>> connecting with thought leaders all around the world, this is a CUBE Conversation. Hi, everybody this is Dave Vellante of theCUBE, and welcome back to my CXO series. I've been running this through really since the start of the COVID-19 crisis to really understand how leaders are dealing with this pandemic. Sri Ambati is here, he's the CEO and founder of H20. Sri, it's great to see you again, thanks for coming on. >> Thank you for having us. >> Yeah, so this pandemic has obviously given people fits, no question, but it's also given opportunities for companies to kind of reassess where they are. Automation is a huge watchword, flexibility, business resiliency and people who maybe really hadn't fully leaned into things like the cloud and AI and automation are now realizing, wow, we have no choice, it's about survival. Your thought as to what you're seeing in the marketplace. >> Thanks for having us. I think first of all, kudos to the frontline health workers who have been ruthlessly saving lives across the country and the world, and what you're really doing is a fraction of what we could have done or should be doing to stay away the next big pandemic. But that apart I think, I usually tend to say BC is before COVID. So if the world was thinking about going digital after COVID-19, they have been forced to go digital and as a result, you're seeing tremendous transformation across our customers, and a lot of application to kind of go in and reinvent their business models that allow them to scale as effortlessly as they could using the digital means. >> So, think about, doctors and diagnosis machines, in some cases, are helping doctors make diagnoses, they're sometimes making even better diagnosis, (mumbles) is informing. There's been a lot of talk about the models, you know how... Yeah, I know you've been working with a lot of healthcare organizations, you may probably familiar with that, you know, the Medium post, The Hammer and the Dance, and if people criticize the models, of course, they're just models, right? And you iterate models and machine intelligence can help us improve. So, in this, you know, you talk about BC and post C, how have you seen the data and in machine intelligence informing the models and proving that what we know about this pandemic, I mean, it changed literally daily, what are you seeing? >> Yeah, and I think it started with Wuhan and we saw the best application of AI in trying to trace, literally from Alipay, to WeChat, track down the first folks who were spreading it across China and then eventually the rest of the world. I think contact tracing, for example, has become a really interesting problem. supply chain has been disrupted like never before. We're beginning to see customers trying to reinvent their distribution mechanisms in the second order effects of the COVID, and the the prime center is hospital staffing, how many ventilator, is the first few weeks so that after COVID crisis as it evolved in the US. We are busy predicting working with some of the local healthcare communities to predict how staffing in hospitals will work, how many PPE and ventilators will be needed and so henceforth, but that quickly and when the peak surge will be those with the beginning problems, and many of our customers have begin to do these models and iterate and improve and kind of educate the community to practice social distancing, and that led to a lot of flattening the curve and you're talking flattening the curve, you're really talking about data science and analytics in public speak. That led to kind of the next level, now that we have somewhat brought a semblance of order to the reaction to COVID, I think what we are beginning to figure out is, is there going to be a second surge, what elective procedures that were postponed, will be top of the mind for customers, and so this is the kind of things that hospitals are beginning to plan out for the second half of the year, and as businesses try to open up, certain things were highly correlated to surgeon cases, such as cleaning supplies, for example, the obvious one or pantry buying. So retailers are beginning to see what online stores are doing well, e-commerce, online purchases, electronic goods, and so everyone essentially started working from home, and so homes needed to have the same kind of bandwidth that offices and commercial enterprises needed to have, and so a lot of interesting, as one side you saw airlines go away, this side you saw the likes of Zoom and video take off. So you're kind of seeing a real divide in the digital divide and that's happening and AI is here to play a very good role to figure out how to enhance your profitability as you're looking about planning out the next two years. >> Yeah, you know, and obviously, these things they get, they get partisan, it gets political, I mean, our job as an industry is to report, your job is to help people understand, I mean, let the data inform and then let public policy you know, fight it out. So who are some of the people that you're working with that you know, as a result of COVID-19. What's some of the work that H2O has done, I want to better understand what role are you playing? >> So one of the things we're kind of privileged as a company to come into the crisis, with a strong balance and an ability to actually have the right kind of momentum behind the company in terms of great talent, and so we have 10% of the world's top data scientists in the in the form of Kaggle Grand Masters in the company. And so we put most of them to work, and they started collecting data sets, curating data sets and making them more qualitative, picking up public data sources, for example, there's a tremendous amount of job loss out there, figuring out which are the more difficult kind of sectors in the economy and then we started looking at exodus from the cities, we're looking at mobility data that's publicly available, mobility data through the data exchanges, you're able to find which cities which rural areas, did the New Yorkers as they left the city, which places did they go to, and what's to say, Californians when they left Los Angeles, which are the new places they have settled in? These are the places which are now busy places for the same kind of items that you need to sell if you're a retailer, but if you go one step further, we started engaging with FEMA, we start engaging with the universities, like Imperial College London or Berkeley, and started figuring out how best to improve the models and automate them. The SEER model, the most popular SEER model, we added that into our Driverless AI product as a recipe and made that accessible to our customers in testing, to customers in healthcare who are trying to predict where the surge is likely to come. But it's mostly about information right? So the AI at the end of it is all about intelligence and being prepared. Predictive is all about being prepared and that's kind of what we did with general, lots of blogs, typical blog articles and working with the largest health organizations and starting to kind of inform them on the most stable models. What we found to our not so much surprise, is that the simplest, very interpretable models are actually the most widely usable, because historical data is actually no longer as effective. You need to build a model that you can quickly understand and retry again to the feedback loop of back testing that model against what really happened. >> Yeah, so I want to double down on that. So really, two things I want to understand, if you have visibility on it, sounds like you do. Just in terms of the surge and the comeback, you know, kind of what those models say, based upon, you know, we have some advanced information coming from the global market, for sure, but it seems like every situation is different. What's the data telling you? Just in terms of, okay, we're coming into the spring and the summer months, maybe it'll come down a little bit. Everybody says it... We fully expect it to come back in the fall, go back to college, don't go back to college. What is the data telling you at this point in time with an understanding that, you know, we're still iterating every day? >> Well, I think I mean, we're not epidemiologists, but at the same time, the science of it is a highly local response, very hyper local response to COVID-19 is what we've seen. Santa Clara, which is just a county, I mean, is different from San Francisco, right, sort of. So you beginning to see, like we saw in Brooklyn, it's very different, and Bronx, very different from Manhattan. So you're seeing a very, very local response to this disease, and I'm talking about US. You see the likes of Brazil, which we're worried about, has picked up quite a bit of cases now. I think the silver lining I would say is that China is up and running to a large degree, a large number of our user base there are back active, you can see the traffic patterns there. So two months after their last research cases, the business and economic activity is back and thriving. And so, you can kind of estimate from that, that this can be done where you can actually contain the rise of active cases and it will take masking of the entire community, masking and the healthy dose of increase in testing. One of our offices is in Prague, and Czech Republic has done an incredible job in trying to contain this and they've done essentially, masked everybody and as a result they're back thinking about opening offices, schools later this month. So I think that's a very, very local response, hyper local response, no one country and no one community is symmetrical with other ones and I think we have a unique situation where in United States you have a very, very highly connected world, highly connected economy and I think we have quite a problem on our hands on how to safeguard our economy while also safeguarding life. >> Yeah, so you can't just, you can't just take Norway and apply it or South Korea and apply it, every situation is different. And then I want to ask you about, you know, the economy in terms of, you know, how much can AI actually, you know, how can it work in this situation where you have, you know, for example, okay, so the Fed, yes, it started doing asset buys back in 2008 but still, very hard to predict, I mean, at this time of this interview you know, Stock Market up 900 points, very difficult to predict that but some event happens in the morning, somebody, you know, Powell says something positive and it goes crazy but just sort of even modeling out the V recovery, the W recovery, deep recession, the comeback. You have to have enough data, do you not? In order for AI to be reasonably accurate? How does it work? And how does at what pace can you iterate and improve on the models? >> So I think that's exactly where I would say, continuous modeling, instead of continuously learning continuous, that's where the vision of the world is headed towards, where data is coming, you build a model, and then you iterate, try it out and come back. That kind of rapid, continuous learning would probably be needed for all our models as opposed to the typical, I'm pushing a model to production once a year, or once every quarter. I think what we're beginning to see is the kind of where companies are beginning to kind of plan out. A lot of people lost their jobs in the last couple of months, right, sort of. And so up scaling and trying to kind of bring back these jobs back both into kind of, both from the manufacturing side, but also lost a lot of jobs in the transportation and the kind of the airlines slash hotel industries, right, sort of. So it's trying to now bring back the sense of confidence and will take a lot more kind of testing, a lot more