>>And welcome to the DockerCon cube cover here on the main stage. So HIRA RA development manager at J Frogg. Welcome to the cube. You guys have been on many times, uh, with J Frogg on the cube, great product you guys are doing great. Congratulations on all the six. Thanks for coming on the cube. >>Thank you. Thank you for having >>Me. So I'm really interested in talking about the supply chain, uh, package management, supply chain, and software workflow, huge discussion. This is one of the hottest issues that's being solved on by, with, with in DevOps and DevSecOps in, in the planet. It's all over the, all over the news, a real challenge, open source, growing so fast and so successful with cloud scale and with automation, as you guys know, you gotta ha you gotta know what's trusted, so you gotta build trust into the, the product itself. So developers don't have to do all the rework. Everyone kind of knows this right now, and this is a key solve problem you guys are solving. So I gotta ask you, what is the package management issue? Why is it such an important topic when you're talking about security? >>Yeah. Uh, so if you look at, uh, look at how software is built today, about 80 to 90% of that is open source. And currently the way we, the way we pull those open source libraries, we just, we just have blind trust in, in repositories that are central, and we rely on whatever mechanism they have built to, to establish that trust, uh, with the developer who is building it. And from, from our experience, uh, we have learned that that is not sufficient, uh, that is not sufficient to tell us that that particular developer built that end product and, uh, whatever code that they build is actually coming out in the end product. So we need, we need something to bridge that gap. We need, we need a trustworthy mechanism there to bridge that gap. And there are, there are a few other, uh, elements to it. >>Um, all these center depositories are prone to, uh, single point of failures. And, you know, in, we have all experience what happens when one of those goes down and how it stops production and how it, how it stops just software, uh, development, right? And we, what we are working on is how do we build a system where we, we can actually have, uh, liquid software as a reality and just continue to build software, regardless of all these systems of being live all the time, uh, and also have a, an implicit, uh, way of mechanism to trust, uh, what is coming out of those systems? >>You know, we've talked with you guys in the past about the building blocks of software and what flows through the pipelines, all that stuff's part of what is automated these days and, and, and important. And what I gotta ask you because security these days is like, don't trust anything, you know, um, here it's, you're, you're trusting software to be in essence verified. I'm simplifying, obviously. So I gotta ask you what is being done to solve this problem, because states change, you know, you got data, you got software injections, and you got, we got containers and Kubernetes right here, helping all this is on the table now, but what is currently being done to solve the problem? Cause it's really hard. >>Yeah, it is. It is a really hard problem. And currently, right, when we develop software, we have a team, uh, which, which we work with and we trust whatever is coming out of the team. And we have, we have a, um, what do you call certified, uh, pro production mechanism to build that software and actually release it to our customers. And when it is done in house, it is easy because we are, we control all the pieces. Now what happens when, when we are doing this with open source, we don't have that chain. We need that chain, which is independent. We just independent of where the software was, you know, produced versus where it is going to be used. We need a way to have Providence of how it was built, which parts actually went in, uh, making, uh, making the end product. Uh, and, and what are the things that we see are, are, are, uh, continuing, uh, uh, continuing evidences that this software can be used. So if there is a vulnerability that is discovered now, that is discovered, and it is released in some database, and we need to do corrective action to say that this vulnerability associated with this version, and there is no, there's no automated mechanism. So we are working on an automated mechanism where, where you can run a command, which will tell you what has happened with this piece of, uh, software, this version of it, and whether it is production worthy or not. >>It's a great goal. I gotta say, but I'll tell you, I can guarantee there's gonna be a ton of skeptics on this security people. Oh, no, I don't. I doubt it's always a back door. Um, what's the relationship with Docker? How do you guys see this evolving? Obviously it's a super important mission. Um, it's not a trend that's gonna go away. Supply chain software is here to stay. Um, it's not gonna go away. And we saw this in hardware and everyone kind of knows kind of what happens when you see these vulnerabilities. Um, you gotta have trusted software, right? This is gonna be continuing what's the relationship with DockerCon? What are you guys doing with dock and here at DockerCon? >>So we, when we actually started working on this project, uh, both Docker and, uh, J frog had had similar ideas in mind of how, how do we make this, uh, this trust mechanism available to anyone, uh, who wants it, whether they're, whether they're in interacting with dock hub or, or regardless of that, right. And how do we actually make it a mechanism, uh, that just, uh, uh, that just provides this kind of, uh, this kind of trust, uh, without, without the developer having to do something. Uh, so what we worked with, uh, with Docker is actually integrating, um, integrating our solution so that anywhere there, uh, there is, uh, Docker being used currently, uh, people don't have to change those, uh, those behaviors or change those code, uh, those code lines, uh, right. Uh, because changing hand, uh, changing this a single line of code in hundreds of systems, hundreds of CI systems is gonna be really hard. Uh, and we wanted to build a seamless integration between Docker and the solution that we are building, uh, so that, so that you can continue to do Docker pro and dock push and, but get, uh, get all the benefits of the supply chain security solution that we have. >>Okay. So let's step back for a minute and let's discuss about the pro what is the project and where's the commercial J Frogg Docker intersect take that, break that apart, just step out the project for us. What's the intended goals. What is the project? Where is it? How do people get involved and how does that intersect with the commercial interest of JRO and Docker? >>Yeah. Yeah. My favorite topic to talk about. So the, the project is called Peria, uh, Peria is, uh, is an open source project. It is, it is an effort that started with JRO and, and Docker, but by no means limited to just JRO and dock contributing, we already have five companies contributing. Uh, we are actually building a working product, uh, which will demo during, uh, during our, uh, our talk. And there is more to come there's more to come. It is being built iteratively, and, and the solution is basically to provide a decentralized mechanism, uh, similar to similar to how, how you, uh, do things with GI, so that you have, you have the, uh, the packages that you are using available at your nearest peer. Uh, there is also going to be a multi load build verification mechanism, uh, and all of the information about the packages that you're going to use will be available on a Providence log. >>So you can always query that and find out what is the latest state of affairs, what ES were discovered and make, make quick decisions. And you don't have to react after the fact after it has been in the news for a while. Uh, so you can react to your customer's needs, um, uh, as quick as they happen. And we feel that the, our emphasis on open source is key here because, uh, given our experience, you know, 80 to 90% of software that is packaged, contains open source, and there is no way currently, which we, uh, or no engineering mechanisms currently that give us that, uh, that confidence that we, whatever we are building and whatever we are dependencies we are pulling is actually worthwhile putting it into production. >>I mean, you really, it's a great service. I mean, you think about like all that's coming out, open source, open source become very social, too. People are starting projects just to code and get, get in the, in the community and hang out, uh, and just get in the fray and just do stuff. And then you see venture capitals coming in funding those projects, it's a new economic system as well, not just code, so I can see this pipeline beautifully up for scale. How do people get involved with this project? Cause again, my, my questions all gonna be around integration, how frictionless it is. That's gonna be the challenge. You mentioned that, so I can see people getting involved. What's what's how do people join? What do they do? What can they do here at Docker con? >>Yeah. Uh, so we have a website, Percy, I P yr S I a.io, and you'll find all kinds of information there. Uh, we have a GI presence. Uh, we have community meetings that are open to public. We are all, we are all doing this under the, uh, under the umbrella limits foundation. We had a boots scrap project within Linux foundation. Uh, so people who have interest in, in all these areas can come in, just, just attend those meetings, uh, add, uh, you know, add comments or just attend our stand up. So we are running it like a, like a agile from, uh, process. We are doing stand up, we are doing retrospectives and we are, we are doing planning and, and we are, we are iteratively building this. So what you'll see at Dr. Conn is, is just a, a little bit of a teaser of what we have built so far and what you, what you can expect to, uh, see in, in future such events. >>So thanks for coming on the queue. We've got 30 seconds left, put a quick plug in for the swamp up, coming up. >>Yeah. Uh, so we, we will talk a lot more about Peria and our open source efforts and how we would like you all to collaborate. We'll be at swamp up, uh, in San Diego on May 26th, uh, May 24th to 26th. Uh, so hope to see you there, hope to discuss more about Peria and, and see what he will do with, uh, with this project. Thank you. >>All right. Thanks for coming on the back to the main stage. I'm John cube. Thanks for watching. >>Thank >>You.

(upbeat music) >> Hey, welcome back to theCUBE's cover of DockerCon Mainstage, I'm John Furrier, host of theCUBE. We're here with Shubha Rao, Senior Manager, Product Manager at AWS, in the container services. Shubha, thanks for coming on theCUBE. >> Hi, thank you very much for having me, excited to be here. >> So obviously, we're doing a lot of coverage with AWS recently, on containers, cloud native, microservices and we see you guys always at the events. But tell me about what your role is in the organization? >> Yeah, so I lead the product management and developer advocacy team, in the AWS Container Services group, where we focus on elastic containers. And what I mean by elastic containers, is that, all the AWS opinionated, out of the box solutions that we have for you, like, you know, ECS and App Runner and Elastic BeanStalk. So where we bring in our services in a way that integrates with the AWS ecosystem. And, you know, my team manages the product management and speaking to customers and developers like you all, to understand how we can improve our services for you to use it more seamlessly. >> So, I mean, I know AWS has a lot of services tha t have containers involved with them and it's a lot of integration within the cloud. Amazon's as cloud native as you're going to get at AWS. If I was a new customer, where do I start with containers if you had to give me advice? And then, where I have a nice roadmap to grow within AWS. >> Yeah, no, that's a great question a lot of customers ask us this. We recommend that the customers choose whatever is the best fit for their application needs and for their operational flexibilities. So, if you have an application which you can use, pretty abstract and like end to end managed by AWS service, we recommend that you start at the highest level of abstraction that's okay to use for your application. And that means something like App Runner, where you can bring in a web application and run it like end to end. And if there are things that you want to control and tweak, then you know, we have services like ECS, where you get control and you get flexibility to tweak it to your needs. Be it needs of like, integrations or running your own agents and running your own partner solutions or even customizing how it scales and all the, you know, characteristics related to it. And of course we have, if there are a lot of our customers also run kubernetes, so that is a requirement for you, if your apps are already packaged to run, you know, easily with the kubernetes ecosystem, then we have, yes, for you. So, like application needs, the operational, how much of the operations do you want us to handle? Or how much of it do you want to actually have control over. And with all that, like the highest level of abstraction so that we can do the work on your behalf, which is the goal of AWS. >> Yeah, well, we always hear that all that heavy lifting, undifferentiated heavy lifting, you guys handle all that. Since you're in product management, I have to ask the question 'cause you guys have a little bit longer view, as you have think about what's on the roadmap. What type of customer trends are you seeing in container services? >> We see a lot of trends about customers who want to have the plugability for their, you know, services of choice. And our EKS offerings actually help in that. And we see customers who want an opinionated, you know, give me an out of the box solution, rather than building blocks. And ECS brings you that experience. The new strengths that we are seeing is that a lot of our customer workloads are also on their data centers and in their on-prem like environments. Be it branch offices or data centers or like, you know, other areas. And so we've recently launched the, anywhere offerings for you. So, ECS anywhere, brings you an experience for letting your workloads run and management that you control, where we manage the scaling and orchestration and the whole like, you know, monitoring and troubleshooting aspects of it. Which is the new trend, which seems to be something that our customers use as a way to migrate their applications to the cloud in the long term or just to get, you know, the same experience and the same, like, constructs that they're familiar with, come onto their data centers and their environments. >> You know, Shubha, we hear a lot about containers. It's becoming standard in the enterprise now, mainstream. But customers, when we talk to them, they kind of have this evolution, they start with containers and they realize how great it is and they become container full, right. And then you start to see kind of, them trying to evolve to the next level. And then you start to see EKS come into the equation. We see that in cloud native. Is EKS a container? Or is it a service? How does that work with everything? >> So EKS is a Amazon managed service, container service, where we do the operational set up, you know, upgrades and other things for the customer on their behalf. So basically, you get the same communities APIs that you get to use for your application but we handle a little bit of the integrations and the operations selected to keeping it up and running with high availability. in a way that actually meets your needs for the applications. >> And more and more people are dipping their toe in the water, as we say, with containers. What are some of the things you've seen customers do when they jump in and start implementing that kind of phase one containers? Also, there's a lot of head room beyond that, as you mentioned. What's the first couple steps that they take? They jump in,, is it a learning process? Is it serverless? Where is the connection points all come together? >> Great, so, I want to say that, no one solution that we have, fits all needs. Like, it's not the best case, best thing for all your use cases, and not for all of your applications. So, how it all comes together is that, AWS gives you a ecosystem of tools and capabilities. Some customers want to really build the, you know, castle themselves with each of the Lego block and some customers want it to be a ready made thing. And I want, you know, one of the things that I speak to customers about is, is to rethink which of the knobs and controls do they really need to have, you know because none of the services we have is a one way door. Like, there is always flexibility and, you know ability to move from one service to the other. So, my recommendation is to always like, start with things where Amazon handles many of the heavy lifting, you know, operations for you. And that means starting with something like, serverless offerings, where, like, for example, with Lambda and Forget, we manage the host, we manage the patching, we manage the monitoring. And that would be a great place for you to use ECS offering and, you know, basically get an end to end experience in a couple of days. And over time, if you have more needs, if you have more control, you know, if you want to bring in your own agents and whatever else you have, the option to use your own EC2 Instances or to take it to other, like, you know, parts of the AWS ecosystem, where you want to, you know, tweak it to your needs. >> Well, we're seeing a lot of great traction here at DockerCon. And all the momentum around containers. And then you're starting to get into trust and security supply chain, as open source becomes more exponentially in growth, it's growing like crazy, which is a great thing. So what can we expect to see from your team in the coming months, as this rolls forward? It's not going away anytime soon. It's going to be integrated and keep on scaling. What do we expect from the team in the next month or so? Couple of months. >> Security and, you know, is our number one job. So you will continue to see more and more features, capabilities and integrations, to ensure that your workloads are secure. Availability and scaling are the things that we do, you know, as keep the lights on. So, you should expect to see all of our services growing to make it like, more user friendly, easier, you know, simpler ways to get the whole availability and scaling to your needs, better. And then like, you know, very specifically, I want to touch on a few services. So App Runner, today we have support for public facing web services. You can expect that the number of use cases that you can meet with app runner is going to increase over time. You want to invest into making it AWS end to end workflow experience for our customers because, that's the easiest journey to the cloud. And we don't want you to actually wait for months and years to actually leverage the benefits of what AWS provides. ECS, we've already launched our, like, you know, Forget and Anywhere, to bring you more flexibility in terms of easier networking capabilities, more granular controls in deployment and more controls to actually help you plug in your preferred, you know, solution ties. And in EKS, we are going to continue to keep the communities, you know, versions and, you know, bring simpler experiences for you. >> A lot of nice growth there, containers, EKS, a lot more goodness in the cloud, obviously. We have 30 seconds left. Tell us what you're most excited about personally. And what should the developers pay attention to in this conference around containers and AWS? >> I would say that AWS has a lot of offerings but, you know, speak to us, like, come to us with your questions or, you know, anything that you have, like in terms of feature requests. We are very, very eager and happy to speak to you all. You know, you can engage with us on the container store map, which is on GitHub. Or you can find, you know, many of us in events like this, AWS Summits and, you know, DockerCon and many of the other meetups. Or find us on LinkedIn, we're always happy to chat. >> Yeah, always open, open source. Open source meets cloud scale, meets commercialization. All happening, all great stuff. Shubha, thank you for coming on theCUBE. Thanks for sharing. We'll send it back now to the DockerCon Mainstage. I'm John Furrier with theCUBE. Thanks for watching. (upbeat music)

(upbeat music) >> Hey, welcome back to Las Vegas. theCUBE is live at AWS re:Invent 2021. I'm Lisa Martin. We have two live sets, theCUBE. We are running one of the largest hybrid tech events, most important events of the year with AWS and its massive ecosystem of partners like as I said. Two live sets, two remote sets. Over a hundred guests on the program talking about the next generation of cloud innovation. I'm pleased to welcome a first timer to theCUBE. Seth Rao, the CEO of FirstEigen joins me. Seth, nice to have you on the program. >> Thank you nice to be here. >> Talk to me about FirstEigen. Also explain to me the name. >> So FirstEigen is a startup company based out of Chicago. The name Eigen is a German word. It's a mathematical term. It comes from eigenvectors and eigenvalues which is used and what it's called is principal component analysis, which is used to detect anomalies, which is related to what we do. So we look for errors in data and hence our name FirstEigen. >> Got it. That's excellent. So talk to me. One of the things that has been a resounding theme of this year's re:Invent is that especially in today's age, every company needs to be a data company. >> Yeah. >> It's all one thing to say it's as a whole other thing to be able to put that into practice with reliable data, with trustworthy data. Talk to me about some of the challenges that you help customers solve 'cause some of the theme about not just being a data company but if you're not a data company you're probably not going to be around much longer. >> Yeah, absolutely .So what we have seen across the board across all verticals, the customers we work with is data governance teams and data management teams are constantly firefighting to find errors in data and fix it. So what we have done is we have created the software DataBuck that autonomously looks at every data set and it will discover errors that are hidden to the human eye. They're hard to find out, hard to detect. Our machine learning algorithms figure out those errors before those errors impact the business. In the usual way, things are sorted out, things are done. It's very laborious, time-consuming and expensive. You have taken a process that takes man-years or even man-months and compressed it to a few hours. >> So dramatic time-savings there. >> Absolutely. >> So six years ago when you guys were founded, you realize this gap in the market, thought it's taking way too long. We don't have this amount of time. Gosh, can you imagine if you guys weren't around the last 22 months when certainly time was of the essence? >> Absolutely. Yeah. Six years ago when we founded the company, my co-founder who's also the CTO. He has extensive experience in validating data and data quality. And my own background and my own experiences in AI and ML. And what we saw was that people are spending an enormous amount of time and yet errors were getting down through to the business side. And at that point it comes back and people are still firefighting. So it was a waste of time, waste of money, waste of effort. >> Right. But also there's the potential for brand damage, brand reputation. Whatever products and services you're producing, if your employees don't have the right data, if there's errors there of what's going out to the consumers is wrong then you've got a big problem. >> Absolutely. Interesting you should mention that because over the summer there was a Danish bank, a very big name Danish bank that had to send apology letters to its customers because they overcharged them on the mortgage because the data in the backend had some errors in it and didn't realize it was inadvertent. But somebody ultimately caught it and did the right thing. Absolutely correct. If the data is incorrect and then you're doing analytics or you're doing reporting or you're sending people a bill that they need to pay it better be very accurate. Otherwise it's a serious brand damage. It has real implications and it has a whole bunch of other issues as well. >> It does and those things can snowball very quickly. >> Yeah. >> So talk to me about one of the things that we've seen in the recent months and years is this explosion of data. And then when the pandemic struck we had this scattering of people and data sources or so much data. The edge is persistent. We've got this work from anywhere environment. What are some of the risks for organizations? They come to you and saying help us ensure that our data is trustworthy. I mean that the trust is key but how do you help organizations that are in somewhat a flux figure out how to solve that problem? >> Yeah. So you're absolutely correct. There is an explosion of data, number one. And along with that, there is also an explosion of analytical tools to mine that data. So as a consequence, there is a big growth. It's exponential growth of microservices, how people are consuming that data. Now in the old world when there were a few consumers of data, it was a lot easier to validate the data. You had few people who are the gatekeepers or the data stewards. But with an explosion of data consumers within a company, you have to take a completely different approach. You cannot now have people manually looking and creating rules to validate data. So there has to be a change in the process. You start validating the data. As soon as the data comes into your system, you start validating if the data is reliable at point zero. >> Okay. >> And then it goes downstream. And every stage the data hops that is a chance that data can get corrupted. And these are called systems risks. Because there are multiple systems and data comes from multiple systems onto the cloud, errors creep in. So you validate the data from the beginning all the way to the end and the kinds of checks you do also increase in complexity as the data is going downstream. You don't want to boil the ocean upfront. You want to do the essential checks. Is my water drinkable at this point, right? I'm not trying to cook as soon as it comes out of the tap. Is it drinkable? - Right. >> Good enough quality. If not then we go back to the source and say, guys, send me better quality data. So sequence, the right process and check every step along the way. >> How much of a cultural shift is FirstEigen helping to facilitate within organizations that now don't... There isn't time to, like we talked about if an error gets in, there's so many downstream effects that can happen, but how do you help organizations shift their mindset? 