(gentle upbeat music) >> Okay, welcome back everyone to theCUBE's live coverage here in Boston, Massachusetts for AWS RE:INFORCE 22. I'm John Furrier, your host with Dave Vellante co-host of theCUBE, and Shreyans Metah, CTO and founder of Cequence Security. CUBE alumni, great to see you. Thanks for coming on theCUBE. >> Yeah. Thanks for having me here. >> So when we chatted you were part of the startup showcase. You guys are doing great. Congratulations on your business success. I mean, you guys got a good product in hot market. >> Yeah. >> You're here before we get into it. I want to get your perspective on the keynote and the talk tracks here and the show. But for the folks that don't know you guys, explain what you guys, take a minute to explain what you guys do and, and key product. >> Yeah, so we are the unified API protection place, but I mean a lot of people don't know what unified API protection is but before I get into that, just just talking about Cequence, we've been around since 2014. But we are protecting close to 6 billion API transactions every day. We are protecting close to 2 billion customer accounts, more than 2 trillion dollars in customer assets and a hundred million plus sort of, data points that we look at across customer base. That's that's who we are. >> I mean, of course we all know APIs is, is the basis of cloud computing and you got successful companies like Stripe, for instance, you know, you put API and you got a financial gateway, billions of transactions. What's the learnings. And now we're in a mode now where single point of failure is a problem. You got more automation you got more reasoning coming a lot more computer science next gen ML, AI there too. More connections, no perimeter. Right? More and more use cases, more in the cloud. >> Yeah. So what, what we are seeing today is, I mean from six years ago to now, when we started, right? Like the monolith apps are breaking down into microservices, right? What effectively, what that means is like every of the every such microservices talking APIs, right? So what used to be a few million web applications have now become billions of APIs that are communicating with each other. I mean, if you look at the, I mean, you spoke about IOT earlier, I call, I call like a Tesla is an application on four wheels that is communicating to its cloud over APIs. So everything is API yesterday. 80% traffic on internet is APIs. >> Now that's dated transit right there. (laughing) Couldn't resist. >> Yeah. >> Fully encrypted too. >> Yeah. >> Yeah, well hopefully. >> Maybe, maybe, maybe. (laughing) We dunno yet, but seriously everything is talking to an API. >> Yeah. >> Every application. >> Yeah. And, and there is no single choke point, right? Like you spoke about it. Like everybody is hosting their application in the cloud environments of their choice, AWS being one of them. But it's not the only one. Right? The, the, your APIs are hosted behind a CDN. Your APIs are hosted on behind an API gateway behind a load balancer in guest controllers. There is no single. >> So what's the problem? What's the problem now that you're solving? Because one was probably I can imagine connecting people, connecting the APIs. Now you've got more operational data. >> Yeah. >> Potential security hacks? More surface area? What's the what's what are you facing? >> Well, I can speak about some of the, our, some of the well known sort of exploits that have been well published, right. Everybody gets exploited, but I mean some of the well knowns. Now, if you, if you heard about Expedian last year there was a third party API that was exposing your your credit scores without proper authentication. Like Facebook had Ebola vulnerability sometime ago, where people could actually edit somebody else's videos online. Peloton again, a well known one. So like everybody is exposed, right. But that is the, the end results. All right? But it all starts with people don't even know where their APIs are and then you have to secure it all the way. So, I mean, ultimately APIs are prone to business logic attacks, fraud, and that's what, what you need to go ahead and protect. >> So is that the first question is, okay, what APIs do I need to protect? I got to take a API portfolio inventory. Is that? >> Yeah, so I think starting point is where. Where are my APIs? Right, so we spoke about there's no single choke point. Right, so APIs could be in, in your cloud environment APIs could be behind your cloud front, like we have here at RE:INFORCE today. So APIs could be behind your AKS, Ingrid controllers API gateways. And it's not limited to AWS alone, right. So, so knowing the unknown is, is the number one problem. >> So how do I find him? I asked Fred, Hey, where are our API? No, you must have some automated tooling to help me. >> Yeah, so, I, Cequence provides an option without any integration, what we call it, the API spider. Whereas like we give you visibility into your entire API attack surface without any integration into any of these services. Where are your APIs? What's your API attack surface about? And then sort of more details around that as well. But that is the number one. Is that agent list or is that an agent? >> There's no agent. So that means you can just sign up on our portal and then, then, then fire it away. And within a few minutes to an hour, we'll give you complete visibility into where your API is. >> So is it a full audit or is it more of a discovery? >> Or both? >> So, so number one, it's it's discovery, but we are also uncovering some of the potential vulnerabilities through zero knowledge. Right? So. (laughing) So, we've seen a ton of lock for J exposed server still. Like recently, there was an article that lock four J is going to be endemic. That is going to be here. >> Long time. >> (laughs) For, for a very long time. >> Where's your mask on that one? That's the Covid of security. >> Yeah. Absolutely absolutely. So, you need to know where your assets are what are they exposing? So, so that is the first step effectively discovering your attack surface. Yeah. >> I'm sure it's a efficiency issue too, with developers. The, having the spider allows you to at least see what's connecting out there versus having a meeting and going through code reviews. >> Yeah. Right? Is that's another big part of it? >> So, it is actually the last step, but you have, you actually go through a journey. So, so effectively, once you're discovering your assets you actually need to catalog it. Right. So, so I know where they're hosted but what are developers actually rolling out? Right. So they are updating your, the API endpoints on a daily basis, if not hourly basis. They have the CACD pipelines. >> It's DevOps. (laughing) >> Welcome to DevOps. It's actually why we'll do it. >> Yeah, and people have actually in the past created manual ways to catalog their APIs. And that doesn't really work in this new world. >> Humans are terrible at manual catalogization. >> Exactly. So, cataloging is really the next step for them. >> So you have tools for that that automate that using math, presumably. >> Exactly. And then we can, we can integrate with all these different choke points that we spoke about. There's no single choke points. So in any cloud or any on-prem environment where we actually integrate and give you that catalog of your APIs, that becomes your second step really. >> Yeah. >> Okay, so. >> What's the third step? There's the third step and then compliance. >> Compliance is the next one. So basically catalog >> There's four steps. >> Actually, six. So I'll go. >> Discovery, catalog, then compliance. >> Yeah. Compliance is the next one. So compliance is all about, okay, I've cataloged them but what are they really exposing? Right. So there could be PII information. There could be credit card, information, health information. So, I will treat every API differently based on the information that they're actually exposing. >> So that gives you a risk assessment essentially. >> Exactly. So you can, you can then start looking into, okay. I might have a few thousand API endpoints, like, where do I prioritize? So based on the risk exposure associated with it then I can start my journey of protecting so. >> That that's the remediation that's fixing it. >> Okay. Keep going. So that's, what's four. >> Four. That was that one, fixing. >> Yeah. >> Four is the risk assessment? >> So number four is detecting abuse. >> Okay. >> So now that I know my APIs and each API is exposing different business logic. So based on the business you are in, you might have login endpoints, you might have new account creation endpoint. You might have things around shopping, right? So pricing information, all exposed through APIs. So every business has a business logic that they end up exposing. And then the bad guys are abusing them. In terms of scraping pricing information it could be competitors scraping pricing. They will, we are doing account take. So detecting abuse is the first step, right? The fifth one is about preventing that because just getting visibility into abuse is not enough. I should be able to, to