masking, a lot more social empathy, I think well, some of the things that we are missing while we are socially distant, we know that we are so connected as a species, we need to kind of start having that empathy for we need to wear a mask, not for ourselves, but for our neighbors and people we may run into. And I think that kind of, the same kind of thinking has to kind of parade, before we can open up the economy in a big way. The data, I mean, we can do a lot of transfer learning, right, sort of there are new methods, like try to model it, similar to the 1918, where we had a second bump, or a lot of little bumps, and that's kind of where your W shaped pieces, but governments are trying very well in seeing stimulus dollars being pumped through banks. So some of the US case we're looking for banks is, which small medium business in especially, in unsecured lending, which business to lend to, (mumbles) there's so many applications that have come to banks across the world, it's not just in the US, and banks are caught up with the problem of which and what's growing the concern for this business to kind of, are they really accurate about the number of employees they are saying they have? Do then the next level problem or on forbearance and mortgage, that side of the things are coming up at some of these banks as well. So they're looking at which, what's one of the problems that one of our customers Wells Fargo, they have a question which branch to open, right, sort of that itself, it needs a different kind of modeling. So everything has become a very highly good segmented models, and so AI is absolutely not just a good to have, it has become a must have for most of our customers in how to go about their business. (mumbles) >> I want to talk a little bit about your business, you have been on a mission to democratize AI since the beginning, open source. Explain your business model, how you guys make money and then I want to help people understand basic theoretical comparisons and current affairs. >> Yeah, that's great. I think the last time we spoke, probably about at the Spark Summit. I think Dave and we were talking about Sparkling Water and H2O our open source platforms, which are premium platforms for democratizing machine learning and math at scale, and that's been a tremendous brand for us. Over the last couple of years, we have essentially built a platform called Driverless AI, which is a license software and that automates machine learning models, we took the best practices of all these data scientists, and combined them to essentially build recipes that allow people to build the best forecasting models, best fraud prevention models or the best recommendation engines, and so we started augmenting traditional data scientists with this automatic machine learning called AutoML, that essentially allows them to build models without necessarily having the same level of talent as these great Kaggle Grand Masters. And so that has democratized, allowed ordinary companies to start producing models of high caliber and high quality that would otherwise have been the pedigree of Google, Microsoft or Amazon or some of these top tier AI houses like Netflix and others. So what we've done is democratize not just the algorithms at the open source level. Now, we've made it easy for kind of rapid adoption of AI across every branch inside a company, a large organization, also across smaller organizations which don't have the access to the same kind of talent. Now, third level, you know, what we've brought to market, is ability to augment data sets, especially public and private data sets that you can, the alternative data sets that can increase the signal. And that's where we've started working on a new platform called Q, again, more license software, and I mean, to give you an idea there from business models endpoint, now majority of our software sales is coming from closed source software. And sort of so, we've made that transition, we still make our open source widely accessible, we continue to improve it, a large chunk of the teams are improving and participating in building the communities but I think from a business model standpoint as of last year, 51% of our revenues are now coming from closed source software and that change is continuing to grow. >> And this is the point I wanted to get to, so you know, the open source model was you know, Red Hat the one company that, you know, succeeded wildly and it was, put it out there open source, come up with a service, maintain the software, you got to buy the subscription okay, fine. And everybody thought that you know, you were going to do that, they thought that Databricks was going to do and that changed. But I want to take two examples, Hortonworks which kind of took the Red Hat model and Cloudera which does IP. And neither really lived up to the expectation, but now there seems to be sort of a new breed I mentioned, you guys, Databricks, there are others, that seem to be working. You with your license software model, Databricks with a managed service and so there's, it's becoming clear that there's got to be some level of IP that can be licensed in order to really thrive in the open source community to be able to fund the committers that you have to put forth to open source. I wonder if you could give me your thoughts on that narrative. >> So on Driverless AI, which is the closest platform I mentioned, we opened up the layers in open source as recipes. So for example, different companies build their zip codes differently, right, the domain specific recipes, we put about 150 of them in open source again, on top of our Driverless AI platform, and the idea there is that, open source is about freedom, right? It is not necessarily about, it's not a philosophy, it's not a business model, it allows freedom for rapid adoption of a platform and complete democratization and commodification of a space. And that allows a small company like ours to compete at the level of an SaaS or a Google or a Microsoft because you have the same level of voice as a very large company and you're focused on using code as a community building exercise as opposed to a business model, right? So that's kind of the heart of open source, is allowing that freedom for our end users and the customers to kind of innovate at the same level of that a Silicon Valley company or one of these large tech giants are building software. So it's really about making, it's a maker culture, as opposed to a consumer culture around software. Now, if you look at how the the Red Hat model, and the others who have tried to replicate that, the difficult part there was, if the product is very good, customers are self sufficient and if it becomes a standard, then customers know how to use it. If the product is crippled or difficult to use, then you put a lot of services and that's where you saw the classic Hadoop companies, get pulled into a lot of services, which is a reasonably difficult business to scale. So I think what we chose was, instead, a great product that builds a fantastic brand, that makes AI, even when other first or second.ai domain, and for us to see thousands of companies which are not AI and AI first, and even more companies adopting AI and talking about AI as a major way that was possible because of open source. If you had chosen close source and many of your peers did, they all vanished. So that's kind of how the open source is really about building the ecosystem and having the patience to build a company that takes 10, 20 years to build. And what we are expecting unfortunately, is a first and fast rise up to become unicorns. In that race, you're essentially sacrifice, building a long ecosystem play, and that's kind of what we chose to do, and that took a little longer. Now, if you think about the, how do you truly monetize open source, it takes a little longer and is much more difficult sales machine to scale, right, sort of. Our open source business actually is reasonably positive EBITDA business because it makes more money than we spend on it. But trying to teach sales teams, how to sell open source, that's a much, that's a rate limiting step. And that's why we chose and also explaining to the investors, how open source is being invested in as you go closer to the IPO markets, that's where we chose, let's go into license software model and scale that as a regular business. >> So I've said a few times, it's kind of like ironic that, this pandemic is as we're entering a new decade, you know, we've kind of we're exiting the era, I mean, the many, many decades of Moore's law being the source of innovation and now it's a combination of data, applying machine intelligence and being able to scale and with cloud. Well, my question is, what did we expect out of AI this decade if those are sort of the three, the cocktail of innovation, if you will, what should we expect? Is it really just about, I suggest, is it really about automating, you know, businesses, giving them more agility, flexibility, you know, etc. Or should we should we expect more from AI this decade? >> Well, I mean, if you think about the decade of 2010 2011, that was defined by software is eating the world, right? And now you can say software is the world, right? I mean, pretty much almost all conditions are digital. And AI is eating software, right? (mumbling) A lot of cloud transitions are happening and are now happening much faster rate but cloud and AI are kind of the leading, AI is essentially one of the biggest driver for cloud adoption for many of our customers. So in the enterprise world, you're seeing rebuilding of a lot of data, fast data driven applications that use AI, instead of rule based software, you're beginning to see patterned, mission AI based software, and you're seeing that in spades. And, of course, that is just the tip of the iceberg, AI has been with us for 100 years, and it's going to be ahead of us another hundred years, right, sort of. So as you see the discovery rate at which, it is really a fundamentally a math, math movement and in that math movement at the beginning of every century, it leads to 100 years of phenomenal discovery. So AI is essentially making discoveries faster, AI is producing, entertainment, AI is producing music, AI is producing choreographing, you're seeing AI in every walk of life, AI summarization of Zoom meetings, right, you beginning to see a lot of the AI enabled ETF peaking of stocks, right, sort of. You're beginning to see, we repriced 20,000 bonds every 15 seconds using H2O AI, corporate bonds. And so you and one of our customers is on the fastest growing stock, mostly AI is powering a lot of these insights in a fast changing world which is globally connected. No one of us is able to combine all the multiple dimensions that are changing and AI has that incredible opportunity to be a partner for every... (mumbling) For a hospital looking at how the second half will look like for physicians looking at what is the sentiment of... What is the surge to expect? To kind of what is the market demand looking at the sentiment of the customers. AI is the ultimate money ball in business and then I think it's just showing its depth at this point. >> Yeah, I mean, I think you're right on, I mean, basically AI is going to convert every software, every application, or those tools aren't going to have much use, Sri we got to go but thanks so much for coming to theCUBE and the great work you guys are doing. Really appreciate your insights. stay safe, and best of luck to you guys. >> Likewise, thank you so much. >> Welcome, and thank you for watching everybody, this is Dave Vellante for the CXO series on theCUBE. We'll see you next time. All right, we're clear. All right.