'Cause that's hard thing to change. >> Fantastic point. In fact, what we see is the mindset change is the biggest wall for companies to have good data. People have been living in the old world where there is a team that is a group, much downstream that is responsible for accurate data. But the volume of data, the complexity of data has gone up so much that that team cannot handle it anymore. It's just beyond their scope. It's not fair for us to expect them to save the world. So the mindshift has to come from an organization leadership that says guys, the data engineers who are upfront who are getting the data into the organization, who are taking care of the data assets have to start thinking of trustable data. Because if they stopped doing it, everything downstream becomes easy. Otherwise it's much, much more complex for these guys. And that's what we do. Our tool provides autonomous solution to monitor the data. It comes out with a data trust score with zero human input. Our software will be able to validate the data and give an objective trust score. Right now it's a popularity contest. People are saying they vote. Yeah, I think I like this. I like this and I like that. That's okay. Maybe it's acceptable. But the reason they do it is because there is no way to objectively say the data is trustable. If there is a small error somewhere, it's a needle in the haystack. It's hard to find out, but we can. With machine learning algorithms our software can detect the errors, the minutest errors, and to give an objective score from zero to a hundred, trust or no trust. So along with a mindset, now they have the tool to implement that mindset and we can make it happen. >> Talk to me about some of the things that you've seen from a data governance perspective, as we've seen, the explosion, the edge, people working from anywhere. This hybrid environment that we're going to be in for quite some time. >> Yeah. >> From a data governance perspective and Dave Vellante did his residency. We're seeing so many more things pop up, you know different regulations. How do you help facilitate data governance for organizations as the data volume is just going to continue to proliferate? >> Absolutely correct. So data governance. So we are a key component of data governance and data quality and data trustworthiness, reliability is a key component of it. And one of the central, one of the central pillars of data governance is the data catalog. Just like a catalog in the library. It's cataloging every data asset. But right now the catalogs, which are the mainstay are not as good as they can be. A key information that is missing is I know where my data is what I don't know is how good is my data? How usable is it? If I'm using it for an accounts receivable or an accounts payable, for example, the data better be very, very accurate. So what our software will do is it'll help data governance by linking with any data governance tool and giving an important component which is data quality, reliability, trustability score, which is objective to every data asset. So imagine I open the catalog. I see where my book is in the library. I also know if there are pages missing in the book is the book readable? So it's not good enough to know that I have a book somewhere but it's how good is it? >> Right >> So DataBuck will make that happen. >> So when customers come to you, how do you help them start? 'Cause obviously the data, the volume it's intimidating. >> Yeah. >> Where do they start? >> Great. This is interestingly enough a challenge that every customer has. >> Right. >> Everybody is ambitious enough to say, no, I want to make the change. But the previous point was, if you want to do such a big change, it's an organizational change management problem. So the way we recommend customers is start with the small problem. Get some early victories. And this software is very easy. Just bring it in, automate a small part. You have your sales data or transactional data, or operational data. Take a small portion of it, automate it. Get reliable data, get good analytics, get the results and start expanding to other places. Trying to do everything at one time, it's just too much inertia, organizations don't move. You don't get anywhere. Data initiatives will fail. >> Right. So you're helping customers identify where are those quick wins? >> Yes. And where are the landmines that we need to be able to find out where they are so we can navigate around them? >> Yeah. We have enough expedience over 20 years of working with different customers. And I know if something can go wrong we know where it'll go wrong and we can help them steer them away from the landmines and take them to areas where they'll get quick wins. 'Cause we want the customer to win. We want them to go back and say, look, because of this, we were able to do better analytics. We are able to do better reporting and so on and so forth. We can help them navigate this area. >> Do you have a favorite example, customer example that you think really articulates that value there, that we're helping customers. We can't boil the ocean like you said. It doesn't make any sense, but customer that you helped with small quick wins that really just opened up the opportunity to unlock the value of trustable data. >> Absolutely. So we're working with a fortune 50 company in the US and it's a manufacturing company. Their CFO is a little in a concern whether the data that she's reporting to the Wall Street is acceptable, does it have any errors? And ultimately she signing off on it. So she had a large team in the technology side that was supporting her and they were doing their best. But in spite of that, she's a very sharp woman. She was able to look and find errors and saying, "Something does not look right here guys. Go back and check". Then it goes back to the IT team and they go, "Oh yeah, actually, there was an error". Some errors had slipped through. So they brought us in and we were able to automate the process, What they could do. They could do a few checks within that audit window. We were able to do an enormous number of checks more. More detailed, more accurate. And we were able to reduce the number of errors that were slipping through by over 98%. >> Big number. >> So, absolutely. Really fast. Really good. Now that this has gone through they feel a lot more comfortable than the question is, okay. In addition to financial reporting, can I use it to iron out my supply chain data? 'Cause they have thousands of vendors. They have hundreds of distributors. They have products all over the globe. Now they want to validate all the data because even if your data is off in a one or 2%, if you're a hundred plus billion dollar company, it has an enormous impact on your balance sheet and your income statement. >> Absolutely. Yeah. >> So we are slowly expanding as soon as they allow us. They like us now they're taking it to other areas from beyond finance. >> Well it sounds like you have not only great technology, Seth but a great plan for helping customers with those quick wins and then learning and expanding within and really developing that trusted relationship between FirstEigen and your customers. Thank you so much for joining me on the program today. Introducing the company, what you guys are doing really cool stuff. Appreciate your time. >> Thank you very much. >> All right. >> Pleasure to be here. >> For Seth Rao, I'm Lisa Martin. You're watching theCUBE. The global leader in live tech coverage. (upbeat music)

(upbeat music) >> Welcome to today's session of theCUBE's presentation of the AWS Startup Showcase, the Next Big Thing in AI, Security & Life Sciences. Today we're joined by Abacus Insights for the Life Sciences track, I'm your host, Natalie Ehrlich. Now we're going to be speaking about creating an innovation enabling data environment to accelerate your healthcare analytics journey, and we're now joined by our guests Krishna Kottapalli, chief commercial officer as well as Sumant Rao, chief product officer, both working at Abacus Insights, thank you very much for joining us. >> Thank you for having us. >> Well let's kick off with our theme Krishna, how can we create innovation enabling data environments in order to facilitate the healthcare analytics? >> Yeah, so I think if you sort of think about this that is a lot of data proliferating inside the healthcare system, and whether it's through the internal sources, external sources, devices, patient monitoring platforms, and so on, and all of this carries yeah all of these essentially carry, have useful data and intelligence, right, and essentially the users are looking to get insights out of it to solve problems. And we're also seeing that the journey that our clients are going through is actually a transformation journey, right so they are thinking about how do we seamlessly interact with our stakeholders, so their stakeholders being members and providers, so that they don't get frustrated and feel like they're interacting with multiple parts of the health plan, right, we typically when you call the health plan you feel like you're calling five different departments, so they want to have a seamless experience, and finally, I think the whole, you know, the data being you know, in the ecosystem within the patients, payers, and providers being able to operate and interact has intelligence. So what we, what we think about this is how do we take all of this and help our clients you know, digitize their, you know, path forward and create a way to deliver, you know enable them to do meaningful analytics. >> Well Sumant, when you think about your customers what are the key benefits that Abacus is providing? >> So that's a good question, so primarily speaking, we approach this as, you know a framework that drives innovation that enables data and analytics. I mean, that's really what we're trying to do here. What Abacus does though, is this is slightly different is how we think about this. So we firmly believe that data analytics is not a linear journey, I mean, you cannot say that, oh I'll build my data foundation first and then, you know have the data and then they shall come that's not how it works. So for us, the way Abacus approaches this is, we focus really heavily on the data foundation part of it first. But along the way in the process, a big part of our value statement is we engage and make sure we are driving business value throughout this piece. So, so general message is, you know make sure innovation for the sake of innovation data is not how you're approaching this, but think about your business users, get them engaged, have it small, milestone driven progress that you make along the way. So, so generally speaking, it's we're not tryna be just a platform who moves bits and bytes of information. The way we think about this is you know we'll help you along this journey, there are steps that happen that take you there. And because of which, the message to most of our customers is you focus on your core competence. You know your business, you have nuances in the data, you have nuances on needs that your customers need, you focus on that. The scale that Abacus brings because this is what we do day in day out is more along area of re-usability. So if within our customers, they've got data assets how do we reuse some of that? How does Abacus re-use the fact that because of our of what we do, we actually have data assets that, you know, we can bring data to life quickly. So, so general guidelines, right, so first is don't innovate for the sake of innovating. I mean, that's not going to get you far, respect the process that this is not a linear path, there's always value that's happening throughout the process, and that's, you know, Abacus will work closely with you to make sure you recognize that value. The second part is within your organization, you have assets. There's like major data assets, there's IP, there's things that can leverage that Abacus will do. And because we are a platform, what we focus on is configurability. We've done this for, I mean, a lot of us on the Abacus team come from healthcare space, we have got big payer DNA, we get this, and what we also know is data rules change. I mean, you know, it's really hard when you build a system that's tightly built and you cannot change and you cannot adapt as data rules change, so we've made that part of it easier. We have, we understand data governance, so we work closely with our payers data governance teams to make sure that part of it happens. And I think the last part of this which is really important, this in the context of this conversation is, all of this is good stuff, I mean, you've got massive data foundation, you've got, you know, healthcare expertise flowing in, you've got partnerships with data governance, all that is great. If you don't have best-in-class infrastructure supporting all of that, then you really, you will really have issues Erlich. I mean, that's just the way it works, and this is why, you know, we're built on the AWS stack which kind of helps us, and also helps our clients along with their cloud journey. So it's kind of an interesting set of events in terms of you know, again, I'm going to repeat this because it's important that we don't innovate for the sake of innovating, re-use your assets, leverage your existing IP, make things configurable, data changes, and then leverage best in class infrastructure, so Abacus strategy progresses across those four dimensions. >> And I mean, that's an excellent point about healthcare data being really nuanced and you know, Krishna would love to get your insights on what you see are the biggest opportunities in healthcare analytics now. >> Yeah, so the biggest opportunities are, you know there are two, we think about it in two dimensions, right, one is really around sort of the analytics use cases, and second is around the operational use cases, right, so if you think about a payer they're trying to solve both, and we see because of, you know, our the way we think about data, which is close to near real time, we are able to essentially serve up our clients with, you know helping them solve both their use cases. So think of this that, when you're a patient, you go to you know, you go to a CVS to do something, and then you go to your doctor's office to do something, right, to be, to be able to take a test. If all of these are known, to your payer care management team, if you will, in close to near real time then know, right, where you've been, what you can do how to be able to sort of intervene and so on and so forth, so from a next best action and operational use cases we see a lot of them emerging, new thanks to the cloud as well as thanks to infrastructure, which can do sort of near real time. So that's our own sort of operational use cases if you will. If, when you think about the analytics right, so, you know, every, all payers struggle with this, Which is you have limited dollars to be able to intervene with you know, a large set of population, right so every piece of data that you know, have about your patient, about the specific provider so on and so forth is able to actually, you know give you analytics to be able to intervene or engage if you will, with the patient in a very one-to-one manner. And what we find is at the end of the day if the patient is not engaged in this and the member or the patient is not engaged, you know in the healthcare, you know, value chain, if you will, then your dollars go to waste, and we feel that, in essentially both of these type of use cases can be sorted up really well with, with a unified data platform, as well as with upstack analytics. >> And now Sumant, I'd love to hear from you, you know you're really involved with the product, how do you see the competitive landscape? How do you make sure that your product is the best out there? >> So I think, I think a lot of that is we think about ourselves across three, three vectors. Talk about it as core platform, which is at a very minimal level of description, it's really moving bits and bytes from point A to point B. That's one part of it, right, and I think there's a, it's a pretty crowded space, it's a whole bunch of folks out there trying to, you know demonstrate that they can successfully land data from one point to the other. We do that too, we do that at scale. Where you'd start differentiating and pulling away from the pack is the second vector, which is enrichment. Now, this is where again, it's you have to understand healthcare data to really build a level of respect for how messy it can get. And you have to understand it and build it in a way where it's easy to keep up with the changes. We spend a lot of time, you know in building out a platform to do that so that we can implement data quickly. I mean, you know, for Abacus to bring a data source to life in less than 45 days, it's pretty straightforward. And it's you're talking on an average 6 or 12 months across the rest. Because we get this, we've got a library of rules, we understand how to bring this piece, so we start pulling away from the competitors, if you may. More along the enrichment vector, because that's where we think, getting high quality rules, getting these re-used, all of this is part of it, but then we bring another level of enrichment where we have, you know, we use public data sets, we use a reference data sets, we tie this, we fill in the blanks in the data. All of this is the end state, let's make the data shovel-ready for analytics. So we do all of that along the way, so now applying our expertise, cleansing data, making sure it's the gaps are all filled out and getting this ready and then comes the next part where we tie this data out. Cause it's one thing to bring in multiple sources quickly at scale high speed and all that good stuff, which is hard work, but you know, it's, it's expected now at the same time how do you put all that together in a meaningful manner with which we can actually, you know, land it and keep it ready? So that's two parts. So first is, the platform, the nuts and bolts, the pipes, all that is good stuff, the second is the enrichment. The third side, which is really where we start differentiating is distribution. We have a philosophy that, you know, really the mission of the whole company was to get data available. To solve use cases like the one Krishna just talked about. So rather than make this a massive change management program that takes five years to implement, and really scares your end users away, our philosophy is like let's have incremental use case all on the way, but let's talk to the users, let them interact with data as easy as they can. So we've built our partnerships on our distribution hub, which makes it easy, so an example is if you have someone in the marketing team, who really wants to analyze a particular population to reach out to them, and all they know is tableau, it is great. It should be as simple as saying, look what's the sliver of data you need to get your job done, how do you interact? So we've our distribution hub, is really is the part where, users come in, interact with the data with you know, we will meet you where you are is the underlying principle and that's how it operates. So, so I think on the first level of platform, yeah a crowded space everyone's fighting for that piece, the second part of it is enrichment where we really start pulling away using our expertise, and then at the end of it you've got the distribution part where you know you just want to make it available to users, and, you know, a lot of work has gone into getting this done but that's how we work. >> And if I could add a couple more things, Natalie, so the other thing is security, right so the reason that healthcare, healthcare players have not gone to the cloud until about three four years back, is the whole concern about security so we have invested a ton of resources and money to make sure that our platform is run in the most secure manner, and giving confidence to our clients, and it's an expensive process, right, even though you're on AWS you have to have your own certification that, so that that gives us a huge differentiator, and the last but not least is how we actually approach the whole data management deployment process, which is, our clients think about us in two dimensions, total cost of ownership, but typically 50 to 60% of what it would cost internally, and secondly, time to value, right, you can't have an infinitely long deployment cycle. So we think about those two and actually put our skin in the game and tie our, you know, tie our success to total cost of ownership and time to value. >> Well, just really quick in 1-2 sentences, would love to get your insight on Abacus's defining contribution to the future of cloud scale. >> Go ahead, Sumant. >> So as I see it, I think so part of it is we've got some of our clients who are payers and we've got them along their cloud journey trusting one of their key assets which is data, and letting us drive it. And this is really driven by domain expertise, a good understanding of data governance, and a great understanding of security, I mean, combining all of this, we've actually got our clients sitting and operating on, you know pretty significant cloud infrastructure successfully day in day out. So I think we've done our part as far as, you know helping folks along that journey. >> Yeah and just to close it out I would say it is speed, right, it is speed to deployment, you don't have to wait. You know, we have set up the infrastructure, set up the cloud and the ability to get things up and running is literally we think about it in weeks, and not months. >> Terrific, well, thank you both very much for insights, fantastic to have you on the show, really fascinating to hear about how Abacus is leveraging healthcare data expertise on its platform , to drive robust analytics, and of course, here we were joined by Abacus Insights, Krishna Kottapalli, the chief commercial officer, as well as Sumant Rao, the chief product officer, thank you again very much for your insights on this program and this session of the AWS Startup Showcase. (upbeat music)

(upbeat electronic music) >> Narrator: From theCUBE studios in Palo Alto and Boston, connecting with thought leaders all around the world, this is a CUBE conversation! >> Hi, I'm Stu Miniman, your host for Cloud Native Insight. When we launched this series, one of the things we wanted to talk about was that we're not just using cloud as a destination, but really enabling new ways of thinking, being able to use the innovations underneath the cloud, and that if you use services in the cloud, that you're not necessarily locked into a solution or can't move forward. And that's why I'm really excited to help welcome to the program, I have the co-founders of Vendia. First we have Dr. Tim Wagner, he is the co-founder and CEO of the company, as well as generally known in the industry as the father of Serverless from the AWS Lambda, and his co-founder, Shruthi Rao, she is the chief business officer at Vendia, also came from AWS where she worked on blockchain solutions. Tim, Shruthi, thanks so much for joining us. >> Thanks for having us in here, Stu. Great to join the show. >> All right, so Shruthi, actually if we could start with you because before we get into Vendia, coming out of stealth, you know, really interesting technology space, you and Tim both learned a lot from working with customers in your previous jobs, why don't we start from you. Block chain of course had a lot of learnings, a lot of things that people don't understand about what it is and what it isn't, so give us a little bit about what you've learned and how that lead towards what you and Tim and the team are doing with Vendia. >> Yeah, absolutely, Stu! One, the most important thing that we've all heard of was this great gravitational pull towards blockchain in 2018 and 2019. Well, I was one of the founders and early adopters of blockchain from Bitcoin and Ethereum space, all the way back from 2011 and onwards. And at AWS I started the Amazon Managed Blockchain and launched Quantum Ledger Database, two services in the block chain category. What I learned there was, no surprise, there was a gold rush to blockchain from many customers. We, I personally talked to over 1,092 customers when I ran Amazon Managed Blockchain for the last two years. And I found that customers were looking at solving this dispersed data problem. Most of my customers had invested in IoT and edge devices, and these devices were gathering massive amounts of data, and on the flip side they also had invested quite a bit of effort in AI and ML and analytics to crunch this data, give them intelligence. But guess what, this data existed in multiple parties, in multiple clouds, in multiple technology stacks, and they needed a mechanism to get this data from wherever they were into one place so they could the AI, ML, analytics investment, and they wanted all of this to be done in real time, and to gravitated towards blockchain. But blockchain had quite a bit of limitations, it was not scalable, it didn't work with the existing stack that you had. It forced enterprises to adopt this new technology and entirely new type of infrastructure. It didn't work cross-cloud unless you hired expensive consultants or did it yourself, and required these specialized developers. For all of these reasons, we've seen many POCs, majority of POCs just dying on the vine and not ever reaching the production potential. So, that is when I realized that what the problem to be solved was not a trust problem, the problem was dispersed data in multiple clouds and multiple stacks problem. Sometimes multiple parties, even, problem. And that's when Tim and I started talking about, about how can we bring all of the nascent qualities of Lambda and Serverless and use all of the features of blockchain and build something together? And he has an interesting story on his own, right. >> Yeah. Yeah, Shruthi, if I could, I'd like to get a little bit of that. So, first of all for our audience, if you're watching this on the minute, probably want to hit pause, you know, go search Tim, go watch a video, read his Medium post, about the past, present, and future of Serverless. But Tim, I'm excited. You and I have talked in the past, but finally getting you on theCUBE program. >> Yeah! >> You know, I've looked through my career, and my background is infrastructure, and the role of infrastructure we know is always just to support the applications and the data that run business, that's what is important! Even when you talk about cloud, it is the applications, you know, the code, and the data that are important. So, it's not that, you know, okay I've got near infinite compute capacity, it's the new things that I can do with it. That's a comment I heard in one of your sessions. You talked about one of the most fascinating things about Serverless was just the new creativity that it inspired people to do, and I loved it wasn't just unlocking developers to say, okay I have new ways to write things, but even people that weren't traditional coders, like lots of people in marketing that were like, "I can start with this and build something new." So, I guess the question I have for you is, you know we had this idea of Platform as a Service, or even when things like containers launched, it was, we were trying to get close to that atomic unit of the application, and often it was talked about, well, do I want it for portability? Is it for ease of use? So, you've been wrangling and looking at this (Tim laughing) from a lot of different ways. So, is that as a starting point, you know, what did you see the last few years with Lambda, and you know, help connect this up to where Shruthi just left off her bit of the story. >> Absolutely. You know, the great story, the great success of the cloud is this elimination of undifferentiated heavy lifting, you know, from getting rid of having to build out a data center, to all the complexity of managing hardware. And that first wave of cloud adoption was just phenomenally successful at that. But as you say, the real thing businesses wrestle with are applications, right? It's ultimately about the business solution, not the hardware and software on which it runs. So, the very first time I sat down with Andy Jassy to talk about what eventually become Lambda, you know, one of the things I said was, look, if we want to get 10x the number of people to come and, you know, and be in the cloud and be successful it has to be 10 times simpler than it is today. You know, if step one is hire an amazing team of distributed engineers to turn a server into a full tolerance, scalable, reliable business solution, now that's going to be fundamentally limiting. We have to find a way to put that in a box, give that capability, you know, to people, without having them go hire that and build that out in the first place. And so that kind of started this journey for, for compute, we're trying to solve the problem of making compute as easy to use as possible. You know, take some code, as you said, even if you're not a diehard programmer or backend engineer, maybe you're just a full-stack engineer who loves working on the front-end, but the backend isn't your focus, turn that into something that is as scalable, as robust, as secure as somebody who has spent their entire career working on that. And that was the promise of Serverless, you know, outside of the specifics of any one cloud. Now, the challenge of course when you talk to customers, you know, is that you always heard the same two considerations. One is, I love the idea of Lamdba, but it's AWS, maybe I have multiple departments or business partners, or need to kind of work on multiple clouds. The other challenge is fantastic for compute, what about data? You know, you've kind of left me with, you're giving me sort of half the solution, you've made my compute super easy to use, can you make my data equally easy to use? And so you know, obviously the part of the genesis of Vendia is going and tackling those pieces of this, giving all that promise and ease of use of Serverless, now with a model for replicated state and data, and one that can cross accounts, machines, departments, clouds, companies, as easily as it scales