detect and prevent, natively on the platform. Because if you send signals to third party platforms like your labs, it's already too late and it's too course grain to be able to act on it. And the last step is around what you actually spoke about developers, right? Like, can I shift security towards the left, but it's not about shifting left. Just about shifting left. You obviously you want to bring in security to your CICD pipelines, to your developers, so that you have a full spectrum of API securities. >> Sure enough. Dave and I were talking earlier about like how cloud operations needs to look the same. >> Yeah. >> On cloud premise and edge. >> Yes. Absolutely. >> Edge is a wild card. Cause it's growing really fast. It's changing. How do you do that? Cuz this APIs will be everywhere. >> Yeah. >> How are you guys going to reign that in? What's the customers journey with you as they need to architect, not just deploy but how do you engage with the customer who says, "I have my environment. I'm not going to be to have somebody on premise and edge. I'll use some other clouds too. But I got to have an operating environment." >> Yeah. "That's pure cloud." >> So, we need, like you said, right, we live in a heterogeneous environment, right? Like effectively you have different, you have your edge in your CDN, your API gateways. So you need a unified view because every gateway will have a different protection place and you can't deal with 5 or 15 different tools across your various different environments. So you, what we provide is a unified view, number one and the unified way to protect those applications. So think of it like you have a data plane that is sprinkled around wherever your edges and gateways and risk controllers are and you have a central brains to actually manage it, in one place in a unified way. >> I have a computer science or computer architecture question for you guys. So Steven Schmidt again said single controls or binary states will fail. Obviously he's talking from a security standpoint but I remember the days where you wanted a single point of control for recovery, you talked about microservices. So what's the philosophy today from a recovery standpoint not necessarily security, but recovery like something goes wrong? >> Yeah. >> If I don't have a single point of control, how do I ensure consistency? So do I, do I recover at the microservice level? What's the philosophy today? >> Yeah. So the philosophy really is, and it's very much driven by your developers and how you want to roll out applications. So number one is applications will be more rapidly developed and rolled out than in the past. What that means is you have to empower your developers to use any cloud and serverless environments of their choice and it will be distributed. So there's not going to be a single choke point. What you want is an ability to integrate into that life cycle and centrally manage that. So there's not going to be a single choke point but there is going to be a single control plane to manage them off, right. >> Okay. >> So you want that unified, unified visibility and protection in place to be able to protect these. >> So there's your single point of control? What about the company? You're in series C you've raised, I think, over a hundred million dollars, right? So are you, where are you at? Are you scaling now? Are you hiring sales people or you still trying to sort of be careful about that? Can you help us understand where you're at? >> Yeah. So we are absolutely scaling. So, we've built a product that is getting, that is deployed already in all these different verticals like ranging from finance, to detail, to social, to telecom. Anybody who has exposure to the outside world, right. So product that can scale up to those demands, right? I mean, it's not easy to scale up to 6 billion requests a day. So we've built a solid platform. We've rolled out new products to complete the vision. In terms of the API spider, I spoke about earlier. >> The unified, >> The unified API protection covers three aspects or all aspects of API life cycle. We are scaling our teams from go to market motion. We brought in recently our chief marketing officer our chief revenue officer as well. >> So putting all the new, the new pieces in place. >> Yeah. >> So you guys are like API observability on steroids. In a way, right? >> Yeah, absolutely. >> Cause you're doing the observability. >> Yes. >> You're getting the data analysis for risk. You're having opportunities and recommendations around how to manage the stealthy attacks. >> From a full protection perspective. >> You're the API store. >> Yeah. >> So you guys are what we call best of breed. This is a trend we're seeing, pick something that you're best in breed in. >> Absolutely. >> And nail it. So you're not like an observability platform for everything. >> No. >> You guys pick the focus. >> Specifically, APS. And, so basically your, you can have your existing tools in place. You will have your CDN, you will have your graphs in place. So, but for API protection, you need something specialized and that stuff. >> Explain why I can't just rely on CDN infrastructure, for this. >> So, CDNs are, are good for content delivery. They do your basic TLS, and things like that. But APIs are all about your applications and business that you're exposing. >> Okay, so you, >> You have no context around that. >> So, yeah, cause this is, this is a super cloud vision that we're seeing of structural change in the industry, a new thing that's happening in real time. Companies like yours are be keeping a focus and nailing it. And now the customer's can assemble these services and company. >> Yeah. - Capabilities, that's happening. And it's happening like right now, structural change has happened. That's called the cloud. >> Yes. >> Cloud scale. Now this new change, best of brief, what are the gaps? Because I'm a customer. I got you for APIs, done. You take the complexity away at scale. I trust you. Where are the other gaps in my architecture? What's new? Cause I want to run cloud operations across all environments and across clouds when appropriate. >> Yeah. >> So I need to have a full op where are the other gaps? Where are the other best of breed components that need to be developed? >> So it's about layered, the layers that you built. Right? So, what's the thing is you're bringing in different cloud environments. That is your infrastructure, right? You, you, you either rely on the cloud provider for your security around that for roll outs and operations. Right? So then is going to be the next layer, which is about, is it serverless? Is it Kubernetes? What about it? So you'll think about like a service mesh type environment. Ultimately it's all about applications, right? That's, then you're going to roll out those applications. And that's where we actually come in. Wherever you're rolling out your applications. We come in baked into that environment, and for giving you that visibility and control, protection around that. >> Wow, great. First of all, APIs is the, is what cloud is based on. So can't go wrong there. It's not a, not a headwind for you guys. >> Absolutely. >> Great. What's a give a quick plug for the company. What are you guys looking to do hire? Get customers who's uh, when, what, what's the pitch? >> So like I started earlier, Cequence is around unified API protection, protecting around the full life cycle of your APIs, ranging from discovery all the way to, to testing. So, helping you throughout the, the life cycle of APIs, wherever those APIs are in any cloud environment. On-prem or in the cloud in your serverless environments. That's what Cequence is about. >> And you're doing billions of transactions. >> We're doing 6 billion requests every day. (laughing) >> Which is uh, which is, >> A lot. >> Unheard for a lot of companies here on the floor today. >> Sure is. Thanks for coming on theCUBE, sure appreciate it. >> Yeah. >> Good, congratulations to your success. >> Thank you. >> Cequence Security here on theCUBE at RE:INFORCE. I'm chatting with Dave Vellante, more coverage after this short break. (upbeat, gentle music)