>> Starting the record, Dave in five, four, three. Hi, everybody this is Dave Vellante, theCUBE, and welcome back to my CXO series. I've been running this through really since the start of the COVID-19 crisis to really understand how leaders are dealing with this pandemic. Sri Ambati is here, he's the CEO and founder of H20. Sri, it's great to see you again, thanks for coming on. >> Thank you for having us. >> Yeah, so this pandemic has obviously given people fits, no question, but it's also given opportunities for companies to kind of reassess where they are. Automation is a huge watchword, flexibility, business resiliency and people who maybe really hadn't fully leaned into things like the cloud and AI and automation are now realizing, wow, we have no choice, it's about survival. Your thought as to what you're seeing in the marketplace. >> Thanks for having us. I think first of all, kudos to the frontline health workers who have been ruthlessly saving lives across the country and the world, and what you're really doing is a fraction of what we could have done or should be doing to stay away the next big pandemic. But that apart I think, I usually tend to say BC is before COVID. So if the world was thinking about going digital after COVID-19, they have been forced to go digital and as a result, you're seeing tremendous transformation across our customers, and a lot of application to kind of go in and reinvent their business models that allow them to scale as effortlessly as they could using the digital means. >> So, think about, doctors and diagnosis machines, in some cases, are helping doctors make diagnoses, they're sometimes making even better diagnosis, (mumbles) is informing. There's been a lot of talk about the models, you know how... Yeah, I know you've been working with a lot of healthcare organizations, you may probably familiar with that, you know, the Medium post, The Hammer and the Dance, and if people criticize the models, of course, they're just models, right? And you iterate models and machine intelligence can help us improve. So, in this, you know, you talk about BC and post C, how have you seen the data and in machine intelligence informing the models and proving that what we know about this pandemic, I mean, it changed literally daily, what are you seeing? >> Yeah, and I think it started with Wuhan and we saw the best application of AI in trying to trace, literally from Alipay, to WeChat, track down the first folks who were spreading it across China and then eventually the rest of the world. I think contact tracing, for example, has become a really interesting problem. supply chain has been disrupted like never before. We're beginning to see customers trying to reinvent their distribution mechanisms in the second order effects of the COVID, and the the prime center is hospital staffing, how many ventilator, is the first few weeks so that after COVID crisis as it evolved in the US. We are busy predicting working with some of the local healthcare communities to predict how staffing in hospitals will work, how many PPE and ventilators will be needed and so henceforth, but that quickly and when the peak surge will be those with the beginning problems, and many of our customers have begin to do these models and iterate and improve and kind of educate the community to practice social distancing, and that led to a lot of flattening the curve and you're talking flattening the curve, you're really talking about data science and analytics in public speak. That led to kind of the next level, now that we have somewhat brought a semblance of order to the reaction to COVID, I think what we are beginning to figure out is, is there going to be a second surge, what elective procedures that were postponed, will be top of the mind for customers, and so this is the kind of things that hospitals are beginning to plan out for the second half of the year, and as businesses try to open up, certain things were highly correlated to surgeon cases, such as cleaning supplies, for example, the obvious one or pantry buying. So retailers are beginning to see what online stores are doing well, e-commerce, online purchases, electronic goods, and so everyone essentially started working from home, and so homes needed to have the same kind of bandwidth that offices and commercial enterprises needed to have, and so a lot of interesting, as one side you saw airlines go away, this side you saw the likes of Zoom and video take off. So you're kind of seeing a real divide in the digital divide and that's happening and AI is here to play a very good role to figure out how to enhance your profitability as you're looking about planning out the next two years. >> Yeah, you know, and obviously, these things they get, they get partisan, it gets political, I mean, our job as an industry is to report, your job is to help people understand, I mean, let the data inform and then let public policy you know, fight it out. So who are some of the people that you're working with that you know, as a result of COVID-19. What's some of the work that H2O has done, I want to better understand what role are you playing? >> So one of the things we're kind of privileged as a company to come into the crisis, with a strong balance and an ability to actually have the right kind of momentum behind the company in terms of great talent, and so we have 10% of the world's top data scientists in the in the form of Kaggle Grand Masters in the company. And so we put most of them to work, and they started collecting data sets, curating data sets and making them more qualitative, picking up public data sources, for example, there's a tremendous amount of job loss out there, figuring out which are the more difficult kind of sectors in the economy and then we started looking at exodus from the cities, we're looking at mobility data that's publicly available, mobility data through the data exchanges, you're able to find which cities which rural areas, did the New Yorkers as they left the city, which places did they go to, and what's to say, Californians when they left Los Angeles, which are the new places they have settled in? These are the places which are now busy places for the same kind of items that you need to sell if you're a retailer, but if you go one step further, we started engaging with FEMA, we start engaging with the universities, like Imperial College London or Berkeley, and started figuring out how best to improve the models and automate them. The SaaS model, the most popular SaaS model, we added that into our Driverless AI product as a recipe and made that accessible to our customers in testing, to customers in healthcare who are trying to predict where the surge is likely to come. But it's mostly about information right? So the AI at the end of it is all about intelligence and being prepared. Predictive is all about being prepared and that's kind of what we did with general, lots of blogs, typical blog articles and working with the largest health organizations and starting to kind of inform them on the most stable models. What we found to our not so much surprise, is that the simplest, very interpretable models are actually the most widely usable, because historical data is actually no longer as effective. You need to build a model that you can quickly understand and retry again to the feedback loop of back testing that model against what really happened. >> Yeah, so I want to double down on that. So really, two things I want to understand, if you have visibility on it, sounds like you do. Just in terms of the surge and the comeback, you know, kind of what those models say, based upon, you know, we have some advanced information coming from the global market, for sure, but it seems like every situation is different. What's the data telling you? Just in terms of, okay, we're coming into the spring and the summer months, maybe it'll come down a little bit. Everybody says it... We fully expect it to come back in the fall, go back to college, don't go back to college. What is the data telling you at this point in time with an understanding that, you know, we're still iterating every day? >> Well, I think I mean, we're not epidemiologists, but at the same time, the science of it is a highly local response, very hyper local response to COVID-19 is what we've seen. Santa Clara, which is just a county, I mean, is different from San Francisco, right, sort of. So you beginning to see, like we saw in Brooklyn, it's very different, and Bronx, very different from Manhattan. So you're seeing a very, very local response to this disease, and I'm talking about US. You see the likes of Brazil, which we're worried about, has picked up quite a bit of cases now. I think the silver lining I would say is that China is up and running to a large degree, a large number of our user base there are back active, you can see the traffic patterns there. So two months after their last research cases, the business and economic activity is back and thriving. And so, you can kind of estimate from that, that this can be done where you can actually contain the rise of active cases and it will take masking of the entire community, masking and the healthy dose of increase in testing. One of our offices is in Prague, and Czech Republic has done an incredible job in trying to contain this and they've done essentially, masked everybody and as a result they're back thinking about opening offices, schools later this month. So I think that's a very, very local response, hyper local response, no one country and no one community is symmetrical with other ones and I think we have a unique situation where in United States you have a very, very highly connected world, highly connected economy and I think we have quite a problem on our hands on how to safeguard our economy while also safeguarding life. >> Yeah, so you can't just, you can't just take Norway and apply it or South Korea and apply it, every situation is different. And then I want to ask you about, you know, the economy in terms of, you know, how much can AI actually, you know, how can it work in this situation where you have, you know, for example, okay, so the Fed, yes, it started doing asset buys back in 2008 but still, very hard to predict, I mean, at this time of this interview you know, Stock Market up 900 points, very difficult to predict that but some event happens in the morning, somebody, you know, Powell says something positive and it goes crazy but just sort of even modeling out the V recovery, the W recovery, deep recession, the comeback. You have to have enough data, do you not? In order for AI to be reasonably accurate? How does it work? And how does at what pace can you iterate and improve on the models? >> So I think that's exactly where I would say, continuous modeling, instead of continuously learning continuous, that's where the vision of the world is headed towards, where data is coming, you build a model, and then you iterate, try it out and come back. That kind of rapid, continuous learning would probably be needed for all our models as opposed to the typical, I'm pushing a model to production once a year, or once every quarter. I think what we're beginning to see is the kind of where companies are beginning to kind of plan out. A lot of people lost their jobs in the last couple of months, right, sort of. And so up scaling and trying to kind of bring back these jobs back both into kind of, both from the manufacturing side, but also lost a lot of jobs in the transportation and the kind of the airlines slash hotel industries, right, sort of. So it's trying to now bring back the sense of confidence and will take a lot more kind of testing, a lot more masking, a lot