oh, I need scale, I can get it, but what do I have to give up asset compliance? So I have to change the way I think about what data is and how I, I can govern it. So there's been these things that deliver on half the promise, but there's never been something that comes together and really drives what we deliver through CIQ is something called expand. So distributed SQL really tied to the SQL Query language, having that asset data. So having everything you need without the compromise built on the cloud allows you to scale out and allows you to think about, I can actually do exponential layers of, of data, data modeling, data querying, complete read, write, driving that forward. And I think it gives us a whole nother dynamic that we can deliver on in a way that hasn't been before. And I think that's kind of the holy grail of what people are looking for is how am I building modern applications and how do I have a database in the cloud that's really gonna support >>It? You know, you talk about distributed, you know, sequel and, and I mean, there's a little mystery behind it, isn't there? Or at least maybe not mystery. There's a little, I guess, confusion or, or just misunderstanding. I mean, I, how, nail that down a little bit. I >>Would say the best way to say it, honestly, this is the great thing, is it people believe it's too good to be true. And I think what we see over and over >>Again, you know, what they say about that. >>But this is the great part is, you know, you know, we've just had two taste studies recently with aws, with HIT labs and Certified power, both on expand, both proof in the pudding. They did the POCs, they're like, oh my God, this works. If you watch the keynote yesterday, you know, Adam had a slide that was, you know, as big as the entire room and it highlighted Samsung and they said, you know, we're doing 80,000 requests per second. So the, you know, the story there is that AWS is able as, as an entity with their scale and their breadth to handle that kind of workload. But guess what that is? That's MariaDB expand underneath there driving all of that utilization. So it's already there, it's already married, it's already in the cloud, and now we're taking it to a completely different level with a fully managed database solution. Right? >>How impressive is that? Right? I mean, you would think that somebody out there who, I mean that that volume, that kind of capacity is, is mind blowing. >>I mean, to your kind of previous point, it's like one of those things, do I see what's coming and it's here, right? You know, it's, is it actually ever gonna be possible? And now we're showing that it really is on a daily basis for some of the biggest brands in the world. We're also seeing companies moving off not only transitioning from, you know, MariaDB or myse, but all of the big licensed, you know, conversions as well. So you think about Oracle DBS Bank is one of our biggest customers, one of the largest Oracle conversions in the world onto MariaDB. And now thinking about what is the promise of connecting that to the cloud? How do you take things that you're currently doing, OnPrem delivering a hybrid model that also then starts to say, Hey, here's my path to cloud modernization. Skye gives me that bridge. And then you take it one layer farther and you think about multi-cloud, right? That's one of the things that's critical that ISVs can really only deliver in a meaningful way, is how can we have a solution for a customer that we can take to any availability zone. We can have performance, proximity, cost, proximity. We're always able to have that total data dexterity across any environment we need and we can build on that for the future. >>So if, if we're talking about cloud database and there's so many good things going forward here. You're talking about easy use and scalability and all that. But as with ever have you talked about this, there's some push and there's some pull. Yeah. So, so what's the, what's the other side that's still, you know, you that you think has to be >>Addressed? And I think that's a great question. So there's, we see that there's poll, right? We've seen these deals, this pipeline growth, this, there's great adoption. But what I think we're still not at the point of massive hockey stick adoption is that customers still don't fully understand the capabilities distributed SQL and the power they can actually deliver. So the more we drive case studies, the more we drive POCs, the more we prove the model, I think you're gonna see just a massive adoption scale. And I also think customers are tired of doing lots of different things in lots of different pockets. So neither one of the key elements of Sky SQL is we can do both transactional and analytical data out of the same database driven by the same proxy. So what, instead of having DBAs and developers try to figure out, okay, I'm gonna pull from this database here. >>Yeah. That there, it's, it's this big spaghetti wire concept that is super expensive and super time intensive. So the ability to write modern applications and pull data from both pockets and really be able to have that as a seamless entity and deliver that to customers is massive. I mean, another part of the keynote yesterday was a new deliverable, like kind of no etl. Adam talked about Aurora and Redshift and the massive complexity of what used to exist for getting data back and forth. You also have to pay for two different databases. It's super expensive. So I think the idea that you can take the real focus of AWS and US is customer value. How do you deliver that next thing that changes the game? Always utilizes AWS delivers on that promise, but then takes a net new technology that really starts to think about how do we bring things together? How do we make it more simple? How do we make it more powerful? And how do we deliver more customer value as we go forward? >>But you know, if, if I'm, I'm still an on-prim guy, just pretend I'm not saying I am. Just pretend I just for the sake of the discussion here, it's like I just can't let it go. Yeah. Right. I, I still, you know, there's control, there's the known versus the unknown. The uncertain. Yeah. So twist my arm just a little bit more and get me over the hum. >>Well, first of all, you don't have to, right? And there's gonna be some industries and some verticals that will always have elements of their business that will be OnPrem. Guess what? We make the best based in the world. It can be MariaDB, but there's those that then say, these, these elements of our business are gonna be far more effective moving to the cloud. So we give you Skye, there's a natural symbiotic bridge between everything we do and how we deliver it. Where you can be hybrid and it's great. You can adopt the cloud as your business needs grow. And you can have multi-cloud. This is that, that idea that you can, can have your cake and eat it too, right? You can literally have all these elements of your business met without these big pressure to say, you gotta throw that away. You gotta move to this. It's really, how do you kind of gracefully adopt the cloud in a way that makes sense for your business? Where are you trying to drive your business? Is it time to value, right? Is it governance? Is it is there's different elements of what matters the most to individual businesses. You know, we wanna address those and we can address >>Those. So you're saying you don't have to dive >>In, you don't have to dive >>In. You, you can, you can go ankle deep, knee deep, whatever you wanna >>Do. Absolutely. And you know, some of the largest MariaDB users still have massive, massive on-prem implementations. And that's okay. But there's elements that are starting to fall behind. There's cost savings, there's things that they need to do in the cloud that they can't do. OnPrem. And that's where expand Skye really says, okay, here is your platform. Grow as you want to, migrate as you want to. And we're there every step along the way. We, we also provide a whole Sky DBA team. Some guys just say, I wanna get outta the database world at all. This is, this is expensive, it's costly and it's difficult to be an expert. So you can bring in our DBA team and they'll man and run, they'll, they'll run your entire environment. They'll optimize it, you know, they'll troubleshoot it, they'll bug fix, they'll do everything for you. So you can just say, I just wanna focus on building phenomenal applications for my customers. And the database game as we knew it is not something that I know I want to invest in anymore. Right. I wanna make that transition >>That makes that really, yeah. You know, I mean really attractive to a lot of people because you are, you talk about a lot of headache there. Yeah. So let's talk about AWS before Sure. I let you go just about that relationship. Okay. You've talked about the platform that it provides you and, and obviously the benefits, but just talk about how you've worked with AWS over the years Yep. And, and how you see that relationship allowing you to expand your services, no pun intended. >>For sure. So, I mean, I would start with the way we even contemplated architecture. You know, we worked with the satisfactory team. We made sure that the things that we built were optimized in their environment. You know, I think it was a lot of collaboration on how does this combined entity really make the most value for our customers? How does it make the most sense for our developers as we build it out? Then we work in the, in the global startup team. So the strategic element of who we are, not all startups are created equal, right? We have, right, we have 75% of the Fortune 100, we've got over a billion downloads. So, you know, we come in with promise. And the reason this partnership is so valuable and the reason there's so much investment going forward is cuz what really, what do the cloud guys care about? >>The very, very most, they want all of these mission critical, big workloads that are on prem to land in their cloud. What do we have a massive, massive TAM sitting out there, these customers that could go to aws. So we both see, like if we can deliver incredible value to that customer base, these big workloads will end up in aws. They'll use other AWS services. And as we scale and grow, you know, we have that platform that's already built for it. So I think that when you go back to like the tenants, the core principles of aws, the one that always stands out, the one that we always kind of lean back on is, are we delivering customer value? Is this the best thing for the customer? Because we do have some competition just like many other, other partners do, right? So there is Aurora and there is rds and there is times when that's a great service for a customer. But when people are really thinking about where do I need my database to go? Where do I really need to be set for the future growth? Where am I gonna get the kind of ROI I need going forward? That's where you can go, Hey, sky sql, expand distributed sql. This is the best game in town. It's built on aws and collectively, you know, we're gonna present that to a customer. I'm >>Sold. Done. >>I love it. Right? >>Maria db, check 'em out, they're on the show floor. Great traffic. I know at at the, at the booth. They're here at AWS Reinvent. So check 'em out. Maria db. Thanks >>Kevin. Hey, thanks John. Appreciate your >>Time. Appreciate Great. That was great. Right back with more, you're watching the cube, the leader in high tech coverage.

>>Good morning from New York city, Lisa Martin and John furrier with the cube. We are at AWS summit NYC. This is a series of summits this year, about 15 summit globally. And we're excited to be here, John, with about 10,000 folks. >>It's crowded. New York is packed big showing here at 80 of us summit. So it's super exciting, >>Super exciting. Just a little bit before the keynote. And we have our first guest, Kevin Farley joins us the director of strategic alliances at Maria DB. Kevin, welcome to >>The program. Thank you very much. Appreciate you guys having us. >>So all of us out from California to NYC. Yeah, lots of eyes. We got keynote with Warner Vogels coming up. We should be some good news, hopefully. Yep. But talk to us about Maria DB Skys cloud native version released a couple years ago. What's going on? >>Yeah, well, it's, you know, Skys SQL for us is really a be on the future. I think when we think about like the company's real mission is it's just creating a database for everyone. It's it's any cloud, any scale, um, any size of performance and really making sure that we're able to deliver on something that really kind of takes advantage of everything we've done in the market to date. If you think about it, there's not very many startups that have a billion downloads and 75% of the fortune 500 already using our service. So what we're really thinking about is how do we bridge that gap? How do we create a natural path for all of these customers? And if you think about not just Maria DB, but anyone else using the sequel query language, all the, my people, what I think most Andy jazzy TK, anyone says, you know, it's about 10% of the market currently is in the clouds. That's 90% of a total addressable market that hasn't done it yet. So creating cloud modernization for us, I think is just a huge opportunity. Do >>You guys have a great history with AWS? I want to just step back, you mentioned some stats on, on success. Can you scope the size and track record of Maria DB for us real quick and set the table? Because I think there's a bigger picture going on that we've been tracking for the past 13 years we address is the role of the database has always been one of those things where they didn't believe a one database fits all things, right. You guys have been part of that track record scope, the size and scale of Maria DB, the usage, the use cases and some of the successes. >>Yeah. I mean, like I said, some of the stats are already threw out there. So, you know, it is pervasive, I think is the best way to put it. I think what you look at what the database market really became is very siloed. Right? I think there was a lot of unique solutions that were built and delivered that had promise, but they also had compromise. And I think once you look at the landscape of a lot of fortune 500 companies, they have probably 10 to 15 different database solutions, right? And they're all doing unique things. They're difficult to manage. They're very costly. So what Marie DB is always kind of focused on is how do we continue to build more and more functionality into the database itself and allow that to be a single source of truth where application developers can seamlessly integrate applications. >>So then the theme of this event in New York city, which is scale dot, dot, dot, anything must align quite well with Maria and your >>Objectives. I mean, honestly, I think when I think of the problems that most database, um, companies, um, face customers, I should say it, it really comes down to performance and scale. Most of them like Maria DB, like you said, they it's like the car, you know, and love you've been driving it for years. You're an expert at it. It works great, but it doesn't have enough range. It doesn't go fast enough. It's hitting walls. That modern data requirements are just breaking. So scale for me is the favorite thing to talk about because what we launched as MariaDB expand, which is a plugable storage engine that is integrated into Skye, and it really gives you dynamic scale. So you can scale in, you can scale out, it's not costly compute to try to get for seasonality. So you can make your black Friday numbers. It's really about the dexterity to be able to come in and out as you need in a share, nothing architecture with full failover sale healing, high availability, married to the cloud for full cloud scale. And that's really the beauty of the AWS partnership. >>Can you elaborate a bit more on the partnership? How long have you guys been partners? Where is it now anything exciting coming out? >>Yeah, it it's, it's actually been a wonderful ride. They've really invested from the very beginning we went for the satisfactory. So they really brought a lot of resources to bear. And I think if you're looking at why it works, um, it's probably two things. I think the number one thing is that we share one of the core tenants and it's customer obsession in a, in a, in an environment where there is co-opetition right. You have to find paths for how do you get the best thing for the customer? And the second is pretty obvious, but if you look at any major cloud, their number one priority is getting large mission critical workloads into their cloud because the revenue is exponential on the backside. So what do we own? Large mission critical workloads. So if you marry that objective with AWS, the partnership is absolutely perfect for driving true revenue, growth scale, and, and revenue across, across both entities in the partner ecosystem. >>So Kevin talk about the, um, the hybrid strategy, cuz you're seeing cloud operations. Yep. Go hybrid. Amazon announced AWS announced outpost like four years ago. Right now edge is super hot. Yeah. So you're seeing like most of the enterprise is saying mm-hmm <affirmative> okay. Love cloud love the cloud database, but I got the on-prem hybrid cloud operations. Right. So it's not just proprietary operations. It's cloud ops. Yeah. How do you guys fit into that? What's the story. >>We, we actually it's. I mean, there's, there's all these new deliverables outposts, you know, come out with a promise. What we have is a reality right now, um, one of the largest, um, networking companies, which I can't mention yet publicly, um, we want a really big sky SQL deal, but what they had manufacturing plants, they needed to have on-prem deployments. So Maria DB naturally syncs with sky SQL. It's the same technology. It works in perfect harmony. So we really already deliver on the promise of hybrid, but of course there's a lot more we can grow in that area. And certainly thinking about app posts and other solutions, um, is definitely on the, the longer term roadmap of what could make sense for in our customer. What, >>What are some of the latest things that, that you guys are doing now that you weren't doing a few years ago that customers should know about the audience should know about? >>I mean, I think the game changer, we're always innovating. I mean, when you're the company that writes the code owns the code, you know, we can do hot fixes, we can do security patches, we can always do the things that give you real time access to what you need. But I think the game changer is what I mentioned a little bit earlier. And I think it's really the, the holy grail of the cloud. It's like, how can we take the, the SQL query language, which is well over 50% of the open source market. Right. And how do we convert that seamlessly into the cloud? How do we help you modernize on that journey? And expand gives you the ability to say, I can be the small, I can be a small startup. I got my C round. I don't wanna manage databases. I can use the exact same service as the largest fortune 100 company that has massive global scale and needs to be able to drive that across globe. Yeah. So I think that's the beauty is that it's really a democratization of the database, >>At least that, you know, we've been covering the big data space for 10 years. Remember all those different conversations had do those days and oh, they have big data and right. But then it's like too hard to set up. Then you had that kind of period where you saw a spark and data lakes emerge. Yeah. Then you, now it almost seems, seems like now more than ever, there's a data revolutions back. Right. It was almost like a lull in the, in, in the, in the market a little bit. Yeah. I'm gonna democratize data science right now. You got data. So now it just seems to be