on a single cloud today. >> Okay, so you covered quite a bit of ground there Tim, if you could just unpack that a little bit, because you're talking about state, cutting across environments. What is it that Vendia is bringing, how does that tie into solutions like, you know, Lamdba as you mentioned, but other clouds or even potentially on premises solutions? So, what is, you know, the IP, the code, the solution that Vendia's offering? >> Happy to! So, let's start with the customer problem here. The thing that every enterprise, every company, frankly, wrestles with is in the modern world they're producing more data than ever, IMT, digital journeys, you know, mobile, edge devices. More data coming in than ever before, at the same time, more data getting consumed than ever before with deep analytics, supply chain optimization, AI, ML. So, even more consumers of ever more data. The challenge, of course, is that data isn't always inside a company's four walls. In fact, we've heard 80% or more of that data actually lives outside of a company's control. So, step one to doing something like AI, ML, isn't even just picking a product or selecting a technology, it's getting all of your data back together again, so that's the problem that we set out to solve with Vendia, and we realized that, you know, and kind of part of the genesis for the name here, you know, Vendia comes from Venn Diagram. So, part of that need to bring code and data together across companies, across tech stacks, means the ability to solve some of these long-standing challenges. And we looked at the two sort of big movements out there. Two of them, you know, we've obviously both been involved in, one of them was Serverless, which amazing ability to scale, but single account, single cloud, single company. The other one is blockchain and distributed ledgers, manages to run more across parties, across clouds, across tech stacks, but doesn't have a great mechanism for scalability, it's really a single box deployment model, and obviously there are a lot of limitations with that. So, our technology, and kind of our insight and breakthrough here was bringing those two things together by solving the problems in each of them with the best parts of the other. So, reimagine the blockchain as a cloud data implementation built entirely out of Serverless components that have all of the scale, the cost efficiencies, the high utilization, like all of the ease of deployment that something like Lambda has today, and at the same time, you know, bring state to Serverless. Give things like Lambda and the equivalent of other clouds a simple, easy, built-in model so that applications can have multicloud, multi-account state at all times, rather than turning that into a complicated DIY project. So, that was our insight here, you know and frankly where a lot of the interesting technology for us is in turning those centralized services, a centralized version of Serverless Compute or Serverless Database into a multi-account, multicloud experience. And so that's where we spent a lot of time and energy trying to build something that gives customers a great experience. >> Yeah, so I've got plenty of background in customers that, you know, have the "information silos", if you will, so we know, when the unstructured data, you know so much of it is not searchable, I can't leverage it. Shruthi, but maybe it might make sense, you know, what is, would you say some of the top things some of your early customers are saying? You know, I have this pain point, that's pointing me in your direction, what was leading them to you? And how does the solution help them solve that problem? >> Yeah, absolutely! One of our design partners, our lead design partners is this automotive company, they're a premier automotive company, they want, their end goal is to track car parts for warranty recall issues. So, they want to track every single part that goes into a particular car, so they're about 30 to 35,000 parts in each of these cars, and then all the way from manufacturing floor to when the car is sold, and when that particular part is replaced eventually, towards the end of the lifecycle of that part. So for this, they have put together a small test group of their partners, a couple of the parts manufacturers, they're second care partners, National Highway Safety Administration is part of this group, also a couple of dealers and service centers. Now, if you just look at this group of partners, you will see some of these parties have high technology, technology backgrounds, just like the auto manufacturers themselves or the part manufacturers. Low modality or low IT-competency partners such as the service centers, for them desktop PCs are literally the IT competency, and so does the service centers. Now, most of, majority of these are on multiple clouds. This particular auto customer is on AWS and manufactures on Azure, another one is on GCP. Now, they all have to share these large files between each other, making sure that there are some transparency and business rules applicable. For example, two partners who make the same parts or similar parts cannot see each other's data. Most of the participants cannot see the PII data that are not applicable, only the service center can see that. National Highway Safety Administration has read access, not write access. A lot of that needed to be done, and their alternatives before they started using Vendia was either use point-to-point APIs, which was very expensive, very cumbersome, it works for a finite small set of parties, it does not scale, as in when you add more participants into this particular network. And the second option for them was blockchain, which they did use, and used Hyperledger Fabric, they used Ethereum Private to see how this works, but the scalability, with Ethereum Private, it's about 14 to 15 transactions per second, with Hyperledger Fabric it taps out at 100, or 150 on a good day, transaction through, but it's not just useful. All of these are always-on systems, they're not Serverless, so just provisioning capacity, our customers said it took them two to three weeks per participant. So, it's just not a scalable solution. With Vendia, what we delivered to them was this virtual data lake, where the sources of this data are on multiple clouds, are on multiple accounts owned by multiple parties, but all of that data is shared on a virtual data lake with all of the permissions, with all of the logging, with all of the security, PII, and compliance. Now, this particular auto manufacturer and the National Highway Safety Administration can run their ML algorithms to gain intelligence off of it, and start to understand patterns, so when certain parts go bad, or what's the propensity of a certain manufacturing unit producing faulty parts, and so on, and so forth. This really shows you this concept of unstructured data being shared between parties that are not, you know, connected with each other, when there are data silos. But I'd love to follow this up with another example of, you know, the democratization, democratization is very important to Vendia. When Tim launched Lambda and founded the AWS Serverless movement as a whole, and at AWS, one thing, very important thing happened, it lowered the barrier to entry for a new wave of businesses that could just experiment, try out new things, if it failed, they scrap it, if it worked, they could scale it out. And that was possible because of the entry point, because of the paper used, and the architecture itself, and we are, our vision and mission for Vendia is that Vendia fuels the next generation of multi-party connected distributed applications. My second design partner is actually a non-profit that, in the animal welfare industry. Their mission is to maintain a no-kill for dogs and cats in the United States. And the number one reason for over populations of dogs and cats in the shelters is dogs lost, dogs and cats lost during natural disasters, like the hurricane season. And when that happens, and when, let's say your dogs get lost, and you want to find a dog, the ID or the chip-reading is not reliable, they want to search this through pictures. But we also know that if you look at a picture of a dog, four people can come up with four different breed names, and this particular non-profit has 2,500 plus partners across the U.S., and they're all low to no IT modalities, some of them have higher IT competency, and a huge turnover because of volunteer employees. So, what we did for them was came up with a mechanism where they could connect with all 2,500 of these participants very easily in a very cost-effective way and get all of the pictures of all of the dogs in all these repositories into one data lake so they can run some kind of a dog facial recognition algorithm on it and identify where my lost dog is in minutes as opposed to days it used to take before. So, you see a very large customer with very sophisticated IT competency use this, also a non-profit being able to use this. And they were both able to get to this outcome in days, not months or years, as, blockchain, but just under a few days, so we're very excited about that. >> Thank you so much for the examples. All right, Tim, before we get to the end, I wonder if you could take us under the hood a little bit here. My understanding, the solution that you talk about, it's universal apps, or what you call "unis" -- >> Tim: Unis? (laughs) >> I believe, so if I saw that right, give me a little bit of compare and contrast, if you will. Obviously there's been a lot of interest in what Kubernetes has been doing. We've been watching closely, you know there's connections between what Kubernetes is doing and Serverless with the Knative project. When I saw the first video talking about Vendia, you said, "We're serverless, and we're containerless underneath." So, help us understand, because at, you know, a super high level, some of the multicloud and making things very flexible sound very similar. So you know, how is Vendia different, and why do you feel your architecture helps solve this really challenging problem? >> Sure, sure, awesome! You know, look, one of the tenets that we had here was that things have to be as easy as possible for customers, and if you think about the way somebody walks up today to an existing database system, right? They say, "Look, I've got a schema, I know the shape of my data." And a few minutes later I can get a production database, now it's single user, single cloud, single consumer there, but it's a very fast, simple process that doesn't require having code, hiring a team, et cetera, and we wanted Vendia to work the same way. Somebody can walk up with a JSON schema, hand it to us, five minutes later they have a database, only now it's a multiparty database that's decentralized, so runs across multiple platforms, multiple clouds, you know, multiple technology stacks instead of being single user. So, that's kind of goal one, is like make that as easy to use as possible. The other key tenet though is we don't want to be the least common denominator of the cloud. One of the challenges with saying everyone's going to deploy their own servers, they're going to run all their own software, they're going to build, you know, they're all going to co-deploy a Kubernetes cluster, one of the challenges with that is that, as Shruthi was saying, first, anyone for whom that's a challenge, if you don't have a whole IT department wrapped around you that's a difficult proposition to get started on no matter how amazing that technology might be. The other challenge with it though is that it locks you out, sort of the universe of a lock-in process, right, is the lock-out process. It locks you out of some of the best and brightest things the public cloud providers have come up with, and we wanted to empower customers, you know, to pick the best degree. Maybe they want to go use IBM Watson, maybe they want to use a database on Google, and at the same time they want to ingest IoT on AWS, and they wanted all to work together, and want all of that to be seamless, not something where they have to recreate an experience over, and over, and over again on three different clouds. So, that was our goal here in producing this. What we designed as an architecture was decentralized data storage at the core of it. So, think about all the precepts you hear with blockchain, they're all there, they all just look different. So, we use a no SQL database to store data so that we can scale that easily. We still have a consensus algorithm, only now it's a high speed serverless and cloud function based mechanism. You know, instead of smart contracts, you write things in a cloud function like Lambda instead, so no more learning Solidity, now you can use any language you want. So, we changed how we think about that architecture, but many of those ideas about people, really excited about blockchain and its capabilities and the vision for the future are still alive and well, they've just been implemented in a way that's far more practical and effective for the enterprise. >> All right, so what environments can I use today for your solution, Shruthi talked about customers spanning across some of the cloud, so what's available kind of today, what's on the roadmap in the future? Will this include beyond, you know, maybe the top five or six hyper scalers? Can I do, does it just require Serverless underneath? So, will things that are in a customer's own data center eventually support that. >> Absolutely. So, what we're doing right now is having people sign up for our preview release, so in the next few weeks, we're going to start turning that on for early access to developers. That'll be, the early access program, will be multi-account, focused on AWS, and then end of summer, well be doing our GA release, which will be multicloud, so we'll actually be able to operate across multiple clouds, multiple cloud services, on different platforms. But even from day one, we'll have API support in there. So, if you got a service, could even be running on a mainframe, could be on-prem, if it's API based you can still interact with the data, and still get the benefits of the system. So, developers, please start signing up, you can go find more information on vendia.net, and we're really looking forward to getting some of that early feedback and hear more from the people that we're the most excited to have start building these projects. >> Excellent, what a great call to action to get the developers and users in there. Shruthi, if you could just give us the last bit, you know, the thing that's been fascinating, Tim, when I look at the Serverless movement, you know, I've talked to some amazing companies that were two or three people (Tim laughing) and out of their basement, and they created a business, and they're like, "Oh my gosh, I got VC funding, and it's usually sub $10,000,000. So, I look at your team, I'd heard, Tim, you're the primary coder on the team. (Tim laughing) And when it comes to the seed funding it's, you know, compared to many startups, it's a small number. So, Shruthi, give us a little bit if you could the speeds and feeds of the company, your funding, and any places that you're hiring. Yeah, we are definitely hiring, lets me start from there! (Tim laughing) We're hiring for developers, and we are also hiring for solution architects, so please go to vendia.net, we have all the roles listed there, we would love to hear from you! And the second one, funding, yes. Tim is our main developer and solutions architect here, and look, the Serverless movement really helped quite a few companies, including us, to build this, bring this to market in record speeds, and we're very thankful that Tim and AWS started taking the stands, you know back in 2014, 2013, to bring this to market and democratize this. I think when we brought this new concept to our investors, they saw what this could be. It's not an easy concept to understand in the first wave, but when you understand the problem space, you see that the opportunity is pretty endless. And I'll say this for our investors, on behalf of our investors, that they saw a real founder market-fit between us. We're literally the two people who have launched and ran businesses for both Serverless and blockchain at scale, so that's what they thought was very attractive to them, and then look, it's Tim and I, and we're looking to hire 8 to 10 folks, and I think we have gotten to a space where we're making a meaningful difference to the world, and we would love for more people to join us, join this movement and democratize this big dispersed data problem and solve for this. And help us create more meanings to the data that our customers and companies worldwide are creating. We're very excited, and we're very thankful for all of our investors to be deeply committed to us and having conviction on us. >> Well, Shruthi and Tim, first of all, congratulations -- >> Thank you, thank you. >> Absolutely looking forward to, you know, watching the progress going forward. Thanks so much for joining us. >> Thank you, Stu, thank you. >> Thanks, Stu! >> All right, and definitely tune in to our regular conversations on Cloud Native Insights. I'm your host Stu Miniman, and looking forward to hearing more about your Cloud Native Insights! (upbeat electronic music)

>> Announcer: Live from Las Vegas, it's theCUBE! Covering AWS re:Invent 2019. Brought to you by Amazon Web Services and Intel, along with its ecosystem partners. >> Welcome back to the Sands Convention Center in Las Vegas everybody, you're watching theCUBE, the leader in live tech coverage. My name is Dave Vellante, I'm here with my cohost Justin Warren, this is day one of our coverage of AWS re:Invent 2019, Naveen Rao here, he's the corporate vice president and general manager of artificial intelligence, AI products group at Intel, good to see you again, thanks for coming to theCUBE. >> Thanks for having me. >> Dave: You're very welcome, so what's going on with Intel and AI, give us the big picture. >> Yeah, I mean actually the very big picture is I think the world of computing is really shifting. The purpose of what a computer is made for is actually shifting, and I think from its very conception, from Alan Turing, the machine was really meant to be something that recapitulated intelligence, and we took sort of a divergent path where we built applications for productivity, but now we're actually coming back to that original intent, and I think that hits everything that Intel does, because we're a computing company, we supply computing to the world, so everything we do is actually impacted by AI, and will be in service of building better AI platforms, for intelligence at the edge, intelligence in the cloud, and everything in between. >> It's really come full circle, I mean, when I first started this industry, AI was the big hot topic, and really, Intel's ascendancy was around personal productivity, but now we're seeing machines replacing cognitive functions for humans, that has implications for society. But there's a whole new set of workloads that are emerging, and that's driving, presumably, different requirements, so what do you see as the sort of infrastructure requirements for those new workloads, what's Intel's point of view on that? >> Well, so maybe let's focus that on the cloud first. Any kind of machine learning algorithm typically has two phases to it, one is called training or learning, where we're really iterating over large data sets to fit model parameters. And once that's been done to a satisfaction of whatever performance metrics that are relevant to your application, it's rolled out and deployed, that phase is called inference. So these two are actually quite different in their requirements in that inference is all about the best performance per watt, how much processing can I shove into a particular time and power budget? On the training side, it's much more about what kind of flexibility do I have for exploring different types of models, and training them very very fast, because when this field kind of started taking off in 2014, 2013, typically training a model back then would take a month or so, those models now take minutes to train, and the models have grown substantially in size, so we've still kind of gone back to a couple of weeks of training time, so anything we can do to reduce that is very important. >> And why the compression, is that because of just so much data? >> It's data, the sheer amount of data, the complexity of data, and the complexity of the models. So, very broad or a rough categorization of the complexity can be the number of parameters in a model. So, back in 2013, there were, call it 10 million, 20 million parameters, which was very large for a machine learning model. Now they're in the billions, one or two billion is sort of the state of the art. To give you bearings on that, the human brain is about a three to 500 trillion model, so we're still pretty far away from that. So we got a long way to go. >> Yeah, so one of the things about these models is that once you've trained them, that then they do things, but understanding how they work, these are incredibly complex mathematical models, so are we at a point where we just don't understand how these machines actually work, or do we have a pretty good idea of, "No no no, when this model's trained to do this thing, "this is how it behaves"? >> Well, it really depends on what you mean by how much understanding we have, so I'll say at one extreme, we trust humans to do certain things, and we don't really understand what's happening in their brain. We trust that there's a process in place that has tested them enough. A neurosurgeon's cutting into your head, you say you know what, there's a system where that neurosurgeon probably had to go through a ton of training, be tested over and over again, and now we trust that he or she is doing the right thing. I think the same thing is happening in AI, some aspects we can bound and say, I have analytical methods on how I can measure performance. In other ways, other places, it's actually not so easy to measure the performance analytically, we have to actually do it empirically, which means we have data sets that we say, "Does it stand up to all the different tests?" One area we're seeing that in is autonomous driving. Autonomous driving, it's a bit of a black box, and the amount of situations one can incur on the road are almost limitless, so what we say is, for a 16 year old, we say "Go out and drive," and eventually you sort of learn it. Same thing is happening now for autonomous systems, we have these training data sets where we say, "Do you do the right thing in these scenarios?" And we say "Okay, we trust that you'll probably "do the right thing in the real world." >> But we know that Intel has partnered with AWS, I ran autonomous driving with their DeepRacer project, and I believe it's on Thursday is the grand final, it's been running for, I think it was announced on theCUBE last year, and there's been a whole bunch of competitions running all year, basically training models that run on this Intel chip inside a little model car that drives around a race track, so speaking of empirical testing of whether or not it works, lap times gives you a pretty good idea, so what have you learned from that experience, of having all of these people go out and learn how to use these ALM models on a real live race car and race around a track? >> I think there's several things, I mean one thing is, when you turn loose a number of developers on a competitive thing, you get really interesting results, where people find creative ways to use the tools to try to win, so I always love that process, I think competition is how you push technology forward. On the tool side, it's actually more interesting to me, is that we had to come up with something that was adequately simple, so that a large number of people could get going on it quickly. You can't have somebody who spends a year just getting the basic infrastructure to work, so we had to put that in place. And really, I think that's still an iterative process, we're still learning what we can expose as knobs, what kind of areas of innovation we allow the user to explore, and where we sort of walk it down to make it easy to use. So I think that's the biggest learning we get from this, is how I can deploy AI in the real world, and what's really needed from a tool chain standpoint. >> Can you talk more specifically about what you guys each bring to the table with your collaboration with AWS? >> Yeah, AWS has been a great partner. Obviously AWS has a huge ecosystem of developers, all kinds of different developers, I mean web developers are one sort of developer, database developers are another, AI developers are yet another, and we're kind of partnering together to empower that AI base. What we bring from a technological standpoint are of course the hardware, our CPUs, our AI ready now with a lot of software that we've been putting out in the open source. And then other tools like OpenVINO, which make it very easy to start using AI models on our hardware, and so we tie that in to the infrastructure that AWS is building for something like DeepRacer, and then help build a community around it, an ecosystem around it of developers. >> I want to go back to the point you were making about the black box, AI, people are concerned about that, they're concerned about explainability. Do you feel like that's a function of just the newness that we'll eventually get over, and I mean I can think of so many examples in my life where I can't really explain how I know something, but I know it, and I trust it. Do you feel like it's sort of a tempest in a teapot? >> Yeah, I think it depends on what you're talking about, if you're talking about the traceability of a financial transaction, we kind of need that maybe for legal reasons, so even for humans we do that. You got to write down everything you did, why did you do this, why'd you do that, so we actually want traceability for humans, even. In other places, I think it is really about the newness. Do I really trust this thing, I don't know what it's doing. Trust comes with use, after a while it becomes pretty straightforward, I mean I think that's probably true for a cell phone, I remember the first smartphones coming out in the early 2000s, I didn't trust how they worked, I would never do a credit card transaction on 'em, these kind of things, now it's taken for granted. I've done it a million times, and I never had any problems, right? >> It's the opposite in social media, most people. >> Maybe that's the opposite, let's not go down that path. >> I quite like Dr. Kate Darling's analogy from MIT lab, which is we already we have AI, and we're quite used to them, they're called dogs. We don't fully understand how a dog makes a decision, and yet we use 'em every day. In a collaboration with humans, so a dog, sort of replace a particular job, but then again they don't, I don't particularly want to go and sniff things all day long. So having AI systems that can actually replace some of those jobs, actually, that's kind of great. >> Exactly, and think about it like this, if we can build systems that are tireless, and we can basically give 'em more power and they keep going, that's a big win for us. And actually, the dog analogy is great, because I think, at least my eventual goal as an AI researcher is to make the interface for intelligent agents to be like a dog, to train it like a dog, reinforce it for the behaviors you want and keep pushing it in new directions that way, as opposed to having to write code that's kind of esoteric. >> Can you talk about GANs, what is GANs, what's it stand for, what does it mean? >> Generative Adversarial Networks. What this means is that, you can kind of think of it as, two competing sides of solving a problem. So if I'm trying to make a fake picture of you, that makes it look like you have no hair, like me, you can see a Photoshop job, and you can kind of tell, that's not so great. So, one side is trying to make the picture, and the other side is trying to guess whether it's fake or not. We have two neural networks that are kind of working against each other, one's generating stuff, and the other one's saying, is it fake or not, and then eventually you keep improving each other, this one tells that one "No, I can tell," this one goes and tries something else, this one says "No, I can still tell." The one that's trying with a discerning network, once it can't tell anymore, you've kind of built something that's really good, that's sort of the general principle here. So we basically have two things kind of fighting each other to get better and better at