(orchestral music) >> Hi, I'm Peter Burris and welcome to another digital community event. As we do with all digital community events, we're gonna start off by having a series of conversations with real thought leaders about a topic that's pressing to today's enterprises as they try to achieve new classes of business outcomes with technology. At the end of that series of conversations, we're gonna go into a crowd chat and give you an opportunity to voice your opinions and ask your questions. So stay with us throughout. So, what are we going to be talking about today? We're going to be talking about the challenge that businesses face as they try to apply AI, ML, and new classes of analytics to their very challenging, very difficult, but nonetheless very value-producing outcomes associated with data. The challenge that all these businesses have is that often, you spend too much time in the infrastructure and not enough time solving the problem. And so what's required is new classes of technology and new classes of partnerships and business arrangements that allow for us to mask the underlying infrastructure complexity from data science practitioners, so that they can focus more time and attention on building out the outcomes that the business wants and a sustained business capability so that we can continue to do so. Once again, at the end of this series of conversations, stay with us, so that we can have that crowd chat and you can, again, ask your questions, provide your insights, and participate with the community to help all of us move faster in this crucial direction for better AI, better ML and better analytics. So, the first conversation we're going to have is with Anant Chintamaneni. Anant's the Vice President of Products at BlueData. Anant, welcome to theCUBE. >> Hi Peter, it's great to be here. I think the topic that you just outlined is a very fascinating and interesting one. Over the last 10 years, data and analytics have been used to create transformative experiences and drive a lot of business growth. You look at companies like Uber, AirBnB, and you know, Spotify, practically, every industry's being disrupted. And the reason why they're able to do this is because data is in their DNA; it's their key asset and they've leveraged it in every aspect of their product development to deliver amazing experiences and drive business growth. And the reason why they're able to do this is they've been able to leverage open-source technologies, data science techniques, and big data, fast data, all types of data to extract that business value and inject analytics into every part of their business process. Enterprises of all sizes want to take advantage of that same assets that the new digital companies are taking and drive digital transformation and innovation, in their organizations. But there's a number of challenges. First and foremost, if you look at the enterprises where data was not necessarily in their DNA and to inject that into their DNA, it is a big challenge. The executives, the executive branch, definitely wants to understand where they want to apply AI, how to kind of identify which huge cases to go after. There is some recognition coming in. They want faster time-to-value and they're willing to invest in that. >> And they want to focus more on the actual outcomes they seek as opposed to the technology selection that's required to achieve those outcomes. >> Absolutely. I think it's, you know, a boardroom mandate for them to drive new business outcomes, new business models, but I think there is still some level of misalignment between the executive branch and the data worker community which they're trying to upgrade with the new-age data scientists, the AI developer and then you have IT in the middle who has to basically bridge the gap and enable the digital transformation journey and provide the infrastructure, provide the capabilities. >> So we've got a situation where people readily acknowledge the potential of some of these new AI, ML, big data related technologies, but we've got a mismatch between the executives that are trying to do evidence-based management, drive new models, the IT organization who's struggling to deal with data-first technologies, and data scientists who are few and far between, and leave quickly if they don't get the tooling that they need. So, what's the way forward, that's the problem. How do we move forward? >> Yeah, so I think, you know, I think we have to double-click into some of the problems. So the data scientists, they want to build a tool chain that leverages the best in-class, open source technologies to solve the problem at hand and they don't want, they want to be able to compile these tool chains, they want to be able to apply and create new algorithms and operationalize and do it in a very iterative cycle. It's a continuous development, continuous improvement process which is at odds with what IT can deliver, which is they have to deliver data that is dispersed all over the place to these data scientists. They need to be able to provide infrastructure, which today, they're not, there's an impotence mismatch. It takes them months, if not years, to be able to make those available, make that infrastructure available. And last but not the least, security and control. It's just fundamentally not the way they've worked where they can make data and new tool chains available very quickly to the data scientists. And the executives, it's all about faster time-to-value so there's a little bit of an expectation mismatch as well there and so those are some of the fundamental problems. There's also reproducibility, like, once you've created an analytics model, to be able to reproduce that at scale, to be then able to govern that and make sure that it's producing the right results is fundamentally a challenge. >> Audibility of that process. >> Absolutely, audibility. And, in general, being able to apply this sort of model for many different business problems so you can drive outcomes in different parts of your business. So there's a huge number of problems here. And so what I believe, and what we've seen with some of these larger companies, the new digital companies that are driving business valley ways, they have invested in a unified platform where they've made the infrastructure invisible by leveraging cloud technologies or containers and essentially, made it such that the data scientists don't have to worry about the infrastructure, they can be a lot more agile, they can quickly create the tool chains that work for the specific business problem at hand, scale it up and down as needed, be able to access data where it lies, whether it's on-prem, whether it's in the cloud or whether it's a hybrid model. And so that's something that's required from a unified platform where you can do your rapid prototyping, you can do your development and ultimately, the business outcome and the value comes when you operationalize it and inject it into your business processes. So, I think fundamentally, this start, this kind of a unified platform, is critical. Which, I think, a lot of the new age companies have, but is missing with a lot of the enterprises. >> So, a big challenge for the enterprise over the next few years is to bring these three groups together; the business, data science world and infrastructure world or others to help with those problems and apply it successfully to some of the new business challenges that we have. >> Yeah, and I would add one last point is that we are on this continuous journey, as I mentioned, this is a world of open source technologies that are coming out from a lot of the large organizations out there. Whether it's your Googles and your Facebooks. And so there is an evolution in these technologies much like we've evolved from big data and data management to capture the data. The next sort of phase is around data exploitation with artificial intelligence and machine learning type techniques. And so, it's extremely important that this platform enables these organizations to future proof themselves. So as new technologies come in, they can leverage them >> Great point. >> for delivering exponential business value. >> Deliver value now, but show a path to delivery value in the future as all of these technologies and practices evolve. >> Absolutely. >> Excellent, all right, Anant Chintamaneni, thanks very much for giving us some insight into the nature of the problems that enterprises face and some of the way forward. We're gonna be right back, and we're gonna talk about how to actually do this in a second. (light techno music) >> Introducing, BlueData EPIC. The leading container-based software platform for distributed AI, machine learning, deep learning and analytics environments. Whether on-prem, in the cloud or in a hybrid model. Data scientists need to build models utilizing various stacks of AI, ML and DL applications and libraries. However, installing and validating these environments is time consuming and prone to errors. BlueData provides the ability to spin up these environments on demand. The BlueData EPIC app store includes, best of breed, ready to run docker based application images. Like TensorFlow and H2O driverless AI. Teams can also add their own images, to provide the latest tools that data scientists prefer. And ensure compliance with enterprise standards. They can use the quick launch button. which provides pre configured templates with the appropriate application image and resources. For example, they can instantly launch a new Sandbox environment using the template for TensorFlow with a Jupyter Notebook. Within just a few minutes, it'll be automatically configured with GPUs and easy access to their data. Users can launch experiments and make GPUs automatically available for analysis. In this case, the H2O environment was set up with one GPU. With BlueData EPIC, users can also deploy end points with the appropriate run time. And the inference run times can use CPUs or GPUs. With a container based BlueData Platform, you can deploy fully configured distributed environments