an explosion at that level. What's your analysis on that? Because you you've been in, in, in the weeds and in the, in the, in this market for 10 years. Yeah. And nothing really changed. It's just now it's more ready. Yeah. I think what's your observation. Why >>Is that? I think that's a really good question. And I love it cuz I mean, what the promise of things like could do and net new technologies sort of, it was always out there, but it required this whole net new lift and how do I do it? How do I manage it? How do I optimize it? The beauty of what we can do with Maria DB is that sky SQLs, which you already know and love. Right? And now we can Del you can deliver a data lake on S3, right? You can pull that data. And we also have the ability to do both analytical data and transactional data from the same database. So you can write applications that can pull column, store data up into, um, your application, but you can also have all of your asset transactions, which are absolutely required for all of your mission critical business. So I think that we're seeing more and more adoption. You've seen other companies start to talk about bringing the different elements in, but we're the only ones that really >>Do it and SQL standardizing that front end. Yeah. Even better than ever before. All the stuff under the covers is all being connected. >>That's the awesome part is right. Is you're literally doing what you already know how to do, but you blow it out on the back end, married to the cloud. And that I think is the real revolution of what makes usability real in the data space. And I think that's what was always the problem before >>When you're in partner conversations, you mentioned co-opetition. Yeah. <laugh> so I think when you're in partner conversations and customer conversations, there is a lot of the, the there's a lot of competition out there. Absolutely. Everyone's got their own key messages. What are the key differentiators that you're saying AWS Marie to be together better? And here's why, >>Yeah. I, I think that certainly you, you start with the global footprint of AWS, right? So what we rely on the most is having the ability to truly deal with global customers in availability zones, they're gonna optimize performance from them. But then when we look at what we do that really changes the game, it comes down to scale and performance. We actually just ran, um, a suspense test against cockroach that also does distributed sequel. Absolutely. You know, the results were off the chart. So we went public and said, we have an open challenge. Anyone that wants to try to beat, um, expand and Skye will we'll if you can, we'll put $25,000 towards charity. So we really are putting our money where our mouth is on that challenge. So we believe the performance cuz we've seen it and we know it's real, but then it's really always about data scale. Modern data requirements are breaking the mold of charting. They're breaking the mold of all these bandaids that people have put in these traditional services. And we give them future. We, we feature proof their investments, so they can say, Hey, I can start here. But if I end up being a startup that becomes Airbnb, I'm already built to blow it out on the back end. I can already use what I have. >>Speaking of startups, being the next Airbnb. If you look at behind us here, you can see, this is a really packed event in New York city events are back, but the ecosystem here is even flourishing. So Dave and I and Lisa were observing that we're still kind of in a growth mode, big time. So yeah, there's some market forces headwinds for the big unicorns, overfunded, you know, public companies, maybe the valuations are a little bit off, but there's still a surge of new innovations, new companies coming out of this. Um, and it's all around data and scale. It's all around new names. We've never heard of. Absolutely. What's your take on >>Reaction? Well, actually another awesome segues cuz in addition to the public clouds, I manage the ecosystem. And one of the things that we've really been focused on with Skys SQL is making it accessible API accessible. So if you're a company that has a huge Marine DB footprint change data capture might be the most important thing for you to say, we wanna do this, but we want you to stay in sync with our environments. Um, things like monitoring, things like BI, all of these are ecosystem plays and current partners that we have, um, that we really think about how do you holistically look at not only the database and what it can do, but how does it deliver value to different segments of your customer base or just your employee base that are using that stuff? So I think that's huge for us. >>Well, you know, one of the things that we talk often about is that every company, these days, regardless of industry, has to be a data company. Yep. You've gotta be able to access the data glean insights from an act on it quickly, whether it's manufacturing, retail, healthcare, are there any verticals in where Maria DB really excels? >>Um, so certainly we Excel in areas like financial services is huge DBS bank. Um, in APAC, one of our biggest customers, also one of the largest Oracle migrations, probably the, that we've ever done. A lot of people trying to get off Oracle, we make it seamless to get into Maria DB. Um, you can think about Samsung cloud and another, their entire consumer cloud is built on Maria DB, why it's integrated with expand right seasonality. So there's customers like that that really bring it home for us as far as ServiceNow tech sector. Right? So these are all different ones, but I think we're really strong in those >>Areas. So this brings up a good point. Dave and I a coined a term called super cloud at reinvent and Lisa and Dave were at multiple events we're together at events. And so a lot of people are getting behind this cuz it's multi-cloud sounds like something's broken. Yes. But so we call it super cloud because customers are building on top of ecosystems like Maria DB and others. Yeah. Not just AWS SOS does all the CapEx absolutely provide the value. So now people are having this new super cloud moment. We' saying we can get all the benefits of cloud scale mm-hmm <affirmative> without actually being a cloud. Right. So this is where the next gen layer comes. What's your reaction to, to super cloud. Do you think it's a thing? >>Well, I think it's a thing in the sense, from our perspective as an ISV, we're, we're laser focused on making sure that we support any cloud and we have a truly multicloud cloud platform. But the beauty of that as well is from a single UI, you're able to deploy databases in different clouds underneath that you're not looking at so you can have performance proximity, but you're still driving it through the same Skys UI. So for us it's, it's unequivocally true. Got it. And I think it's only ISVs like Maria DB that can deliver on that value because >>You're enabling, >>We're enabling it. Right. We partner, we build on top of everything. Right. So we can access everything underneath >>And they can then build on top of you. >>Sure, exactly. And that's exactly where it goes. Right? Yeah. So that, I think in that sense, the super cloud is actually already somewhat real. >>It's interesting. You look at the old, it spend, you take a big company. I won't say a name, but a leader in a, a vertical, they have such a big spend. Now they can leverage that spend in with the super cloud model. They then could become a service provider in the vertical. Absolutely capital one S doing it. Yeah. You're seeing, um, Goldman Sachs doing it. They have the power on the spend that they're leveraging in for their business and servicing their vertical and the smaller players. Do you see that trend? >>Well, I think that's the reality is that everyone is getting this place where if you're talking about sort of this broader super concept, you're talking about global scale, right? That's if in order to deliver a backbone that can service that model, you have to have the right data structure and the right database footprint to be able to scale. And I think that's what they all need to be able to do. And that's what we're really well positioned with Skys >>To enable companies, as we talked about a minute ago to truly become data companies. Yeah. And to be competitive and to scale on their own, where are your customer conversations? Are they at the C-suite level? Has that changed in the last couple of years? >>Uh, that's actually a really great way to state that question because I think you would've traditionally probably talked more to, um, the DBAs, right? They're the people that are having headaches. They're having problems. They're, they're trying to solve. We see a lot of developers now tons, right? They're thinking about, I have this, I have this new thing that I need to do to deliver this new application. And here's the requirements and the current model's broken. It doesn't optimize that it's a lot of work and it's hard to manage. So I think that we're in a great position to be able to take that to that next phase and deliver. And then of course, as you get deeper in with AWS, you're talking about, you know, CIO level, CISO level, they're they need to understand how do you fit into our larger paradigm. And many of these guys have, you know, hundreds of million dollar commits with AWS. So they think of their investment in the sense of the cloud stack. And we're part of that cloud stack, just like AWS services. So those conversations continue to happen certainly with our larger customers, cuz it truly is married. >>It is. And they continue to evolve. Kevin, thank you so much >>For joining. You're welcome. Great, >>John and me talking about what's going on with Maria >>D. Thank you, John. Thank you, Lisa. On behalf of Maria B, it was wonderful. Really >>Appreciate it. Fantastic as well for John furrier. I'm Lisa Martin. You're watching the cube live from New York city at AWS summit NYC, John and I we're back with our next guest in a minute.

>> Narrator: Live from the Silicon Valley, it's theCUBE. (bright music) Covering Google Cloud Next 17. >> Hi, and welcome to the second day of live coverage here of theCUBE covering Google Next 2017. We're at the heart of Silicon Valley here at our 4,500 square foot new studio in Palo Alto. We've got a team of reporters and analysts up in San Francisco checking out everything that's happening in Google. I was up there for the day two keynote, and happy to have with me is the first guest of the day, friend of theCUBE, Marc Farley, Vulcancast, guy that knows clouds, worked for one the big three in the past and going to help me break down some of what's going on in the marketplace. Mark, it's great to see you. >> Oh, it's really nice to be here, Stu, thanks for asking me on. >> Always happy to have you-- >> And what a lot of fun stuff to get into. >> Oh my god, yeah, this is what we love. We talked about, I wonder, Amazon Reinvent is like the Superbowl of the industry there. What's Google there if, you know-- >> Well, Google pulls a lot of resources for this. And they can put on a very impressive show. So if this is, if Invent is the Superbowl, then maybe this, maybe Next is the college championship game. I hate to call it college, but it's got that kind of draw, it's a big deal. >> Is is that, I don't want to say, arena football, it's the up and coming-- >> Oh, it's a lot better than that. Google really does some spectacular things at events. >> They're Google, come on, we all use Google, we all know Google, 10,000 people showed up, there's a lot of excitement. So what's your take of the show so far in Google's positioning in cloud? >> It's nothing like the introduction of Glass. And of course, Google Glass is a thing of the past, but I don't know if you remember when they introduced that, when they had the sky diver. Sky divers diving out of an airplane and then climbing up the outside of the building and all that, it was really spectacular. Nobody can ever reach that mark again, probably not even the Academy Awards. But you asked the second part of the question, what's Google position with cloud, I think that's going to be the big question moving forward. They are obviously committed to doing it, and they're bringing unique capabilities into cloud that you don't see from either Amazon or Microsoft. >> Yeah. I mean, coming into it, there's certain things that we've been hearing forever about Google, and especially when you talk about Google in the enterprise. Are they serious, is this just beta, are they going to put the money in? I thought Eric Schmidt did a real good job yesterday in the close day keynote, he's like, "Look, I've been telling Google to push hard "in the enterprise for 17 years. "Look, I signed a check for 30 billion dollars." >> 30 billion! >> Yeah, and I talked to some people, they're a little skeptical, and they're like, "Oh, you know, that's not like it all went to build "the cloud, some of it's for their infrastructure, "there's acquisitions, there's all these other things." But I think it was infrastructure related. Look, there shouldn't be a question that they're serious. And Diane Greene said, in a Q&A she had with the press, that thing about, we're going to tinker with something and then kill it, I want to smash that perception because there's certain things you can do in the consumer side that you cannot get away with on the enterprise side, and she knows that, they're putting a lot of effort to transform their support, transform the pricing, dig in with partners and channels. And some of it is, you know, they've gotten the strategy together, they've gotten the pieces together, we're moving things from beta to GA, and they're making good progress. I think they have addressed some of the misperceptions, that being said, everybody usually, it's like, "I've been hearing this for five years, "it's probably going to take me a couple of years "to really believe it." >> Yeah, but you know, the things is, for people that know Diane Greene and have watched VMware over the years, and then her being there at Google is a real commitment. And she's talking about commitment when she talks about that business. It's full pedal to the metal, this is a very serious, the things that's interesting about it, it's a lot more than infrastructure as a service. >> Yeah. >> The kinds of APIs and apps and everything that they're bringing, this is a lot more than just infrastructure, this is Google developed, Google, if you will, proprietary technology now that they're turning to the external world to use. And there's some really sophisticated stuff in there. >> Yes, so before we get into some of the competitive landscape, some of the things you were pretty impressed with, I think everybody was, the keynote this morning definitely went out much better, day one keynote, a little rocky. Didn't hear, the biggest applauses were around some of the International Women's Day, which is great that they do that, but it's nice when they're like, "Oh, here's some cool new tech," or they're like, oh, wow, this demo that they're doing, some really cool things and products that people want to get their hands on. So what jumped out at you at the keynote this morning? >> I'm trying to remember what it's called. The stuff from around personal identifiable information. >> Yeah, so that's what they call DLP or it's the Data Loss Prevention API. Thank goodness for my Evernote here, which I believe runs on Google cloud, keeping up to date, so I'm-- >> Data loss prevention shouldn't be so hard to remember. >> And by the way, you said proprietary stuff. One thing about Google is, that Data Loss Prevention, it's an API, they want to make it easy to get in, a lot of what they do is open source. They feel that that's one of their differentiations, is to be, we always used to say on the infrastructure side, it's like everybody's pumping their chest. Who's more open than everybody else? Google. Lots of cool stuff, everything from the TensorFlow and Kubernetes that's coming out, where some of us are like, "Okay, how will they actually make money on some of this, "will it be services?" But yeah, Data Loss Prevention API, which was a really cool demo. It's like, okay, here's a credit card, the video kind of takes it and it redacts the number. It can redact social security numbers, it's got that kind of machine learning AI with the video and all those things built in to try to help security encrypt and protect what you're doing. >> It's mind boggling. You think about, they do the facial recognition, but they're doing content recognition also. And you could have a string of numbers there that might not be a phone number, it might not be a social security number, and the question is, what DLP flagged that to, who knows, it doesn't really matter. What matters is that they can actually do this. And as a storage person, you're getting involved, and compliance and risk and mitigation, all these kinds of things over the years. And it's hard for software to go in and scan a lot of data to just look for text. Not images of numbers on a photograph, but just text in a document, whether it's a Word file or something. And you say, "Oh, it's not so hard," but when you try to do that at scale, it's really hard at scale. And that's the thing that I really wonder about DLP, are they going to be able to do this at large scale? And you have to think that that is part of the consideration for them, because they are large scale. And if they can do that, Stu, that is going to be wildly impressive. >> Marc, everything that Google does tends to be built for scale, so you would think they could do that. And I'd think about all the breaches, it was usually, "Oh, oops, we didn't realize we had this information, "didn't know where it was," or things like that. So if Google can help address that, they're looking at some of those core security issues they talked about, they've got a second form factor authentication with a little USB tab that can go into your computer, end to end encryption if you've got Android and Chrome devices, so a lot of good sounding things on encryption and security. >> One of the other things they announced, I don't know if this was part of the same thinking, but they talk about 64 core servers, and they talk about, or VMs, I should say, 64 core VMs, and they're talking about getting the latest and greatest from Intel. What is it, Skylink, Sky-- >> Stu: Skylake. >> Skylake, yeah, thanks. >> They had Raejeanne actually up on stage, Raejeanne Skillern, Cube alumn, know her well, was happy to see her up on stage showing off what they're doing. Not only just the chipset, but Intel's digging in, doing development on Kubernetes, doing development on TensorFlow to help with really performance. And we've seen Intel do this, they did this with virtualization with the extensions that they did, they're doing it with containers. Intel gets involved in these software pieces and makes sure that the chipset's going to be optimized, and great to see them working with Google on it. >> My guess is they're going to be using a lot of cycles for these security things also. The security is really hard, it's front and center in our lives these days, and just everything. I think Google's making a really interesting play, they take their own internal technology, this security technology that they've been using, and they know it's compute heavy. The whole thing about DLP, it's extremely compute heavy to do this stuff. Okay, let's get the biggest, fastest technology we can to make it work, and then maybe it can all seem seamless. I'm really impressed with how they've figured out to take the assets that they have in different places, like from YouTube. These other things that you would think, is YouTube really an enterprise app? No, but there's technology in YouTube that you can use for enterprise cloud services. Very smart, I