a particular task. >> Like deepfakes. >> I use that because it is relevant in this case, and that's kind of where it came from, is from GANs. >> All right, okay, and so wow, obviously relevant with 2020 coming up. I'm going to ask you, how far do you think we can take AI, two part question, how far can we take AI in the near to mid term, let's talk in our lifetimes, and how far should we take it? Maybe you can address some of those thoughts. >> So how far can we take it, well, I think we often have the sci-fi narrative out there of building killer machines and this and that, I don't know that that's actually going to happen anytime soon, for several reasons, one is, we build machines for a purpose, they don't come from an embattled evolutionary past like we do, so their motivations are a little bit different, say. So that's one piece, they're really purpose-driven. Also, building something that's as general as a human or a dog is very hard, and we're not anywhere close to that. When I talked about the trillions of parameters that a human brain has, we might be able to get close to that from a engineering standpoint, but we're not really close to making those trillions of parameters work together in such a coherent way that a human brain does, and efficient, human brain does that in 20 watts, to do it today would be multiple megawatts, so it's not really something that's easily found, just laying around. Now how far should we take it, I look at AI as a way to push humanity to the next level. Let me explain what that means a little bit. Simple equation I always sort of write down, is people are like "Radiologists aren't going to have a job." No no no, what it means is one radiologist plus AI equals 100 radiologists. I can take that person's capabilities and scale it almost freely to millions of other people. It basically increases the accessibility of expertise, we can scale expertise, that's a good thing. It makes, solves problems like we have in healthcare today. All right, that's where we should be going with this. >> Well a good example would be, when, and probably part of the answer's today, when will machines make better diagnoses than doctors? I mean in some cases it probably exists today, but not broadly, but that's a good example, right? >> It is, it's a tool, though, so I look at it as more, giving a human doctor more data to make a better decision on. So, what AI really does for us is it doesn't limit the amount of data on which we can make decisions, as a human, all I can do is read so much, or hear so much, or touch so much, that's my limit of input. If I have an AI system out there listening to billions of observations, and actually presenting data in a form that I can make better decisions on, that's a win. It allows us to actually move science forward, to move accessibility of technologies forward. >> So keeping the context of that timeframe I said, someday in our lifetimes, however you want to define that, when do you think that, or do you think that driving your own car will become obsolete? >> I don't know that it'll ever be obsolete, and I'm a little bit biased on this, so I actually race cars. >> Me too, and I drive a stick, so. >> I kind of race them semi-professionally, so I don't want that to go away, but it's the same thing, we don't need to ride horses anymore, but we still do for fun, so I don't think it'll completely go away. Now, what I think will happen is that commutes will be changed, we will now use autonomous systems for that, and I think five, seven years from now, we will be using autonomy much more on prescribed routes. It won't be that it completely replaces a human driver, even in that timeframe, because it's a very hard problem to solve, in a completely general sense. So, it's going to be a kind of gentle evolution over the next 20 to 30 years. >> Do you think that AI will change the manufacturing pendulum, and perhaps some of that would swing back to, in this country, anyway, on-shore manufacturing? >> Yeah, perhaps, I was in Taiwan a couple of months ago, and we're actually seeing that already, you're seeing things that maybe were much more labor-intensive before, because of economic constraints are becoming more mechanized using AI. AI as inspection, did this machine install this thing right, so you have an inspector tool and you have an AI machine building it, it's a little bit like a GAN, you can think of, right? So this is happening already, and I think that's one of the good parts of AI, is that it takes away those harsh conditions that humans had to be in before to build devices. >> Do you think AI will eventually make large retail stores go away? >> Well, I think as long as there are humans who want immediate satisfaction, I don't know that it'll completely go away. >> Some humans enjoy shopping. >> Naveen: Some people like browsing, yeah. >> Depends how fast you need to get it. And then, my last AI question, do you think banks, traditional banks will lose control of the payment systems as a result of things like machine intelligence? >> Yeah, I do think there are going to be some significant shifts there, we're already seeing many payment companies out there automate several aspects of this, and reducing the friction of moving money. Moving money between people, moving money between different types of assets, like stocks and Bitcoins and things like that, and I think AI, it's a critical component that people don't see, because it actually allows you to make sure that first you're doing a transaction that makes sense, when I move from this currency to that one, I have some sense of what's a real number. It's much harder to defraud, and that's a critical element to making these technologies work. So you need AI to actually make that happen. >> All right, we'll give you the last word, just maybe you want to talk a little bit about what we can expect, AI futures, or anything else you'd like to share. >> I think it's, we're at a really critical inflection point where we have something that works, basically, and we're going to scale it, scale it, scale it to bring on new capabilities. It's going to be really expensive for the next few years, but we're going to then throw more engineering at it and start bringing it down, so I start seeing this look a lot more like a brain, something where we can start having intelligence everywhere, at various levels, very low power, ubiquitous compute, and then very high power compute in the cloud, but bringing these intelligent capabilities everywhere. >> Naveen, great guest, thanks so much for coming on theCUBE. >> Thank you, thanks for having me. >> You're really welcome, all right, keep it right there everybody, we'll be back with our next guest, Dave Vellante for Justin Warren, you're watching theCUBE live from AWS re:Invent 2019. We'll be right back. (techno music)

>> Announcer: Live from San Diego, California, it's theCUBE. Covering KubeCon and CloudNativeCon brought to you by Red Hat, the Cloud Native Computing Foundation, and its ecosystem partners. >> Welcome back to theCUBE here in San Diego for KubeCon CloudNativeCon, with John Troyer, I'm Stu Miniman, and happy to welcome to the program two guests, first time guests, I believe. Julio Tapia, who's the director of Cloud BU partner and community with Red Hat and Gou Rao, who's the founder and CEO at Portworx. Gentlemen, thanks so much for joining us. >> Thank you, happy to be here. >> Thanks for having us. >> Alright, let's start with community, ecosystem, it's a big theme we have here at the show. Tell us your main focus, what the team's doing here. >> Sure, so I'm part of a product team, we're responsible for OpenShift, OpenStack and Red Hat virtualization. And my responsibility is to build a partner ecosystem and to do our community development. On the partner front, we work with a lot of different partners. We work with ISVs, we work with OEMs, SIs, COD providers, TelCo partners. And my role is to help evangelize, to help on integrations, a lot of joint solutions, and then do a little bit of go to market as well. And the community side, it's to evangelize with upstream projects or customers with developers, and so forth. >> Alright, so, Gou, actually, it's not luck, but I had a chance to catch up with the Red Hat storage team. Back when I was on the vendor side I partnered with them. Red Hat doesn't sell gear, they're a software company. Everything open-source, and when it comes to data and storage, obviously they're working with partners. So put Portworx into the mix and tell us about the relationship and what you both do together. >> Sure, yeah, we're a Red Hat OpenShift partner. We've been working with them for quite some time now, partner with IBM as well. But yeah, Portworx, we focus on enabling cloud native storage, right? So we complement the OpenShift ecosystem. Essentially we enable people to run stateful services in OpenShift with a lot of agility and we bring DR backup functionality to OpenShift. I'm sure you're familiar with this, but, people, when they deploy OpenShift, they're running fleets of OpenShift clusters. So, multi-cluster management and data accessibility across clusters is a big topic. >> Yeah, if you could, I hear the term cloud native storage, what does that really mean? You know, back a few years ago, containers were stateless, I didn't have my persistent storage, it was super challenging as to how we deal with this. And now we have some options, but what is the goal of what we're doing here? >> There really is no notion of a stateless application, right? Especially when it comes to enterprise applications. What cloud native storage means is, to us at least, it signifies a couple of things. First of all, the consumer of storage is not a machine anymore, right? Typical storage systems are designed to provide storage to either a virtual machine or a hardware server. The consumer of storage is now a container that's running inside of a machine. And in fact, an application is never just one container, it's many containers running on different systems so it's a distributed problem. So what cloud native storage means is the following things. Providing container granular data services, being application aware, meaning that you're providing services to many containers that are running on different systems, and facilitating the data life cycle management of those applications from a Kubernetes way, right? The user experience is now driven through Kubernetes as opposed to a storage admin driving that functionality so it's these three things that make a platform cloud native. >> I want to dig into the operator concept for a little bit here, as it applies to storage. So, first, Operators. I first heard of this a couple years back with the CoreOS folks, who are now part of Red Hat and it's a piece of technology that came into the Kubernetes ecosystem, seems to be very well adopted, they talked about it today on the keynote. And I'd love to hear a little bit more about the ecosystem. But first I want to figure out what it is and in my head, I didn't quite understand it and I'm like, well, okay, automation and life cycle, I get it. There's a bunch of things, Puppet and Chef and Ansible and all sorts of things there. There's also things that know about cloud like Terraform, or Cloudform, or Halloumi, all these sort of things here. But this seems like this is a framework around life cycle, it might be a little higher in the semantic level or knows a little bit more about what's going on inside Kubernetes. >> I'll just touch on this, so Operators, it's a way to codify business logic into the application, so how to manage, how to install, how to manage the life cycle of the application on top of the Kubernetes cluster. So it's a way of automating. >> Right, but-- >> And just to add to that, you mentioned Ansible, Salt, right? So, as engineers, we're always trying to make our lives easier. And so, infrastructure automation certainly is a concept here. What Operators does is it elevates those same needs to more of an application construct level, right? So it's a piece of intelligent software that is watching the entire run-time of an application as opposed to provisioning infrastructure and stepping out of the way. Think of it as a living being, it is constantly running and reacting to what the application is doing and what its needs are. So, on one hand you have automation that sets things up and then the job is done. Here the job is never done, you're sort of, right there as a side car along with the application. >> Nice, but for any sort of life cycle or for any sort of project like this, you have to have code sharing and contributing, right? And so, Julio, can you tell us a little about that? >> What we do is we're obviously all in on Operators. And so we've invested a great deal in terms of documentation and training and workshops. We have certification programs, we're really helping create the ecosystem and facilitate the whole process. You may be familiar, we announced Operator Framework a year ago, it includes Operator SDKs. So we have an Operator SDK for Helm, for Ansible, for Go. We also have announced Operator Life Cycle Manager which does the install, the maintenance and the whole life cycle management process. And then earlier this year we did introduce also, Operatorhub.io which is a community of our Operators, we have about 150 Operators as part of that. >> How does the Operator Framework relate to OpenShare versus upstream Kubernetes? Is it an OpenShift and Red Hat specific thing, or? >> Yes, so, Operatorhub.io is a listing of Operators that includes community Operators. And then we also have certified Operators. And the community Operators run on any Kubernetes instance. The certified Operators make sure that we run on OpenShift specifically. So that's kind of the distinction between those two. >> I remember a Red Hat summit where you talked about some bits. So, give us a little walk around the show, some of the highlights from Operators, the ecosystem, obviously, we've got Portworx here but there's a broad ecosystem. >> Yeah, so we have a huge huge ecosystem. The ISVs play a big part of this. So we've got Operators database partners, security partners, app monitoring partners, storage partners. Yesterday we had an OpenShift commons event, we showcased five of our big Operator partnerships with Couchbase, with MongoDB, with Portworx obviously, with StorageOS and with Dynatrace. But we have a lot of partners in a lot of different areas that are creating these Operators, are certifying them, and they're starting to get a lot of use with customers so it's pretty exciting stuff. >> Gou, I'd love your viewpoint on this because of course, Portworx, good Red Hat partner but you need to work with all the Kubernetes opt-ins out there so, what's the importance of Operators to your business? >> Yeah, you know. OpenShift, obviously, it's one of the leading platforms for Kubernetes out there and so, the reason that is, it's because it's the expectations that it sets to an enterprise customer. It's that Red Hat experience behind it and so the notion of having an Operator that's certified by Red Hat and Red Hat going through the vetting process and making sure that all of the components that it is recommending from its ecosystem that you're putting onto OpenShift, that whole process gives a whole new level of enterprise experience, so, for us, that's been really good, right? Working with Red Hat, going through the process with them and making sure that they are actually double clicking on everything we submit, and there's a real, we iterate with them. So the quality of the product that's put out there within OpenShift is very high. So, we've deployed these Operators now, the Operator that Portworx just announced, right? We have it running in customers' hands so these are real end users, you'll be talking to Ford later on today. Harvard, for example, and so the level of automation that it has provided to them in their platform, it's quite high. >> I was kind of curious to shift maybe to the conference here that you all have a long history. With organizations and both of you personally in the Kubernetes world and cloud native world. We're here at KubeCon CloudNativeCon, North America, 2019. It's pretty big. And I see a lot of folks here, a lot of vendors, a lot of engineers, huge conference, 12,000 people. I mean, any perspective? >> So I've been at Red Hat a little over six years and I was at the very first KubeCon many years ago in San Francisco, I think we had about 200 people there. So this show has really grown over the years. And we're obviously big supporters, we've participated in KubeCon in Shanghai and Barcelona, we're obviously here. We're just super excited about seeing the ecosystem and the whole community grow and expand, so, very exciting. >> Gou? >> Yeah, I mean, like Julio mentioned, right? So, all the way from DockerCon to where we are today and I think last year was 8000 people in Seattle and I think there're probably I've heard numbers like 12? So it's also equally interesting to see the maturity of the products around Kubernetes. And that level of consistency and lack of fracture, right? From mainstream Kubernetes to how it's being adopted in OpenShift, there's consistency across the different Kubernetes platforms. Also, it's very interesting to see how on-prem and public cloud Kubernetes are coexisting. Four years ago we were kind of worried on how that would turn out, but I think it's enabling those hybrid-cloud workloads and I think today in this KubeCon we see a lot of people talking about that and having interest around it. >> That's a really great point there. Julio, want to give you the final word, for people that aren't yet engaged in the ecosystem of Operators, how can they learn more and get involved? >> Yeah, so we're excited to work with everybody, our ecosystem includes customers, partners, contributors, so as long as you're all in on Operators, we're ready to help. We've got tools, we've documentation, we have workshops, we have training, we have certification programs. And we also can help you with go to market. We're very fortunate to have a huge customer footprint, and so for those partners that have solutions, databases, storage solutions, there's a lot of joint opportunities out there that we can participate in. So, really excited to do that. >> Julio, Gou, thank you so much, you have a final word, Gou? >> I was just going to say, so, to follow up on the Operator comment on the certification that Julio mentioned earlier, so the Operator that we have, we were able to achieve level five certification. The level five signifies just the amount of automation that's built into it, so the concept of having Operators help people deploy these complex applications, that's a very important concept in Kubernetes itself. So, glad to be a Red Hat partner. >> That's actually a really good point, we have an Operator maturity model, level one, two, three, four, five. Level one and two are more your installations and upgrades. But the really highly capable ones, the fours and fives, are really to be commended. And Portworx is one of those partners. So we're excited to be here with them. >> That is a powerful statement, we talk about the complexity and how many pieces are in there. Everybody's looking to really help cross that chasm, get the vast majority of people. We need to allow environments to have more automation, more simplicity, a story I heard loud and clear at AnsibleFest earlier this year and through the partner ecosystem. It's good to see progress, so congratulations and thank you both for joining us. >> Thank you, thank you. >> Thank you. >> All right, for John Troyer, I'm Stu Miniman, back with lots more here from KubeCon CloudNativeCon 2019, thanks for watching theCUBE. (electronic music)

>> Announcer: Live from San Diego, California, it's theCUBE covering Cisco Live US 2019, brought to you by Cisco and its ecosystem partners. >> Good morning, welcome to theCUBE's second day of coverage of Cisco Live 2019 from San Diego. I'm Lisa Martin, my co-host is Stu Miniman, and Stu and I have a couple of guests from Cisco's Data Center Networking group with us. To my right, Thomas Scheibe, VP of Product Management and to his right, Ranga Rao, Senior Director of Product Management. Guys, welcome to theCUBE. Welcome back, Ranga. >> Thank you very much. >> Thomas, great to have you. >> Happy to be here, yeah. >> So, here we are in the DevNet Zone, this is, Ranga you were saying this is probably one of the busiest locations within all of Cisco Live. It's been jammed since this morning. >> Stu: You got the ACI Takeover going on right now. >> It is! >> Yes, yes. >> So with that said, Thomas, with ACI, application-centric infrastructure, all these changes to the network, what's going on on day two? >> It's fun, it's actually, yeah, as you say, it's a little busier. We have a good set of news coming this week and yeah, you're going to hear more but let me give a little bit of a hint here as to what we're doing. We talked about how do we extend ACI into the cloud, or, as they say, anywhere. We did this two or three months ago with AWS. We're following up with the same for Azure, as well as having extension into the IBM Cloud, so that's really really exciting, opens up a lot of capabilities not just for the networking teams, how to extend into the cloud, we also have some interesting things around how we actually can start with ACI in the cloud first for the app developers, and then come back if you want to deploy either in the cloud or on prem. So, really an extension of what is doable for the networking team, as well as actually for the app teams. >> So, any ACI, any platform, any location, any workload? Any hypervisor, any container platform you want, yeah. Flexibility, that's what this is about. So, Ranga, spoke with you earlier this year at one of your partner shows, talking about all the latest, you know, AI, cognitive learning and all of those pieces there. Partner's obviously a big piece of the ACI story here. >> Thomas: Yeah. >> Thomas was giving a little talk about some of the cloud provider, what more can you share about what's happening with how your product integrates with a vast ecosystem? >> Absolutely, when we built ACI, we sort of anticipated this moment when this crowd in the DevNet area that we are seeing, right? Like, from people shifting from a very CLI focused approach to a developer focused, and integrated solution-focused approach. So we built ACI as an open platform. We have 65 plus partners, and with new integrations coming on like every so often. Just this at Cisco Live, we are launching an integration with F5. F5 is building, has built, an app for the Cisco ACI App Center, and a whole number of tools and integrations for developers. We have essentially built integrations with Ansible, Terraform, and new Python modules, so these are all exciting new things coming at Cisco Live. >> So, when you guys talk with customers, being in product management, I know you talk to customers all the time. There's presumably a very bidirectional symbiotic relationship. When you're talking with customers, Thomas, what are some of the values that they're looking for ACI to help them deliver, especially as it relates to being able to get more value out of the data that they've got? >> Right, so there are a couple of things that are probably standing out. One is, if you turn to the networking team, it's all around network automation and segmentation. There are the two biggest things everybody's after. Particularly if you look as the data is more distributed it's become harder and harder to do those all manually. You want to automate your date one activity as well as you want to make sure you can enforce segmentation of the data lost in the cloud, and on prem, all over the place. So these are the two big ones that really every network operations team is after. And then the second piece that we see obviously more and more is, what about day two? Give me better visibility, in particular, as they say, if the data is so dissolute, get me better visibility. What is going on? And then be able to tie this back to the user, which is the application team. Is there an impact on the application or not? And so there are a lot of interesting tools that we have and we're going to demo those all here, that is available for the networking team. The other piece, really, and you asked for the values, as I said, the app team, right? What is today, if an app team is developing they're typically then handed over in production. This is where this friction happens. How long does it take to go from here to here? If I can shorten that one and just take the blueprints out of the app development process, and map it directly to the automation capability of ACI, I can shorten the cycle to deploy, and so that's a tremendous value that we do see from customers. >> Great, lots of discussion in the keynote about the ever changing architectures that are happening here. Give us the update, you know? We've been down this path for ACI for a few years now, but where are your customers at? You know, what are the new things that are causing them challenges and opportunities, Thomas? >> Yeah, I probably use instead of ever changing I would say ever expanding, but you're absolutely right, because what we saw when we started this office rollaround how do I automate my data center? How do I get a cloud experience in my data center? What we see changing, and what I find is driven by this whole app refactoring process, that customers want to deploy apps maybe in the cloud, maybe develop in the cloud and so they need an extension to their automated data center into the cloud, and so really what you see from us is an expansion of that ACI concept, to Ranga's point, we actually really didn't change. We're just extending it to container development platforms, to different cloud environments, but it's the same idea, how do I automate the end to end network reach as well as these segmentations? >> Ranga, maybe can you expand on some of the automated piece of it, even, you know, I look at one of the things that jumped out at me this week is there's some changes to the CCIE program. It's not just, okay, I've done it, and I do my test. It's, well, we understand that things are changing year to year, and therefor how I get my certification, How I keep up on these, it is going to change. Where does automation play in all of this? >> Absolutely, I mean, when you think about automation there are two key parts to it. One is the automation that happens within the fabric that the controller manages and there's a lot of that. To extend on what Thomas was saying, right, in terms of how quickly the fabric can be brought up, how quickly applications can be deployed on the fabric and so on. Beyond that, there's automation that we have built leveraging the modern devo apps and CICD tools that are very popular among the developer community. Like I said, we have built integrations with Ansible, Terraform and so on, but we have also made rich APIs available across our platform. Every piece of information that the controller or the switches have is very much accessible for developers. That's really a back breaking approach to networking where developers have access to everything and can program to the network. So I think that's where the world is going, and that's what we plan to support with automation. >> Let me comment on this, because there's an interesting piece where we did, right? We have this fabric controller called the APIC. It's actually an App posting platform as well and so where we're actually taking advantage of that, everything is code in ACI, and you can write as a partner customer, apps, right on top, and so like the F5 integration that we have done is literally an F5-written and app residing there, right? And so it's really, really really flexible to build workflows around what you want to do on any infrastructure, for customers themself, for any partner themself. >> Yeah, it's an interesting piece, cause when you think, you know, I think back at my career. How much did the network really, the network architect think about the applications? Like, oh, okay, how much throughput and sure, I need it to go from north to south to east, west. Or, oh, wait, this thing needed some extra buffer credits, but usually, you know, the business owner, application owner and the network people, they were throwing things over the wall between each other and tweaking some dials here. Now when I look around this show, it's we're talking about building applications at the core of it and it's happening together. Can you speak a little bit to some of the activities going around, and that