within a matter of minutes. Whether on-prem, in the public cloud, or in a hybrid a architecture. BlueData was recently acquired by Hewlett Packward Enterprise. And now, HPE and BlueData are joining forces to help you on your AI journey. (light techno music) To learn more, visit www.BlueData.com >> And we're back. I'm Peter Burris and we're continuing to have this conversation about how businesses are turning experience with the problems of advance analytics and the solutions that they seek into actual systems that deliver continuous on going value and achieve the business capabilities required to make possible these advanced outcomes associated with analytics, AI and ML. And to do that, we've got two great guests with us. We've got Kumar Sreekanti, who is the co-founder and CEO of BlueData. Kumar, welcome back to theCUBE. >> Thank you, it is nice to be here, back again. >> And Kumar, you're being joined by a customer. Ramesh Thyagarajan, is the executive director of the Advisory Board Company which is part of Optum now. Ramesh, welcome to theCUBE. >> Great to be here. >> Alright, so Kumar let's start with you. I mentioned up front, this notion of turning technology and understanding into actual business capabilities to deliver outcomes. What has been BlueData's journey along, to make that happen? >> Yeah, it all started six years ago, Peter. It was a bold vision and a big idea and no pun intended on big data which was an emerging market then. And as everybody knows, the data was enormous and there was a lot of innovation around the periphery. but nobody was paying attention to how to make the big data consumable in enterprise. And I saw an enormous opportunity to make this data more consumable in the enterprise and to give a cloud-like experience with the agility and elasticity. So, our vision was to build a software infrastructure platform like VMware, specially focused on data intensity distributed applications and this platform will allow enterprises to build cloud like experiences both on enterprise as well as on hybrid clouds. So that it pays the journey for their cloud experience. So I was very fortunate to put together a team and I found good partners like Intel. So that actually is the genesis for the BlueData. So, if you look back into the last six years, big data itself has went through a lot of evolution and so the marketplace and the enterprises have gone from offline analytics to AI, ML based work loads that are actually giving them predictive and descriptive analytics. What BlueData has done is by making the infrastructure invisible, by making the tool set completely available as the tool set itself is evolving and in the process, we actually created so many game changing software technologies. For example, we are the first end-to-end content-arised enterprise solution that gives you distributed applications. And we built a technology called DataTap, that provides computed data operation so that you don't have to actually copy the data, which is a boom for enterprises. We also actually built multitenancy so those enterprises can run multiple work loads on the same data and Ramesh will tell you in a second here, in the healthcare enterprise, the multitenancy is such a very important element. And finally, we also actually contributed to many open source technologies including, we have a project called KubeDirector which is actually is our own Kubernetes and how to run stateful workloads on Kubernetes. which we have actually very happy to see that people like, customers like Ramesh are using the BlueData. >> Sounds like quite a journey and obviously you've intercepted companies like the advisory board company. So Ramesh, a lot of enterprises have mastered or you know, gotten, understood how to create data lakes with a dupe but then found that they still weren't able to connect to some of the outcomes that they saw. Is that the experience that you had. >> Right, to be precise, that is one of the kind of problems we have. It's not just the data lake that we need to be able to do the workflows or other things, but we also, being a traditional company, being in the business for a long time, we have a lot of data assets that are not part of this data lake. We're finding it hard to, how do we get the data, getting them and putting them in a data lake is a duplication of work. We were looking for some kind of solutions that will help us to gather the benefits of leaving the data alone but still be able to get into it. >> This is where (mumbles). >> This is where we were looking for things and then I was lucky and fortunate to run into Kumar and his crew in one of the Hadoop conferences and then they demonstrated the way it can be done so immediately hit upon, it's a big hit with us and then we went back and then did a POC, very quickly adapt to the technology and that is also one of the benefits of corrupting this technology is the level of contrary memorization they are doing, it is helping me to address many needs. My data analyst, the data engineers and the data scientists so I'm able to serve all of them which otherwise wouldn't be possible for me with just this plain very (mumbles). >> So it sounds as though the partnership with BlueData has allowed you to focus on activities and problems and challenges above the technology so that you can actually start bringing data science, business objectives and infrastructure people together. Have I got that right? >> Absolutely. So BlueData is helping me to tie them all together and provide an excess value to my business. We being in the healthcare, the importance is we need to be able to look at the large data sets for a period of time in order to figure out how a patient's health journey is happening. That is very important so that we can figure out the ways and means in which we can lower the cost of health care and also provide insights to the physician, they can help get people better at health. >> So we're getting great outcomes today especially around, as you said that patient journey where all the constituents can get access to those insights without necessarily having to learn a whole bunch of new infrastructure stuff but presumably you need more. We're talking about a new world that you mentioned before upfront, talking about a new world, AI, ML, a lot of changes. A lot of our enterprise customers are telling us it's especially important that they find companies that not only deliver something today but demonstrate a commitment to sustain that value delivery process especially as the whole analytics world evolves. Are you experiencing that as well? >> Yes, we are experiencing and one of the great advantage of the platform, BlueData platform that gave me this ability to, I had the new functionality, be it the TensorFlow, be it the H2O, be it the heart studio, anything that I needed, I call them, they give me the images that are plug-and-play, just put them and all the prompting is practically transparent to nobody need to know how it is achieved. Now, in order to get to the next level of the predictive and prescriptive analytics, it is not just you having the data, you need to be able to have your curated data asset set process on top of a platform that will help you to get the data scientists to make you. One of the biggest challenges that are scientist is not able to get their hands on data. BlueData platform gives me the ability to do it and ensure all the security meets and all the compliances with the various other regulated compliances we need to make. >> Kamar, congratulations. >> Thank you. >> Sounds like you have a happy customer. >> Thank you. >> One of the challenges that every entrepreneur faces is how did you scale the business. So talk to us about where you are in the decisions that you made recently to achieve that. >> As an entrepreneur, when you start a company, odds are against you, right? You're always worried about it, right. You make so many sacrifices, yourself and your team and all that but the the customer is the king. The most important thing for us to find satisfied customers like Rameshan so we were very happy and BlueData was very successful in finding that customer because i think as you pointed out, as Ramesh pointed out, we provide that clean solution for the customer but as you go through this journey as a co-founder and CEO, you always worry about how do you scale to the next level. So we had partnerships with many companies including HPE and we found when this opportunity came in front of me with myself and my board, we saw this opportunity of combining the forces of BlueData satisfied customers and innovative technology and the team with the HPs brand name, their world-class service, their investment in R&D and they have a very long, large list of enterprise customers. We think putting these two things together provides that next journey in the BlueData's innovation and BlueData's customers. >> Excellent, so once again Kumar Sreekanti, co-founder and CEO of BlueData and Ramesh Thyagarajan who is the executive director of the advisory board company and part of Optum, I want to thank both of you for