give them a lot of credit for looking broadly throughout their organization which, in a lot of respects, traditionally has been a consumer oriented experience, and they're taking some of these technologies now and making it available to enterprise. It's really, really hard. >> Absolutely. They did a bunch of enhancements on the G Suite product line. It felt at times a little bit, it's like, okay, wait, I've got the cloud and I've got the applications. There are places that they come together, places that data and security flow between them, but it still feels like a couple of different parts, and how they put together the portfolio, but building a whole solution for the enterprise. We see similar things from Microsoft, not as much from Amazon. I'm curious what your take is as to how Google stacks up against Microsoft who, disclaimer, you did work for one time on the infrastructure side. >> Yeah, that's a whole interesting thing. Google really wants to try to figure out how to get enterprises that run on Microsoft technology moving to Google cloud, and I think it's going to be very tough for them. Satya Nadella and Microsoft are very serious about making a seamless experience for end users and administrators and everybody along managing the systems and using their systems. Okay, can Google replicate that? Maybe on the user side they can, but certainly not on the administration side. And there are hooks between the land-based technology and the cloud-based technology that Microsoft's been working on for years. Question is, can Google come close to replicating those kinds of things, and on Microsoft's side, do customers get enough value, is there enough magic there to make that automation of a hybrid IT experience valuable to their customers. I just have to think though that there's no way Google's going to be able to beat Microsoft at hybrid IT for Microsoft apps. I just don't believe it. >> Yeah, it's interesting. I think one of the not so secret weapons that Google has there is what they're doing with Kubernetes. They've gotten Kubernetes in all the public clouds, it's getting into a lot of on premises environment. Everything from we were at the KubeCon conference in Seattle a couple of months ago. I hear DockerCon and OpenStacks Summit are going to have strong Kubernetes discussions there, and it's growing, it's got a lot of buzz, and that kind of portability and mobility of workload has been something that, especially as guys that have storage background, we have a little bit of skepticism because physics and the size of data and that whole data gravity thing. But that being said, if I can write applications and have ways to be able to do similar things across multiple environments, that gives Google a way to spread their wings beyond what they can do in their Google cloud. So I'm curious what you think about containers, Kubernetes, serverless type activity that they're doing. >> I think within the Google cloud, they'll be able to leverage that technology pretty effectively. I don't think it's going to be very effective, though, in enterprise data centers. I think the OpenStack stuff's been a really hard road, and it's a long time coming, I don't know if they'll ever get there. So then you've got a company like Microsoft that is working really hard on the same thing. It's not clear to me what Microsoft's orchestrate is going to be, but they're going to have one. >> Are you bullish on Asure Stack that's coming out later this year? >> No, not really. >> Okay. >> I think Asure Stack's a step in the right direction, and Microsoft absolutely has to have it, not so much for Google, but for AWS, to compete with AWS. I think it's a good idea, but it's such a constrained system at this point. It's going to take a while to see what it is. You're going to have HPE and Lenovo and Cisco, all have, and Dell, all having the same basic thing. And so you ask yourself, what is the motivation for any of these companies to really knock it out of the park when Microsoft is nailing everybody's feet to the floor on what the options are to offer this? And I understand Microsoft wanting to play it safe and saying, "We want to be able to support this thing, "make sure that, when customers install it, "they don't have problems with it." And Microsoft always wants to foist the support burden onto somebody else anyway, we've all been working for Microsoft our whole lives. >> It was the old Dilbert cartoon, as soon as you open that software, you're all of a sudden Microsoft's pool boy. >> (laughs) I love that, yeah. Asure Stack's going to be pretty constrained, and they keep pushing it further out. So what's the reality of this? And Asure Pack right now is a zombie, everybody's waiting for Asure Stack, but Asure Stack keeps moving out and Asure Stack's going to be small and constrained. This stuff is hard. There's a reason why it's taking everybody a long time to get it out, there's a reason why OpenStack hasn't had the adoption that people first expected, there's going to be a reason why I think Asure Stack does not have the adoption that Microsoft hoped for either. It's going to be an interesting thing to watch over what will play out over the next five or six years. >> Yeah, but for myself, I've seen this story play out a few times on the infrastructure side. I remember the original precursor, the Vblock with Acadia and the go-to-market. VMware, when they did the VSAN stuff, the generation one of Evo really went nowhere, and they had to go, a lot of times it takes 18 to 24 months to sort out some of those basic pricing, packaging, partnering, positioning type things, and even though Asure Stack's been coming for a while, I want to say TP3 is like here, and we're talking about it, and it's going to GA this summer, but it's once we really start getting this customer environment, people start selling it, that we're going to find out what it is and what it isn't. >> It's interesting. You know how important that technology is to Microsoft. It's, in many respects, Satya's baby. And it's so important to them, and at the same time, it's not there, it's not coming, it's going to be constrained. >> So Marc, unfortunately, you and I could talk all day about stuff like this, and we've had many times, at conferences, that we spend a long time. I want to give you just the final word. Wrap up the intro for today on what's happening at Google Next and what's interesting you in the industry. >> Well, I think the big thing here is that Google is showing that they put their foot down and they're not letting up. They're serious about this business, they made this commitment. And we sort of talk and we give lip service, a little bit, to the big three, we got Asure, we got Amazon, and then there's Google. I think every year it's Google does more, and they're proving themselves as a more capable cloud service provider. They're showing the integration with HANA is really interesting, SAP, I should say, not HANA but SAP. They're going after big applications, they've got big customers. Every year that they do this, it's more of an arrival. And I think, in two years time, that idea of the big three is actually going to be big three. It's not going to be two plus one. And that is going to accelerate more of the movement into cloud faster than ever, because the options that Google is offering are different than the others, these are all different clouds with different strengths. Of the three of them, Google, I have to say, has the most, if you will, computer science behind it. It's not that Microsoft doesn't have it, but Google is going to have a lot more capability and machine learning than I think what you're going to see out of Amazon ever. They are just going to take off and run with that, and Microsoft is going to have to figure out how they're going to try to catch up or how they're going to parley what they have in machine learning. It's not that they haven't made an investment in it, but it's not like Google has made investment in it. Google's been making investment in it over the years to support their consumer applications on Google. And now that stuff is coming, like I said before, the stuff is coming into the enterprise. I think there is a shift now, and we sort of wonder, is machine learning going to happen, when it's going to happen? It's going to happen, and it's going to come from Google. >> All right, well, great way to end the opening segment here. Thank you so much, Marc Farley, for joining us. We've got a full day of coverage here from our 4,500 square foot studio in the heart of Silicon Valley. You're watching theCUBE. (bright music)

>>from San Francisco. It's the Cube covering. Get lab commit 2020 Brought to you by get lab. >>Welcome back. I'm Stew Minutemen, and this is get lab Commit 2020 here in San Francisco. Happy to welcome to the program. First time guests and TN Who is the co founder and CEO of Matter Most in. Nice to meet you. >>Thanks. Thanks for having me. >>Alright. S O. I always love. When you get the founders, we go back to a little bit of the why. And just from our little bit of conversation, there is a connection with get lab. You have relationships, Syd, Who's the co founder and CEO of get lab? So bring us back and tell us a little bit about that. >>Yeah, thanks. So I'm you know, I'm ex Microsoft. So I came from collaboration for many years there. And then, you know what I did after Microsoft's I started my own started a sort of video game company was backed by Y Combinator and, you know, we had were doing 85. Game engine is very, very fun on. We ran the entire company off of a messaging product. Misses, You know, a little while ago and it happens that messing product got bought by a big company and that got kind neglected. It started crashing and lose data. We were super unhappy. We tried to export and they wouldn't let us export. We had 26 gigs of all information. And when we stop paying our subscription, they would pay one less for our own information. So, you know, very unhappy. And we're like, holy cats. Like what? I'm gonna d'oh! And rather than go to another platform, we actually realized about 10 million hours of people running messaging and video games. Well, why don't we kind of build this ourselves? So we kind of build a little prototype, started using ourselves internally and because, you know, Sid was this a 2015 and said was out of my Combinator, We were y commoner would invent and we started talking. I was showing him what we built and sits like. You should open source that. And he had this really compelling reason. He's like, Well, if you open source it and people like it, you can always close source it again because it's a prototype. But if you open source, it and no one cares. You should stop doing what you do. And he was great. Kind of send me like this email with all the things you need to dio to run open source business. And it was just wonderful. And it just it is a start taking off. We started getting these wonderful, amazing enterprise customers that really saw what mattered most was at the very beginning, which was You know, some people call us open source slack, but what it really is, it's a collaborates, a collaboration platform for real Time Dev ops and it release. For people who are regulated, it's gonna offer flexibility and on Prem deployment and a lot of security and customization. So that's kind of we started and get lab is we kind of started Farley. We started following get labs footsteps and you'll find today with get lab is we're we're bundled with the omnibus. So all you have to do is put what your own would you like matter most on one. Get lab reconfigure and europe running. >>Yeah, I love that. That story would love you to tease out a little bit when you hear you know, open source. You know, communications and secure might not be things that people would necessarily all put together. So help us understand a little bit the underlying architecture. This isn't just, you know, isn't messaging it, Z how is it different from things that people would be familiar with? >>Yeah, that's a great question. So how do you get more secure with open source products? And the one thing look at, I'll just give you one example. Is mobility right? So, in mobile today, if you're pushing them, if you're setting a push notification to an Iowa, sir. An android device, It has a route through, like Google or Android. Right? And whatever app that you're using to send those notifications they're going to see you're going to see your notifications. They have to, right? So you just get encryption all that stuff in order to send to Google and Andrew, you have to send it on encrypted. And you know these applications are not there, not yours. They're owned by another organization. So how do you make that private how to make it secure? So with open source communication, you get the source code. It's an extreme case like we have you know, perhaps you can views, and it's really simple in turnkey. But in the if you want to go in the full privacy, most security you have the full source code. APS. You have the full source code to the system, including what pushes the messages to your APS, and you can compiling with your own certificates. And you can set up a system where you actually have complete privacy and no third party can actually get your information. And why enterprises in many cases want that extreme privacy is because when you're doing incident response and you have information about a vulnerability or breach that could really upset many, many critical systems. If that information leaked out, you really can't. Many people don't want ever to touch 1/3 party. So that's one example of how open source lets you have that privacy and security, because you because you control everything >>all right, what we threw a little bit the speeds and feeds. How many employees do you have? How many did you share? How many customers you have, where you are with funding? >>So where we are funding is, you know, last year we announced a 20 million Siri's A and A 50 million Siri's be who went from about 40 folks the beginning the aired about 100 a t end of the year. We got over 1000 people that contribute to matter most, and what you'll find is what you'll find is every sort of get lab on the bus installations. Gonna have a matter most is gonna have the ability to sort of turn on matter most so very broad reach. It's sort of like one step away. There's lots of customers. You can see it. Get lab commit that are running matter. Most get lab together, so customers are going to include Hey, there's the I T K and Agriculture that's got six times faster deployments running. Get lab in Madame's together, you've got world line. It's got 3000 people in the system, so you've got a lot of so we're growing really quickly. And there's a lot of opportunity working with Get lab to bring get lab into mobile into sort of real times. Dev up scenarios. >>Definitely One of the themes we hear the at the show is that get labs really enabling the remote workforce, especially when you talk about the developers. It sounds like that's very much in line with what matters most is doing. >>Absolutely. Madam Mrs Moat. First, I don't actually know. We're probably in 20 plus countries, and it's it's a remote team. So we use use matter most to collaborate, and we use videoconferencing and issue tracking across a bunch of different systems. And, yeah, it's just it's remote. First, it's how it's how we work. It's very natural. >>Yeah, it just give us a little bit of the inside. How do you make sure, as a CEO that you, you know, have the culture and getting everyone on the same page when many of them, you know, you're not seeing them regularly? Some of them you've probably never met in person, so >>that's a great question. So how do you sort of maintain that culture 11? The concert that get lips pioneered is a continent boring solutions, and it's something that we've taken on as well. What's the most boring solution to preserve culture and to scale? And it's really do what get labs doing right? So get love's hand, looked up. Get lab dot com. We've got handbook that matter most dot com. It's really writing down all the things that how we operate, what our culture is and what are values are so that every person that onboard is gonna get the same experience, right? And then what happens is people think that if you're building, you're gonna have stronger culture because, you know, sort of like, you know, absorbing things. What actually happens is it's this little broken telephone and starts echoing out, and it's opposed to going one source of truth. It's everyone's interpretation. We have a handbook and you're forced to write things down. It's a very unnatural act, and when you force people to write things down, then you get that consistency and every we can go to a source of truth and say, like, This is the way we operate. >>2019 was an interesting year for open source. There were certain companies that were changing their models as toe how they do things. You started it open source to be able to get, you know, direct feedback. But how do you position and talk to people about you know, the role of open source on still being ableto have a business around that >>so open source is, I think there's a generation of open source cos there's three ways you can really make money from open source, right? You can host software, you can provide support, and service is where you can do licensing, which is an open core model. When you see his categories of companies like allowed, you see categories like elastic like Hash corporate Terra Form involved with Get Lab that have chosen the open core model. And this is really becoming sort of a standard on what we do is we fall that standard, and we know that it supports public companies and supports companies with hyper growth like get Lab. So it's a very it's becoming a model that I'm actually quite familiar to the market, and what we see is this this sort of generation, this sort of movement of okay, there was operating systems Windows Circle. Now there's now there's more servers running Lennix than Windows Server. On Azure, you seen virtual ization technology. You've seen databases all sort of go the open source way and we see that it's a natural progression of collaboration. So it's really like we believe collaboration will go the open source way we believe leading the way to do that is through open core because you can generate a sustainable, scalable business that's going to give enterprises the confidence to invest in the right platform. >>All right, in what's on deck for matter most in 2020. >>It's really we would definitely want to work with. Get lab a lot more. We really want to go from this concept of concurrent Dev ops that get labs really champion to say Real time de Bob's. So we've got Dev ops in the world that's taking months and weeks of cycle times. And bring that down to minutes. We want to take you know, all your processes that take hours and take it down to seconds. So what really people, developers air sort of clamoring for a lot is like, Well, how do we get these if I'm regulated if I have a lot of customization needs? If I'm on premise, if I'm in a private network, how do I get to mobile? How do I get quicker interactions on? We really want to support that with instant response with deficit cock use cases and with really having a complete solution that could go from all your infrastructure in your data center, too. You know, that really important person walking through the airport. And that's that's how you speed cycle times and make Deb sec cops available anywhere. And you do it securely and in do it privately. >>All right, thanks so much for meeting with us. And great to hear about matter most. >>Well, thank you. Still >>all right. Be sure to check out the cube dot net for all the coverage that we will have throughout 2020 I'm still minimum. And thanks for watching the cue.