more social empathy, I think well, some of the things that we are missing while we are socially distant, we know that we are so connected as a species, we need to kind of start having that empathy for we need to wear a mask, not for ourselves, but for our neighbors and people we may run into. And I think that kind of, the same kind of thinking has to kind of parade, before we can open up the economy in a big way. The data, I mean, we can do a lot of transfer learning, right, sort of there are new methods, like try to model it, similar to the 1918, where we had a second bump, or a lot of little bumps, and that's kind of where your W shaped pieces, but governments are trying very well in seeing stimulus dollars being pumped through banks. So some of the US case we're looking for banks is, which small medium business in especially, in unsecured lending, which business to lend to, (mumbles) there's so many applications that have come to banks across the world, it's not just in the US, and banks are caught up with the problem of which and what's growing the concern for this business to kind of, are they really accurate about the number of employees they are saying they have? Do then the next level problem or on forbearance and mortgage, that side of the things are coming up at some of these banks as well. So they're looking at which, what's one of the problems that one of our customers Wells Fargo, they have a question which branch to open, right, sort of that itself, it needs a different kind of modeling. So everything has become a very highly good segmented models, and so AI is absolutely not just a good to have, it has become a must have for most of our customers in how to go about their business. (mumbles) >> I want to talk a little bit about your business, you have been on a mission to democratize AI since the beginning, open source. Explain your business model, how you guys make money and then I want to help people understand basic theoretical comparisons and current affairs. >> Yeah, that's great. I think the last time we spoke, probably about at the Spark Summit. I think Dave and we were talking about Sparkling Water and H2O or open source platforms, which are premium platforms for democratizing machine learning and math at scale, and that's been a tremendous brand for us. Over the last couple of years, we have essentially built a platform called Driverless AI, which is a license software and that automates machine learning models, we took the best practices of all these data scientists, and combined them to essentially build recipes that allow people to build the best forecasting models, best fraud prevention models or the best recommendation engines, and so we started augmenting traditional data scientists with this automatic machine learning called AutoML, that essentially allows them to build models without necessarily having the same level of talent as these Greek Kaggle Grand Masters. And so that has democratized, allowed ordinary companies to start producing models of high caliber and high quality that would otherwise have been the pedigree of Google, Microsoft or Amazon or some of these top tier AI houses like Netflix and others. So what we've done is democratize not just the algorithms at the open source level. Now, we've made it easy for kind of rapid adoption of AI across every branch inside a company, a large organization, also across smaller organizations which don't have the access to the same kind of talent. Now, third level, you know, what we've brought to market, is ability to augment data sets, especially public and private data sets that you can, the alternative data sets that can increase the signal. And that's where we've started working on a new platform called Q, again, more license software, and I mean, to give you an idea there from business models endpoint, now majority of our software sales is coming from closed source software. And sort of so, we've made that transition, we still make our open source widely accessible, we continue to improve it, a large chunk of the teams are improving and participating in building the communities but I think from a business model standpoint as of last year, 51% of our revenues are now coming from closed source software and that change is continuing to grow. >> And this is the point I wanted to get to, so you know, the open source model was you know, Red Hat the one company that, you know, succeeded wildly and it was, put it out there open source, come up with a service, maintain the software, you got to buy the subscription okay, fine. And everybody thought that you know, you were going to do that, they thought that Databricks was going to do and that changed. But I want to take two examples, Hortonworks which kind of took the Red Hat model and Cloudera which does IP. And neither really lived up to the expectation, but now there seems to be sort of a new breed I mentioned, you guys, Databricks, there are others, that seem to be working. You with your license software model, Databricks with a managed service and so there's, it's becoming clear that there's got to be some level of IP that can be licensed in order to really thrive in the open source community to be able to fund the committers that you have to put forth to open source. I wonder if you could give me your thoughts on that narrative. >> So on Driverless AI, which is the closest platform I mentioned, we opened up the layers in open source as recipes. So for example, different companies build their zip codes differently, right, the domain specific recipes, we put about 150 of them in open source again, on top of our Driverless AI platform, and the idea there is that, open source is about freedom, right? It is not necessarily about, it's not a philosophy, it's not a business model, it allows freedom for rapid adoption of a platform and complete democratization and commodification of a space. And that allows a small company like ours to compete at the level of an SaaS or a Google or a Microsoft because you have the same level of voice as a very large company and you're focused on using code as a community building exercise as opposed to a business model, right? So that's kind of the heart of open source, is allowing that freedom for our end users and the customers to kind of innovate at the same level of that a Silicon Valley company or one of these large tech giants are building software. So it's really about making, it's a maker culture, as opposed to a consumer culture around software. Now, if you look at how the the Red Hat model, and the others who have tried to replicate that, the difficult part there was, if the product is very good, customers are self sufficient and if it becomes a standard, then customers know how to use it. If the product is crippled or difficult to use, then you put a lot of services and that's where you saw the classic Hadoop companies, get pulled into a lot of services, which is a reasonably difficult business to scale. So I think what we chose was, instead, a great product that builds a fantastic brand, that makes AI, even when other first or second.ai domain, and for us to see thousands of companies which are not AI and AI first, and even more companies adopting AI and talking about AI as a major way that was possible because of open source. If you had chosen close source and many of your peers did, they all vanished. So that's kind of how the open source is really about building the ecosystem and having the patience to build a company that takes 10, 20 years to build. And what we are expecting unfortunately, is a first and fast rise up to become unicorns. In that race, you're essentially sacrifice, building a long ecosystem play, and that's kind of what we chose to do, and that took a little longer. Now, if you think about the, how do you truly monetize open source, it takes a little longer and is much more difficult sales machine to scale, right, sort of. Our open source business actually is reasonably positive EBITDA business because it makes more money than we spend on it. But trying to teach sales teams, how to sell open source, that's a much, that's a rate limiting step. And that's why we chose and also explaining to the investors, how open source is being invested in as you go closer to the IPO markets, that's where we chose, let's go into license software model and scale that as a regular business. >> So I've said a few times, it's kind of like ironic that, this pandemic is as we're entering a new decade, you know, we've kind of we're exiting the era, I mean, the many, many decades of Moore's law being the source of innovation and now it's a combination of data, applying machine intelligence and being able to scale and with cloud. Well, my question is, what did we expect out of AI this decade if those are sort of the three, the cocktail of innovation, if you will, what should we expect? Is it really just about, I suggest, is it really about automating, you know, businesses, giving them more agility, flexibility, you know, etc. Or should we should we expect more from AI this decade? >> Well, I mean, if you think about the decade of 2010 2011, that was defined by software is eating the world, right? And now you can say software is the world, right? I mean, pretty much almost all conditions are digital. And AI is eating software, right? (mumbling) A lot of cloud transitions are happening and are now happening much faster rate but cloud and AI are kind of the leading, AI is essentially one of the biggest driver for cloud adoption for many of our customers. So in the enterprise world, you're seeing rebuilding of a lot of data, fast data driven applications that use AI, instead of rule based software, you're beginning to see patterned, mission AI based software, and you're seeing that in spades. And, of course, that is just the tip of the iceberg, AI has been with us for 100 years, and it's going to be ahead of us another hundred years, right, sort of. So as you see the discovery rate at which, it is really a fundamentally a math, math movement and in that math movement at the beginning of every century, it leads to 100 years of phenomenal discovery. So AI is essentially making discoveries faster, AI is producing, entertainment, AI is producing music, AI is producing choreographing, you're seeing AI in every walk of life, AI summarization of Zoom meetings, right, you beginning to see a lot of the AI enabled ETF peaking of stocks, right, sort of. You're beginning to see, we repriced 20,000 bonds every 15 seconds using H2O AI, corporate bonds. And so you and one of our customers is on the fastest growing stock, mostly AI is powering a lot of these insights in a fast changing world which is globally connected. No one of us is able to combine all the multiple dimensions that are changing and AI has that incredible opportunity to be a partner for every... (mumbling) For a hospital looking at how the second half will look like for physicians looking at what is the sentiment of... What is the surge to expect? To kind of what is the market demand looking at the sentiment of the customers. AI is the ultimate money ball in business and then I think it's just showing its depth at this point. >> Yeah, I mean, I think you're right on, I mean, basically AI is going to convert every software, every application, or those tools aren't going to have much use, Sri we got to go but thanks so much for coming to theCUBE and the great work you guys are doing. Really appreciate your insights. stay safe, and best of luck to you guys. >> Likewise, thank you so much. >> Welcome, and thank you for watching everybody, this is Dave Vellante for the CXO series on theCUBE. We'll see you next time. All right, we're clear. All right.

