 From our studios, in the heart of Silicon Valley, Palo Alto, California, this is a CUBE Conversation. Hello everyone, welcome to this special CUBE Conversation here in Palo Alto, California. The CUBE studios, I'm John Furrier, host of the CUBE. We're here at Shreya Badi, he's the founder and CEO of H2O.ai, CUBE alum. Hot startup right at the, in the action of all the machine learning, artificial intelligence, really the democratization, the role of data in the future. It's all happening with the Cloud 2.0, DevOps 2.0, great to see you. So tell us what 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. We started about seven years ago and Dot AI was just a new fad that arrived into Silicon Valley. 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. We made it easy for people to start adapting data science and machine learning and different functions inside their large, inside the large organizations and apply that for different use cases across financial services, insurance, healthcare. We leapfrogged in 2016 and built our first closed source product, Trivallus AI. We made it on GPUs using the latest hardware, software innovations, open source AI has funded the rise of 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 machine learning to a very significant crunch time for AI so people can consume AI better. You know, this is one of the things I love about the current state of the market right now for 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. Like the old AIs, I get to be a PhD and we're seeing this in data science. I mean, you don't have to be a Python coder. This democratization is not just a tagline, it's actually the reality of a business opportunity of whoever can provide the infrastructure and the systems for people to do it is an opportunity. You guys are doing that. This is a real dynamic. This isn't a new way, a new kind of dynamic in the industry. The three real characteristics on ability to adopt AI is one is data is a team sport, which means that you got to bring different dimensions within your organization to be able to take advantage of data and AI and you 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 our strategists to make those decisions that data is showing up. So that takes a multi-dimensional workforce to work closely together. So the real problem in adoption of AI today is not just technology, it's also culture. And so we're kind of bringing those aspects together in form of 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 decision, 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's deciding and explaining and breaking that down into English, which can, 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 track, but I want to get the hard news out there. You guys have some news, driverless AI is one of your core things. What's the hard, explain the news, what's the big news? The big news has been that AI is the 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 was no participation between the 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, monetize the data and become AI 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 around Hadoop, 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 because they're the ones who have the data. And you mentioned verticals. This is another interesting point and 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. This kind of supports your idea that the AI is about the companies that are using it, not the suppliers of the technology. Our vision has always been how do I make our customers successful, right? So we 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 very closely with some of our leading customers was that we need to get them to participate and take AI's algorithms and platforms that can tune automatically the algorithms and 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 on their data architecture today, which data sets I'm not using in my current problem I'm solving. That's a reasonable problem we're 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 that automatic fit for their particular race. If you think about this, as we built all the components of a race car, they're going to take it and apply it for that particular race to win. 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, which is in some they're taking the data scientists giving them a power tool that historically before automatic machine learning, driverless is in the umbrella of automatic machine learning, they would fine tune, learn 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 these trees are going to be, how many layers of deep learning they need, what particular variation in deep learning they should put and in a natural language processing, what context they need to their long-term, short-term memory, all these pieces they had to learn themselves. And there were only a few grandmasters 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, so you're simplifying that. You get the big brains to work on it and democratization means people can then participate and the machines also can learn, both humans and the machines. Between our open source and the very maker-centric culture, we've been able to attract one of the world's top data scientists, physicists and compiler engineers to bring in a form factor that businesses can use. And today, 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 conversation with Kent Libby who works with me and our platform team. We have all this data with theCUBE and we were just talking, we need to hire data scientists and AI specialists. And you go out and look around, you get Google and Amazon, all these big players spending between three to four million dollars per machine learning engineer. And that might be someone under the age of 30 and with no experience. 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 talent shortage in China, there's talent shortage in India, there's talent shortage in Europe and we have offices in Europe and India, the talent shortage in Toronto and Ottawa, right? So it's a global shortage of physicists and mathematicians and data scientists. So that's where our tools can help. And we see of that, we see driverless AI as a way you can drive to New York or you can fly to New York. I was talking to my son the other days taking computer science classes in AI school. I'm 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, does 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 AI possibility is. Machines have to be taught something. It'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've 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 where we brought AI to help with AI. Driverless AI is a concept that bringing AI to simplify it's an expert system to do AI better. So you can actually give it to the hands of a new data scientist. So you can perform at the power of a advanced data scientist. So we're not disempowering the data scientist. The product's still for a data scientist because he cannot, when you talk about the confusion matrix, false positives, false negatives, that's something a data scientist can understand. When you're talking 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 automate it on the latest hardware. That's where the time is coming into. GPUs, FPGAs, TPUs, different form of clouds cheaper. So faster, cheaper and easier. That's the democratization aspect. But it's really targeted to data scientists to prevent experimental error. In science, data science is a search for truth, but it's a lot of experiments to get to truth. And if you can make the cost of experiment really simple, cheaper, and prevent overfitting. That's a common problem in our science. Prevent bias, accidental bias that we introduced because the data is biased. So trying to kind of prevent the common false in doing data science leakage, usually your signal leaks and how do you prevent those common pieces. That's kind of 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 is courage. And you don't have to look for founders to look for courage and imagination. There are a lot of entrepreneurs in large companies who are trying to bring change to their organizations. You know, we always say that in actual property games changing from, you know, I got the protocols, this is locked in and patented. So 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 workflows, in the applications, new opportunities. Do you agree with that? Absolutely, the leapfrog from here is businesses will come up with new business processes. It's a, we looked at business optimization and globalization kind of help there. But AI, as you quite fully said earlier, is training computers, not just programming them. They're schooling a host of computers that can now with data think almost at the same level as a go player, right? 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 seven at the rate at which I can assemble machines and feed data. Data creation becomes, or making new data becomes the real value that AI can. H2O.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, recurring license, a software license, people can download on our website, get the three week trial, try it on their own. Free trial. Free trial, our recipes are open source, but 100 recipes built by grandmasters have been made open source and they can be plugged and tried and they can, 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 so we can, 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 of the largest customers. So my problem of hiring an AI person, you could help me solve that. Right today. Okay, so when I was watching, please get their stuff and come get a job opening here. That's the goal. But that's the dream, that is the dream. And we want AI in our system. I have watched you over the last 10 years. You've been an entrepreneur with a fierce passion. We 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 war laws we have and data privacy is 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, 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, H2O.AI. Here in theCUBE, I'm John Furrier with Breaking News here in Silicon Valley from Hot Startup H2O.AI. Thanks for watching. Thank you.