>> Welcome to the Intel AI Lounge. Today, we're very excited to share with you the Precision Medicine panel discussion. I'll be moderating the session. My name is Kay Erin. I'm the general manager of Health and Life Sciences at Intel. And I'm excited to share with you these three panelists that we have here. First is John Madison. He is a chief information medical officer and he is part of Kaiser Permanente. We're very excited to have you here. Thank you, John. >> Thank you. >> We also have Naveen Rao. He is the VP and general manager for the Artificial Intelligence Solutions at Intel. He's also the former CEO of Nervana, which was acquired by Intel. And we also have Bob Rogers, who's the chief data scientist at our AI solutions group. So, why don't we get started with our questions. I'm going to ask each of the panelists to talk, introduce themselves, as well as talk about how they got started with AI. So why don't we start with John? >> Sure, so can you hear me okay in the back? Can you hear? Okay, cool. So, I am a recovering evolutionary biologist and a recovering physician and a recovering geek. And I implemented the health record system for the first and largest region of Kaiser Permanente. And it's pretty obvious that most of the useful data in a health record, in lies in free text. So I started up a natural language processing team to be able to mine free text about a dozen years ago. So we can do things with that that you can't otherwise get out of health information. I'll give you an example. I read an article online from the New England Journal of Medicine about four years ago that said over half of all people who have had their spleen taken out were not properly vaccinated for a common form of pneumonia, and when your spleen's missing, you must have that vaccine or you die a very sudden death with sepsis. In fact, our medical director in Northern California's father died of that exact same scenario. So, when I read the article, I went to my structured data analytics team and to my natural language processing team and said please show me everybody who has had their spleen taken out and hasn't been appropriately vaccinated and we ran through about 20 million records in about three hours with the NLP team, and it took about three weeks with a structured data analytics team. That sounds counterintuitive but it actually happened that way. And it's not a competition for time only. It's a competition for quality and sensitivity and specificity. So we were able to indentify all of our members who had their spleen taken out, who should've had a pneumococcal vaccine. We vaccinated them and there are a number of people alive today who otherwise would've died absent that capability. So people don't really commonly associate natural language processing with machine learning, but in fact, natural language processing relies heavily and is the first really, highly successful example of machine learning. So we've done dozens of similar projects, mining free text data in millions of records very efficiently, very effectively. But it really helped advance the quality of care and reduce the cost of care. It's a natural step forward to go into the world of personalized medicine with the arrival of a 100-dollar genome, which is actually what it costs today to do a full genome sequence. Microbiomics, that is the ecosystem of bacteria that are in every organ of the body actually. And we know now that there is a profound influence of what's in our gut and how we metabolize drugs, what diseases we get. You can tell in a five year old, whether or not they were born by a vaginal delivery or a C-section delivery by virtue of the bacteria in the gut five years later. So if you look at the complexity of the data that exists in the genome, in the microbiome, in the health record with free text and you look at all the other sources of data like this streaming data from my wearable monitor that I'm part of a research study on Precision Medicine out of Stanford, there is a vast amount of disparate data, not to mention all the imaging, that really can collectively produce much more useful information to advance our understanding of science, and to advance our understanding of every individual. And then we can do the mash up of a much broader range of science in health care with a much deeper sense of data from an individual and to do that with structured questions and structured data is very yesterday. The only way we're going to be able to disambiguate those data and be able to operate on those data in concert and generate real useful answers from the broad array of data types and the massive quantity of data, is to let loose machine learning on all of those data substrates. So my team is moving down that pathway and we're very excited about the future prospects for doing that. >> Yeah, great. I think that's actually some of the things I'm very excited about in the future with some of the technologies we're developing. My background, I started actually being fascinated with computation in biological forms when I was nine. Reading and watching sci-fi, I was kind of a big dork which I pretty much still am. I haven't really changed a whole lot. Just basically seeing that machines really aren't all that different from biological entities, right? We are biological machines and kind of understanding how a computer works and how we engineer those things and trying to pull together concepts that learn from biology into that has always been a fascination of mine. As an undergrad, I was in the EE, CS world. Even then, I did some research projects around that. I worked in the industry for about 10 years designing chips, microprocessors, various kinds of ASICs, and then actually went back to school, quit my job, got a Ph.D. in neuroscience, computational neuroscience, to specifically understand what's the state of the art. What do we really understand about the brain? And are there concepts that we can take and bring back? Inspiration's always been we want to... We watch birds fly around. We want to figure out how to make something that flies. We extract those principles, and then build a plane. Don't necessarily want to build a bird. And so Nervana's really was the combination of all those experiences, bringing it together. Trying to push computation in a new a direction. Now, as part of Intel, we can really add a lot of fuel to that fire. I'm super excited to be part of Intel in that the technologies that we were developing can really proliferate and be applied to health care, can be applied to Internet, can be applied to every facet of our lives. And some of the examples that John mentioned are extremely exciting right now and these are things we can do today. And the generality of these solutions are just really going to hit every part of health care. I mean from a personal viewpoint, my whole family are MDs. I'm sort of the black sheep of the family. I don't have an MD. And it's always been kind of funny to me that knowledge is concentrated in a few individuals. Like you have a rare tumor or something like that, you need the guy who knows how to read this MRI. Why? Why is it like that? Can't we encapsulate that knowledge into a computer or into an algorithm, and democratize it. And the reason we couldn't do it is we just didn't know how. And now we're really getting to a point where we know how to do that. And so I want that capability to go to everybody. It'll bring the cost of healthcare down. It'll make all of us healthier. That affects everything about our society. So that's really what's exciting about it to me. >> That's great. So, as you heard, I'm Bob Rogers. I'm chief data scientist for analytics and artificial intelligence solutions at Intel. My mission is to put powerful analytics in the hands of every decision maker and when I think about Precision Medicine, decision makers are not just doctors and surgeons and nurses, but they're also case managers and care coordinators and probably most of all, patients. So the mission is really to put powerful analytics and AI capabilities in the hands of everyone in health care. It's a very complex world and we need tools to help us navigate it. So my background, I started with a Ph.D. in physics and I was computer modeling stuff, falling into super massive black holes. And there's a lot of applications for that in the real world. No, I'm kidding. (laughter) >> John: There will be, I'm sure. Yeah, one of these days. Soon as we have time travel. Okay so, I actually, about 1991, I was working on my post doctoral research, and I heard about neural networks, these things that could compute the way the brain computes. And so, I started doing some research on that. I wrote some papers and actually, it was an interesting story. The problem that we solved that got me really excited about neural networks, which have become deep learning, my office mate would come in. He was this young guy who was about to go off to grad school. He'd come in every morning. "I hate my project." Finally, after two weeks, what's your project? What's the problem? It turns out he had to circle these little fuzzy spots on these images from a telescope. So they were looking for the interesting things in a sky survey, and he had to circle them and write down their coordinates all summer. Anyone want to volunteer to do that? No? Yeah, he was very unhappy. So we took the first two weeks of data that he created doing his work by hand, and we trained an artificial neural network to do his summer project and finished it in about eight hours of computing. (crowd laughs) And so he was like yeah, this is amazing. I'm so happy. And we wrote a paper. I was the first author of course, because I was the senior guy at age 24. And he was second author. His first paper ever. He was very, very excited. So we have to fast forward about 20 years. His name popped up on the Internet. And so it caught my attention. He had just won the Nobel Prize in physics. (laughter) So that's where artificial intelligence will get you. (laughter) So thanks Naveen. Fast forwarding, I also developed some time series forecasting capabilities that allowed me to create a hedge fund that I ran for 12 years. After that, I got into health care, which really is the center of my passion. Applying health care to figuring out how to get all the data from all those siloed sources, put it into the cloud in a secure way, and analyze it so you can actually understand those cases that John was just talking about. How do you know that that person had had a splenectomy and that they needed to get that pneumovax? You need to be able to search all the data, so we used AI, natural language processing, machine learning, to do that and then two years ago, I was lucky enough to join Intel and, in the intervening time, people like Naveen actually thawed the AI winter and we're really in a spring of amazing opportunities with AI, not just in health care but everywhere, but of course, the health care applications are incredibly life saving and empowering so, excited to be here on this stage with you guys. >> I just want to cue off of your comment about the role of physics in AI and health care. So the field of microbiomics that I referred to earlier, bacteria in our gut. There's more bacteria in our gut than there are cells in our body. There's 100 times more DNA in that bacteria than there is in the human genome. And we're now discovering a couple hundred species of bacteria a year that have never been identified under a microscope just by their DNA. So it turns out the person who really catapulted the study and the science of microbiomics forward was an astrophysicist who did his Ph.D. in Steven Hawking's lab on the collision of black holes and then subsequently, put the other team in a virtual reality, and he developed the first super computing center and so how did he get an interest in microbiomics? He has the capacity to do high performance computing and the kind of advanced analytics that are required to look at a 100 times the volume of 3.2 billion base pairs of the human genome that are represented in the bacteria in our gut, and that has unleashed the whole science of microbiomics, which is going to really turn a lot of our assumptions of health and health care upside down. >> That's great, I mean, that's really transformational. So a lot of data. So I just wanted to let the audience know that we want to make this an interactive session, so I'll be asking for questions in a little bit, but I will start off with one question so that you can think about it. So I wanted to ask you, it looks like you've been thinking a lot about AI over the years. And I wanted to understand, even though AI's just really starting in health care, what are some of the new trends or the changes that you've seen in the last few years that'll impact how AI's being used going forward? >> So I'll start off. There was a paper published by a guy by the name of Tegmark at Harvard last summer that, for the first time, explained why neural networks are efficient beyond any mathematical model we predict. And the title of the paper's fun. It's called Deep Learning Versus Cheap Learning. So there were two sort of punchlines of the paper. One is is that the reason that mathematics doesn't explain the efficiency of neural networks is because there's a higher order of mathematics called physics. And the physics of the underlying data structures determined how efficient you could mine those data using machine learning tools. Much more so than any mathematical modeling. And so the second thing that was a reel from that paper is that the substrate of the data that you're operating on and the natural physics of those data have inherent levels of complexity that determine whether or not a 12th layer of neural net will get you where you want to go really fast, because when you do the modeling, for those math geeks in the audience, a factorial. So if there's 12 layers, there's 12 factorial permutations of different ways you could sequence the learning through those data. When you have 140 layers of a neural net, it's a much, much, much bigger number of permutations and so you end up being hardware-bound. And so, what Max Tegmark basically said is you can determine whether to do deep learning or cheap learning based upon the underlying physics of the data substrates you're operating on and have a good insight into how to optimize your hardware and software approach to that problem. >> So another way to put that is that neural networks represent the world in the way the world is sort of built. >> Exactly. >> It's kind of hierarchical. It's funny because, sort of in retrospect, like oh yeah, that kind of makes sense. But when you're thinking about it mathematically, we're like well, anything... The way a neural can represent any mathematical function, therfore, it's fully general. And that's the way we used to look at it, right? So now we're saying, well actually decomposing the world into different types of features that are layered upon each other is actually a much more efficient, compact representation of the world, right? I think this is actually, precisely the point of kind of what you're getting at. What's really exciting now is that what we were doing before was sort of building these bespoke solutions for different kinds of data. NLP, natural language processing. There's a whole field, 25 plus years of people devoted to figuring out features, figuring out what structures make sense in this particular context. Those didn't carry over at all to computer vision. Didn't carry over at all to time series analysis. Now, with neural networks, we've seen it at Nervana, and now part of Intel, solving customers' problems. We apply a very similar set of techniques across all these different types of data domains and solve them. All data in the real world seems to be hierarchical. You can decompose it into this hierarchy. And it works really well. Our brains are actually general structures. As a neuroscientist, you can look at different parts of your brain and there are differences. Something that takes in visual information, versus auditory information is slightly different but they're much more similar than they are different. So there is something invariant, something very common between all of these different modalities and we're starting to learn that. And this is extremely exciting to me trying to understand the biological machine that is a computer, right? We're figurig it out, right? >> One of the really fun things that Ray Chrisfall likes to talk about is, and it falls in the genre of biomimmicry, and how we actually replicate biologic evolution in our technical solutions so if you look at, and we're beginning to understand more and more how real neural nets work in our cerebral cortex. And it's sort of a pyramid structure so that the first pass of a broad base of analytics, it gets constrained to the next pass, gets constrained to the next pass, which is how information is processed in the brain. So we're discovering increasingly that what we've been evolving towards, in term of architectures of neural nets, is approximating the architecture of the human cortex and the more we understand the human cortex, the more insight we get to how to optimize neural nets, so when you think about it, with millions of years of evolution of how the cortex is structured, it shouldn't be a surprise that the optimization protocols, if you will, in our genetic code are profoundly efficient in how they operate. So there's a real role for looking at biologic evolutionary solutions, vis a vis technical solutions, and there's a friend of mine who worked with who worked with George Church at Harvard and actually published a book on biomimmicry and they wrote the book completely in DNA so if all of you have your home DNA decoder, you can actually read the book on your DNA reader, just kidding. >> There's actually a start up I just saw in the-- >> Read-Write DNA, yeah. >> Actually it's a... He writes something. What was it? (response from crowd member) Yeah, they're basically encoding information in DNA as a storage medium. (laughter) The company, right? >> Yeah, that same friend of mine who coauthored that biomimmicry book in DNA also did the estimate of the density of information storage. So a cubic centimeter of DNA can store an hexabyte of data. I mean that's mind blowing. >> Naveen: Highly done soon. >> Yeah that's amazing. Also you hit upon a really important point there, that one of the things that's changed is... Well, there are two major things that have changed in my perception from let's say five to 10 years ago, when we were using machine learning. You could use data to train models and make predictions to understand complex phenomena. But they had limited utility and the challenge was that if I'm trying to build on these things, I had to do a lot of work up front. It was called feature engineering. I had to do a lot of work to figure out what are the key attributes of that data? What are the 10 or 20 or 100 pieces of information that I should pull out of the data to feed to the model, and then the model can turn it into a predictive machine. And so, what's really exciting about the new generation of machine learning technology, and particularly deep learning, is that it can actually learn from example data those features without you having to do any preprogramming. That's why Naveen is saying you can take the same sort of overall approach and apply it to a bunch of different problems. Because you're not having to fine tune those features. So at the end of the day, the two things that have changed to really enable this evolution is access to more data, and I'd be curious to hear from you where you're seeing data come from, what are the strategies around that. So access to data, and I'm talking millions of examples. So 10,000 examples most times isn't