(upbeat music) >> Announcer: From our studios, in the heart of Silicon Valley, Palo Alto, California, this is a Cube conversation. >> Everyone, welcome to this special Cube conversation here in Palo Alto Cube studios. I'm John Furrier, host of the Cube. We have special breaking news here, with Sri Ambati who is the founder and CEO of H2O.ai with big funding news. Great to see you Cube alumni, hot startup, you got some hot funding news, share with us. >> We are very excited to announce our Series D. Goldman Sachs, one of our leading customers and Ping An from China are leading our round. It's a round of $72 million, and bringing our total fundraise to 147. This is an endorsement of their support of our mission to democratize AI and an endorsement of the amazing teamwork behind the company and its customer centricity. Customers have now come to lead two of our rounds. Last round was Series C led by Wells Fargo and NVIDIA and I think it just goes to say how critical a thing we are for their success in AI. >> Well congratulations, I've been watching you guys build this company from scratch, we've had many conversations going back to 2013, '14 on The Cube. You call it-- >> You covered us long before. >> You guys were always on the wave, and you really created a category, this is a new category that Cloud 2.0 is creating which is a DevOps mindset, entrepreneurial mindset, creating a category to enable people to have the kind of infrastructure and tooling and software to enable them to do all the heavy lifting of AI without doing the heavy lifting. As the quote for cloud is, that Amazon always quotes is you do all of the undifferentiated heavy lifting that's required to stand up stuff and then provide tooling for the heavy differentiated lifting to make it easy to use. This has been a key thing. Has that been the-- >> Customers have be core to our, company building. H2O is here to build an amazing piece of innovation and technology and innovation is not new for Silicon Valley, as you know. But I think innovation, with a purpose and with a focus of customer success is something we represent and that's been kind of the key north finder for us. In terms of making things simpler, when we started, it was a grassroots movement in open source and we wanted the mind share of millions of users worldwide and that mind share got us a lot of feedback. And that feedback is how we then built the second generation of the product lines, which is driverless AI. We are also announcing our mission to make every company an AI company, this funding will power that transformation of several businesses that can then go on to build the AI superpower. >> And certainly, cloud computing, more compute more elastic resources is always a great tailwind. What are you guys going to do with the funding in terms of focus? >> You mentioned cloud which is a great story. We're obviously going to make things easier for folks who are doing the cloud, but they are the largest players, as well, Google, Microsoft, Amazon. They're right there, trying to innovate. AI is at the center of every software moment because AI eating software, software is eating the world. And so, all the software players are right there, trying to build a large AI opportunity for the world and we think in ecosystems, not just empires. So our mission is to uplift the entire AI to the place where businesses can use it, verticalize it, build new products, globalize. We are building our sales and marketing efforts now with a much bigger, faster systems-- >> So a lot of, go to market expansion, more customer focus. More field sales and support kind of thing. >> Build our center for AI research in Prague, within the CND, now we are building it in Chennai and Ottawa, and so globalizing the operation, going to China, going to build focus in Asia as well. >> So nice step up on funding at 72 million, you said? >> 72.5 million. >> 72.5 million, that's almost double what you've raised to date, nice kickup. So global expansion, nice philosophy. That's important to you guys, isn't it? >> The world has become a small village. There's no changing that, and data is global. Things are a wide global trend, it's amazing to see that AI is not just transforming the US, it's also transforming China, it's also transforming India. It's transforming Africa. Pay through mobile is a very common theme worldwide and I think data is being collected globally. I think there is no way to unbox it and box it back to a small place, so our vision is very borderless and global and we want the AI companies of the valley to also compete in a global arena and I think that's kind of why we think it's important to be-- >> Love competition, that's certainly going to force everyone to be more open. I got to ask you about the role of the developer. I love the democratization, putting AI in the hands of everybody, it's a great mission. You guys do a lot of AI for Good efforts. So congratulations on that, but how does this change the nature of the developer, because you're seeing with cloud and DevOps, developers are becoming closer to the front lines, they're becoming kingmakers. They're becoming really, really important. So the role of the developer is important. How do you change that role, if any. How do you expand it, what happens? >> There are two important transformations happening right now in the tech world. One is the role of data scientists and the role of the software engineer. Right, so they're coming closer in many ways, in actually in some of the newer places, software engineers are deploying data science models, data scientists are deploying software engineering. So Python has been a good new language, the new languages that are coming up that help that happen more closely. Software engineering as we know it, which was looking at data creating the rules and the logic that runs a program is now being automated to a degree where that logic is being generated from data using data science. So that's where the brains behind how programs run how computers build is now being, is AI inside. And so that's where the world is transforming, software engineers now get to do a lot more with a lot less of tinkering on a daily basis for little modules. They can probably build a whole slew of an application what would take 18 months to build is now compressing into 18 weeks or 18 days. >> Sri, I love how you talk about software engineering and data scientists, very specific. I was having a debate with my young son around what is computer science was the question. Well, computer science is the study of computers the science of computers. It used to be if you were a CS or a comp sci major which is not cool to say anymore but, when you were a computer science major, you were really a software engineer, that was the discipline. Now, computer science as a field has spread so far and so broad, you've got software engineering you've got data science, you have newer roles are emerging. But that brings up the question I want to put to you which is, the whole idea of, I'm a full stack developer. Well, if what you're saying you're doing is true, you're essentially cutting the stack in half. So it's a half stack developer on one end and a data scientist that's got the other half. So the notion of the full stack developer kind of goes away with the idea of horizontally scalable infrastructure and vertically specialized data and AI. Your thoughts, what's your reaction to that? >> I think the most... I would say the most scarce resource in the world is empathy, right? When developers have empathy for their users, they now start building design that cares for the users. So the design becomes still the limiting factor where you can't really automate a lot of that design. So the full stack engineer is now going closer to the front and understanding their users and making applications that are perceptive of how the users are using them and building that empathy into the product. A lot of the full stack, we used to learn how to build up a kernel, deploy it on cloud, scale it on your own servers. All of that is coming together in reasonably easier ways. With cloud is helping there, AI is helping there, data is helping there, and lessons from the data. But I think what has not gone away is imagination, creativity, and how to power that creativity with AI and get it in the hands of someone quickly. Marketing has become easier in the new world. So it's not just enough to make products, you have to make markets for your products and then deliver and get that success for customers-- >> So what you're saying-- >> The developers become-- >> The consistency of the lower end of the stack of wiring together the plumbing and the kernel and everything else is done for you. So you can move up. >> Up the stack. >> So the stack's growing, so it's still kind of full. No one calls themselves a half stack developer. I haven't met anyone say "Yeah I'm a half stack developer." They're full stack developers, but the roles are changing. >> I think what-- >> There's more to do on the front end of creativity so the stack's extending. >> Creativity is changing, I think the one thing we have learned. We've gone past Moore's Law in the valley and people are innovating architectures to run AI faster. So AI is beginning to eat hardware. So you've seen the transformation in microprocessors as well I think once AI starts being part of the overall conversation, you'll see a much more richer coexistence with being how a human programmer and a computer programmer is going to be working closely. But I think this is just the beginning of a real richness when you talk about rich interactive applications, you're going to talk about rich interactive appliances, where you start seeing intelligence really spread around the form. >> Sri, if we really want to have some fun we can just talk about what a 10x engineer is. No I'm only kidding, we're not going to go there. It's always a good debate on Twitter what a 10x engineer is. Sri, congratulations on the funding. $72.5 million in finance for global expansion on the team side as well as in geographies, congratulations. >> Thank you. >> H2O.ai >> The full stack engineer of the future is, finishing up your full stack engineer conversation is going to get that courage and become a leader. Going from managers to leaders, developers to founders. I think it's become easier to democratize entrepreneurship now than ever before and part of our mission as a company is to democratize things, democratize AI, democratize H2O like in the AI for Good, democratize water. But also democratize the art of making more entrepreneurs and remove the common ways to fail and that's also a way to create more opportunity more ownership in the world and so-- >> And I think society will benefit from this globally because in the data is truth, in the data is the notion of being transparent, if it's all there and we're going to get to the data faster and that's where AI helps us. >> That's what it is. >> Sri, congratulations, $72 million of funding for H2O. We're here with the founder and CEO Sri Ambati. Great success story here in Silicon Valley and around the world. I'm John Furrier with the Cube, thanks for watching. >> Sri: Thank you. (upbeat music)