trend? >> No, absolutely, it's actually exciting. I actually, because my background is I did programming long long time back, and it's actually- >> Back when you called it programming, not coding? >> Correct. (laughing) it was programming. It is actually exciting to see, and I can tell you over the fast four, five years, when we run these techtorials we ask, cause, like architecture and programming, how many people are interested programming? And it used to be, I dunno, 10%. Now it's literally 60 to 70% of the people in the room are saying we're using automation frameworks like Ansible, and they actually see what we're doing and the value and they want to learn more. So there's a significant shift in terms of what people expect, what they want to do as a network infrastructure, versus what it was in the past. It's just a reflection on, as I said, the agility that is needed out of the infrastructure, and how do we react to what the developers, the users, want to do that put the apps on, so. >> In the spirit of Cisco's bridge to possible which was the Barcelona theme, is this a bridge to IT and business working better together? >> Absolutely. I mean the way... I dunno whether I can say much, but it's absolutely, how do I bridge, we call it initially, how do I bridge between what you called out, the networking and application team? It's the bridge to possible. It's not like, oh, it's your problem, it's my problem. We can do it together or these two teams can do it together, absolutely. That's actually a very good reference. >> To add to that, when we were in 2012 thinking about what should ACI be, everyone in the industry was somehow thinking that all the network engineers will magically become programmers, right? So programmability is a big part of what the network needs but also being aware of the application and being able to respond to the right needs of the application at the right moment is a pretty big thing, and that's what we have built with ACI, with the first class support for programmability. >> And the programmability that we're seeing and hearing about, Ranga, how is that a differentiator for Cisco? >> So, I think, first of all, the network, we have always believed, is the nervous system of the enterprise, so a lot really interesting information goes through the network, so unlocking the value of the network for these different use cases is what's made possible with the programmability approaches that we have taken, right? The only reason why we have 65 plus partners programming to our platform, is because we have these open APIs. We have a ton of channel partners using the open APIs to build apps and to, like, support videos, different use cases for our customers, with ad hoc automation or even using some of the automation framework, so it has really, evolved the network from being CLI-centric to being solution and programmability centric. >> Maybe one point to make since he said open APIs, and I can't overemphasize this, we're truly open APIs, right? Because sometimes there's in this, not naming names, but people saying, oh, you have to be a certification to use these APIs. That is not the case for Cisco APIs on ACI. They are open. Everybody, customers, partners, quite frankly, even competitors could use those to program. We're standing behind our APIs. They can be used as-is. >> So it is quite a big change. I mean, people know historically, Cisco it's like, well, Cisco solves customer problems, and then they would drive it through the standards. Here, you know, we we've watched the ascendency of the DevNet group, and you know, hundreds of thousands of people now helping to build code. It's the API economy, so, you know, very much it's not the Cisco I thought about a generation ago. >> Thank you. You know, we take this as a compliment. We're actually really excited to see how much development is possible by opening this platform up with APIs and I think that somebody else said this, so it's not all mine, but your more APIs to have, you have user lists to integrate, the faster we jointly develop and actually achieve what we want, so that's the bridge. >> And the beautiful thing about APIs is customers and other developers learn things that we wouldn't have and we learn, and we are seeing a lot of that, so that's another way of unlocking innovation for our customers, and we are seeing a lot of that, you know? >> So when we talk with any customer, any business, we always talk about speed scale, speed to innovation. With the wave of connectivity, the expansion of 5G, WiFi six, the proliferation of mobile data that's going to be traversing the networks the next few years. It's going to be video. How is an application-centric infrastructure going to allow customers to take advantage of the demands on the network that need for speed, so that your customers can be as competitive as they need to be? >> Let me try to kind of tune down to the essence there. What really is going on, you have all these different applications, as you point out, all of these users and endpoints, and what you want to do is you want to have an ability to correlate between what the user wants to run on the infrastructure and how the infrastructure has to behave. And then also, you want to correlate back as to infrastructure issues, who is impacted? And really was ACI was about is not rebuilding applications. It was about, provide this glue, this bridge, between what's going on in the infrastructure to what the user experience is, and if I can do this it becomes so much more efficient and it's so much easier to roll out all these new applications on an ACI infrastructure. >> Exciting stuff, guys. Thomas, Ranga, thank you for joining Stu and me on theCUBE today. Lots of exciting stuff. We'll be listening for those announcements that you said are coming out later today. >> Yeah, okay. You will see them. >> All right, excellent. Thanks, guys, we appreciate your time. >> Thanks, same here, appreciate it. >> Thank you. >> Our pleasure. For Stu Miniman, I am Lisa Martin. You're watching theCUBE from our second day of coverage of Cisco Live San Diego. Thanks for watching. (energetic theme music)

from our studios in the heart of Silicon Valley Palo Alto California this is a cute conversation hello everyone welcome to this cube conversation here in Palo Alto California I'm John Fourier host of the cube we are in the cube studios we're here with Ranga Rao's Senior Director of Product Management Cisco Networking group and David link the CEO of science logic tell what their part is guys great to see you thanks I come in it glad to be here so you guys had a great event by the way symposium in DC thanks a lot of momentum yeah it was fun to watch he was there eight videos up on YouTube but you guys are classic partnership here with Cisco talk about the relationship you guys are meeting in the channel got a lot of joint customers a lot of innovation talk about the relationship between science logic and Cisco Cisco and science logic have been working together because our customers demand us to work together right so across more for more than 10 years science logic has been a strong part of for Cisco working across various different business units when we started working on ACI which is application centric infrastructure which is the product that my group works on we thought science logic was a perfect partner to work with and in fact some of our joint customers including Cisco IT which is a customer of science logic two came to the table and said we needed an integration with science logic so customers are a huge part of the genesis of the partnership and that's what keeps us going together and the fact that we have such strong synergies from a technology perspective makes it really easy for us to collaborate and the fact that we have both open platforms with strong api's makes it really easy for us to collaborate and this is the real haveá-- we're here to the chalk tracks around you know these API so this abstraction software layer zci has kind of gone the next level we covered that at Cisco live Dave we talked before about your value proposition you're on the front lines Cisco obviously has this new programmability model where their goal is to leverage the value the network abstract away the complexities and allow people to get more value out of it how is that working in what and how do you guys tie into Cisco it's really an ecosystem of technologies and partnerships that deliver outcomes for customers Cisco's advancing technology so fast we've seen an innovation sprint from Cisco it's actually causing us to sprint with them on behalf of their customers but ultimately we've had to introduce deep integrated monitoring across all the fabrics that they support for software-defined and that includes visibility into the ACCAC eye fabrics but it also goes through into the virtual machines the storage layers the operating systems the application layer so when you pull all of that together that's a day to challenge for the enterprise to make sure they're delivering outcomes to the customer that are above expectations recently we supported support for multi-site and multi pod ACI fabric we're working on a CI anywhere in the cloud we're really Cisco's extending the datacenter hyper-converged solutions to deliver value propositions no matter where the applications live so that's a huge step forward and then it causes operational initiatives to say how are we going to solve that problem for our customers no matter where the application lives so that's really where we're focused helping solve problems on that day to side of make all these technologies come to collect together to deliver a great outcome for the end-user and so they're enabling you with the with the ACI if a hyper converges to go out and do your thing yes so we instrument all those different really abstracted components because what we have with container management with Software Defined it's abstraction on top of core routes which and server and hypervisor technologies that bring it together in an intuitive way ultimately what we've seen from the enterprise and service providers is they really want infrastructure delivered as a service and that's really where Cisco's headed helping make that a reality with these products we just helped bring them together with the instrumentation analytics to operate them as one system you know ranking this is a great example of what we're seeing in this modern era with the data center on premises modernization growth of the cloud the advent of you know real hybrid cloud and private cloud as well as public cloud you guys are in a good position so I want to kind of dig into some talk tracks one you mentioned day two operates I've heard that term kicked around before cuz this is kind of speaks to this modernization in IT operations you know enabling an environment for you know Network compute storage to work seamlessly this is this is the real deal what is day to operations can you like define what that is yeah so our customers essentially go through the journey of building and network for some purpose to deliver an application or a service to their end users so the we think of the process of them building the network is day zero configuring the network for the particular operator a particular service as day one and everything that they deal with which is a lot of complexity which is they where they spend 80% of their time 90% of their budget has data operations which is a very complex domain so this is the area that we have been focusing within our business unit to make our customers lives easier with products that essentially solve some of these problems and collaborating with partners like science logic to make the operations of our customers much easier a important part of data operations is making sure that we provide the light right kind of abstractions for our customers initially customers used to configure switches on a switch by switch basis using command line interfaces with obscure commands what we have done within the data center as of like 2014 is brought in the application centric abstraction so customers can configure the network in the language of the application which is the intent interesting you know I'm old enough to remember those days of standing those networks up day one day zero on day one but I think day two has become really the new environment because day 2 operates was simply you know make sure the lights are on provision the switches top of racks all that stuff that went on and then you managed it you had your storage administrator all these things we're kind of static perimeter based security all that now is kind of thrown away so I think day two is almost like the reality of the situation because you now have micro services you've got apps and DevOps manding to have the agility and programmability the network so I got to ask is if that's true and you got cloud over the top happening this means that the software has to be really rock-solid because it's not getting less complex it's getting more complex so it's what is science logic fit in today too we've been focused on all these different technologies you bring together so from an intent based perspective Cisco's been really focused on intent based solutions but that lines up to a business service the business service is made up of a lot of different technologies that can come from many different locations to deliver you an application to you where you're super satisfied with an outcome its delivering productivity to you all the great things that you're hoping to experience when you interact with an application but behind the scenes there's a whole myriad of technologies that we instrument from a fault configuration performance analytics and really an analysis perspective to see all these multivariate data streams coming together in a hub where we can analyze them and understand the relationships the context of how all of those data feeds come together to enable a service so if we know that service view again no matter where the service is coming from in Cisco is now supporting ECI anywhere so that service could be sitting in a lot of different places today and we're seeing more and more hybrid applications and I think that's around for a long time to stay for good reasons security compliance and other reasons you've got to bring all that together and understand real time the real time operational viewpoint of how is it now and more so that proactive insight to know if you have an anomaly across any one of these performance variants how that may impact the service so is it going to be impacting the service and really help operations stay proactive I think that's where the DevOps and focuses right now look at evolution of DevOps gene King was on the cube said 3% of enterprises have adopted Ewa certainly there's the early adopters we all know who they are they're there DevOps cloud native from day one but really the adoption of DevOps is not yet there on mainstream is getting there but you're speaking to day two operations as kind of like operations you mentioned developers the apps that needs to be built to require all this infrastructure program ability this is where I think a CIS can I guess you need intrument ation so you need IT ops and you got to have program ability of the network but everyone's talking about automation so to me it sounds like there's an automation story in here if you got an instrument everything you got to have move beyond command line and configuring is that how does that fit it how's the automation finish yeah absolutely first of all within the ACI fabric we have we have a controller based approach so there's a single place of managing the entire infrastructure today we have customers who use two hundred-plus physicals which is being managed by a single point that's a huge amount of automation for provisioning the network from the perspective of managing the network the controller continuously looks at what's going on and essentially we have a product core network assurance engine which look which are which is a second pair of eyes which will tell you proactively if there are problems in the network right but a broader automation is needed where you can actually look at information from various different silos because network as important as we as cisco think is one part of the whole puzzle rate information comes from many different places so there's a platform that's needed where people can funnel in pieces of information from various different places and analyze that pieces of information figure out trends find the things that are of interest to them and operate in a data-driven fashion they want to get your thoughts on this next talk track around the impact of the cloud because if this happens the automations is pretty much agreed upon the industry therefore we've gotta automate things that are repetitive mundane tasks and certainly the network's a lot of command line stuff that can be automated away value will shift to other places but with the impact of cloud operation the operational side of the data center is looking more and more cloud like so in a way whether the debate of moving to the private cloud versus on-premises goes away and it becomes more of a cloud operation story on-premises multi-cloud on public clouds is kind of a new system this is the operational shift this is where all the action is talk about your perspective on this because this is kind of like you know it's not a simple saying lift and shift and moving into the cloud it's I want cloud like economics I want cloud like elasticity I want all that benefit on premises that's day two in my opinion would you agree I do and I think that's sometimes lost on the industry that we have a lot of clouds that we have to serve and for good purpose they're gonna live in different places but back to the earlier comment you've got to then pull information into a data hub I'll just call it in an architecture of data where you've got it from multiple sources whether it's clouds or private hyper-converged the wireless to the end-user all these different layers often that are being abstracted we've got to really understand how that relates to a user experience so when we think about what are the end results we're trying to achieve we're trying to be proactive so among the things that we're working on from a vision perspective instead of thinking about waiting for a system across any one of these tiers to have a fault where it tells you I've got a problem here's a trap here's a log I've experienced this problem we really want to do a lot more on the front end that performance analytics the anomaly detection to get across multivariate all these very 'it's mean this kind of performance health and in a performance score cisco has been investing heavily here we have as well jointly for some of our customers what I'd like to see in the future our vision is that you rely less on fault management and more on the proactive analytics side so that you understand anomalous behavior and how that could impact your experience as an end-user and fix it through automation before there is a problem so that's a very different thought I'd love to say our industry should in the future worry less about event correlation and more about predictive behavior so that's where we're spending a lot I don't like so wherever the false look for the goodness to I mean where's the zag lesion but you have to have all these data streams and you have to understand how they can textually relate to one another to make those important decisions and recommendations well you know I've always said this on the cube you can you know in this world of digital you can instrument everything so you soon it's going to be a matter of time for seeing what everything's happening but knowing what to look at it's kind of like what you're getting at yes hey Rach I talked about your your perspective because again and one of the things that Dave Volante and I just do many we talk about all the time on the cube is we debate this cloud conversation because I think my opinion is it's one big distributed architecture second operating system the cliff it's all the cloud they're all edges nodes and arcs on a distributed a dissenter certainly isn't going away but if everything is a network connection well that's the edge your data center you got this is you're in the business of networking right what's your take on all this because you know if it's a cloud operations it's a shift from the old IT to the new IT what's your perspective on this so the moniker that we have been using this year is that there's nothing centered about the data centers like you said there are workloads that reside in many different environments including the cloud so customers are demanding consistent operations and consistent management capabilities across this many different environments right so you're right the data center itself is turning out more to be like a cloud and we have even seen large cloud providers like offer solutions that sit within a customer's data center right so that's one area in which the words evolving another area is in terms of all the tools that are coming together to solve some of the operational problems to be more predictive and more proactive yeah you know I like to draw horns sometimes too many minute we keep on coin the term private cloud years ago and everyone was throwing hate at them you know on the way I don't know what's this private cloud nonsense if that's what's happening there's a private cloud it's a hybrid cloud multiple clouds you have public cloud and again you're gonna have multi purpose pick the right cloud for the workload kind of environment going on kind of like the way the tools business would but it's still platform so so guys thanks for coming in and sharing your insights I really appreciate that before we go take a minute to get the plug in for what you guys are working on give the company update what's going on you're hiring revenues up what's happening give us a state of the science logic what's going on so we had a great first quarter the best first quarter in the history of the company the health of the business is good I think the underlying theme is the transform of infrastructure is causing a lot of people to rethink the monitoring tooling as to how do we need to manage in this new operating environment you mentioned DevOps I think the real key there is the developer really wants to have the application be infrastructure aware and he needs good information coming from not 50 places but from a trusted place where he can make sure the application knows about how all the infrastructure that's supporting it no matter where it is is behaving and that's really the wind behind the sales driving our business we grew quarter-over-quarter sequentially with our subscription over a hundred percent in q1 so we're really thrilled with where the business is headed excited about the momentum and this is a really important partnership for us because everybody uses Cisco all of our enterprise customer service provider government customers Cisco is embedded in virtually every customer that we work with so we have to have the best support kind of that thought leadership of support for our customers for them to entrust management of those core applications through our platform right it gives a quick plug for the data center networking group what's happening there what's the the hot items what's the with give the plug quickly so very quickly I think the journey that we have been on is a CI any where to take a CI and its management and operations paradigm to many different environments we introduced support for AWS earlier in this year we are working on support for Azure and soon we'll have support for Google public clouds in all these environments we want our customers to have consistent experience and the way we get that is through solutions working with partners where we offer consistent solutions across all these environments for our customers and working with science logic as a very important partner to solve problems for our joint customers and you guys have always had a great Channel great ecosystem now you have not new for you to partner yeah we have like open API is open platform 65 plus partners that we work with so all customer focus well let me put you on the spot one last question got you here because your guru and networking and you know you've been around the block you've seen the different waves what's the biggest wake-up call that customers are having with respect to the old way of doing networking and the new way cuz clearly everyone has come to the realization that the perimeter based security model and static networking has to be more dynamic what's the big wake-up call that you think customers are seeing now with this new modern era I think customers are realizing more and more how important technologies part of it as part of their business sometimes it even drives the techni drives the business and helps customers make ditions on what's the right path to take for their business so what this applications become really important and the nerve center which is the network that supports the application becomes really important so customers are demanding us to build the best network possible to support this modern world that's continuously evolving so did you think a stab at that customer wake-up call what's your perspective on this what's the big R from your experience over the years you can't use tools that were built 20 years ago to continue operating global networks so we see a lot of the industry it's about a ten billion dollar total addressable market changing over because the market fit of the old tools that people have relied upon for many years aren't solving modern problems Oh guys thanks for the insight appreciating and good to see the partnership doing well thanks for coming into the cube studio we have Ranga Rao senior director of product management Cisco Networking group and David Lynch CEO of science logic here for cube conversation I'm Sean Fourier thanks for watching you [Music] you