going to cut it. But millions of examples will do it. And then, the other piece is the computing capability to actually take millions of examples and optimize this algorithm in a single lifetime. I mean, back in '91, when I started, we literally would have thousands of examples and it would take overnight to run the thing. So now in the world of millions, and you're putting together all of these combinations, the computing has changed a lot. I know you've made some revolutionary advances in that. But I'm curious about the data. Where are you seeing interesting sources of data for analytics? >> So I do some work in the genomics space and there are more viable permutations of the human genome than there are people who have ever walked the face of the earth. And the polygenic determination of a phenotypic expression translation, what are genome does to us in our physical experience in health and disease is determined by many, many genes and the interaction of many, many genes and how they are up and down regulated. And the complexity of disambiguating which 27 genes are affecting your diabetes and how are they up and down regulated by different interventions is going to be different than his. It's going to be different than his. And we already know that there's four or five distinct genetic subtypes of type II diabetes. So physicians still think there's one disease called type II diabetes. There's actually at least four or five genetic variants that have been identified. And so, when you start thinking about disambiguating, particularly when we don't know what 95 percent of DNA does still, what actually is the underlining cause, it will require this massive capability of developing these feature vectors, sometimes intuiting it, if you will, from the data itself. And other times, taking what's known knowledge to develop some of those feature vectors, and be able to really understand the interaction of the genome and the microbiome and the phenotypic data. So the complexity is high and because the variation complexity is high, you do need these massive members. Now I'm going to make a very personal pitch here. So forgive me, but if any of you have any role in policy at all, let me tell you what's happening right now. The Genomic Information Nondiscrimination Act, so called GINA, written by a friend of mine, passed a number of years ago, says that no one can be discriminated against for health insurance based upon their genomic information. That's cool. That should allow all of you to feel comfortable donating your DNA to science right? Wrong. You are 100% unprotected from discrimination for life insurance, long term care and disability. And it's being practiced legally today and there's legislation in the House, in mark up right now to completely undermine the existing GINA legislation and say that whenever there's another applicable statute like HIPAA, that the GINA is irrelevant, that none of the fines and penalties are applicable at all. So we need a ton of data to be able to operate on. We will not be getting a ton of data to operate on until we have the kind of protection we need to tell people, you can trust us. You can give us your data, you will not be subject to discrimination. And that is not the case today. And it's being further undermined. So I want to make a plea to any of you that have any policy influence to go after that because we need this data to help the understanding of human health and disease and we're not going to get it when people look behind the curtain and see that discrimination is occurring today based upon genetic information. >> Well, I don't like the idea of being discriminated against based on my DNA. Especially given how little we actually know. There's so much complexity in how these things unfold in our own bodies, that I think anything that's being done is probably childishly immature and oversimplifying. So it's pretty rough. >> I guess the translation here is that we're all unique. It's not just a Disney movie. (laughter) We really are. And I think one of the strengths that I'm seeing, kind of going back to the original point, of these new techniques is it's going across different data types. It will actually allow us to learn more about the uniqueness of the individual. It's not going to be just from one data source. They were collecting data from many different modalities. We're collecting behavioral data from wearables. We're collecting things from scans, from blood tests, from genome, from many different sources. The ability to integrate those into a unified picture, that's the important thing that we're getting toward now. That's what I think is going to be super exciting here. Think about it, right. I can tell you to visual a coin, right? You can visualize a coin. Not only do you visualize it. You also know what it feels like. You know how heavy it is. You have a mental model of that from many different perspectives. And if I take away one of those senses, you can still identify the coin, right? If I tell you to put your hand in your pocket, and pick out a coin, you probably can do that with 100% reliability. And that's because we have this generalized capability to build a model of something in the world. And that's what we need to do for individuals is actually take all these different data sources and come up with a model for an individual and you can actually then say what drug works best on this. What treatment works best on this? It's going to get better with time. It's not going to be perfect, because this is what a doctor does, right? A doctor who's very experienced, you're a practicing physician right? Back me up here. That's what you're doing. You basically have some categories. You're taking information from the patient when you talk with them, and you're building a mental model. And you apply what you know can work on that patient, right? >> I don't have clinic hours anymore, but I do take care of many friends and family. (laughter) >> You used to, you used to. >> I practiced for many years before I became a full-time geek. >> I thought you were a recovering geek. >> I am. (laughter) I do more policy now. >> He's off the wagon. >> I just want to take a moment and see if there's anyone from the audience who would like to ask, oh. Go ahead. >> We've got a mic here, hang on one second. >> I have tons and tons of questions. (crosstalk) Yes, so first of all, the microbiome and the genome are really complex. You already hit about that. Yet most of the studies we do are small scale and we have difficulty repeating them from study to study. How are we going to reconcile all that and what are some of the technical hurdles to get to the vision that you want? >> So primarily, it's been the cost of sequencing. Up until a year ago, it's $1000, true cost. Now it's $100, true cost. And so that barrier is going to enable fairly pervasive testing. It's not a real competitive market becaue there's one sequencer that is way ahead of everybody else. So the price is not $100 yet. The cost is below $100. So as soon as there's competition to drive the cost down, and hopefully, as soon as we all have the protection we need against discrimination, as I mentioned earlier, then we will have large enough sample sizes. And so, it is our expectation that we will be able to pool data from local sources. I chair the e-health work group at the Global Alliance for Genomics and Health which is working on this very issue. And rather than pooling all the data into a single, common repository, the strategy, and we're developing our five-year plan in a month in London, but the goal is to have a federation of essentially credentialed data enclaves. That's a formal method. HHS already does that so you can get credentialed to search all the data that Medicare has on people that's been deidentified according to HIPPA. So we want to provide the same kind of service with appropriate consent, at an international scale. And there's a lot of nations that are talking very much about data nationality so that you can't export data. So this approach of a federated model to get at data from all the countries is important. The other thing is a block-chain technology is going to be very profoundly useful in this context. So David Haussler of UC Santa Cruz is right now working on a protocol using an open block-chain, public ledger, where you can put out. So for any typical cancer, you may have a half dozen, what are called sematic variance. Cancer is a genetic disease so what has mutated to cause it to behave like a cancer? And if we look at those biologically active sematic variants, publish them on a block chain that's public, so there's not enough data there to reidentify the patient. But if I'm a physician treating a woman with breast cancer, rather than say what's the protocol for treating a 50-year-old woman with this cell type of cancer, I can say show me all the people in the world who have had this cancer at the age of 50, wit these exact six sematic variants. Find the 200 people worldwide with that. Ask them for consent through a secondary mechanism to donate everything about their medical record, pool that information of the core of 200 that exactly resembles the one sitting in front of me, and find out, of the 200 ways they were treated, what got the best results. And so, that's the kind of future where a distributed, federated architecture will allow us to query and obtain a very, very relevant cohort, so we can basically be treating patients like mine, sitting right in front of me. Same thing applies for establishing research cohorts. There's some very exciting stuff at the convergence of big data analytics, machine learning, and block chaining. >> And this is an area that I'm really excited about and I think we're excited about generally at Intel. They actually have something called the Collaborative Cancer Cloud, which is this kind of federated model. We have three different academic research centers. Each of them has a very sizable and valuable collection of genomic data with phenotypic annotations. So you know, pancreatic cancer, colon cancer, et cetera, and we've actually built a secure computing architecture that can allow a person who's given the right permissions by those organizations to ask a specific question of specific data without ever sharing the data. So the idea is my data's really important to me. It's valuable. I want us to be able to do a study that gets the number from the 20 pancreatic cancer patients in my cohort, up to the 80 that we have in the whole group. But I can't do that if I'm going to just spill my data all over the world. And there are HIPAA and compliance reasons for that. There are business reasons for that. So what we've built at Intel is this platform that allows you to do different kinds of queries on this genetic data. And reach out to these different sources without sharing it. And then, the work that I'm really involved in right now and that I'm extremely excited about... This also touches on something that both of you said is it's not sufficient to just get the genome sequences. You also have to have the phenotypic data. You have to know what cancer they've had. You have to know that they've been treated with this drug and they've survived for three months or that they had this side effect. That clinical data also needs to be put together. It's owned by other organizations, right? Other hospitals. So the broader generalization of the Collaborative Cancer Cloud is something we call the data exchange. And it's a misnomer in a sense that we're not actually exchanging data. We're doing analytics on aggregated data sets without sharing it. But it really opens up a world where we can have huge populations and big enough amounts of data to actually train these models and draw the thread in. Of course, that really then hits home for the techniques that Nervana is bringing to the table, and of course-- >> Stanford's one of your academic medical centers? >> Not for that Collaborative Cancer Cloud. >> The reason I mentioned Standford is because the reason I'm wearing this FitBit is because I'm a research subject at Mike Snyder's, the chair of genetics at Stanford, IPOP, intrapersonal omics profile. So I was fully sequenced five years ago and I get four full microbiomes. My gut, my mouth, my nose, my ears. Every three months and I've done that for four years now. And about a pint of blood. And so, to your question of the density of data, so a lot of the problem with applying these techniques to health care data is that it's basically a sparse matrix and there's a lot of discontinuities in what you can find and operate on. So what Mike is doing with the IPOP study is much the same as you described. Creating a highly dense longitudinal set of data that will help us mitigate the sparse matrix problem. (low volume response from audience member) Pardon me. >> What's that? (low volume response) (laughter) >> Right, okay. >> John: Lost the school sample. That's got to be a new one I've heard now. >> Okay, well, thank you so much. That was a great question. So I'm going to repeat this and ask if there's another question. You want to go ahead? >> Hi, thanks. So I'm a journalist and I report a lot on these neural networks, a system that's beter at reading mammograms than your human radiologists. Or a system that's better at predicting which patients in the ICU will get sepsis. These sort of fascinating academic studies that I don't really see being translated very quickly into actual hospitals or clinical practice. Seems like a lot of the problems are regulatory, or liability, or human factors, but how do you get past that and really make this stuff practical? >> I think there's a few things that we can do there and I think the proof points of the technology are really important to start with in this specific space. In other places, sometimes, you can start with other things. But here, there's a real confidence problem when it comes to health care, and for good reason. We have doctors trained for many, many years. School and then residencies and other kinds of training. Because we are really, really conservative with health care. So we need to make sure that technology's well beyond just the paper, right? These papers are proof points. They get people interested. They even fuel entire grant cycles sometimes. And that's what we need to happen. It's just an inherent problem, its' going to take a while. To get those things to a point where it's like well, I really do trust what this is saying. And I really think it's okay to now start integrating that into our standard of care. I think that's where you're seeing it. It's frustrating for all of us, believe me. I mean, like I said, I think personally one of the biggest things, I want to have an impact. Like when I go to my grave, is that we used machine learning to improve health care. We really do feel that way. But it's just not something we can do very quickly and as a business person, I don't actually look at those use cases right away because I know the cycle is just going to be longer. >> So to your point, the FDA, for about four years now, has understood that the process that has been given to them by their board of directors, otherwise known as Congress, is broken. And so they've been very actively seeking new models of regulation and what's really forcing their hand is regulation of devices and software because, in many cases, there are black box aspects of that and there's a black box aspect to machine learning. Historically, Intel and others are making inroads into providing some sort of traceability and transparency into what happens in that black box rather than say, overall we get better results but once in a while we kill somebody. Right? So there is progress being made on that front. And there's a concept that I like to use. Everyone knows Ray Kurzweil's book The Singularity Is Near? Well, I like to think that diadarity is near. And the diadarity is where you have human transparency into what goes on in the black box and so maybe Bob, you want to speak a little bit about... You mentioned that, in a prior discussion, that there's some work going on at Intel there. >> Yeah, absolutely. So we're working with a number of groups to really build tools that allow us... In fact Naveen probably can talk in even more detail than I can, but there are tools that allow us to actually interrogate machine learning and deep learning systems to understand, not only how they respond to a wide variety of situations but also where are there biases? I mean, one of the things that's shocking is that if you look at the clinical studies that our drug safety rules are based on, 50 year old white guys are the peak of that distribution, which I don't see any problem with that, but some of you out there might not like that if you're taking a drug. So yeah, we want to understand what are the biases in the data, right? And so, there's some new technologies. There's actually some very interesting data-generative technologies. And this is something I'm also curious what Naveen has to say about, that you can generate from small sets of observed data, much broader sets of varied data that help probe and fill in your training for some of these systems that are very data dependent. So that takes us to a place where we're going to start to see deep learning systems generating data to train other deep learning systems. And they start to sort of go back and forth and you start to have some very nice ways to, at least, expose the weakness of these underlying technologies. >> And that feeds back to your question about regulatory oversight of this. And there's the fascinating, but little known origin of why very few women are in clinical studies. Thalidomide causes birth defects. So rather than say pregnant women can't be enrolled in drug trials, they said any woman who is at risk of getting pregnant cannot be enrolled. So there was actually a scientific meritorious argument back in the day when they really didn't know what was going to happen post-thalidomide. So it turns out that the adverse, unintended consequence of that decision was we don't have data on women and we know in certain drugs, like Xanax, that the metabolism is so much slower, that the typical dosing of Xanax is women should be less than half of that for men. And a lot of women have had very serious adverse effects by virtue of the fact that they weren't studied. So the point I want to illustrate with that is that regulatory cycles... So people have known for a long time that was like a bad way of doing regulations. It should be changed. It's only recently getting changed in any meaningful way. So regulatory cycles and legislative cycles are incredibly slow. The rate of exponential growth in technology is exponential. And so there's impedance mismatch between the cycle time for regulation cycle time for innovation. And what we need to do... I'm working with the FDA. I've done four workshops with them on this very issue. Is that they recognize that they need to completely revitalize their process. They're very interested in doing it. They're not resisting it. People think, oh, they're bad, the FDA, they're resisting. Trust me, there's nobody on the planet who wants to revise these review processes more than the FDA itself. And so they're looking at models and what I recommended is global cloud sourcing and the FDA could shift from a regulatory role to one of doing two things, assuring the people who do their reviews are competent, and assuring that their conflicts of interest are managed, because if you don't have a conflict of interest in this very interconnected space, you probably don't know enough to be a reviewer. So there has to be a way to manage the conflict of interest and I think those are some of the keypoints that the FDA is wrestling with because there's type one and type two errors. If you