>> from our studios in the heart of Silicon Valley, Palo ALTO, California It is a cute conversation. >> Hello and welcome to this Special Cube conversation here in Palo Alto, California Cubes Studios Jon for your host of the Q. We retreat embodies the founder and CEO of H 20 dot ay, ay, Cuba Lem hot. Start up right in the action of all the machine learning artificial intelligence with the democratization, the role of data in the future, it's all happening with the cloud 2.0, Dev Ops 2.0, great to see you, The test. But the company What's going on, you guys air smoking hot? Congratulations. You got the right formally here with a I explain what's going on. It started about seven >> years ago on Dottie. I was was just a new fad that arrived into Silicon Valley. Today we have thousands of companies in the eye and we're very excited to be partners in making more companies becoming I first. And our region here is to democratize the eye and we've made simple are open source made it easy for people to start adapting data signs and machine learning and different functions inside their large and said the large organizations and apply that for different use cases across financial service is insurance healthcare. >> We leapfrog in 2016 and build our first closer. It's chronic traveler >> C I. We made it on GPS using the latest hardware software innovations Open source. I has funded the rice off automatic machine learning, which >> further reduces the need for >> extraordinary talent to build machine learning. >> No one has time >> today and then we're trying to really bring that automatic mission learning a very significant crunch. Time free, I so people can consuming. I better. >> You know, this is one of the things I love about the current state of the market right now. Entrepreneur Mark, as well as start of some growing companies Go public is that there's a new breed of entrepreneurship going on around large scale, standing up infrastructure, shortening the time it takes to do something like provisioning like the old eyes. I get a phD and we're seeing this in data science. I mean, you don't have to be a python coder. This democratisation is not just a tagline. It's actually the reality is of a business opportunity of whoever can provide the infrastructure and the systems four people to do. It is an opportunity. You guys were doing that. This is a real dynamic. This isn't a new way, a new kind of dynamic in the industry. The three real character >> sticks on ability to adopt. Hey, Iris Oneness Data >> is a team, a team sport, which means that you gotta bring different dimensions within your organization to be able to take advantage of data and the I and, um, you've got to bring in your domain. Scientists work closely with your data. Scientists were closely with your data. Engineers produce applications that can be deployed and then get your design on top of it. That can convince users are our strategist to make those decisions. That delays is showing up, so that takes a multi dimensional workforce to work closely together. So the rial problem, an adoption of the AI today is not just technology, it's also culture. And so we're kind of bringing those aspects together and form of products. One of our products, for example, explainable. Aye, aye. It's helping the data. Scientists tell a story that businesses can understand. Why is the model deciding? I need to take discretion. This'll direction. Why's this moral? Giving this particular nurse a high credit score? Even though she is, she has a very she doesn't have a high school graduation. That kind of figuring out those Democratic democratization goes all the way down there. It's wise, a mortal deciding what's deciding and explaining and breaking that down into English, which which building trust is a huge aspect in a >> well. I want to get to the the talent in the time and the trust equation on the next talk track, but I want to get the hard news out there. You guys are have some news driverless a eyes, your one of your core things. What's the hard Explain the news. What's the big news? >> The big news has Bean, that is, the money ball from business and money Ball, as it has been played out, has been. The experts >> were left out of the >> field and all garden is taking over and there is no participation between experts, the domain scientists and the data scientists and what we're bringing with the new product in travel see eyes, an ability for companies to take away I and become a I companies themselves. The rial air races not between the Googles and the Amazons and Microsoft's and other guy companies, software companies. The relay race is in the word pickles. And how can a company, which is a bank or an insurance giant or a health care company take a I platforms and become, take the data, monetize the data and become a I companies themselves? >> You know, that's a really profound state. I would agree with 100% on that. I think we saw that early on in the big data world round Doop doop kind of died by the wayside. But day Volonte and we keep on team have observed and they actually predicted that the most value was gonna come from practitioners, not the vendors, because they're the ones who have the data. And you mentioned verticals. This is another interesting point. I want to get more explanation from you on Is that APS are driven by data data needs domain specific information. So you can't just say I have data. Therefore, magic happens. It's really at the edge of the domain speak or the domain feature of the application. This is where the data is this kind of supports your idea that the eyes with the company's not that are using it, not the suppliers of the technology. >> Our vision has always being hosted by maker customer service for right to be focused on the customer, and through that we actually made customer one of the product managers inside the company. And the way that the doors that opened from working where it closed with some of our leading customers was that we need to get them to participate and take a eyes, algorithms and platforms that can tune automatically. The algorithms and the right hyper parameter organizations, right features and amend the right data sets that they have. There's a whole data lake around there on their data architecture today, which data sets them and not using in my current problem solving. That's a reasonable problem in looking at that combination of these Berries. Pieces have been automated in travel a, C I. A. And the new version that we're not bringing to market is able to allow them to create their own recipes, bring your own transformers and make that automatic fit for their particular race. Do you think about this as a rebuilt all the components of a race car. They're gonna take it and apply for that particular race to win. >> So that's where driverless comes in its travels in the sense of you don't really need a full operator. It kind of operates on its own. >> In some sense, it's driver less, which is in some there taking the data scientists giving them a power tool that historically before automatic machine learning your valises in the umbrella automatic machine learning they would find tune learning the nuances off the data and the problem, the problem at hand, what they're optimizing for and the right tweaks in the algorithm. So they have to understand how deep the streets are gonna be home, any layers off, off deep learning they need what particular variation and deploying. They should put in a natural language processing what context they need to the long term, short term memory. All these pieces, they have to learn themselves. And they were only a few Grand masters are big data scientist in the world who could come up with the right answer for different problems. >> So you're spreading the love of a I around. So you simplifying that you get the big brains to work on it and democratization. People can then participate in. The machines also can learn both humans and machines between >> our open source and the very maker centric culture we've been able to attract on the world's top data scientists, physicists and compiler engineers to bring in a form factor that businesses can use. And today it one data scientist in a company like Franklin Templeton can operate at the level of 10 or hundreds of them and then bring the best in data science in a form factor that they can plug in and play. >> I was having a cautious We can't Libby, who works with being our platform team. We have all this data with the Cube, and we were just talking. Wait higher data science and a eye specialist and you go out and look around. You get Google and Amazon all these big players, spending between 3 to $4,000,000 per machine learning engineer, and that might be someone under the age of 30. And with no experience or so the talent war is huge. I mean the cost to just hire these guys. We can't hire these people. It's a >> global war. >> There's no there's a talent shortage in China. There's talent shortage in India. There stand shortage in Europe and we have officers in in Europe and in India. The talent shortage in Toronto and Ottawa writes it is. It's a global shortage off physicists and mathematicians and data scientists. So that's where our tools can help. And we see that you see travelers say I as a wave you can drive to New York or you can fly to me >> off. I started my son the other days taking computer science classes in school. I'm like, Well, you know, the machine learning at a eyes kind like dog training. You have dog training. You train that dog to do some tricks that some tricks. Well, if you're a coder, you want to train the machines. This is the machine training. This is data science is what a. I possibilities that machines have to