being on theCUBE. >> Thank you >> Thank you, great to be here. >> Now let's hear a little bit more about how this notion of bringing BlueData and HPE together is generating new classes of value that are making things happen today but are also gonna make things happen for customers in the future and to do that we've got Dave Velante who's with Silicon Angle Wiki Bond joined by Patrick Osbourne who's with HPE in our Marlborough studio so Dave over to you. >> Thanks Peter. We're here with Patrick Osbourne, the vice president and general manager of big data and analytics at Hewlett Packard Enterprise. Patrick, thanks for coming on. >> Thanks for having us. >> So we heard from Kumar, let's hear from you. Why did HPE purchase, acquire BlueData? >> So if you think about it from three angles. Platform, people and customers, right. Great platform, built for scale addressing a number of these new workloads and big data analytics and certainly AI, the people that they have are amazing, right, great engineering team, awesome customer success team, team of data scientists, right. So you know, all the folks that have some really, really great knowledge in this space so they're gonna be a great addition to HPE and also on the customer side, great logos, major fortune five customers in the financial services vertical, healthcare, pharma, manufacturing so a huge opportunity for us to scale that within HP context. >> Okay, so talk about how it fits into your strategy, specifically what are you gonna do with it? What are the priorities, can you share some roadmap? >> Yeah, so you take a look at HPE strategy. We talk about hybrid cloud and specifically edge to core to cloud and the common theme that runs through that is data, data-driven enterprises. So for us we see BlueData, Epic platform as a way to you know, help our customers quickly deploy these new mode to applications that are fueling their digital transformation. So we have some great plans. We're gonna certainly invest in all the functions, right. So we're gonna do a force multiplier on not only on product engineering and product delivery but also go to market and customer success. We're gonna come out in our business day one with some really good reference architectures, with some of our partners like Cloud Era, H2O, we've got some very scalable building block architectures to marry up the BlueData platform with our Apollo systems for those of you have seen that in the market, we've got our Elastic platform for analytics for customers who run these workloads, now you'd be able to virtualize those in containers and we'll have you know, we're gonna be building out a big services practice in this area. So a lot of customers often talk to us about, we don't have the people to do this, right. So we're gonna bring those people to you as HPE through Point Next, advisory services, implementation, ongoing help with customers. So it's going to be a really fantastic start. >> Apollo, as you mentioned Apollo. I think of Apollo sometimes as HPC high performance computing and we've had a lot of discussion about how that's sort of seeping in to mainstream, is that what you're seeing? >> Yeah absolutely, I mean we know that a lot of our customers have traditional workloads, you know, they're on the path to almost completely virtualizing those, right, but where a lot of the innovation is going on right now is in this mode two world, right. So your big data and analytics pipeline is getting longer, you're introducing new experiences on top of your product and that's fueling you know, essentially commercial HPC and now that folks are using techniques like AI and modeling inference to make those services more scalable, more automated, we're starting to bringing these more of these platforms, these scalable architectures like Apollo. >> So it sounds like your roadmap has a lot of integration plans across the HPE portfolio. We certainly saw that with Nimble, but BlueData was working with a lot of different companies, its software, is the plan to remain open or is this an HPE thing? >> Yeah, we absolutely want to be open. So we know that we have lots of customers that choose, so the HP is all about hybrid cloud, right and that has a couple different implications. We want to talk about your choice of on-prem versus off-prem so BlueData has a great capability to run some of these workloads. It essentially allows you to do separation of compute and storage, right in the world of AI and analytics we can run it off-prem as well in the public cloud but then we also have choice for customers, you know, any customer's private cloud. So that means they want to run on other infrastructure besides HPE, we're gonna support that, we have existing customers that do that. We're also gonna provide infrastructure that marries the software and the hardware together with frameworks like Info Site that we feel will be a you know, much better experience for the customers but we'll absolutely be open and absolutely have choice. >> All right, what about the business impact to take the customer perspective, what can they expect? >> So I think from a customer perspective, we're really just looking to accelerate deployment of AI in the enterprise, right and that has a lot of implications for us. We're gonna have very scalable infrastructure for them, we're gonna be really focused on this very dynamic AI and ML application ecosystems through partnerships and support within the BlueData platform. We want to provide a SAS experience, right. So whether that's GPUs or accelerators as a service, analytics as a service, we really want to fuel innovation as a service. We want to empower those data scientists there, those are they're really hard to find you know, they're really hard to retain within your organization so we want to unlock all that capability and really just we want to focus on innovation of the customers. >> Yeah, and they spend a lot of time wrangling data so you're really going to simplify that with the cloud (mumbles). Patrick thank you, I appreciate it. >> Thank you very much. >> Alright Peter, back to you in Palo Alto. >> And welcome back, I'm Peter Burris and we've been talking a lot in the industry about how new tooling, new processes can achieve new classes of analytics, AI and ML outcomes within a business but if you don't get the people side of that right, you're not going to achieve the full range of benefits that you might get out of your investments. Now to talk a little bit about how important the data science practitioner is in this equation, we've got two great guests with us. Nanda Vijaydev is the chief data scientists of BlueData. Welcome to theCUBE. >> Thank you Peter, happy to be here. >> Ingrid Burton is the CMO and business leader at H2O.AI, Ingrid, welcome to the CUBE. >> Thank you so much for having us. >> So Nanda Vijaydev, let's start with you. Again, having a nice platform, very, very important but how does that turn into making the data science practitioner's life easier so they can deliver more business value. >> Yeah thank you, it's a great question. I think end of the day for a data scientist, what's most important is, did you understand the question that somebody asked you and what is expected of you when you deliver something and then you go about finding, what do I need for them, I need data, I need systems and you know, I need to work with people, the experts in the process to make sure that the hypothesis I'm doing is structured in a nice way where it is testable, it's modular and I have you know, a way for them to go back to show my results and keep doing this in an iterative manner. That's the biggest thing because the satisfaction for a data scientist is when you actually take this and make use of it, put it in production, right. To make this whole thing easier, we definitely need some way of bringing it all together. That's really where, especially compared to the traditional data science where everything was monolithic, it was one system, there was a very set way of doing things but now it is not so you know, with the growing types of data, with the growing types of computation algorithms that's available, there's a lot of opportunity and at the same time there is a lot of uncertainty. So it's really about putting that structure and it's really making sure you get the best of everything and still deliver the results, that is the focus that all data scientists strive for. >> And especially you wanted, the data scientists wants to operate in the world of uncertainty related to the business question and reducing uncertainty and not deal with the underlying some uncertainty associated with the infrastructure. >> Absolutely, absolutely you know, as a data scientist a lot of time used to spend in the past about where is the data, then the question was, what data do you want and give it to you because the data always came in a nice structured, row-column format, it had already lost a lot of context of what we had to look for. So it is really not about you know, getting the you know, it's really not about going back to systems that are pre-built or