>> 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.