>> from San Francisco. It's the Cube covering the M Wear Radio twenty nineteen brought to you by the M where >> welcome to Special Cube conversation here in San Francisco for PM wears radio event there. Top engineers air here for once a year Get together and show in the best stuff road map to get to great guests. Here we got Mike Petrillo is the senior director of Blockchain of'em were here where their journey is, where they come from and gone to today where they are up to and for Team A Gluckman Engineering leader Blockchain engine both with GM Where great to see you guys. Thanks for coming on. Appreciate spending the time My favorite topic block Jane our favorite topic to thanks for joining me. So, Mike, let's start with you. Take a street where you guys are now Because we talked about a year ago. Just getting the putting the team together. You were here last year radio kind of getting some core mo mentum. Where have you guys come from? And where are you now? Yeah, >> it's been quite a journey. You know, over the past five years, we've been doing a lot of research that research culminated in an open source project called Project Concorde that we announced last year. Then we wrapped some commercial offerings around it around really the operational side. How do you operate a blockchain at scale in an enterprise setting we introduced? That is Veum wear Blockchain. It's a very descriptive on the naming, and it really focuses on three core things. Enterprise grade, decentralized trust, not distributed trust but really decentralized trust. So being able to deploy it across multiple different cloud environments as well as on prim, it concentrates on robust Day two operations. How do you operate it at scale in an enterprise setting? How do you deal with stuff like GDP are the right to be forgotten? You know, data sovereignty issues, things like that, which is much different than other block Janes. And then the third thing is really being developer friendly. Last year we were fully Ethereum compatible. We had the Ethereum language sitting on top of our block chain. Since then, we've added support for Djamel from digital asset. So another language and we're adding more and more languages so the developers can develop in the framework that they're used to on the best scaleable. You know, Enterprise Supporter >> brings him Dev Ops Mojo Concepts to blockchain. Absolutely, absolutely somewhat. The demo you guys did you get on stage? I want to get too, because it's a really use case. So again, rnd concept jewel years of research started putting together making some progress on developer. So the solutions that you guys presented, we really take us through that. >> So the ocean plastics demo that we talked about a radio basically solves this problem or, you know, just the plastics polluted, polluting our oceans today. So if you look at the numbers are staggering and the BP that you actually a consume, you know, in fish it's pretty scary. The other thing is, you know, it's been predicted that we'll have mohr plastic in our oceans than fish by twenty fifty. And one of the things Dell is trying to do is clean up the environment, and they're building these reusable trays or packaging material for their Dell laptops. And so this use case was providing them that functionality. And if you look at del they you know, they have a massive supply chain. They've got hundreds and thousands of renders that you know, they use despite systems that don't actually talk to each other, they've got complicated work flows. There's also a lot off corruption in their supply chain. And one of the things that can really solve a lot of those problems is bmr blockchain. And they have an instance running on our service, which runs on VM CNW s and I walked through the devil of just going through an aggregator to getting to a manufacturer and then assembling these laptops with the trays and shipping them off to >> think this was something we covered. A del technology world. I just wanna point out for the folks watching Dell's taking recycled material from the ocean, using it as materials into their laptops as a part of sustainability. Great business. You know, Malcolm for del the supply chain pieces. Interesting. So you guys are using blockchain and track the acquisition of the plastic out of the ocean? >> Yes. To manufacturing and to end >> Yes. In tow? Yes. >> Using of'em were blocking. >> Yes. So who coded >> this up? It was actually >> del Del developers coded it up. So they quoted up. They took two weeks to code it. We had absolutely no support issues that came away, which really talks about these. A fuse of our platform. And, you know, just the applications that running. >> How does someone get involved real quick at the plug for they have someone joins the bm where? Blockchain initiative? Yes, Via more block >> change. That's ah, manage service offering from us. So it's a license, you know, product. If they're interested, they can go to the inn where dot com slash blockchain or emails? The blockchain dash info beyond where dotcom get it signed up on the beta. We have an active beta. We have lots of enterprise customers all around the planet using it today. You know, at scale >> is a free servicers are licensed paid license. >> There will >> be a paid license for it. You know, we're invader right now, so it is free, right? >> I get it. But we're gonna get you in the snow. So it is. It is a licence service. Yeah, It's Amanda's >> service offering on. That's you know, the beauty of it is that you don't have to worry about updating it, you know, keeping the nose live anything like that. We don't see the transactional data. We don't manage the nodes or anything like that. But we deploy and keep him updated. Keeping refresh. >> You know, one of the benefits Ray Farrell's on. Just how about the ape revolution? How I change the world with the Internet and the web that blockchain has that same kind of inflection point impact you mentioned GDP are that implies, Reed. You know, changing the the values on the block chain will dwell in the Ganges is immutable. How do you handle that? Because if it's already immutable on encrypted, how does Tootie pr work? >> Yeah, just doesn't take >> care of you. Why wait? If it's encrypted, no one can see it. >> Yeah, well, you know, block chains, not about encrypting the data, right? There are some block change that do encrypted data. People get confused because they associated the cryptography with it, which links the blocks together. But the data in there is still visible. Right? We're working on privacy solutions to make privacy per transaction. We're working on GDP our issues right now, because that is an issue. When you get into a regulated environment, which there isn't really a non regulated environment these days. You have to worry about these things. You know, Blockchain gives you a mutability and that gives you to trust. But really, Blockchain is about trust. It's about this decentralized trust. And when you think about it in that context, you say, Well, if I trust that I want to be able to delete that data and we reach consensus on it and we still maintain the order, right, the proper order of the bits, which is really what Blockchain is doing, is giving you trust on that order. Bits and I agree, is a consensus to delete one of those pieces out of the order bit, and we can still maintain trust of the order bits. And that's fine. Now I can't get into details on this engineering secret sauce. >> I. So one of the things I want to ask >> you guys just engineers, because I think this is one of the things that I see is that Blockchain is attractive. There's a lot of unknowns that coming down the pike, but we do know one thing. It's a distributed, decentralized kind of concept that people like it. I see a new generation of attracted the blockchain new generation of entrepreneurs, a new generation of young people, engineers who see use cases that others from old school industry might not. So you start to see. I won't say it's the hipster or cutting edge. It's just that it's attracting this kind of new generation developer for engineer. >> Why do you >> think that's the case? Why, and and is that right assumption to look at? Because, you know, when asked his blockchain, certainly state of the art. Yeah, it's not as fast as a database of I wanted to do something. Technically, that's like saying the Internet dial up was bad. But what happened after? So you know, a lot of people making these arguments, But I see it definitely resonating with young people. Yes, look at Facebook who tried looking blockchain and moving their entire a broken system. Teo blocked, Jammed. Try to fix that so you can feel these indicators. What's your thoughts? >> I think >> part of it. And I definitely saw this, you know, last Friday and Saturday, I was eighty three up in New York City, right, and it was very much that hipster crowd, and I was really attached to the crypto currency phase. Cryptocurrency allowed individuals to make investments. You know, the kind of millennials to make investments. They didn't have to go to a e trade or they didn't have to go to some broker. It wasn't caught up in anything. They could, you know, make these bets. And now they can build applications that are directly attached to that currency. They can make up their own currency. They can make up their own value system. You know, you've done some of that with a cube, right? We've launched an application that provides value around the content and token eyes. Is that value? And now it can transfer that value So it opens up the transfer of that value, the trust of that value. And I think, you know, we're in a generation of trust and transparency. That's what's powering the world right now is about trust and transparency, and that's what Blockchain gives you, gives you trust in the system that no one person or no one government owns, and >> I really like that. >> But one thing important is I mean, we just have to demystify this like we just have to say, This is not about Cryptocurrency. That's one thing, and what I am is doing is enterprise blockchain and, you know, and Mike, you've talked about this. You always say, you know, black jeans, not going toe, you know, save the world or, you know it's not going to get rid of poverty. But there's four use cases that we've drilled down to in the supply chain realm, and there's the financial services. And so those are some of the things we're tackling, and I think it's important to like talk about that. And, you know, there's these hipsters every time we go and talk anything with regarding the blockchain, we know a big chunk of people, therefore Cryptocurrency and apparently at consensus in New York, they reduce their audience from I can't remember the numbers. When >> I was >> in the two thousand >> last year were about House kicked out all the crypto >> currency people, and so it's important to make that distinction. >> I think the crypto winter probably hurt them more than taking up, because last year there was a lot of hype there, but I think the bubble was already burst around February last year. But this piece of the good point is something that we've been kind of covering on silicon angle. The Cube is there's infrastructure dynamic. The engineering goodness. I think that certainly is intoxicating to think about Blockchain as an impact. Engineering wise, the token conversation brings up utility, the decentralized crypto currency, the icy, his initial coin offerings. The fraud part or regulated part has caused a lot of problems. So to me, well, I tell people was looking the CEO kind of scams and fraud kind of put a shadow on token economics. And Blockchain is a technology so supply chain no doubt is great. Blockchain. That's where you guys are focused. That's what the enterprise want. Way start getting into tokens. Tokens is a form of measurement. Uh huh. And that's where I think the regulators to do your point earlier is it's caused a lot of problems. So you know, the says if you got a utility token and you're selling >> it, it's Zane exchanges, not kill each other. So that's you're >> called. A lot of it would call those app developers. >> Yeah, but the up developers were still out there, right? And what's nice is these app developers that are on the side building these unique little applications. They still end up working for these larger companies and driving interesting solutions through like we're doing with supply chain like we're doing financial services like what we're doing in Telco and Media. You look at the people that we're dealing with in these companies. They came from building those applications. Heck, some of our own product managers came from building unique things mining rigs and mining companies. So you still have that background. They still have that entrepreneurial, you know, asset. And that's what's changing these cos they're driving change these companies saying, Hey, look, we can use the blockchain for this really unique thing that opens up a brand new business line, you know, for this large corporation, >> you know, I showed you our check preview. We did a quick preview of'Em World last year with our block changing me with a cube coin token, kind of total experimental thing. And it was interesting time because I think you hit the nail on the head. We as entrepreneurial developers. I had this great application we want to do for the Cube community, but we were stalled by the you know, the crypto winter, and you know, we're Apple developers, so there's many use cases of such scenarios like that. That's kind of people are kind of halfway between a Z A B. What's your advice toe to us, or folks like us who were out there who want to get the project back on track? What what what should we and application will do? They should they free focus on the infrastructure peace? What's your advice for the marketplace? >> It's so early, it's It's so early to actually really comment on that. I mean, I would say Just keep at it because you never know. It's I feel like we're so early in the game that though we can solve world hunger, there's so many use cases and applications that come out of it and we just have to keep going. And I think the developer community is what's going to make this successful, you know, and even emerging standards. I think that's one thing is standards across, You know, these block chains like we don't have that right now, and that's something we really need to need to do. And >> I don't know we program in Ethereum. >> So the question is, is that a bad choice is a lot of cognitive dissonance around with the right to. >> That's what I was just going to bring up is that >> you know, you brought up the point of your an app developer and you become stalled, you know, in your project. And we see that exactly same thing happening in the enterprise. We go into account after account where they've chosen some block change solution that's out there and to become what I call a stalled pioneer. They've gone through. They develop that application. But they either hit a scale, ability issue and then, you know, throughput or the number of nodes. They hit an operations thing. You know, operations comes in and says, Whoa, how are you going to do an audit on that thing? What about data Sovereign? What about GPR? What about this one? How are we? My God, you're gonna operate it inside of my environment? You know, what's the security side? So it's really round scale ability. It's around operations. It's around security. Those are three things we hit on over and over and over again with the stalled pioneers. So those of the accounts that we go into and rescue them essentially right, we say we can provide you the scale. We can provide you the through, but we can provide you the operations. For twenty years, the Empire has been taking large, complex distributed systems and making you operate them at scale in an enterprise setting. Where the experts at it So we're doing that would block chain now and allowing your blockchain projects to succeed and >> really find that term. Yep. Yeah. Okay, So, radio. What's the feedback here? Obviously got. Got the demo. What's been some of the peer review Give us the the four one one on peer review here. People liking what's going on? >> I think the demo really talk to people. It was relatable. You're a There was a social good a demo. I think it really impacted them. Um, but some of the cool stuff we're doing is also like in the financial services side. You know, we've got Mohr interesting stuff on the supply chain, so the feedbacks been great. Ah, lot of focuses on VM are blockchain, which is also cool. We didn't quite have that last year. In radio, we had everyone running off in different directions. So now it's via more blockchain And what Mike talked about installed pioneers is you know, we were seeing scalability throughput on numbers. And, you know, we talked about it at the immoral Barcelona A numbers. They're looking really great. And, you know, we're we're optimizing pushing our platform so we could get to, you know, perhaps the papal numbers rave, and someday visa, >> you have high availability. You guys know scale. Yeah. You happy where you are right now? >> Very happy where we >> are right >> now. I mean, we've got great customers. Great feedback, you know, great solution that solving real world problems. You know, engineers like doing two things shipping code and solving stuff that's going to help the world. At least you're of'em where that's our culture, right? And And we're able to do that day in and day out and the entire block chains the cornerstone to that. That's what makes people happy. >> Mike Protein, We following your journey. Great. Teo, check in Great to hear the progress. Congratulations on the great demo reel Use cases in supply chain. We'LL be following you guys and keep in touch. Thanks for coming on the key. Absolutely. Thank you for >> the time >> chauffeur here with Lisa Martin here in San Francisco for work. You coverage of radio, the top engineering event where they all come together internally with GM, where one of a few press outlets here, the Cube bringing exclusive coverage. Thanks for watching.

>> from Palo Alto, California It's the Cube covering the EM Where women Transforming technology twenty nineteen. Brought to You by VM Wear >> Hi Lisa Martin with the Cube on the ground at the end. Where in Palo Alto, California, for the fourth annual Women Transforming Technology, even W. T. Squared on event that is near and dear to my heart. Excited to welcome back to the Cube pretty much. Rog Lachman, engineering leader, blocked in at the end where pretty much It's so great to have you back on the Cube. Thank you, Lisa. It's amazing to be here, and I can't believe it's been a year, a year. And so last year, when Protein was here, she launched her book. Nevertheless, she persistent love the title You just Did a session, which we'LL get to in a second, but I'd love to get your your experiences in the last year about the book launch. What's the feedback? Ben? What are some of the things that have made me feel great and surprised you at the same time? It's been fantastic. I wasn't expecting that when I started to write this book. It was more like I want to impact one woman's life. But what was interesting is I delivered around twenty twenty five talks last year. My calendar's booked for this year, but every time I go give a talk, my Lincoln goes crazy and I'm connecting with all these women and men. And it's just fantastic because they're basically resonating with everything I talk about in the book. I spoke at the Federal Reserve. Wow, I was like, This is a book on tech and they were like, No, this impacts all of us And I spoke to a group of lawyers and actually, law firms have fifty fifty when they get into law, right when they get into whenever I mean live, I'm not that familiar with it. But getting to partner is where they don't have equality or diversity, and it's resonated. So now I'm like, maybe I should just take the word check out What? You It's been impactful. And so last year was all about companies, so I did. You know, I spoke at uber I spoken Veum, where spoken nutanix it's looking a lot of these companies last year. This year is all about schools, fantastic schools of all different type, so I you know, I've done a talk at San Jose State. I went to CMU. They invited me over Carnegie Mellon. I supported the robotics team, which is all girls team. Nice. And it was fantastic because these girls high school kids were designing robots. They were driving these robots. They were coding and programming these robots and was an all girls team. And I asked them, I said, But you're excluding the men and the boys and they said no. When it's a combined boy girls team, the women end up the girls and organizing the men of the boys are actually writing the code. They're doing the drilling there, doing all that. And so the girls don't get to do any of that. And I was looking at just the competition and as watching these teams, the boy girl steams and those were all organizing. And I thought, this is exactly what happens in the workforce. You're right. Yeah. We come into the workforce, were busy organizing, coordinating and all that, and the men are driving the charge. And that's why these kids where this is at high school, Yeah, thirteen to seventeen, where this is becoming part of their cultural upbringing. Exactly. Pretty. In great. Yes, yes. And a very young age. So that was fascinating. I think that surprised me. You know, you were asking me what surprised you that surprised me. And what also surprised me was the confidence. Though these girls were doing all these things. I've never built a robot. I would love to. I haven't built a robot, and they were doing all these amazing things, and I thought, Oh, my God, >> they're like, >> confident women. But they were not. And it was because they felt that there was too much to lose. They don't want to take risks, they don't want to fail. And it was that impostor syndrome coming back so that conditioning happens way more impossible syndrome is something that I didn't even know what it wass until maybe the last five or six years suddenly even just seeing that a very terse description of anyone Oh, my goodness, it's not just me. And that's really a challenge that I think the more the more it's brought to light, the more people like yourself share stories. But also what your book is doing is it's not just like you were surprised to find out It's not just a tech. This is every industry, Yes, but his pulse syndrome is something that maybe people consider it a mental health issue and which is so taboo to talk about. But I just think it's so important to go. You're not alone. Yeah, vast majority men, women, whatever culture probably have that. Let's talk about that. Let's share stories. So that your point saying why I was surprised that these young girls had no confidence. Maybe we can help. Yes, like opening up. You know, I'm sharing it being authentic. Yeah. So I'm looking at my second book, which basically says what the *** happens in middle school? Because what happens is somewhere in middle school, girls drop out, so I don't know what it is. I think it's Instagram or Facebook or boys or sex. I don't know what it is, but something happens there. And so this year of my focus is girls and you know, young girls in schools and colleges. And I'm trying to get as much research as I can in that space to see what is going on there, because that totally surprised me. So are you kind of casting a wide net and terms like as you're. Nevertheless, she persisted. Feedback has shown you it's obviously this is a pervasive, yes issue cross industry. This is a global pandemic, yes, but it's your seeing how it's starting really early. Tell me a little bit about some of the things that we can look forward to in that book. So one thing that's important is bravery, Which reshma So Johnny, who's the CEO off girls code? She has this beautiful quote, she says. We raise our voice to be brave, and we'd raise our girls to be perfect, pretty telling. And so we want to be perfect. We won't have the perfect hair, the perfect bodies. We want a perfect partner. That never happens. But we want all that and because we want to be perfect, we don't want to take risks, and we're afraid to fail. So I want to focus on that. I want to talk to parents. I want to talk to the kids. I want to talk to teachers, even professors, and find out what exactly it is like. What is that conditioning that happens, like, why do we raise our girls to be perfect because that impacts us at every step of our lives. Not even careers. It's our lives. Exactly. It impacts us because we just can't take that risk. That's so fascinating. So you had a session here about persistent and inclusive leadership at W T squared forth and you will tell me a little bit about that session today. What were some of the things that came up that you just said? Yes, we're on the right track here. So I started off with a very depressing note, which is twenty eighty five. That's how long it's gonna take for us to see equality. But I talked about what we can do to get to twenty twenty five because I'm impatient. I don't want to wait twenty eighty five I'LL be dead by them. We know you're persistent book title. You know, my daughter will be in the seventies. I just don't want that for her. So, through my research, what I found is we need not only women to lean in. You know, we've have cheryl sound. We're talking about how women need to lean in, and it's all about the women. And the onus is on the woman the burdens on the woman. But we actually need society. Selena. We need organizations to lean in, and we need to hold them accountable. And that's where we're going to start seeing that changes doing that. So if you take the m r. I. You know, I've been with him for ten years, and I always ask myself, Why am I still here? One of the things we're trying to do is trying to take the Cirrus early this morning rail Farrell talked about like on the panel. He said, We are now Our bonuses are tied to, you know, domestic confusion, like we're way have to hire, you know, not just gender, right, Like underrepresented communities as well. We need to hire from there, and they're taking this seriously. So they're actually making this kind of mandatory in some sense, which, you know, it kind of sucks in some ways that it has to be about the story that weighing they're putting a stake in the ground and tying it to executive compensation. Yes, it's pretty bold. Yes. So organizations are leaning in, and we need more of that to happen. Yeah. So what are some of the things that you think could, based on the first *** thing you talked about the second one that you think could help some of the women that are intact that are leaving at an alarming rate for various reasons, whether it's family obligations or they just find this is not an environment that's good for me mentally. What are some of the things that you would advise of women in that particular situation? First thing is that it's to be equal partnership at home. A lot of women leave because they don't have that. They don't have that support on having that conversation or picking the right partner. And if you do pick the wrong partner, it's having that conversation. So if you have equal partnership at home, then it's both a careers that's important. So you find that a lot of women leave tech or leave any industry because they go have babies, and that happens. But it's just not even that, like once they get past that, they come backto work. It's not satisfying because they don't get exciting projects to work on that you don't get strategic projects, they don't have sponsors, which is so important toward the success, and they they're you know, people don't take a risk on them, and they don't take a risk. And so these are some of those things that I would really advice women. And, you know, my talk actually talked about that. Talked about how to get mail allies, how to get sponsors. Like what? You need to actually get people to sponsor you. Don't talk to me a little bit more about that. We talk about mentors a lot. But I did talk this morning with one of our guests about the difference between a sponsor and a mentor. I'd love you to give Sarah some of your advice on how women can find those sponsors. And actually, we activate that relationship. So mentors, uh, talk to you and sponsors talk about okay. And the way to get a sponsor is a is. You do great work. You do excellent work. Whatever you do, do it well. And the second thing is B is brag about it. Talk about it. Humble bragging, Yeah. Humble bragging talkabout it showcases demo it and do it with people who matter in organizations, people who can notice your work building that brand exactly. And you find that women are all the men toward and under sponsored. Interesting, Yes. How do you advise that they change that? There was a Harvard study on this. They found that men tend to find mentors are also sponsors. So what they do is, you know, I like you to stick pad girl singer, he says. Andy Grove was his mentor, but Andy Grove was also his sponsor in many ways, in for his career at Intel, he was a sponsor and a mental. What women tend to do is we find out like even me, like I have female spot him. Mentors were not in my organization, and they do not have the authority to advocate for me. They don't They're not sitting in an important meeting and saying, Oh, patina needs that project for team needs to get promoted. And so I'm not finding the right mentors who can also be my sponsors, or I'm not finding this one says right, and that's happens to us all the time. And so the way we have to switch this is, you know, mentors, a great let's have mentors. But let's laser focus on sponsors, and I've always said this all of last year. I'm like the key to your cell. Success is sponsorship, and I see that now. I am in an organization when my boss is my sponsor, which is amazing, because every time I go into a meeting with him, he says, This is about pretty much grew up. This is a pretty mers group. It's not me asking him. He's basically saying It's pretty nose grow, which is amazing to hear because I know he's my mentor in sponsor as well. And it's funny when I gave him a copy of my book and I signed it and I said, And he's been my sponsor to be more for like ten years I said, Thank you for being my sponsor and he looked at me. He said, Oh, I never realized it was your sponsor So that's another thing is men themselves don't know they're in this powerful position to have an impact, and they don't know that they are sponsors as well. And so we need. We need women to Fox and sponsors. I always say find sponsors. Mentorship is great, but focus of sponsors Look, I think it's an important message to get across and something I imagine we might be reading about in your next book to come. I know. Yeah, well, we'LL see. Artie, thank you so much for stopping by the Cube. It's great to talk to you and to hear some of the really interesting things that you've learned from nevertheless you persistent and excited to hear about book number two and that comes out. You got a combined studio. I'd love to thank you and thank you. I'm Lisa Martin. You're watching the queue from BM Where? At the fourth Annual Women Transforming Technology event. Thanks for watching.