pre-processed, it's getting access to that real, raw data. It's getting access to the information as it came so you can actually make the best judgment of how to go forward with it. >> So you describe the world with business, technology and data science practitioners are working together but let's face it, there's an enormous amount of change in the industry and quite frankly, a deficit of expertise and I think that requires new types of partnerships, new types of collaboration, a real (mumbles) approach and Ingrid, I want to talk about what H2O.AI is doing as a partner of BlueData, HPE to ensure that you're complementing these skills in pursuit or in service to the customer's objectives. >> Absolutely, thank you for that. So as Nanda described, you know, data scientists want to get to answers and what we do at H2O.AI is we provide the algorithms, the platforms for data scientist to be successful. So when they want to try and solve a problem, they need to work with their business leaders, they need to work with IT and they actually don't want to do all the heavy lifting, they want to solve that problem. So what we do is we do automatic machine learning platforms, we do that with optimizing algorithms and doing all the kind of, a lot of the heavy lifting that novice data scientists need and help expert data scientists as well. I talk about it as algorithms to answers and actually solving business problems with predictions and that's what machine learning is really all about but really what we're seeing in the industry right now and BlueData is a great example of kind of taking away some of the hard stuff away from a data scientist and making them successful. So working with BlueData and HPE, making us together really solve the problems that businesses are looking for, it's really transformative and we've been through like the digital transformation journey, all of us have been through that. We are now what I would term an AI transformation of sorts and businesses are going to the next step. They had their data, they got their data, infrastructure is kind of seamlessly working together, the clusters and containerization that's very important. Now what we're trying to do is get to the answers and using automatic machine learning platforms is probably the best way forward. >> That's still hard stuff but we're trying to get rid of data science practitioners, focusing on hard stuff that doesn't directly deliver value. >> It doesn't deliver anything for them, right. They shouldn't have to worry about the infrastructure, they should worry about getting the answers to the business problems they've been asked to solve. >> So let's talk a little bit about some of the new business problems that are going to be able to be solved by these kinds of partnerships between BlueData and H2O.AI. Start, Nanda, what do you, what gets you excited when we think about the new types of business problems that customers are gonna be able to solve. >> Yeah, I think it is really you know, the question that comes to you is not filtered through someone else's lens, right. Someone is trying an optimization problem, someone is trying to do a new product discovery so all this is based on a combination of both data-driven and evidence-based, right. For us as a data scientist, what excites me is that I have the flexibility now that I can choose the best of the breed technologies. I should not be restricted to what is given to me by an IT organization or something like that but at the same time, in an organization, for things to work, there has to be some level of control. So it is really having this type of environments or having some platforms where some, there is a team that can work on the control aspect but as a data scientist, I don't have to worry about it. I have my flexibility of tools of choice that I can use. At the same time, when you talk about data, security is a big deal in companies and a lot of times data scientists don't get access to data because of the layers and layers of security that they have to go through, right. So the excitement of the opportunity for me is if someone else takes care of the problem you know, just tell me where is the source of data that I can go to, don't filter the data for me you know, don't already structure the data for me but just tell me it's an approved source, right then it gives me more flexibility to actually go and take that information and build. So the having those controls taken care of well before I get into the picture as a data scientist, it makes it extremely easy for us to focus on you know, to her point, focus on the problem, right, focus on accessing the best of the breed technology and you know, give back and have that interaction with the business users on an ongoing basis. >> So especially focus on, so speed to value so that you're not messing around with a bunch of underlying infrastructure, governance remaining in place so that you know what are the appropriate limits of using the data with security that is embedded within that entire model without removing fidelity out of the quality of data. >> Absolutely. >> Would you agree with those? >> I totally agree with all the points that she brought up and we have joint customers in the market today, they're solving very complex problems. We have customers in financial services, joint customers there. We have customers in healthcare that are really trying to solve today's business problems and these are everything from, how do I give new credit to somebody? How do I know what next product to give them? How do I know what customer recommendations can I make next? Why did that customer churn? How do I reach new people? How do I do drug discovery? How do I give a patient a better prescription? How do I pinpoint disease than when I couldn't have seen it before? Now we have all that data that's available and it's very rich and data is a team sport. It takes data scientists, it takes business leaders and it takes IT to make it all work together and together the two companies are really working to solve problems that our customers are facing, working with our customers because they have the intellectual knowledge of what their problems are. We are providing the tools to help them solve those problems. >> Fantastic conversation about what is necessary to ensure that the data science practitioner remains at the center and is the ultimate test of whether or not these systems and these capabilities are working for business. Nanda Vijaydev, chief data scientist of BlueData, Ingrid Burton CMO and business leader, H2O.AI, thank you very much for being on theCUBE. >> Thank you. >> Thank you so much. >> So let's now spend some time talking about how ultimately, all of this comes together and what you're going to do as you participate in the crowd chat. To do that let me throw it back to Dave Velante in our Marlborough studios. >> We're back with Patrick Osbourne, alright Patrick, let's wrap up here and summarize. We heard how you're gonna help data science teams, right. >> Yup, speed, agility, time to value. >> Alright and I know a bunch of folks at BlueData, the engineering team is very, very strong so you picked up a good asset there. >> Yeah, it means amazing technology, the founders have a long lineage of software development and adoption in the market so we're just gonna, we're gonna invested them and let them loose. >> And then we heard they're sort of better together story from you, you got a roadmap, you're making some investments here, as I heard. >> Yeah, I mean so if we're really focused on hybrid cloud and we want to have all these as a services experience, whether it's through Green Lake or providing innovation, AI, GPUs as a service is something that we're gonna be you know, continuing to provide our customers as we move along. >> Okay and then we heard the data science angle and the data science community and the partner angle, that's exciting. >> Yeah, I mean, I think it's two approaches as well too. We have data scientists, right. So we're gonna bring that capability to bear whether it's through the product experience or through a professional services organization and then number two, you know, this is a very dynamic ecosystem from an application standpoint. There's commercial applications, there's certainly open source and we're gonna bring a fully vetted, full stack experience for our customers that they can feel confident in this you know, it's a very dynamic space. >> Excellent, well thank you very much. >> Thank you. Alright, now it's your turn. Go into the crowd chat and start talking. Ask questions, we're gonna have polls, we've got experts in there so let's crouch chat.