underregulate, you end up with another thalidomide and people born without fingers. If you overregulate, you prevent life saving drugs from coming to market. So striking that balance across all these different technologies is extraordinarily difficult. If it were easy, the FDA would've done it four years ago. It's very complicated. >> Jumping on that question, so all three of you are in some ways entrepreneurs, right? Within your organization or started companies. And I think it would be good to talk a little bit about the business opportunity here, where there's a huge ecosystem in health care, different segments, biotech, pharma, insurance payers, etc. Where do you see is the ripe opportunity or industry, ready to really take this on and to make AI the competitive advantage. >> Well, the last question also included why aren't you using the result of the sepsis detection? We do. There were six or seven published ways of doing it. We did our own data, looked at it, we found a way that was superior to all the published methods and we apply that today, so we are actually using that technology to change clinical outcomes. As far as where the opportunities are... So it's interesting. Because if you look at what's going to be here in three years, we're not going to be using those big data analytics models for sepsis that we are deploying today, because we're just going to be getting a tiny aliquot of blood, looking for the DNA or RNA of any potential infection and we won't have to infer that there's a bacterial infection from all these other ancillary, secondary phenomenon. We'll see if the DNA's in the blood. So things are changing so fast that the opportunities that people need to look for are what are generalizable and sustainable kind of wins that are going to lead to a revenue cycle that are justified, a venture capital world investing. So there's a lot of interesting opportunities in the space. But I think some of the biggest opportunities relate to what Bob has talked about in bringing many different disparate data sources together and really looking for things that are not comprehensible in the human brain or in traditional analytic models. >> I think we also got to look a little bit beyond direct care. We're talking about policy and how we set up standards, these kinds of things. That's one area. That's going to drive innovation forward. I completely agree with that. Direct care is one piece. How do we scale out many of the knowledge kinds of things that are embedded into one person's head and get them out to the world, democratize that. Then there's also development. The underlying technology's of medicine, right? Pharmaceuticals. The traditional way that pharmaceuticals is developed is actually kind of funny, right? A lot of it was started just by chance. Penicillin, a very famous story right? It's not that different today unfortunately, right? It's conceptually very similar. Now we've got more science behind it. We talk about domains and interactions, these kinds of things but fundamentally, the problem is what we in computer science called NP hard, it's too difficult to model. You can't solve it analytically. And this is true for all these kinds of natural sorts of problems by the way. And so there's a whole field around this, molecular dynamics and modeling these sorts of things, that are actually being driven forward by these AI techniques. Because it turns out, our brain doesn't do magic. It actually doesn't solve these problems. It approximates them very well. And experience allows you to approximate them better and better. Actually, it goes a little bit to what you were saying before. It's like simulations and forming your own networks and training off each other. There are these emerging dynamics. You can simulate steps of physics. And you come up with a system that's much too complicated to ever solve. Three pool balls on a table is one such system. It seems pretty simple. You know how to model that, but it actual turns out you can't predict where a balls going to be once you inject some energy into that table. So something that simple is already too complex. So neural network techniques actually allow us to start making those tractable. These NP hard problems. And things like molecular dynamics and actually understanding how different medications and genetics will interact with each other is something we're seeing today. And so I think there's a huge opportunity there. We've actually worked with customers in this space. And I'm seeing it. Like Rosch is acquiring a few different companies in space. They really want to drive it forward, using big data to drive drug development. It's kind of counterintuitive. I never would've thought it had I not seen it myself. >> And there's a big related challenge. Because in personalized medicine, there's smaller and smaller cohorts of people who will benefit from a drug that still takes two billion dollars on average to develop. That is unsustainable. So there's an economic imperative of overcoming the cost and the cycle time for drug development. >> I want to take a go at this question a little bit differently, thinking about not so much where are the industry segments that can benefit from AI, but what are the kinds of applications that I think are most impactful. So if this is what a skilled surgeon needs to know at a particular time to care properly for a patient, this is where most, this area here, is where most surgeons are. They are close to the maximum knowledge and ability to assimilate as they can be. So it's possible to build complex AI that can pick up on that one little thing and move them up to here. But it's not a gigantic accelerator, amplifier of their capability. But think about other actors in health care. I mentioned a couple of them earlier. Who do you think the least trained actor in health care is? >> John: Patients. >> Yes, the patients. The patients are really very poorly trained, including me. I'm abysmal at figuring out who to call and where to go. >> Naveen: You know as much the doctor right? (laughing) >> Yeah, that's right. >> My doctor friends always hate that. Know your diagnosis, right? >> Yeah, Dr. Google knows. So the opportunities that I see that are really, really exciting are when you take an AI agent, like sometimes I like to call it contextually intelligent agent, or a CIA, and apply it to a problem where a patient has a complex future ahead of them that they need help navigating. And you use the AI to help them work through. Post operative. You've got PT. You've got drugs. You've got to be looking for side effects. An agent can actually help you navigate. It's like your own personal GPS for health care. So it's giving you the inforamation that you need about you for your care. That's my definition of Precision Medicine. And it can include genomics, of course. But it's much bigger. It's that broader picture and I think that a sort of agent way of thinking about things and filling in the gaps where there's less training and more opportunity, is very exciting. >> Great start up idea right there by the way. >> Oh yes, right. We'll meet you all out back for the next start up. >> I had a conversation with the head of the American Association of Medical Specialties just a couple of days ago. And what she was saying, and I'm aware of this phenomenon, but all of the medical specialists are saying, you're killing us with these stupid board recertification trivia tests that you're giving us. So if you're a cardiologist, you have to remember something that happens in one in 10 million people, right? And they're saying that irrelevant anymore, because we've got advanced decision support coming. We have these kinds of analytics coming. Precisely what you're saying. So it's human augmentation of decision support that is coming at blazing speed towards health care. So in that context, it's much more important that you have a basic foundation, you know how to think, you know how to learn, and you know where to look. So we're going to be human-augmented learning systems much more so than in the past. And so the whole recertification process is being revised right now. (inaudible audience member speaking) Speak up, yeah. (person speaking) >> What makes it fathomable is that you can-- (audience member interjects inaudibly) >> Sure. She was saying that our brain is really complex and large and even our brains don't know how our brains work, so... are there ways to-- >> What hope do we have kind of thing? (laughter) >> It's a metaphysical question. >> It circles all the way down, exactly. It's a great quote. I mean basically, you can decompose every system. Every complicated system can be decomposed into simpler, emergent properties. You lose something perhaps with each of those, but you get enough to actually understand most of the behavior. And that's really how we understand the world. And that's what we've learned in the last few years what neural network techniques can allow us to do. And that's why our brain can understand our brain. (laughing) >> Yeah, I'd recommend reading Chris Farley's last book because he addresses that issue in there very elegantly. >> Yeah we're seeing some really interesting technologies emerging right now where neural network systems are actually connecting other neural network systems in networks. You can see some very compelling behavior because one of the things I like to distinguish AI versus traditional analytics is we used to have question-answering systems. I used to query a database and create a report to find out how many widgets I sold. Then I started using regression or machine learning to classify complex situations from this is one of these and that's one of those. And then as we've moved more recently, we've got these AI-like capabilities like being able to recognize that there's a kitty in the photograph. But if you think about it, if I were to show you a photograph that happened to have a cat in it, and I said, what's the answer, you'd look at me like, what are you talking about? I have to know the question. So where we're cresting with these connected sets of neural systems, and with AI in general, is that the systems are starting to be able to, from the context, understand what the question is. Why would I be asking about this picture? I'm a marketing guy, and I'm curious about what Legos are in the thing or what kind of cat it is. So it's being able to ask a question, and then take these question-answering systems, and actually apply them so that's this ability to understand context and ask questions that we're starting to see emerge from these more complex hierarchical neural systems. >> There's a person dying to ask a question. >> Sorry. You have hit on several different topics that all coalesce together. You mentioned personalized models. You mentioned AI agents that could help you as you're going through a transitionary period. You mentioned data sources, especially across long time periods. Who today has access to enough data to make meaningful progress on that, not just when you're dealing with an issue, but day-to-day improvement of your life and your health? >> Go ahead, great question. >> That was a great question. And I don't think we have a good answer to it. (laughter) I'm sure John does. Well, I think every large healthcare organization and various healthcare consortiums are working very hard to achieve that goal. The problem remains in creating semantic interoperatability. So I spent a lot of my career working on semantic interoperatability. And the problem is that if you don't have well-defined, or self-defined data, and if you don't have well-defined and documented metadata, and you start operating on it, it's real easy to reach false conclusions and I can give you a classic example. It's well known, with hundreds of studies looking at when you give an antibiotic before surgery and how effective it is in preventing a post-op infection. Simple question, right? So most of the literature done prosectively was done in institutions where they had small sample sizes. So if you pool that, you get a little bit more noise, but you get a more confirming answer. What was done at a very large, not my own, but a very large institution... I won't name them for obvious reasons, but they pooled lots of data from lots of different hospitals, where the data definitions and the metadata were different. Two examples. When did they indicate the antibiotic was given? Was it when it was ordered, dispensed from the pharmacy, delivered to the floor, brought to the bedside, put in the IV, or the IV starts flowing? Different hospitals used a different metric of when it started. When did surgery occur? When they were wheeled into the OR, when they were prepped and drapped, when the first incision occurred? All different. And they concluded quite dramatically that it didn't matter when you gave the pre-op antibiotic and whether or not you get a post-op infection. And everybody who was intimate with the prior studies just completely ignored and discounted that study. It was wrong. And it was wrong because of the lack of commonality and the normalization of data definitions and metadata definitions. So because of that, this problem is much more challenging than you would think. If it were so easy as to put all these data together and operate on it, normalize and operate on it, we would've done that a long time ago. It's... Semantic interoperatability remains a big problem and we have a lot of heavy lifting ahead of us. I'm working with the Global Alliance, for example, of Genomics and Health. There's like 30 different major ontologies for how you represent genetic information. And different institutions are using different ones in different ways in different versions over different periods of time. That's a mess. >> Our all those issues applicable when you're talking about a personalized data set versus a population? >> Well, so N of 1 studies and single-subject research is an emerging field of statistics. So there's some really interesting new models like step wedge analytics for doing that on small sample sizes, recruiting people asynchronously. There's single-subject research statistics. You compare yourself with yourself at a different point in time, in a different context. So there are emerging statistics to do that and as long as you use the same sensor, you won't have a problem. But people are changing their remote sensors and you're getting different data. It's measured in different ways with different sensors at different normalization and different calibration. So yes. It even persists in the N of 1 environment. >> Yeah, you have to get started with a large N that you can apply to the N of 1. I'm actually going to attack your question from a different perspective. So who has the data? The millions of examples to train a deep learning system from scratch. It's a very limited set right now. Technology such as the Collaborative Cancer Cloud and The Data Exchange are definitely impacting that and creating larger and larger sets of critical mass. And again, not withstanding the very challenging semantic interoperability questions. But there's another opportunity Kay asked about what's changed recently. One of the things that's changed in deep learning is that we now have modules that have been trained on massive data sets that are actually very smart as certain kinds of problems. So, for instance, you can go online and find deep learning systems that actually can recognize, better than humans, whether there's a cat, dog, motorcycle, house, in a photograph. >> From Intel, open source. >> Yes, from Intel, open source. So here's what happens next. Because most of that deep learning system is very expressive. That combinatorial mixture of features that Naveen was talking about, when you have all these layers, there's a lot of features there. They're actually very general to images, not just finding cats, dogs, trees. So what happens is you can do something called transfer learning, where you take a small or modest data set and actually reoptimize it for your specific problem very, very quickly. And so we're starting to see a place where you can... On one end of the spectrum, we're getting access to the computing capabilities and the data to build these incredibly expressive deep learning systems. And over here on the right, we're able to start using those deep learning systems to solve custom versions of problems. Just last weekend or two weekends ago, in 20 minutes, I was able to take one of those general systems and create one that could recognize all different kinds of flowers. Very subtle distinctions, that I would never be able to know on my own. But I happen to be able to get the data set and literally, it took 20 minutes and I have this vision system that I could now use for a specific problem. I think that's incredibly profound and I think we're going to see this spectrum of wherever you are in your ability to get data and to define problems and to put hardware in place to see really neat customizations and a proliferation of applications of this kind of technology. >> So one other trend I think, I'm very hopeful about it... So this is a hard problem clearly, right? I mean, getting data together, formatting it from many different sources, it's one of these things that's probably never going to happen perfectly. But one trend I think that is extremely hopeful to me is the fact that the cost of gathering data has precipitously dropped. Building that thing is almost free these days. I can write software and put it on 100 million cell phones in an instance. You couldn't do that five years ago even right? And so, the amount of information we can gain from a cell phone today has gone up. We have more sensors. We're bringing online more sensors. People have Apple Watches and they're sending blood data back to the phone, so once we can actually start gathering more data and do it cheaper and cheaper, it actually doesn't matter where the data is. I can write my own app. I can gather that data and I can start driving the correct inferences or useful inferences back to you. So that is a positive trend I think here and personally, I think that's how we're going to solve it, is by gathering from that many different sources cheaply. >> Hi, my name is Pete. I've very much enjoyed the conversation so far but I was hoping perhaps to bring a little bit more focus into Precision Medicine and ask two questions. Number one, how have you applied the AI technologies as you're emerging so rapidly to your natural language processing? I'm particularly interested in, if you look at things like Amazon Echo or Siri, or the other voice recognition systems that are based on AI, they've just become incredibly accurate and I'm interested in specifics about how I might use technology like that in medicine. So where would I find a medical nomenclature and perhaps some reference to a back end that works that way? And the second thing is, what specifically is Intel doing, or making available? You mentioned some open source stuff on cats and dogs and stuff but I'm the doc, so I'm looking at the medical side of that. What are you guys providing that would allow us who are kind of geeks on the software side, as well as being docs, to experiment a little bit more thoroughly with AI technology? Google has a free AI toolkit. Several other people have come out with free AI toolkits in order to accelerate that. There's special hardware now with graphics, and different processors, hitting amazing speeds. And so I was wondering, where do I go in Intel to find some of those tools and perhaps learn a bit about the fantastic work that you guys are already doing at Kaiser? >> Let me take that first part and then we'll be able to talk about the MD part. So in terms of technology, this is what's extremely exciting now about what Intel is focusing on. We're providing those