be taught. Something is a base in foot. Machines just aren't self learning on their own. So as you look at the science of a I, this becomes the question on the talent gap. Can the talent get be closed by machines and you got the time you want speed low, latent, see and trust. All these things are hard to do. All three. Balancing all three is extremely difficult. What's your thoughts on those three variables? >> So that's where we brought a I to help the day >> I travel A. C. I's concept that bringing a I to simplify it's an export system to do a I better so you can actually give it to the hands of a new data scientists so you can perform it the power off a Dead ones data centers if you're not disempowering. The data sent that he is a scientist, the park's still foreign data scientist, because he cannot be stopped with the confusion matrix, false positives, false negatives. That's something a data scientists can understand. What you're talking about featured engineering. That's something a data scientists understand. And what travelers say is really doing is helping him may like do that rapidly and automated on the latest hardware. That's what the time is coming into GPS that PTSD pews different form off clouds at cheaper, faster, cheaper and easier. That's the democratization aspect, but it's really targeted. Data Scientist to Prevent Excrement Letter in Science data sciences is a search for truth, but it's a lot of extra minutes to get the truth and law. If you can make the cost of excrement really simple, cheaper on dhe prevent over fitting. That's a common problem in our science. Prevent by us accidental bites that you introduced because the data is last right, trying to kind of prevent the common pitfalls and doing data science leakage. Usually your signal leaks. And how do you prevent those common those pieces? That's kind of weird, revolutionize coming at it. But if you put that in the box, what that really unlocks is imagination. The real hard problems in the world are still the same. >> Aye aye for creative people, for instance. They want infrastructure. They don't wanna have to be an expert. They wanted that value. That's the consumer ization, >> is really the co founder for someone who's highly imaginative and his courage right? And you don't have to look for founders to look for courage and imagination that a lot of intra preneurs in large companies were trying to bring change to that organization. >> You know, we always say that it's intellectual property game's changing from you know I got the protocol. This is locked and patented. Two. You could have a workflow innovation change. One little tweak of a process with data and powerful. Aye, aye, that's the new magic I P equation. It's in the workforce, in the applications, new opportunities. Do you agree with that? >> Absolutely. That the leapfrog from here is businesses will come up with new business processes that we looked at. Business process optimization and globalization can help there. But a I, as you rightfully said earlier, is training computers, not just programming them. Their schooling most of computers that can now with data, think almost at the same level as a go player. Right there was leading Go player. You can think at the same level off an expert in that space. And if that's happening now, I can transform. My business can run 24 by seven at the rate at which I can assembled machines and feed a data data creation becomes making new data becomes the real value that hey, I can >> h 20 today I announcing driverless Aye, aye. Part of their flagship problem product around recipes and democratization. Ay, ay, congratulations. Final point take a minute to explain for the folks just the product, how they buy it. What's it made of? What's the commitment? How did they engage with you >> guys? It's an annual license recruit. License this software license people condone load on our website, get a three week trial, try it on their own retrial. Pretrial recipes are open source, but 100 recipes built by then Masters have been made open source and they could be plugged and tried and taken. Customers, of course, don't have to make their software open source. They can take this, make it theirs. And our region here is to make every company in the eye company. And and that means that they have to embrace it. I learn it. Ticket. Participate some off. The leading conservation companies are giving it back so you can access in the open source. But the real vision here is to build that community off. A practitioners inside large formulations were here or teams air global. And we're here to support that transformation off some of the largest customers. >> So my problem of hiring an aye aye person You could help you solve that right today. Okay, So it was watching. Please get their stuff and come get a job opening here. That's the goal. But that's that's the dream. That is the dream. And we we want to be should one day. I have watched >> you over the last 10 years. You've been an entrepreneur. The fierce passion. We want the eye to be a partner so you can take your message to wider audience and build monetization or on the data you have created. Businesses are the largest after the big data warlords we have on data. Privacy is gonna come eventually. But I think I did. Businesses are the second largest owners of data. They just don't know how to monetize it. Unlock value from it. I will have >> Well, you know, we love day that we want to be data driven. We want to go faster. I love the driverless vision travel. Say I h 20 dot ay, ay here in the Cuban John for it. Breaking news here in Silicon Valley from that start of h 20 dot ay, ay, thanks for watching. Thank you.

(upbeat music) >> Woman Voiceover: From our studios in the heart of Silicon Valley, Palo Alto, California this is a CUBE Conversation. >> Hello and welcome to this special CUBE Conversation here in Palo Alto, California, CUBE Studios, I'm John Furrier, host of theCUBE, here with Sri Ambati. He's the founder and CEO of H20.ai. CUBE Alum, hot start up right in the action of all the machine learning, artificial intelligence, with democratization the role of data in the future, it's all happening with Cloud 2.0, DevOps 2.0, Sri, great to see you. Thanks for coming by. You're a neighbor, you're right down the street from us at our studio here. >> It's exciting to be at theCUBE Com. >> That's KubeCon, that's Kubernetes Con. CUBEcon, coming soon, not to be confused with KubeCon. Great to see you. So tell us about the company, what's going on, you guys are smoking hot, congratulations. You got the right formula here with AI. Explain what's going on. >> It started about seven years ago, and .ai was just a new fad that arrived that arrived in Silicon Valley. And today we have thousands of companies in AI, and we're very excited to be partners in making more companies become AI-first. And our vision here is to democratize AI, and we've made it simple with our open source, made it easy for people to start adapting data science and machine learning in different functions inside their large organizations. And apply that for different use cases across financial services, insurance, health care. We leapfrogged in 2016 and built our first closed source product, Driverless AI, we made it on GPUs using the latest hardware and software innovations. Open source AI has funded the rise of automatic machine learning, Which further reduces the need for extraordinary talent to fill the machine learning. No one has time today, and then we're trying to really bring that automatic machine learning at a very significant crunch time for AI, so people can consume AI better. >> You know, this is one of the things that I love about the current state of the market right now, the entrepreneur market as well as startups and growing companies that are going to go public. Is that there's a new breed of entrepreneurship going on around large scale, standing up infrastructure, shortening the time it takes to do something. Like provisioning. The old AIs, you got to be a PHD. And we're seeing this in data science, you don't have to be a python coder. This democratization is not just a tag line, actually the reality is of a business opportunity. Whoever can provide the infrastructure and the systems for people to do it. It is an opportunity, you guys are doing that. This is a real dynamic. This is a new way, a new kind of dynamic and an industry. >> The three real characteristics on ability to adopt AI, one is data is a team sport. Which means you've got to bring different dimensions within your organization to be able to take advantage of data and AI. And you've got to bring in your domain scientists, work closely with your data scientists, work closely with your data engineers, produce applications that can be deployed, and then get your design on top of it that can convince users or strategists to make those decisions that data is showing up So that takes a multi-dimensional workforce to work closely together. The real problem in adoption of AI today is not just technology, it's also culture. So we're kind of bringing those aspects together in formal products. One of our products, for example, Explainable AI. It's helping the data scientists tell a story that businesses can understand. Why is the model deciding I need to take this test in this direction? Why is this model giving this particular nurse a high credit score even though she doesn't have a high school graduation? That kind of figuring out those democratization goes all the way down. Why is the model deciding what it's deciding, and explaining and breaking that down into English. And building a trust is a huge aspect in AI right now. >> Well I want to get to the talent, and the time, and the trust equation on the next talk, but I want to get the hard news out there. You guys have some news, Driverless AI is one of your core things. Explain the news, what's the big news? >> The big news has been that... AI's a money ball for business, right? And money ball as it has been played out has been the experts were left out of the field, and algorithms taking over. And there is no participation between experts, the domain scientists, and the data scientists. And what we're bringing with the new product in Driverless AI, is an ability for companies to take our AI and become AI companies themselves. The real AI race is not between the Googles and the Amazons and the Microsofts and other AI companies, AI software companies. The real AI race is in the verticals and how can a company which is a bank, or an insurance giant, or a healthcare company take AI platforms and become, take the data and monetize the data and become AI companies themselves. >> Yeah, that's a really profound statement I would agree with 100% on that. I think we saw that early on in the big data world around Hadoop, well Hadoop kind of died by the wayside, but Dave Vellante and the WikiBon team have observed, and they actually predicted, that the most value was going to come from practitioners, not the vendors. 