>> Hello, everyone. Welcome to the special CUBE conversation. I'm John Furrier, the co-founder of SiliconANGLE Media, co-host of theCUBE. We're here at Sundance Film Festival, the Intel Tech Lounge for a one on one conversation with Kane Lee, who's the head of content at Baobab Studios in California. Thanks for joining me here at the Intel Tech Lounge. >> Really excited to be here. >> You know we just had a panel on the new creative here, and Intel is showing some great technology. Things like volumetric, all kinds of really hardcore tech. Really powering some of the VR, AR, mixed reality, all the trends that are happening around user experience. But, a new creative artist is out there. A new storyteller. It could be a 12 year old to a 50 year old. You're in the middle of it. You're an award winning producer. So you're building the stories, you're building the content. What's the biggest thing happening here at Sundance? >> I think it's really interesting, because content has always been my passion. Good storytelling. And growing up, it was always books and films, and all these traditional mediums that inspired me to sort of dream, and right here in Sundance, we're in the middle of a great sea change going on, because technology and art are coming together in such a fast pace, to really usher in the new generation of storytelling, and we're all very fortunate to be in the middle of that. This is a very unique period in our history as humans, and our culture, to challenge what storytelling really means, because VR, for us at Baobab, is the next great medium. And Sundance recognizes that. Technology companies like Intel recognize that. So we're all coming together at the film festival, and working together to define what that will mean. >> Kane, you're an Emmy award winning producer. Baobab's doing some cutting edge work. Take a minute to talk about what Baobab is doing, and why is it so relevant? We know it's cool. We've interviewed the CEO and Founder before. Share with the audience, what is Baobab doing? Why is it so relevant? >> So, we formed a couple years ago, and at the time, VR was, and it still is, in its very nascent stage. One thing that we recognized, was an opportunity to try to create content that would appeal for people from the ages of five to 105. There was a lot of documentaries, there was a lot of experiential art house type of material. And there was a lot of gaming type of content for VR. For us, we're big lovers of animation and how that unites families, kids, grandparents, teenagers, and we saw an opportunity to try to create content that could appeal to all of these different types of people through animation. So that's sort of our mission, is to inspire your childlike sense of wonder, using two mediums that are so meant for each other, which are animation and VR. >> I'd like to talk about some of the work you got going on a little bit later, but I want to talk about that 12 year old in his room, or the 16 year old that's got a full rig, tricked out with the keyboard, they're laying down music, they're building music, they're gaming, they might be creating art. They are a living, breathing creative. And, they're self learning. They're jumping on Youtube. They're jumping into VR meetups and groups. They're self learning. >> Kane: Absolutely. >> How do you connect to them? What do they do? What's the playbook? How do these people go to the next level? What's the industry doing around this? >> I think, one example I'll give is, I was at Annecy Film Festival, and that's one of the biggest animation focused film festivals in the world, and I was showcasing our very first piece, it was called Invasion, starring Ethan Hawke, where you're actually in the body of a bunny rabbit, and you meet another bunny rabbit. You create a bond. And together you thwart an alien invasion on Earth. What was so interesting to me, was I had never seen that sort of, that demo, that teenage demo, where young boys and girls would actually bring their parents back to the experience, and say this is what I want to study in college. This is what I want to do in art school. So, I think that they, growing up with all this new technology, really sort of get the idea of being in realtime, and having storytelling in realtime. And seeing that level of interest from that age group was very sort of affirming to us that we're on the right track, in terms of the next generation of storytelling. >> Well you guys are definitely on the right track, I can say that. But I think what your point confirms, and connects the dots for people that might not be in the industry is that the old tech world was, the geeks did it, software was an art and you had to be in that CS club. The democratization is a big trend here, and what you're talking about is, people are humanizing, they can see real emotional, practical examples. So the young guns, the young kids, they don't have baggage. They look at it with a clean slate and going, I want that. I can see myself using this. I can self actualize with this. So really kind of tips the scales, and proves the point. >> Absolutely. We world premiered Asteroids, our second VR experience, starring Elizabeth Banks, and one of the biggest millennial stars, Ingrid Nilson, last year at Sundance. Even had the first red carpet VR premiere in Sundance history. And watching the younger generation, it was our first piece where we actually used the controllers that had just come out in that past year. And watching them go in with no preconceived notions on what using controllers could be, to be a character in the experience, it was just fascinating, because they picked it up faster than anyone, and learned the language of being a character, and having hand controllers as a robot, so you could play fetch with an alien dog, or you could mirror their actions, or they might mirror yours, and creating these bonds and these experiences. So, that sort of fresh perspective is really exciting. >> Talk about the role of these experiences, and how they connect people, because one of the big trends also online today, in today's, I would say, yeah the peg the evolution is, you're really getting into the immersive experience, I believe that. But, content creates bonds between people, and good experiences creates glue between relationships, and forges new ones, maybe enhances existing ones. This is a big part of the media. >> Absolutely. For us, emotional connection is the key to getting people to put on headsets, and to come back to our experiences. And that emotional connection for us, is what we've witnessed, in terms of people forming bonds with our characters. So, everyone knows that VR can bring you to brand new worlds, and exciting places, and immerse you in places that you can never go. But, the one thing that I think we learned in our experience with VR, is that if you can create a bond between the user and other characters in the experience that they believe is real, and we use psychology, technology, and storytelling to do that, then they want to come back again and again. So, one of the trickiest parts of VR is trying to get people to have repeat views. And the feedback we've gotten from a lot of the technology platforms is people come back time and time again, and it seems to be because they actually believe these characters are real, and that they're friends. >> So talk about your journey, because you're at the front end of this wave, and you're participating, you're creating art, you're creating work product. You're building technology with the Baobab Studios. What would you do if you were 16? If you were a sophomore in high school, knowing what you know, and you could go back in time, or you could be today what you know at 16, what would you do? >> When I was 16, I had no idea what I was going to do. When I graduated from college, I had no idea what I was going to do. But what I will say is, VR is really unique because it's so interdisciplinary. So, it actually invites people from all different fabrics of society, and different types of education. The most, I would encourage 16 year olds to just be who they are, and to play. And if I talked to my 16 year old self, I would have just encouraged myself to follow my interest and pursuits more, because many years later, actually VR has brought me back to a lot of my roots, and different things that I studied growing up, and was fascinated by. >> So it ignited your passion. >> Absolutely. >> Or things that you were really into, that you might have forgotten. Is that- >> Yeah, I studied something called symbolic systems at Stanford University, and I had no idea what I was doing. It combined computer science, psychology, linguistics, and philosophy. And the first thing I did after college was pursue potentially a career as a lawyer. But now it all makes sense. VR makes, brings everything together. >> What could have been, you know? >> Absolutely. >> Well, a lot of neural network, symbolic systems, this is the underpinnings of this complex fabric that is powering this content market. So I'd love to get your thoughts. Is there a success formula that you're seeing emerging, I know there's no silver bullet yet. A lot of experimentation. A lot of new things happening. But as this technology, and the scaffolding around it is being built, while also original content is being built, it's still evolving. What's the success formula, and what's the pitfall? What to stay away from? >> I think it's about, it's really about good storytelling. And I think it's a time to be courageous and brave, and put forward stories