pieces. So you can actually assemble and build the application. How you build that application specific for MDs and the use cases is up to you or the one who's filling out the application. But we're going to power that technology for multiple perspectives. So Intel is already the main force behind The Data Center, right? Cloud computing, all this is already Intel. We're making that extremely amenable to AI and setting the standard for AI in the future, so we can do that from a number of different mechanisms. For somebody who wants to develop an application quickly, we have hosted solutions. Intel Nervana is kind of the brand for these kinds of things. Hosted solutions will get you going very quickly. Once you get to a certain level of scale, where costs start making more sense, things can be bought on premise. We're supplying that. We're also supplying software that makes that transition essentially free. Then taking those solutions that you develop in the cloud, or develop in The Data Center, and actually deploying them on device. You want to write something on your smartphone or PC or whatever. We're actually providing those hooks as well, so we want to make it very easy for developers to take these pieces and actually build solutions out of them quickly so you probably don't even care what hardware it's running on. You're like here's my data set, this is what I want to do. Train it, make it work. Go fast. Make my developers efficient. That's all you care about, right? And that's what we're doing. We're taking it from that point at how do we best do that? We're going to provide those technologies. In the next couple of years, there's going to be a lot of new stuff coming from Intel. >> Do you want to talk about AI Academy as well? >> Yeah, that's a great segway there. In addition to this, we have an entire set of tutorials and other online resources and things we're going to be bringing into the academic world for people to get going quickly. So that's not just enabling them on our tools, but also just general concepts. What is a neural network? How does it work? How does it train? All of these things are available now and we've made a nice, digestible class format that you can actually go and play with. >> Let me give a couple of quick answers in addition to the great answers already. So you're asking why can't we use medical terminology and do what Alexa does? Well, no, you may not be aware of this, but Andrew Ian, who was the AI guy at Google, who was recruited by Google, they have a medical chat bot in China today. I don't speak Chinese. I haven't been able to use it yet. There are two similar initiatives in this country that I know of. There's probably a dozen more in stealth mode. But Lumiata and Health Cap are doing chat bots for health care today, using medical terminology. You have the compound problem of semantic normalization within language, compounded by a cross language. I've done a lot of work with an international organization called Snowmed, which translates medical terminology. So you're aware of that. We can talk offline if you want, because I'm pretty deep into the semantic space. >> Go google Intel Nervana and you'll see all the websites there. It's intel.com/ai or nervanasys.com. >> Okay, great. Well this has been fantastic. I want to, first of all, thank all the people here for coming and asking great questions. I also want to thank our fantastic panelists today. (applause) >> Thanks, everyone. >> Thank you. >> And lastly, I just want to share one bit of information. We will have more discussions on AI next Tuesday at 9:30 AM. Diane Bryant, who is our general manager of Data Centers Group will be here to do a keynote. So I hope you all get to join that. Thanks for coming. (applause) (light electronic music)

>> live from the Mandalay Bay Convention Center in Las Vegas. It's the king covering via World 2016 brought to you by IBM Wear and its ecosystem sponsors. Now here's your host stool minimum, >> including I Welcome back to the Q bomb stew. Minuteman here with my co host for this segment, Mark Farley, and we'll get the emerald 2016 here in Las Vegas. It's been five years since we've been in Vegas, and a lot of changes in five years back Elsa do this morning was talking about five years from now. They expect that to be kind of a crossover between public Cloud becomes majority from our research. We think that flash, you know, capacities. You know, you really are outstripping, You know, traditional hard disk drives within five years from now. So the two guests I have for this program, Brian Vials, is the CEO of Day Tree. Um, it's been a year since we had you on when you came out of stealth on really excited cause your customer along. We love having customers on down from Alaska, you know, within sight view of of of Russia. Maybe on Did you know Ben Craig, who's the c i O of Northern Bank. Thank you so much for coming. All right, so we want to talk a lot to you, but real quick. Ryan, why do you give us kind of the update on the company? What's happened in the last year where you are with the product in customer deployments? >> Sure. Last year, when we talked, daydream was just coming out of stealth mode. So we were introducing the notion of what we're doing. Starting in kind of mid Q. One of this year, we started shipping and deploying. Thankfully, one of our first customers was Ben. And, uh, you know, our our model of, ah, sort of convergence is different from anything else that you'll see a v m world. I think hearing Ben tell about his experience in deployment philosophy. What changed for him is probably the best way to understand what we do. >> All right, so and great leading. Start with first. Can you tell us a little bit about north from bank? How many locations you have your role there. How long you've been there? Kind of a quick synopsis. >> Sure. Where we're growing. Bank one of three publicly traded publicly held companies in the state of Alaska. We recently acquired residential mortgage after acquiring the last Pacific Bank. And so we have locations all the way from Fairbanks, Alaska, where it gets down to negative 50 negative, 60 below Fahrenheit down to Bellevue, Washington. And to be perfectly candid, what's helped propel some of that growth has been our virtual infrastructure and our virtual desktop infrastructure, which is predicated on us being able to grow our storage, which kind of ties directly into what we've got going on with a tree and >> that that that's great. Can you talk to you know what we're using before what led you to day tree? Um, you know, going with the startup is you know, it's a little risky, right? I thought, Cee Io's you buy on risk >> Well, and as a very conservative bank that serves a commercial market, risk is not something that way by into a lot. But it's also what propels some of our best customers to grow with us. And in this case, way had a lot of faith in the people that joined the company. From an early start, I personally knew a lot of the team from sales from engineering from leadership on That got us interested. Once we kind of got the hook way learned about the technology and found out that it was really the I dare say we're unicorn of storage that we've been looking for. And the reason is because way came from a ray based systems and we have the same revolution that a lot of customers did. We started out with a nice, cosy, equal logic system. We evolved into a nimble solution the hybrid era, if you will, of a raise. And we found that as we grew, we ran into scalability problems. A soon as we started tackling beady eye, we found that we immediately needed to segregate our workloads. Obviously, because servers and production beauty, I have a completely different read right profile. As we started looking at some of the limitations as we grew our video structure, we had to consider upgrading all our processors, all of our solid state drives, all of the things that helped make that hybrid array support our VD infrastructure, and it's costly. And so we did that once and then we grew again because maybe I was so darn popular. within our organization. At that time, we kind of caught wind of what was going on with the atrium, and it totally turned the paradigm on top of its head for what we were looking for. >> How did it? Well, I just heard that up, sir. How did the date Reum solution impact the or what did you talk about? The reed, Right balance? What was it about the day trim solution that solved what was the reed right? Balance you there for the >> young when we ran out of capacity with our equal logic, we had to go out and buy a whole new member when he ran out of capacity with are nimble, had to go out and buy a whole new controller. When we run out of capacity with day tree, um, solution, we literally could go out and get commoditized solid state drives one more into our local storage and end up literally impacting our performance by a magnifier. That's huge. So the big difference between day trim and these >> are >> my words I'm probably gonna screw this up, Bryant, So feel free to jump in, and in my opinion day trip starts out with a really good storage area network appliance, and then they basically take away all of you. I interface to it and stick it out on the network for durable rights. Then they move all of the logic, all of the compression, all of the D duplication. Even the raid calculations on to software that I call a hyper driver that runs the hyper visor level on each host. So instead of being bound by the controller doing all the heavy lifting, you now have it being done by a few extra processors, a few extra big of memory out on their servers. That puts the data as close as humanly possible, which is what hyper converging. But it also has this very durable back end that ensures that your rights are protected. So instead of having to span my storage across all of my hosts, I still have all the best parts of a durable sand on all the best parts of high performance. By bringing that that data closer to where the host. So that's why Atrium enabled us to be able to grow our VD I infrastructure literally overnight. Whenever we ran out of performance, we just pop in another drive and go and the performances is insane. We just finished writing a 72 page white paper for VM, where we did our own benchmarking. Um, using my OMETER sprayers could be using our secondary data center Resource is because they were, frankly, somewhat stagnant, and we knew that we'd be able to get with most level test impossible. And we found that we were getting insane amounts of performance, insane amounts of compression. And by that I can quantify we're getting 132,000 I ops at a little bit over a gig a sec running with two 0.94 milliseconds of late and see that's huge. And one of the things that we always used to compare when it came to performance was I ops and throughput. Whenever we talk to any storage vendor, they're always comparing. But we never talked about lately because Leighton See was really network bound and their storage bender could do anything about that. But by bringing the the brain's closer to the hosts, it solves that problem. And so now our latent C that was like a 25 minutes seconds using a completely unused, nimble storage sand was 2.94 milliseconds. What that translated into was about re X performance increase. So when we went from equal logic to nimble, we saw a multiplier. There we went from nimble toed D atrium. We saw three Export Supplier, and that translated directly into me being able to send our night processors home earlier. Which means less FT. Larger maintenance window times, faster performance for all of our branches. So it went on for a little bit there. But that's what daydreams done for us, >> right? And just to just to amplify that part of the the approached atrium Staking is to assume that host memory of some kind or another flash for now is going to become so big and so cheap that reads will just never leave the host at some point. And we're trying to make that point today. So we've increased our host density, for example, since last year, flash to 16 terabytes per host. Raw within line di Dupin compression. That could be 50 a 100 terabytes. So we have customers doing fairly big data warehouse operations where the reeds never leave the host. It's all host Flash Leighton see and they can go from an eight hour job to, ah, one hour job. It's, you know, and in our model, we sell a system that includes a protected repositories where the rights go. That's on a 10 big network. You buy hosts that have flash that you provisions from your server vendor? Um, we don't charge extra for the software that we load on the host. That does all the heavy lifting. It does the raid compression d do cloning. What have you It does all the local cashing. So we encourage people to put as much flash and as many hosts as possible against that repositories, and we make it financially attractive to do that. >> So how is the storage provisioned? Is it a They're not ones. How? >> So It all shows up, and this is one of the other big parts that is awesome for us. It shows up his one gigantic NFS datastore. Now it doesn't actually use NFS. Itjust presents that way to be anywhere. But previously we had about 34 different volumes. And like everybody else on the planet who thin provisions, we had to leave a buffer zone because we'd have developers that would put a bm where snapshot on something patches. Then forget about it, Philip. The volume bring the volume off lying panic ensues. So you imagine that 30 to 40% of buffer space times each one of those different volumes. Now we have one gigantic volume and each VM has its performance and all of its protection managed individually at the bm level. And that's huge because no longer do you have to set protection performance of the volume level. You can set it right in the B m. Um, >> so you don't even see storage. >> You don't ever have to log into the appliance that all you >> do serve earless storage lists. Rather, this is what we're having. It's >> all through the place. >> And because because all the rights go off, host the rights, don't interrupt each other the host on interrupt together. So we actually going to a lot of links to make sure that happens. So there's an isolation host, a host. That means if you want a provisional particular host for a particular set of demands, you can you could have VD I next door to data warehouse and you know the level of intensity doesn't matter to each other. So it's very specifically enforceable by host configuration or by managing the VM itself. Justus, you would do with the M where >> it gets a lot more flexibility than we would typically get with a hyper converge solution that has a very static growth and performance requirements. >> So when you talk about hyper convergence, the you know, number one, number two and number three things that we usually talk about is, you know, simplicity. So you're a pretty technical guy. You obviously understand this. Well, can you speak to beyond the, you know, kind of ecological nimble and how you scale that house kind of the day's your experience. How's the ongoing, how much you after, you know, test and tweak and adjust things? And how much is it? Just work? >> Well, this is one of the reasons that we went with the atrium is well, you know, when it comes down to it with a hyper converge solution, you're spanning all of your storage across your host, right? We're trying to make use of those. Resource is, but we just recently had one of our server's down because it had a problem with his bios for a little over 10 days. Troubleshooting it. It just doesn't want to stay up. If we're in a full hyper converged infrastructure and that was part of the cluster, that means that our data would've had to been migrated off of that hostess. Well, which is kind of a big deal. I love the idea of having a rock solid, purpose built, highly available device that make sure that my rights are there for me, but allows me to have the elastic configuration that I need on my host to be able to grow them as I see fit. And also to be able to work directly with my vendors to get the pricing points that I need for each. My resource is so our Oracle Servers Exchange Server sequel servers. We could put in some envy Emmy drives. It'll screen like a scalded dog, and for all of our file print servers, I t monitoring servers. We can go with Cem Samsung 8 50 e b o. Drives pop him in a couple of empty days, and we're still able to crank out the number of I ops that we need to be able. Thio appreciate between those at a very low cost point, but with a maximum amount of protection on that data. So that was a big song. Points >> are using both envy. Emmy and Block. >> We actually going through a server? Refresh. Right now, it's all part of the white paper that way. Just felt we decided to go with Internal in Vienna drives to start with two two terabyte internal PC cards. And then we have 2.5 inch in Vienna ready on the front load. But we also plumbed it to be able to use solid state drive so that we have that flexibility in the future to be able to use those servers as we see fit. So again, very elastic architecture and allows us to be kind of a control of what performance is assigned to each individual host. >> So what APS beyond VD? I Do you expect to use this for? Are you already deploying it further? >> VD I is our biggest consumer of resource is our users have come to expect that instant access to all of their applications eventually way have the ability to move the entire data center onto the day trim and so One of the things that we're currently completing this year is the rollout of beady eye to the remaining 40% of our branches. 60% of them are already running through the eye. And then after that, we're probably gonna end up taking our core servers and migrating them off and kind of through attrition, using some of our older array based technology for testing death. All >> right, so I can't let you go without asking you a bit. Just you're in a relationship with GM Ware House Veum. We're meeting your needs. Is there anything from GM wear or the storage ecosystem around them that would kind of make your job easier? >> Yes. If they got rid of the the Sphere Web client, that would be great. I am not a fan of the V Sphere Web client at all, and I wish they'd bring back the C Sharp client like to get that on the record because I tried to every single chance I could get. No, the truth is the integration between the day tree, um and being where is it's super tight. It's something I don't have to think about. It makes it easy for me to be able to do my job at the end of the day. That's what we're looking for. So I think the biggest focus that a lot of the constituents that air the Anchorage being where user group leader of said group are looking for stability and product releases and trying to make sure that there's more attention given to que es on some of the recent updates that they have. Hyper visor Weber >> Brian, I'll give you the final word takeaways that you want people to know about your company, your customers coming out. >> Of'em World. We're thrilled to be here for the second year, thrilled to be here with Ben. It's a It's a great, you know, exciting period for us. As a vendor, we're just moving into sort of nationwide deployment. So check us out of here at the show. If you're not, check us out on the Web. There's a lot of exciting things happening in convergence in general and atriums leading the way in a couple of interesting ways. All >> right, Brian and Ben, thank you so much for joining us. You know, I don't think we've done a cube segment in Alaska yet. so maybe we'll have to talk to you off camera about that. Recommended. All right. We'll be back with lots more coverage here from the emerald 2016. Thanks for watching the Cube. >> You're good at this. >> Oh, you're good.