'Cause they're the ones who have the data. And you mentioned verticals, this is another interesting point I want to get more explanation from you on, is that apps are driven by data. Data needs domain-specific information. So you can't just say "I have data, therefore magic happens" it's really at the edge of the domain speak or the domain feature of the application. This is where the data is, so this kind of supports your idea that the AI's about the companies that are using it, not the suppliers of the technology. >> Our vision has always been how we make our customers satisfied. We focus on the customer, and through that we actually make customer one of the product managers inside the company. And the doors that open from working very closely with some of our leading customers is that we need to get them to participate and take AIs, algorithms, and platforms, that can tune automatically the algorithms, and have the right hyper parameter optimizations, the right features. And augment the right data sets that they have. There's a whole data lake around there, around data architecture today. Which data sets am I not using in my current problem I'm solving, that's a reasonable problem I'm looking at. That combination of these various pieces have been automated in Driverless AI. And the new version that we're now bringing to market is able to allow them to create their own recipes, bring their own transformers, and make an automatic fit for their particular race. So if you think about this as we built all the components of a race car, you're going to take it and apply it for that particular race to win. >> John: So that's the word driverless comes in. It's driverless in the sense of you don't really need a full operator, it kind of operates on its own. >> In some sense it's driverless. They're taking the data scientists, giving them a power tool. Historically, before automatic machine learning, driverless is in the umbrella of machine learning, they would fine tune, learning the nuances of the data, and the problem at hand, what they're optimizing for, and the right tweaks in the algorithm. So they have to understand how deep the streets are going to be, how many layers of deep learning they need, what variation of deep learning they should put, and in a natural language crossing, what context they need. Long term shot, memory, all these pieces they have to learn themselves. And there were only a few grand masters or big data scientists in the world who could come up with the right answer for different problems. >> So you're spreading the love of AI around. >> Simplifying that. >> You get the big brains to work on it, and democratization means people can participate and the machines also can learn. Both humans and machines. >> Between our open source and the very maker-centric culture, we've been able to attract some of the world's top data scientists, physicists, and compiler engineers. To bring in a form factor that businesses can use. One data scientist in a company like Franklin Templeton can operate at a level of ten or hundreds of them, and then bring the best in data science in a form factor that they can plug in and play. >> I was having a concert with Kent Libby, who works with me on our platform team. We have all this data with theCUBE, and we were just talking, we need to hire a data scientist and AI specialist. And you go out and look around, you've got Google, Amazon, all these big players spending between 3-4 million per machine learning engineer. And that might be someone under the age of 30 with no experience. So the talent bore is huge. The cost to just hire, we can't hire these people. >> It's a global war. There's talent shortage in China, there's talent shortage in India, there's talent shortage in Europe, and we have offices in Europe and India. There's a talent shortage in Toronto and Ottawa. So it's a global shortage of physicists and mathematicians and data scientists. So that's where our tools can help. And we see Driverless AI as, you can drive to New York or you can fly to New York. >> I was talking to my son the other day, he's taking computer science classes in night school. And it's like, well you know, the machine learning in AI is kind of like dog training. You have dog training, you train the dog to do some tricks, it does some tricks. Well, if you're a coder you want to train the machine. This is the machine training. This is data science, is what AI possibility is there. Machines have to be taught something. There's a base input, machines just aren't self-learning on their own. So as you look at the science of AI, this becomes the question on the talent gap. Can the talent gap be closed by machines? And you got the time, you want speed, low latency, and trust. All these things are hard to do. All three, balancing all three is extremely difficult. What's your thoughts on those three variables? >> So that's why we brought AI to help with AI. Driverless AI is a concept of bringing AI to simplify. It's an expert system to do AI better. So you can actually give to the hands of the new data scientists, so you can perform at the power of an advanced data scientist. We're not disempowering the data scientist, the part's still for a data scientist. When you start with a confusion matrix, false positives, false negatives, that's something a data scientist can understand. When you talk about feature engineering, that's something a data scientist can understand. And what Driverless AI is really doing is helping him do that rapidly, and automated on the latest hardware, that's where the time is coming into. GPUs, FPGAs, TPUs, different form of clouds. Cheaper, right. So faster, cheaper, easier, that's the democratization aspect. But it's really targeted at the data scientist to prevent experimental error. In science, the data science is a search for truth, but it's a lot of experiments to get to truth. If you can make the cost of experiments really simple, cheaper, and prevent over fitting. That's a common problem in our science. Prevent bias, accidental bias that you introduce because the data is biased, right. So trying to prevent the flaws in doing data science. Leakage, usually your signal leaks, and how do you prevent those common pieces. That's where Driverless AI is coming at it. But if you put that in a box, what that really unlocks is imagination. The real hard problems in the world are still the same. >> AI for creative people, for instance. They want infrastructure, they don't want to have to be an expert. They want that value. That's the consumerization. >> AI is really the co founder for someone who's highly imaginative and has courage, right. And you don't have to look for founders to look for courage and imagination. A lot of entrepreneurs in large companies, who are trying to bring change to their organizations. >> Yeah, we always say, the intellectual property game is changing from protocols, locked in, patented, to you could have a workflow innovation. Change one little tweak of a process with data and powerful AI, that's the new magic IP equation. It's in the workflow, it's in the application, it's new opportunities. Do you agree with that? >> Absolutely. The leapfrog from here is businesses will come up with new business processes. So we looked at business process optimization, and globalization's going to help there. But AI, as you rightfully said earlier, is training computers. Not just programming them, you're schooling them. A host of computers that can now, with data, think almost at the same level as a Go player. The world's leading Go player. They can think at the same level of an expert in that space. And if that's happening, now I can transform. My business can run 24 by 7 and the rate at which I can assemble machines and feed it data. Data creation becomes, making new data becomes, the real value that AI can- >> H20.ai announcing Driverless AI, part of their flagship product around recipes and democratizing AI. Congratulations. Final point, take a minute to explain to the folks just the product, how they buy it, what's it made of, what's the commitment, how do they engage with you guys? >> It's an annual license, a software license people can download on our website. Get a three week trial, try it on their own. >> Free trial? >> A free trial, our recipes are open-source. About a hundred recipes, built by grand masters have been made open source. And they can be plugged, and tried. Customers of course don't have to make their software open source. They can take this, make it theirs. And our vision here is to make every company an AI company. And that means that they have to embrace AI, learn it, tweak it, participate, some of the leading conservation companies are giving it back in the open source. But the real vision here is to build that community of AI practitioners inside large organizations. We are here, our teams are global, and we're here to support that transformation of some large customers. >> So my problem of hiring an AI person, you could help me solve that. >> Right today. >> Okay, so anyone who's watching, please get their stuff and come get an opening here. That's the goal. But that is the dream, we want AI in our system. >> I have watched you the last ten years, you've been an entrepreneur with a fierce passion, you want AI to be a partner so you can take your message to wider audience and build monetization around the data you have created. Businesses are the largest, after the big data warlords we have, and data privacy's going to come eventually, but I think businesses are the second largest owners of data they just don't know how to monetize it, unlock value from it, and AI will help. >> Well you know we love data, we want to be data-driven, we want to go faster. Love the driverless vision, Driverless AI, H20.ai. Here in theCUBE I'm John Furrier with breaking news here in Silicon Valley from hot startup H20.ai. Thanks for watching.