that wouldn't have otherwise been told in the more traditional mediums. Our latest project in production that I'm so personally excited about, is called Legend of Crow. It stars John Legend as a beautiful bird with the most beautiful feathers, and the most gorgeous voice, who during dark and cold times, must go on a heroes journey to bring light back to the world. Something I feel like in this day and age, a lot of people can relate to. But, on top of this story being based upon a beautiful Native American legend that hasn't really been exposed to the world, we've taken the opportunity to take the themes of diversity and self sacrifice, and self acceptance, to create an all star cast of minorities and women, and that's something I feel the younger generations can really relate to, because having worked a lot in Hollywood as a producer in traditional TV and film, things take a while, and there's a certain way of casting and doing things that follow an older model, and I think younger audiences are excited to have a character like Moth in our experience who speaks both Spanish and English, because that's the way the world is today. >> So I got to ask you a quick, you brought up diversity and inclusion kind of in your comment. I got to bring this up, because you guys do hit a nice demographic that I think is super relevant and important, the younger generation. So I talk to a lot of young people all the time. I say things like, you don't need to be a computer scientist to get into this game. You can be super smart. You don't need to learn how to code hardcore coding to get into this. And they respond to that. And that's one kind of, I would say, narrative that conventional wisdom might not be right. And the other one is the diversity. So my son, 16 year old, says, "Dad, your generation is so politically correct. All this nonsense." So, the younger generation is not living what we're living in, in these dark times, I would say, certainly with diversity, but how does VR really equalize? And will the storm pass? Diversity, inclusion, all that great stuff that are core issues, certainly are being worked on. But, do we see hope here? >> Absolutely. I think disruption in the form of a new technology and a new medium is, while scary to some people, is actually the most exciting and fertile time to equalize. Our CEO, Maureen Fan, who is a college classmate of mine, always wanted to work in animation. And she finally saw the right opportunity when VR came, and we put on headsets for the first time, and saw how there could be a new wave of exciting animators, through this disruptive technology. Because everyone else in more traditional animation is so focused on the old model, and the old ways of doing things, of getting things off the ground, of financing, of creating certain kinds of content that have been proven over time, in the old sort of studio model. >> What were some of those things that were instrumental in this breakout, to forge this new ground? >> I think a lot of it is the technology finally being ready. Our CTO, Larry Cutler, actually studied virtual reality at Stanford a decade before Maureen and I were there, and he had always been waiting for the right time to go into VR. >> Does he preach down, hey kids, I used to walk in the snow with bare feet to you guys, or has he, what's his role, how's he doing? >> He's amazing. He was the head of global character tech for all of Dreamworks animation, and like I said, I think one thing that distinguishes us from some of the other people in VR is that we're so focused on characters, so focused on them making eye contact with you, or with their facial features reacting in realtime, and being very believable, and forging that bond between you and that character. So, for us, that character technology, and having the top people in that space work with us, is the long term thing that is going to differentiate us from the crowd. >> I'd like to get your reaction to my comment about the computer science, and that's mainly, mostly a Silicon Valley thing, living in Palo Alto, so, but people are struggling when they go to college. What should I major in? And there's a narrative right now, oh you got to learn how to code, you got to be a computer science major. You don't. You don't have to be a CS major. Some of the most creative and technical brilliance can also come from other disciplines. What's your reaction to that, and what's your advice? >> I think people should just follow their effort. Because, if you follow what naturally comes to you, what you're good at, and that also has meaning and interest to you, and something that you can get feedback along the way, which is the great thing about being in a growing space, you are going to just spend your, you're going to spend a lot of late nights doing that stuff, and you can always bring it into your career path when that happens. And I think, we're in a very DIY time in VR. No one knows anything. We're constantly making mistakes, but then learning from them. And that's the most exciting process of being where we are. So, to people who are of college age, I would just tell them follow your effort. If you're interested in VR, it's an exciting time to just do it yourself. Learn from your mistakes. And then, and try to create something new. >> What does the new creative mean to you. When you hear that, new creative, what does that mean to you? >> You know, it's interesting being at these talks and panels, and at all these festivals, because I feel like a lot of people are looking for that new innovator who comes out of nowhere, and sort of just redefines the industry. And that could very well happen. But I actually think what's really exciting about right now is, it's more about having, understanding the bridge between all the different mediums and disciplines. I think new things are created when you combine areas that have not been traditionally aligned. So for example, Orson Welles arguably created one of the first great cinematic masterpieces in Citizen Kane, but he was able to do so by bringing values from theater, and from radio, and areas where he sort of learned the art of storytelling. And he was able to combine them in new and interesting ways that people hadn't seen before. So, for me it's less about looking for that silver bullet of a creative person who comes out of nowhere, but these younger generations who understand these different mediums, combining them and creating connections with them in an exciting way. >> Brooks Brown from Starbury Studios said on the panel, the next breakout star is going to be the kid in the basement that no one's ever heard of. >> Very possibly, but that kid in the basement, he needs to be passionate about a lot of different disciplines. So, what we've tried to emulate in doing so, is bringing the best people in gaming, bringing the best people from traditional film, bringing people who had interests in a lot of different areas, different art forms, and letting them kind of play together and learn from each other. Argue with each other, you know? And then come up with something that no one's seen before. >> We're going to have to come up with a camera, so that could be like an experiment. Like it's just a reality show in and of itself. All that talent, multi discipline together. >> Absolutely. >> John: It's like dynamite ready to explode. >> It's the challenge, it's the blessing, it's the curse and the blessing of our medium right now, because there's so much more to discover, but if people come in and have an open mind, and are willing ... If the people from Hollywood are willing to learn from the people who do gaming in Silicon Valley, who are open to learning from the people in New York who grew up on live theater, I feel those, finding that intersection, finding those beautiful intersections are where we're going to thrive. >> Well you guys highlight that multi disciplinary thing, but also highlights why diversity is so important. Diversity brings the most perspectives to the table, the most data, most contribution. It might be a little bit longer to work through the arguments, right? You got to be patient. >> Absolutely you have to be patient. We're really lucky to be working with John Legend on our VR piece. He had actually been looking for several years to find, wanting to play in this space, but not wanting to do it with the wrong partner at the wrong time. So, it's, there's an art to timing in everything that we do right now, and when we presented to him the story we're doing with the Legend of Crow, it felt like the perfect sort of match. >> Legend of Crow coming out. Head of Content, Kane Lee here, Baobab Studios. Thanks for spending the time here on the Cube Conversation. What's the timing of the release of the program? >> Probably late spring, but we're going to be announcing some news around that soon, and we have some more exciting updates about it that I can't wait to share. >> Alright, we are here at the Intel Tech Lounge as the Cube's Conversation at Sundance Film Festival, part of our coverage of Sundance 2018. I'm John Furrier, thanks for watching.