>> Live, from Times Square, in New York City, it's theCUBE covering IBM's Change the Game: Winning with AI. Brought to you by IBM. >> Welcome back to the Big Apple, everybody. I'm Dave Vellante, and you're watching theCUBE, the leader in live tech coverage, and we're here covering a special presentation of IBM's Change the Game: Winning with AI. IBM's got an analyst event going on here at the Westin today in the theater district. They've got 50-60 analysts here. They've got a partner summit going on, and then tonight, at Terminal 5 of the West Side Highway, they've got a customer event, a lot of customers there. We've talked earlier today about the hard news. Seth Dobern is here. He's the Chief Data Officer of IBM Analytics, and he's joined by Shreesha Rao who is the Senior Manager of IT Applications at California-based Niagara Bottling. Gentlemen, welcome to theCUBE. Thanks so much for coming on. >> Thank you, Dave. >> Well, thanks Dave for having us. >> Yes, always a pleasure Seth. We've known each other for a while now. I think we met in the snowstorm in Boston, sparked something a couple years ago. >> Yep. When we were both trapped there. >> Yep, and at that time, we spent a lot of time talking about your internal role as the Chief Data Officer, working closely with Inderpal Bhandari, and you guys are doing inside of IBM. I want to talk a little bit more about your other half which is working with clients and the Data Science Elite Team, and we'll get into what you're doing with Niagara Bottling, but let's start there, in terms of that side of your role, give us the update. >> Yeah, like you said, we spent a lot of time talking about how IBM is implementing the CTO role. While we were doing that internally, I spent quite a bit of time flying around the world, talking to our clients over the last 18 months since I joined IBM, and we found a consistent theme with all the clients, in that, they needed help learning how to implement data science, AI, machine learning, whatever you want to call it, in their enterprise. There's a fundamental difference between doing these things at a university or as part of a Kaggle competition than in an enterprise, so we felt really strongly that it was important for the future of IBM that all of our clients become successful at it because what we don't want to do is we don't want in two years for them to go "Oh my God, this whole data science thing was a scam. We haven't made any money from it." And it's not because the data science thing is a scam. It's because the way they're doing it is not conducive to business, and so we set up this team we call the Data Science Elite Team, and what this team does is we sit with clients around a specific use case for 30, 60, 90 days, it's really about 3 or 4 sprints, depending on the material, the client, and how long it takes, and we help them learn through this use case, how to use Python, R, Scala in our platform obviously, because we're here to make money too, to implement these projects in their enterprise. Now, because it's written in completely open-source, if they're not happy with what the product looks like, they can take their toys and go home afterwards. It's on us to prove the value as part of this, but there's a key point here. My team is not measured on sales. They're measured on adoption of AI in the enterprise, and so it creates a different behavior for them. So they're really about "Make the enterprise successful," right, not "Sell this software." >> Yeah, compensation drives behavior. >> Yeah, yeah. >> So, at this point, I ask, "Well, do you have any examples?" so Shreesha, let's turn to you. (laughing softly) Niagara Bottling -- >> As a matter of fact, Dave, we do. (laughing) >> Yeah, so you're not a bank with a trillion dollars in assets under management. Tell us about Niagara Bottling and your role. >> Well, Niagara Bottling is the biggest private label bottled water manufacturing company in the U.S. We make bottled water for Costcos, Walmarts, major national grocery retailers. These are our customers whom we service, and as with all large customers, they're demanding, and we provide bottled water at relatively low cost and high quality. >> Yeah, so I used to have a CIO consultancy. We worked with every CIO up and down the East Coast. I always observed, really got into a lot of organizations. I was always observed that it was really the heads of Application that drove AI because they were the glue between the business and IT, and that's really where you sit in the organization, right? >> Yes. My role is to support the business and business analytics as well as I support some of the distribution technologies and planning technologies at Niagara Bottling. >> So take us the through the project if you will. What were the drivers? What were the outcomes you envisioned? And we can kind of go through the case study. >> So the current project that we leveraged IBM's help was with a stretch wrapper project. Each pallet that we produce--- we produce obviously cases of bottled water. These are stacked into pallets and then shrink wrapped or stretch wrapped with a stretch wrapper, and this project is to be able to save money by trying to optimize the amount of stretch wrap that goes around a pallet. We need to be able to maintain the structural stability of the pallet while it's transported from the manufacturing location to our customer's location where it's unwrapped and then the cases are used. >> And over breakfast we were talking. You guys produce 2833 bottles of water per second. >> Wow. (everyone laughs) >> It's enormous. The manufacturing line is a high speed manufacturing line, and we have a lights-out policy where everything runs in an automated fashion with raw materials coming in from one end and the finished goods, pallets of water, going out. It's called pellets to pallets. Pellets of plastic coming in through one end and pallets of water going out through the other end. >> Are you sitting on top of an aquifer? Or are you guys using sort of some other techniques? >> Yes, in fact, we do bore wells and extract water from the aquifer. >> Okay, so the goal was to minimize the amount of material that you used but maintain its stability? Is that right? >> Yes, during transportation, yes. So if we use too much plastic, we're not optimally, I mean, we're wasting material, and cost goes up. We produce almost 16 million pallets of water every single year, so that's a lot of shrink wrap that goes around those, so what we can save in terms of maybe 15-20% of shrink wrap costs will amount to quite a bit. >> So, how does machine learning fit into all of this? >> So, machine learning is way to understand what kind of profile, if we can measure what is happening as we wrap the pallets, whether we are wrapping it too tight or by stretching it, that results in either a conservative way of wrapping the pallets or an aggressive way of wrapping the pallets. >> I.e. too much material, right? >> Too much material is conservative, and aggressive is too little material, and so we can achieve some savings if we were to alternate between the profiles. >> So, too little material means you lose product, right? >> Yes, and there's a risk of breakage, so essentially, while the pallet is being wrapped, if you are stretching it too much there's a breakage, and then it interrupts production, so we want to try and avoid that. We want a continuous production, at the same time, we want the pallet to be stable while saving material costs. >> Okay, so you're trying to find that ideal balance, and how much variability is in there? Is it a function of distance and how many touches it has? Maybe you can share with that. >> Yes, so each pallet takes about 16-18 wraps of the stretch wrapper going around it, and that's how much material is laid out. About 250 grams of plastic that goes on there. So we're trying to optimize the gram weight which is the amount of plastic that goes around each of the pallet. >> So it's about predicting how much plastic is enough without having breakage and disrupting your line. So they had labeled data that was, "if we stretch it this much, it breaks. If we don't stretch it this much, it doesn't break, but then it was about predicting what's good enough, avoiding both of those extremes, right? >> Yes. >> So it's a truly predictive and iterative model that we've built with them. >> And, you're obviously injecting data in terms of the trip to the store as well, right? You're taking that into consideration in the model, right? >> Yeah that's mainly to make sure that the pallets are stable during transportation. >> Right. >> And that is already determined how much containment force is required when your stretch and wrap each pallet. So that's one of the variables that is measured, but the inputs and outputs are-- the input is the amount of material that is being used in terms of gram weight. We are trying to minimize that. So that's what the whole machine learning exercise was. >> And the data comes from where? Is it observation, maybe instrumented? >> Yeah, the instruments. Our stretch-wrapper machines have an ignition platform, which is a Scada platform that allows us to measure all of these variables. We would be able to get machine variable information from those machines and then be able to hopefully, one day, automate that process, so the feedback loop that says "On this profile, we've not had any breaks. We can continue," or if there have been frequent breaks on a certain profile or machine setting, then we can change that dynamically as the product is moving through the manufacturing process. >> Yeah, so think of it as, it's kind of a traditional manufacturing production line optimization and prediction problem right? It's minimizing waste, right, while maximizing the output and then throughput of the production line. When you optimize a production line, the first step is to predict what's going to go wrong, and then the next step would be to include precision optimization to say "How do we maximize? Using the constraints that the predictive models give us, how do we maximize the output of the production line?" This is not a unique situation. It's a unique material that we haven't really worked with, but they had some really good data on this material, how it behaves, and that's key, as you know, Dave, and probable most of the people watching this know, labeled data is the hardest part of doing machine learning, and building those features from that labeled data, and they had some great data for us to start with. >> Okay, so you're collecting data at the edge essentially, then you're using that to feed the models, which is running, I don't know, where's it running, your data center? Your cloud? >> Yeah, in our data center, there's an instance of DSX Local. >> Okay. >> That we stood up. Most of the data is running through that. We build the models there. And then our goal is to be able to deploy to the edge where we can complete the loop in terms of the feedback that happens. >> And iterate. (Shreesha nods) >> And DSX Local, is Data Science Experience Local? >> Yes. >> Slash Watson Studio, so they're the same thing. >> Okay now, what role did IBM and the Data Science Elite Team play? You could take us through that. >> So, as we discussed earlier, adopting data science is not that easy. It requires subject matter, expertise. It requires understanding of data science itself, the tools and techniques, and IBM brought that as a part of the Data Science Elite Team. They brought both the tools and the expertise so that we could get on that journey towards AI. >> And it's not a "do the work for them." It's a "teach to fish," and so my team sat side by side with the Niagara Bottling team, and we walked them through the process, so it's not a consulting engagement in the traditional sense. It's how do we help them learn how to do it? So it's side by side with their team. Our team sat there and walked them through it. >> For how many weeks? >> We've had about two sprints already, and we're entering the third sprint. It's been about 30-45 days between sprints. >> And you have your own data science team. >> Yes. Our team is coming up to speed using this project. They've been trained but they needed help with people who have done this, been there, and have handled some of the challenges of modeling and data science. >> So it accelerates that time to --- >> Value. >> Outcome and value and is a knowledge transfer component -- >> Yes, absolutely. >> It's occurring now, and I guess it's ongoing, right? >> Yes. The engagement is unique in the sense that IBM's team came to our factory, understood what that process, the stretch-wrap process looks like so they had an understanding of the physical process and how it's modeled with the help of the variables and understand the data science modeling piece as well. Once they know both side of the equation, they can help put the physical problem and the digital equivalent together, and then be able to correlate why things are happening with the appropriate data that supports the behavior. >> Yeah and then the constraints of the one use case and up to 90 days, there's no charge for those two. Like I said, it's paramount that our clients like Niagara know how to do this successfully in their enterprise. >> It's a freebie? >> No, it's no charge. Free makes it sound too cheap. (everybody laughs) >> But it's part of obviously a broader arrangement with buying hardware and software, or whatever it is. >> Yeah, its a strategy for us to help make sure our clients are successful, and I want it to minimize the activation energy to do that, so there's no charge, and the only requirements from the client is it's a real use case, they at least match the resources I put on the ground, and they sit with us and do things like this and act as a reference and talk about the team and our offerings and their experiences. >> So you've got to have skin in the game obviously, an IBM customer. There's got to be some commitment for some kind of business relationship. How big was the collective team for each, if you will? >> So IBM had 2-3 data scientists. (Dave takes notes) Niagara matched that, 2-3 analysts. There were some working with the machines who were familiar with the machines and others who were more familiar with the data acquisition and data modeling. >> So each of these engagements, they cost us about $250,000 all in, so they're quite an investment we're making in our clients. >> I bet. I mean, 2-3 weeks over many, many weeks of super geeks time. So you're bringing in hardcore data scientists, math wizzes, stat wiz, data hackers, developer--- >> Data viz people, yeah, the whole stack. >> And the level of skills that Niagara has? >> We've got actual employees who are responsible for production, our manufacturing analysts who help aid in troubleshooting problems. If there are breakages, they go analyze why that's happening. Now they have data to tell them what to do about it, and that's the whole journey that we are in, in trying to quantify with the help of data, and be able to connect our systems with data, systems and models that help us analyze what happened and why it happened and what to do before it happens. >> Your team must love this because they're sort of elevating their skills. They're working with rock star data scientists. >> Yes. >> And we've talked about this before. A point that was made here is that it's really important in these projects to have people acting as product owners if you will, subject matter experts, that are on the front line, that do this everyday, not just for the subject matter expertise. I'm sure there's executives that understand it, but when you're done with the model, bringing it to the floor, and talking to their peers about it, there's no better way to drive this cultural change of adopting these things and having one of your peers that you respect talk about it instead of some guy or lady sitting up in the ivory tower saying "thou shalt." >> Now you don't know the outcome yet. It's still early days, but you've got a model built that you've got confidence in, and then you can iterate that model. What's your expectation for the outcome? >> We're hoping that preliminary results help us get up the learning curve of data science and how to leverage data to be able to make decisions. So that's our idea. There are obviously optimal settings that we can use, but it's going to be a trial and error process. And through that, as we collect data, we can understand what settings are optimal and what should we be using in each of the plants. And if the plants decide, hey they have a subjective preference for one profile versus another with the data we are capturing we can measure when they deviated from what we specified. We have a lot of learning coming from the approach that we're taking. You can't control things if you don't measure it first. >> Well, your objectives are to transcend this one project and to do the same thing across. >> And to do the same thing across, yes. >> Essentially pay for it, with a quick return. That's the way to do things these days, right? >> Yes. >> You've got more narrow, small projects that'll give you a quick hit, and then leverage that expertise across the organization to drive more value. >> Yes. >> Love it. What a great story, guys. Thanks so much for coming to theCUBE and sharing. >> Thank you. >> Congratulations. You must be really excited. >> No. It's a fun project. I appreciate it. >> Thanks for having us, Dave. I appreciate it. >> Pleasure, Seth. Always great talking to you, and keep it right there everybody. You're watching theCUBE. We're live from New York City here at the Westin Hotel. cubenyc #cubenyc Check out the ibm.com/winwithai Change the Game: Winning with AI Tonight. We'll be right back after a short break. (minimal upbeat music)

>> From Sunnyvale California, in the heart of Silicon Valley. It's theCUBE, covering, Accelerate Your Journey to AI, Brought to you by NetApp. >> Hi I'm Peter Burris, welcome to another conversation here from the Data Visionary Center at NetApp's headquarters in beautiful Sunnyvale California. I'm being joined today by Santosh Rao. Santosh is the Senior Technical Director at NetApp, Specifically Santosh we're going to talk about some of the challenges and opportunities associated with AI and how NetApp is making that possible. Welcome to theCUBE. >> Thank you Peter, I'm excited to be here. Thank you for that. >> So, Santosh what is your role at Netapp? Why don't we start there. >> Wonderful, glad to be here, my name is Santosh Rao, I'm a Senior Technical Director at NetApp, part of the Product Operations group, and I've been here 10 years. My role is to drive up new lines of opportunity for NetApp, build up new product businesses. The most recent one has been AI. So I've been focused on bootstrapping and incubating the AI effort at NetApp for the last nine months now. Been excited to be part of this effort now. >> So nine months of talking, both internally, but spending time with customers too. What are customers telling you that are NetApp's opportunities, and what NetApp has to do to respond to those opportunities? >> That's a great question. We are seeing a lot of focus around expanding the digital transformation to really get value out of the data, and start looking at AI, and Deep Learning in particular, as a way to prove the ROI on the opportunities that they've had. AI and deep learning requires a tremendous amount of data. We're actually fascinated to see the amount of data sets that customers are starting to look at. A petabyte of data is sort of the minimum size of data set. So when you think about petabyte-scale data lakes. The first think you want to think about is how do you optimize the TCO for the solution. NetApp is seen as a leader in that, just because of our rich heritage of storage efficiency. A lot of these are video image and audio files, and so you're seeing a lot of unstructured data in general, and we're a leader in NFS as well. So a lot of that starts to come together from a NetApp perspective. And that's where customers see us as the leader in NFS, the leader in files, and the leader in storage efficiency, all coming together. >> And you want to join that together with some leadership, especially in GPU's, so that leads to NVIDIA. So you've announced an interesting partnership between NetApp and NVIDIA. How did that factor into your products, and where do you think that goes? >> It's kind of interesting how that came about, because when you look at the industry it's a small place. Some of the folks driving the NVIDIA leadership have been working with us in the past, when we've bootstrapped converged infrastructures with other vendors. We're known to have been a 10 year metro vendor in the converged infrastructure space. The way this came about was NVIDIA is clearly a leader in the GPU and AI acceleration from a computer perspective. But they're also seen as a long history of GPU virtualization and GPU graphics acceleration. When they look at NetApp, what NetApp brings to NVIDIA is just the converged infrastructure, the maturity of that solution, the depth that we have in the enterprise and the rich partner ecosystem. All of that starts to come together, and some of the players in this particular case, have had aligned in the past working on virtualization based conversion infrastructures in the past. It's an exciting time, we're really looking forward to working closely with NVIDIA. >> So NVIDIA brings these lighting fast machines, optimized for some of the new data types, data forms, data structures associated with AI. But they got to be fed, got to get the data to them. What is NetApp doing from a standpoint of the underlying hardware to improve the overall performance, and insure that these solutions really scream for customers? >> Yeah, it's kind of interesting, because when you look at how customers are designing this. They're thinking about digital transformation as, "What is the flow of that data? "What am I doing to create new sensors "and endpoints that create data? "How do I flow the data in? "How do I forecast how much data I'm going to "create quarter over quarter, year over year? "How many endpoints? what is the resolution of the data?" And then as that starts to come into the data center, they got to think about, where are the bottlenecks. So you start looking at a wide range of bottlenecks. You look at the edge data aggregation, then you start looking at network bandwidth to push data into the core data centers. You got to think smart about some of these things. For example, no matter how much network bandwidth you throw at it, you want to reduce the amount of data you're moving. Smart data movement technologies like SnapMirror, which NetApp brings to the table, are some things that we uniquely enable compared to others. The fact of the matter is when you take a common operating system, like ONTAP, and you can lear it across the Edge, Core and Cloud, that gives us some unnatural advantages. We can do things that you can't do in a silo. You've got a commodities server trying to push data, and having to do raw full copies of data into the data center. So we think smart data movement is a huge opportunity. When you look at the core, obviously it's a workhorse, and you've got the random sampling of data into this hardware. And we think the A800 is a workhorse built for AI. It is a best of a system in terms of performance, it does about 25 gigabytes per second just on a dual controller pair. You'll recall that we spent several number of years building out the foundation of Clustered ONTAP to allow us to scale to gigantic sizes. So 24 node or 12 controller pad A800 gets us to over 300 gigabytes per second, and over 11 million IOPS if you think about that. That's over about four to six times greater than anybody else in the industry. So when you think about NVIDIA investment in DGX and they're performance investment they've made there. We think only NetApp can keep up with that, in terms of performance. >> So 11 million IOPS, phenomenal performance for today. But the future is going to demand ever more. Where do you think these trends go? >> Well nobody really knows for sure. The most exciting part of this journey, is nobody knows where this is going. This is where you need to future proof customers, and you need to enable the technology to have sufficient legs, and the architecture to have sufficient legs. That no matter how it evolves and where customers go, the vendors working with customers can go there with them. And actually when customers look at NetApp and say, "You guys are working with the Cloud partners, "you're now working with NVIDIA. "And in the past you worked with a "variety of data source vendors. "So we think we can work with NetApp because, "you're not affiliated to any one of them, "and yet you're giving us that full range of solutions." So we think that performance is going to be key. Acceleration of compute workloads is going to demand orders of magnitude performance improvement. We think data set efficiencies and storage efficiencies is absolutely key. And we think you got to really look at PCO, because customers want to build these great solutions for the business, but they can't afford it unless vendors give them viable options. So it's really up to partners like NVIDIA and NetApp to work together to give customers the best of breed solutions that reduce the TCO, accelerate compute, accelerate the data pipeline, and yet, bring the cost of the overall solution down, and make it simple to deploy and pre integrated. These are the things customers are looking for and we think we have the best bet at getting there. >> So that leads to... Great summary, but that leads to some interesting observations on what customers should be basing their decisions on. What would you say are the two or three most crucial things that customers need to think about right now as a conceptualized, where to go with their AI application, or AI workloads, their AI projects and initiatives? >> So when customers are designing and building these solutions, they're thinking the entire data lifecycle. "How am I getting this new type of "data for digital transformation? "What is the ingestion architecture? "What are my data aggregation endpoints for ingestion? "How am I going to build out my AI data sources? "What are the types of data? "Am I collecting sensor data? Is it a variety of images? "Am I going to add in audio transcription? "Is there video feeds that come in over time?" So customers are having to think about the entire digital experience, the types of data, because that leads to the selection of data sources. For example, if you're going to be learning sensor data, you want to be looking at maybe graph databases. If you want to be learning log data, you're going to be looking at log analytics over time, as well as AI. You're going to look at video image and audio accordingly. Architecting these solutions requires an understanding of, what is your digital experience? How does that evolve over time? What is the right and optimal data source to learn that data, so that you get the best experience from a search, from an indexing, from a tiering, from analytics and AI? And then, what is the flow of that data? And how do you architect it for a global experience? How do you build out these data centers where you're not having to copy all data maybe, into your global headquarters. If you're a global company with presence across multiple Geo's, how do you architect for regional data centers to be self contained? Because we're looking at exabyte scale opportunities in some of these. I think that's pretty much the two or three things that I'd say, across the entire gamut of space here. >> Excellent, turning that then into some simple observations about the fact that data still is physical. There's latency issues, there's the cost of bandwidth issues. There's other types of issues. This notion of Edge, Core, Cloud. How do you see the ONTAP operating system, the ONTAP product set, facilitating being able to put data where it needs to be, while at the same time creating the options that a customer needs to use data as they need to use it? >> The fact of the matter is, these things cannot be achieved overnight. It takes a certain amount of foundational work, that, frankly, takes several years. The fact that ONTAP can run on small, form factor hardware at the edge is a journey that we started several years ago. The fact that ONTAP can run on commodity white box hardware, has been a journey that we have run over the last three, four years. Same thing in the Cloud, we have virtualized ONTAP to the point that it can run on all hyperscalers and now we are in the process of consuming ONTAP as a service, where you don't even know that it is an infrastructure product, or has been. So the process of building an Edge, Core, and Cloud data pipeline leverages the investments that we've made over time. When you think about the scale of compute, data and performance needed, that's a five to six year journey in Clustered ONTAP, if you look at NetApp's past. These are all elements that are coming together from a product and solution perspective. But the reality is that leveraging years and years of investment that NetApp engineering has made. In a a way that the industry really did not invest in the same areas. So when we compare and contrast what NetApp has done versus the rest of the industry. At a time when people were building monolithic engineered systems, we were building software defined architectures. At a time when they were building tightly cobbled system for traditional enterprise, we were building flexible, scale out systems, that assumed that you would want to scale in modular increments. Now as the world has shifted from enterprise into third platform and Webscale. We're finding all those investments NetApp made over the years is really starting to pay off for us. >> Including some of the investments in how AI can be used to handle how ONTAP operates at each of those different levels of scale. >> Absolutely, yes. >> Sontash Rao, Technical Director at NetApp, talking about AI, some of the new changes in the relationships between AI and storage. Thanks very much for being on theCUBE. >> Thank you, appreciate it.