live from the mandalay bay convention center in las vegas it's the cues covering vmworld 2016 rock you by vmware and its ecosystem sponsors it's legal yeah everything's legal welcome inside walls here on the cube as we continue our coverage here at vmworld once again we're back or what is going to be an exciting three days here in Mandalay Bay and i'm joined by my partner in crime you might say mark farley the producer Vulcan cast a host of Vulcan cast and tell us about Vulcan kestrel quick mark well you've seen comedy in cars you've seen singing in cars with carpool karaoke this is discussions about technology and cars it's tech talk and cars I see it on you can see it on Vulcan cast calm what a novel name for a website I'm pretty you figure good all day coming up with that one didn't you yeah but it's cool you know what it's like to look for a name absolutely benefit but it's a neat neat concept Tech Talk comes the cars you're kind of like the the james corbett of tech there you go except we don't sing about it I'm more like the Jerry Seinfeld maybe that's the next time we're joined by a couple of guests who are they become partners to more or less here in the business and solutely with Vaughn Stewart who is the enterprise architect and chief evangelist I love that by the way of on a pure storage and that evangelist looked up you do have it you getting the whole thing today and kimbark is a CEO of cata logic software and gentlemen ulcers thank you for being here we appreciate that so if you would start off by telling us a little bit about your individual companies you know what you do and then the marriage you to have partnered up here for the past four months came together pretty quickly and what that's all about and if you would bomb what you go first sure so pure storage is recognized widely as being the number one independent all-flash storage vendor we've been recognized for three years as being the leader in gartner's solid-state array Magic Quadrant we've really allowed flash to be consumed by the masses by making it more affordable than traditional disk based storage arrays and deliver all the promise of of the performance of flash kent and in a nutshell cattle objects software's that spin out three years ago from the syncsort company and what we've got about twenty nine patents we're working hard what we did is we evolved our technology to this whole copy data management space which is very exciting and when you marry copy data management to flash technology you drive some really serious effects and catback savings for customers so it's kind of a peanut butter and chocolate on here right was together really really does right so let's talk about your relationship then this has only been four months in the making you've known each other for a long time but you put together your business venture here very quickly what brought it together so fast and how did it make that kind of sense that boom it just happened almost overnight like that to start going on with the Kent listen we were lucky enough that these guys actually found us that a trade show it was a mug event Vav mug event in Austin Texas they found some for a show they have been absolutely brilliant to work with in the business that we're in we're what's called in place copy data management and why that's important is because we get to pick our partners and it's a lot easier to build a technology if you have a partner that cooperates and these guys have been so cooperative that's what made this thing tick they saw a gap that we could fill they were kind enough they sent us a box up to work with the team culturally has been aligned I mean we we've kind of do things all up and down the stack the same way pricing I think we're very similar channel driven we're similar the way we we look at at working together is very similar say just been brilliant and that's kind of what it is it's a neat at the end of the date and to try to squeeze the effects and capex savings out for customers that's kind of the do yeah and we're also seeing a lot of requests from our from our customer base we have a large number of joint customers as well as customers that were interested in purchasing the other technology but we're waiting for a point of integration and so as we're seeing this shift in the the mid market and the enterprise to a more DevOps centric model more of infrastructure teams converging their their server and their compute management or application owners into owning the entire stack there was this this need for taking the data management constructs that we had and allowing an end-to-end ecosystem enable meant so that dev teams could just you know at the push of a button and refresh their data sets move they move their development efforts forward and get rid of all the old legacy time centric based provisioning models yeah I mean I mean CDM has kind of become you know one of these hot buzzwords right all of a sudden as as our data storage just become more capable and has become cheaper we tend to hoard more stuff right now listen we're hanging on things a lot longer so what is the gap exactly you're talking about that you're filling and what's the need that you're addressing specifically then you have all this data at your disposal and and and I guess with Flash movie great question John so what what happens is when you first of all let's talk about what's driving the flash analogy right why why flash is so popular right now everybody that we've talked to is either moving to flash or thinking about moving to flash simply for their primary applications you know those are things like databases virtualization filers you know SharePoint right and as you start to move you get you get really good benefits around effects from using your flash because the speed and the performance particularly with what they do they've got some compression stuff that's unbelievable and then what we do is we overlay that so if you take CDM which was your question if you look at CDM what CDM does copy data management it allows you to deal with all of these copies in the in the world today you've got so many of the vendors that are taking different snapshots at different times and you end up at any given time I think IDC did a study what was it like 50 50 versions of an email that you've got floating around is any given time floating in your organization right so what Vaughn was referring to let's take one example in a test dev environment right we could drive home on that which they do a lot more than that but if you take the test stab and let's say you're a developer and you have an Oracle database that you really want to test the latest data right now without flash without CDM what happens is you make a copy of that database you move it to the developers and getting that copy if you're a developer getting that copy away from the internal IT infrastructure department can take you hours can take days go ahead we've we've got customers whose current copy data management process is it is is fulfilled by either a full-time employee or a staff that runs around doing arm and restores or restores from tape and development teams have to try to anticipate weeks in advance when a new copy of the data that model has been the the de facto standard in the industry for a decade or more and in what you're seeing from from all conversations around DevOps is agility it's time to how can I no increase the rate at which we innovate part of it is by bringing agility into your development process and so so this is a real nice pairing of technologies the performance capabilities within a flash a flash array allows you to scale a large number of instances the instant ability to clone the data set gives you gives you the agility but it's just an engine I still have to take care of the rest of the stack I got a role based access which users get to see which data do I need a datum ask the data or do they get direct access are they having a virtual copy or a physical right and best part can I make it a portal or can I make it right into their native workflow so they never hit the storage team or even the infrastructure team so let's talk about how customers are going to use this right pure has been a big leader not just in flash but and also digital efficiency capacity efficiency and you've had to be that right from the get-go people are saying well how am I going to be able to get the cost you know the effect of costs down of this flash well you have dee doop and you have compression and now you're adding this application layer or higher layer if you will another layer of the stack towards you know data density do you think this is going to have you done run the numbers on what kind of percentage or anything like that that customers will see absolutely kind of kind of absolute ken so I'm actually doing in the solution booth I think 430 tomorrow's solution a the vmworld booth we've got a customer six flags theme park operator that doing this test dev case we saved ninety percent affects efficiency for these guys so there's some really solid number again 90 90 90 / such a big number what's a huge number but it's what is what Vaughn was trying to say if you start marrying the workflow if you take their ability to make the storage and the moving the data more efficient and you'll ever their tool and then you overlay it with our api's we have rest api is that you can tie into a customer environment and then we've got to work flow this workflow engine that we call full stack automation the customer can start automating a lot of the stuff that they're trying to do and it's a home run yeah let's be let's be get a little bit in greater depth here but not too deep yeah these capabilities have existed in market for a long time yeah but the customers had to assemble and build their own scriptures in a fool's the phone and again we're not talking just copying of the data yeah we're giving you an efficiency in the copy data engine with it running on the flash array right what cata logic is doing is giving you a single interface either via portal or API for the entire orc for the orchestration of the entire stack the test Network the virtual machines the physical servers the volume managers all the way down to the copy of the data absolutely so I'm going to dive even deeper bond what kind of skill set be careful what did I get wet what kind of skill set does a customer need to have to take advantage of this solution so that's that's a beautiful question because it goes back to the synergy between our two companies right we're known for being able to set up storage in under an hour that requires no administrative skill set right nothing to tune much like very much like an iphone right kind of out of the box there's no manual right cata logics in the same boat you download an ova you're up and running in 30 minutes you're connected to the pure array in four at 40 minutes yeah you're connected ad and 50 and you're running you're off to the races right we don't have any boxes no appliance versus our competitors out there right we don't have any agents to install no appliances it's just it's the perfect match simplistic and we're running and through api's right we're getting we're getting consistent application consistent copies of the data sets right and we're orchestrating through the built in infrastructures that that already exists whether we're looking at vSphere or the rest of the ecosystem so say a customer does their own development and they've got they've got people that know how to use api's program for api's will they be able to will they be any faster be able to do more with it or does it really not what it does this gets back to the effects issue right so so with our REST API they can tie it in and we've already got a lot of things that are tied in like some of the development tools out there chef puppet bluemix from IBM I mean these are all things that we we can kind of work with to complete the environment and allowed them to lever is amazing platform does that answer your question I think yeah so what about the market for this right it a happy data management took a while to take off right it's one of those things in data management has always been a tough thing and it takes a while for customers to sort of get a what what I'm going to say a group think and the critical mass of people thinking about it it looks like you've had some help in the last year with other vendors getting in well and popularizing it you know EMC has theirs and commvault I think is doing something in my response is talking about it now you know 18 months ago those of what he did but what started it mark and this is and that's a great question is what I was alluding to earlier once flash comes on the scene and particularly flash vendors that can do what they do that have got a huge cat-back saving or opik savings for the customer then you can start working in their workflow in their processes and saving them even more money so it actually is copy data management with flash storage can becomes almost to have to have versus and the other things that we were doing a year ago it was a nice to have what i call a nice to have right because if you start looking at how to save yourself money from an effects perspective you might as well look at how to go all the way and sometimes you can triple to 10 times your savings geometrically by adding see the right CDM what i call enhance CDM what our customers sometimes say is they call us a CDM on steroids copy data management on steroids that's energy is a big thing if you've looked at the industry historically what you've seen as storage vendors put out their own homogeneous right automation walls right point bond and then you've seen a number of heterogeneous vendors to play their tools but they don't want to have any correlation with any hardware vendor right right and so and so as a storage provider right and customers are looking to say well look I don't wanna get locked in a particular storage provider and right so that's one aspect as a storage vendor we're sitting there saying we'd like to have greater integration your ecosystem so we can bubble up our value cattle logics kind of hit that sweet spot and said we're going to be heterogeneous we're going to be multi-platform and we're going to leverage leverage the channel right hundred percent channel driven and we're going to leverage the API and the data management ecosystem the storage vendors so they've kind of got a perfect storm going on in terms of a technology and market momentum if you like ok so let's talk about how the solution is going to be delivered you sell it do you sell it do you sell into pure accounts you talked about channel we're getting we're going to meet in the channel okay we're also talking about doing some more creative things possibly up for right now it's a meet in the channel we think there's enough enough good networking the teams are in touch with each other you know the value proposition proves itself right if somebody when's it going to be available in another month or so so there are demonstrations available both in the cat illogic and in the pure storage booth here at vmworld I so we would we would encourage those who are interested in seeing the power of this this solution to stop by either booth at any time we're going to speaking sessions in each others as well this week absolutely up and we are currently targeting for somewhere between mid to end-september for a ga release right and I need to say one other thing going back to this the reason this works is because these guys have but one care and they are customer driven right they don't have an ego they are driving to the customer and fulfill the needs because as he said it's sometimes hard for a heterogeneous vendor that controls a lot to be welcomed as much as we've been welcomed with this group it's because they know they want to drive it through the customer get the best solution in the world of the customer so on the customer side you've talked about the perfect storm of services and products who's the perfect customer who's the optimal customer something like this that I i think the low-hanging fruit is any development team that has as some requirement where they are taking copies of their current data set and are developing off of that platform I think that's the low-hanging fruit I think at a more macro level any organization that says they have a DevOps initiative and particularly they want to turn key DevOps platform to be riding with and launch launch ahead versus a try to acquire talent to build their own this is rate rate within your wheelhouse good deal no brainer and if people aren't looking at that right now you know they're not they're not in this century right because everybody's moving to flash for the primary all the projections are going forward to going off the charts in terms of the growth of flash of what's gonna happen at any what's changed with flash right where four years ago sure had to kind of get over the hurdle of the price berry for flat right we did that with industry-leading data reduction that's still two x better than the rest of the industry but as flash prices keep coming down not what you're seeing as a pivot around around value is around making multiple data sets I mean if you get into a depth use case and I'm making ten copies of a data footprint that's already reduced by x 5x and you're getting to a price point that you just you can't you can't meet with with this because you couldn't drive enough performance either death actually that's not possible yeah well before I let you go I want to tell you it's just disappointing to us that you're not more enthusiastic so and super a little it's really impressed today we had a long night life maybe tomorrow things will pick up but congratulations on the business venture and wish you the best of luck down the road thanks for being well thank you thank you guys for having us on really enjoyed it appreciate it thank huh thank you back with more from vmworld right after this here on the cube