(electronic music) >> Announcer: From the VMware campus in Palo Alto, California, it's theCUBE! Covering women transforming technology. >> Hi, welcome to theCube. Lisa Martin on the ground at the 3rd Annual Women Transforming Technology event at VMware in Palo Alto, and I'm joined by an author and a senior VMware engineer, Pratima Rao Gluckman. Welcome to the Cube, Pratima. >> Thank you, Lisa. It's great to be here >> It's great to have you here. So you have been an engineer here for about ten years. You knew from when you were a kid, love this, engineer, you knew you wanted to be that. You fell in love with your first programming class. It was like a Jerry McGuire, you complete me kind of moment I'm imagining. Tell me a little bit about your career in engineering and specifically as a female. >> Okay, so I was raised, born and raised, in India, and I grew up in an environment where I was gender blind. You know, my oldest sister played cricket for the country. >> Lisa: Wow! >> And it was a man's game! You know and a lot of people kind of talked about that, but it wasn't like she couldn't do it, right? So, I always grew up with this notion that I could do anything, and I could be whoever I wanted to be. And then I came to the United States, and that whole narrative stayed with me, the meritocracy narrative. Like you work hard, you know, society, the world will take care of you, and good things will happen, but it wasn't until 2016 was when I had this aha moment, and that's when I suddenly felt, suddenly I was aware of my gender, and I was like, okay I'm a female in tech, and there's lots of challenges for women in tech. And I didn't quite realize that. It was just that aha moment, and VMware has been a great company. I've been with VMware for nine years, I started as an engineer, and I moved into engineering management. We had Diane Greene who founded the company, the culture was always meritocratic, but I think something in 2016 kind of made me just thinking about my career and thinking about the careers of the women around me, I felt like we were stuck. But at the same time be focused on the women that were successful, for instance Yanbing Li, who's our senior VP and general manager of our storage business. And we were talking about her, and I said, this is what I said, I said, "There are some women who are successful despite everything "that we're dealing with, and I just want "to know their stories, and I'm going to write this book." The moment I said that it just felt right. I felt like this was something I wanted to do, and the stories in this book are inspiring stories of these women, just listening to Laila Ali this morning, her inspirational story, and this book has around 19 stories of these executive women, and they're just not role models, I mean every story offers strategies of how to thrive in the tech world. >> So interesting that first of all I love the title, Pratima, of this book, "Nevertheless She Persisted." So simple, so articulate, and so inspiring. So interesting, though, that you were working as an engineer for quite a few years before you realized, kind of looked around, like, whoa, this is a challenge that I'm actually living in. Yanbing is a CUBE alumni, I love her Twitter handle. So you said all right, I want to talk to some women who have been persistent and successful in their tech careers, as kind of the genesis of the book. Talk to us about, maybe, of those 19 interviews that range from, what, c-levels to VPs to directors. What are some of the stories that you found, what kind of blew your mind of, wow, I didn't know that you came from that kind of background? >> So when I started off I was very ambitious. I said I'd go interview CEO women, and I did a lot of research, and I found some very disturbing facts. You know, Fortune Magazine lists Fortune 500 companies, and they rank them based on their prior year's fiscal revenues, and from that data there were 24 women CEOs in 2014. That number dropped to 21 in 2015, and it dropped again in 2016, but it went up slightly in 2017 to 32 women, which is promising, but back in 2018 we're down to 24. So we have very very few women CEOs, and when I started off I said I'll talk to the CEO women, and I couldn't find any CEO women, my network, my friends' network, And so I dropped one level and I said let me go talk to SVPs and when I looked at VMware and VMware's network, Yanbing was one of them, so she's in the book, and then I reached out to contacts outside of my network. So I have some women from LinkedIn, I have Google, I have Facebook, I have some women from startups. So I have around four CEOs in the book, I've got, and what's great about this book is it's got a diverse set of women. Right? They have different titles; I've got directors, senior directors, VPs, Senior VPs, GMs, and CEOs. And some of them have PhDs, some of them have a Master's Degree, and some actually don't have formal training in computer science. I thought this would be interesting because a woman with any background can relate to it. Right? And so that was helpful. And so that's kind of how I went off and I started to write this book. And when I interviewed these women, there was a common theme that just kept emerging, and that was persistence. And they persisted against gender bias, stereotype threat, just the negative messages from media and society. I mean like Laila Ali was talking about just even the messages she got from her dad. >> Right. >> Right? Someone who was so close to her who basically said "Women can't box." And that didn't stop her; I mean she persisted. When I was listening to her, she didn't use the word, but, you know, she said she was believing in herself and all that, but she persisted through all those negative messages, right? And she said no one can tell her what to do. (laughs) >> Yeah her confidence is very loud and clear, and I think that you do find women, and I imagine some of them are some of the interviewees in your book, who have that natural confidence, and as you were saying when Muhammad Ali was trying to talk her out of it, and trying to, as she said, "He tried to get me think it was my idea," but she just knew, well no, this is what I want to do. And she had that confidence. Did you find that a lot of the women leaders in this book had that natural confidence? Like you grew up in an environment where you just believed "I can do this, my sister's playing cricket." Did you find that was a common thread, or did you find some great examples of women who wanted to do something, but just thought "Can I do this?" And "How do I do that?" What was the kind of confidence level that you saw? >> I was surprised because I had a question on imposter syndrome, and I asked these women, Telle Whiteney, who's the CEO, she was the CEO, ex-CEO >> Lisa: Grace Hopper >> Yes. The founder of Grace Hopper. I asked her about imposter syndrome and this is what she told me, she said "I feel like I'm not good enough" and that actually gave me goosebumps. I remember I was sitting in front of greatness and this is what she was telling me. And then I asked her "How do you overcome it?" and she said "I just show up the next day." And that actually helped me with this book because I am not an author. >> That's persistence. >> I mean I am an author now but 2 years ago when I started to write this, writing is not my forte. I'm a technologist, I build teams, I manage teams, I ship products, I ship technical products, but everyday I woke up and I said, "I'm feeling like an imposter." It was just her voice right? Yanbing also feels the same way, I mean she does feel times where she feels like, "I'm lacking confidence here." Majority of the people actually, pretty much all the women, this one woman, Patty Hatter, didn't feel like she had imposter syndrome but the rest of them face it everyday. Talia Malachi who's a principal engineer at VMWare, it's very hard to be a PE, she said that she fights it every day, and that was surprising to me, right? Because I was sitting in front of all these women, they were confident, they've achieved so much, but they struggle with that every day. But all they do is they persist, they show up the next day. They take those little steps and they have these goals and they're very intentional and purposeful, I mean just like what Layla said, right? She said, "Everything that I've done in the last 20 years "has been intentional and purposeful." And that's what these women did. And I learned so much from them because 20 years ago I was a drifter (laughs) you know I just kind drifted and I didn't realize that I could set a goal and I could reach it and I could do all these amazing things, and I didn't think any of this was possible for me. But I'm hoping that some girl somewhere can read this book and say "You know what this is possible", right? This is possible and you know role models, I think we need lots of these role models. >> We do I think, you know imposter syndrome I've suffered for it for so long before I even knew what it was and I'll be honest with you even finding out that it was a legitimate issue was (exhales) okay I'm not the only one. So I think it's important that you, that these women and youth are your voice, in your book, identified it. This is something I face everyday even though you may look at me on the outside and think, "She's so successful, she's got everything." And we're human. And Laila Ali talked about of having to revisit that inner lawyer, that sometimes she goes silent, sometimes the pilot light goes out and needs to be reignited or turned back up. I think that is just giving people permission, especially women, and I've felt that in the keynote, giving us permission to go, "Ah, you're not going to feel that everyday, "you're not going to feel it everyday." Get up the next day to your point, keep persisting and pursuing your purpose is in and of itself so incredibly empowering. >> Right but also imposter syndrome is good for you and I talk about that a little bit in the book. And you know why it's good for you? It's you getting out of your comfort zone, you're trying something different, and it's natural to feel that way, but once you get over it, you've mastered that, and Laila talked about it too today she said, "You get uncomfortable to the point "where you get comfortable." >> Lisa: Yes. >> So every time that you find that you have this imposter syndrome, just remember that greatness is right around the corner. >> Yep. I always say "Get uncomfortably uncomfortable". >> Pratima: Yes. >> And I loved how she said that today. So one of the big news of the day is VMWare with Stanford announcing that they are investing $15,000,000 in a new Women's Leadership Innovation Lab at Stanford. Phenomenal. >> Pratima: Yes. >> And they're really going to start studying diversity and there's so many different gaps that we face, wage gap, age gap, gender gap, you know mothers vs motherless gap, and one of the things that was really interesting that, I've heard this before, that the press release actually cited a McKinsey report that says, "Companies with diversity "on their executive staff are 21% more profitable." >> Yes. >> And that just seems like a, no duh, Kind of thing to me for organizations like VMWare and your other partners in this consortium of Wt Squared to get on board to say, "Well of course." Thought diversity is so important and it actually is demonstrated to impact a companies' profitability. >> Right, yeah. And that's true, I just hope that more people listen to it and internalize it, and organizations internalize that, and what VMWare's doing is fantastic. I mean I'm so proud to be part of this company that's doing this. And you Shelly talked about change right? She said, "I think, right now the way I feel "about this whole thing, is we need to stop talking about "diversity and inclusion, we just need to say "enough is enough, this is important, let's just do it." >> Lisa: We should make this a part of our DNA. >> Exactly. Just make it, why do we have to fight for all this, right? It's just pointless and you know, men have wives and daughters and mothers and you know, It impacts societies as a whole and organizations, and we have so much research on this and what I like about what the Stanford Research Lab is doing is, they're actually working with woman all the way from middle-school to high-school to the executive suite, and that's amazing because research has now shown, there was a report in March 2014 by a senior fellow at the Center for American Progress, for Judith Warner, and so she documented, just with the rate of change, like I talked with all the percentages and the number of women CEOs, just with that rate of change, the equality of men and women at the top will not occur until 2085. >> Lisa: Oh my goodness. >> That's 63 years from now. That means all our daughters would be retired by then. My daughters was born on 2013 and so she won't live in a world of female leaders that's representative of the population. And so that realization actually really, really, really broke my heart and that made me want to write this book, to create these role models. And what Stanford is doing, is they're going to work on this and I'm hoping that they can make that transition sooner. Like we don't have to wait 'till 2085. I want this for my daughter. >> It has to be accelerated, yes. >> It has to be accelerated and I think all of us need to do that, our daughters should be in the 20s, 30s when this happens, not when they're in their 70s. >> Lisa: And retired. >> And retired, I mean we don't want that. And we don't know how that number's going to get pushed further, right? Like if we don't do anything now... It. (exhales) >> Lisa: Right. 2085 becomes, what? >> I know! It's insane. >> In the spirit of being persistent, with the theme of this 3rd annual Wt Squared being Inclusion in Action, you're a manager and in a people or hiring role, tell me about the culture on your team and how your awareness and your passion for creating change here, lasting change. How are you actually creating that inclusion through action in your role at VMWare? >> So what I do is when I have to hire engineers on my team, I talk to my recruiter, have a conversation, I'm like, "I need more diversity." It's just not women, I want diversity with the men too. I want different races, different cultures because I believe that if I have a diverse team I'm going to be successful. So it's almost like I'm being selfish but that is very important. So I have that conversation with my recruiters, so I kind have an expectation set. And then we go through their hiring process and I'm very aware of just the hiring panel, like who I put on the panel, I make sure to have at least a women on the panel and have some diversity. My team right now is not really that diverse and I'm working hard to make that because it is hard, you know the pipeline has to get built at a certain point, and then start getting those resumes, but I try to have at least one female on the panel, and during the selection process the first thing I'll tell them is, let's get the elephant out of the room, age, gender, whatever, like let's take that out, let's just talk about skills and how well this person has done in an interview. And that's how I conducted and you know I've had fairly good success of hiring women on the team. But I've also seen that it's hard to retain women because they tend to drop-out faster than the men and so it's constant, it's just constant work to make that happen. >> Yeah. I wish we had more time to talk about retention because it is a huge issue. So the book is Nevertheless, She Persisted. Where can people get a copy of the book? >> So you can get it on Amazon, that's, I think, the best place to get it. You can also get it from my publisher's site which is FriesenPress. >> Excellent well Pratima thank you so much for stopping by. >> Thank you. >> And sharing your passion, how your persisting, and how you're also helping more of us learn how to find that voice and pursue our passions, thank you. >> Thank you. >> We want to thank you for watching. We are TheCUBE on the ground at VMWare for the Third Annual Women Transforming Technology Event. I'm Lisa Martin thanks for watching. (upbeat music)

(bright music) >> Narrator: Live from Austin, Texas. It's theCUBE, covering South by Southwest 2017. Brought to you by Intel. Now here's John Furrier. >> We're here live in South by Southwest Austin, Texas. Silicon Angle, theCUBE, our broadcast, we go out and extract the signal from noise. I'm John Furrier, I'm here with Naveene Rao, the vice president general manager of the artificial intelligence solutions group at Intel. Welcome to theCUBE. >> Thank you, yeah. >> So we're here, big crowd here at Intel, Intel AI lounge. Okay, so that's your wheelhouse. You're the general manager of AI solutions. >> Naveene: That's right. >> What is AI? (laughs) I mean-- >> AI has been redefined through time a few times. Today AI means generally applied machine learning. Basically ways to find useful structure in data to do something with. It's a tool, really, more than anything else. >> So obviously AI is a mental model, people can understand kind of what's going on with software. Machine learning and IoT gets kind of in the industry, it's a hot area, but this really is points to a future world where you're seeing software tackling new problems at scale. So cloud computing, what you guys are doing with the chips and software has now created a scale dynamic. Similar to Moore's, but Moore's Law is done for devices. You're starting to see software impact society. So what are some of those game changing impacts that you see and that you're looking at at Intel? >> There are many different thought labors that many of us will characterize as drudgery. For instance, if I'm an insurance company, and I want to assess the risk of 10 million pages of text, I can't do that very easily. I have to have a team of analysts run through, write summaries. These are the kind of problems we can start to attack. So the way I always look at it is what a bulldozer was to physical labor, AI is to data. To thought labor, we can really get through much more of it and use more data to make our decisions better. >> So what are the big game changing things that are going on that people can relate to? Obviously, autonomous vehicles is one that we can all look at and say, "Wow, that's mind blowing." Smart cities is one that you say, "Oh my god, I'm a resident of a community. "Do they have to re-change the roads? "Who writes the software, is there a budget for that?" Smart home, you see Alexa with Amazon, you see Google with their home product. Voice bots, voice interfaces. So the user interface is certainly changing. How is that impacting some of the things that you guys are working on? >> Well, to the user interface changing, I think that has an entire dynamic on how people use tools. Easier something is, the more people use, the more pervasive it becomes, and we start discovering these emergent dynamics. Like an iPod, for instance. Storing music in a digital form, small devices around before the iPod. But when it made it easy to use, that sort of gave rise to the smartphone. So I think we're going to start seeing some really interesting dynamics like that. >> One of the things that I liked about this past week in San Francisco, Google had their big event, their cloud event, and they talked a lot about, and by the way, Intel was on stage with the new Xeon processor, up to 72 cores, amazing compute capabilities, but cloud computing does bring that scale together. But you start thinking about data science has moved into using data, and now you have a tsunami of data, whether it's taking an analog view of the world and having now multiple datasets available. If you can connect the dots, okay, a lot of data, now you have a lot of data plus a lot of datasets, and you have almost unlimited compute capability. That starts to draw in some of the picture a little bit. >> It does, but actually there's one thing missing from what you just described, is that our ability to scale data storage and data collection has outpaced our ability to compute on it. Computing on it typically is some sort of quadratic function, something faster than when your growth on amount of data. And our compute has really not caught up with that, and a lot of that has been more about focus. Computers were really built to automate streams of tasks, and this sort of idea of going highly parallel and distributed, it's something somewhat new. It's been around a lot in academic circles, but the real use case to drive it home and build technologies around it is relatively new. And so we're right now in the midst of transforming computer architecture, and it's something that becomes a data inference machine, not just a way to automate compute tasks, but to actually do data inference and find useful inferences in data. >> And so machine learning is the hottest trend right now that kind of powers AI, but also there's some talk in the leader circles around learning machines. Data learning from engaged data, or however you want to call it, also brings out another question. How do you see that evolving, because do we need to have algorithms to police the algorithms? Who teaches the algorithms? So you bring in this human aspect of it. So how does the machine become a learning machine? Who teaches the machine, is it... (laughs) I mean, it's crazy. >> Let me answer that a little bit with a question. Do you have kids? >> Yes, four. >> Does anyone police you on raising your kids? >> (laughs) Kind of, a little bit, but not much. They complain a lot. >> I would argue that it's not so dissimilar. As a parent, your job is to expose them to the right kind of biases or not biased data as much as possible, like experiences, they're exactly that. I think this idea of shepherding data is extremely important. And we've seen it in solutions that Google has brought out. There are these little unexpected biases, and a lot of those come from just what we have in the data. And AI is no different than a regular intelligence in that way, it's presented with certain data, it learns from that data and its biases are formed that way. There's nothing inherent about the algorithm itself that causes that bias other than the data. >> So you're saying to me that exposing more data is actually probably a good thing? >> It is. Exposing different kinds of data, diverse data. To give you an example from the biological world, children who have never seen people of different races tend to be more, it's something new and unique and they'll tease it out. It's like, oh, that's something different. Whereas children who are raised with people of many diverse face types or whatever are perfectly okay seeing new diverse face types. So it's the same kind of thing in AI, right? It's going to hone in on the trends that are coming, and things that are outliers, we're going to call as such. So having good, balanced datasets, the way we collect that data, the way we sift through it and actually present it to an AI is extremely important. >> So one of the most exciting things that I like, obviously autonomous vehicles, I geek out on because, not that I'm a car head, gear head or car buff, but it just, you look at what it encapsulates technically. 5G overlay, essentially sensors all over the car, you have software powering it, you now have augmented reality, mixed reality coming into it, and you have an interface to consumers and their real world in a car. Some say it's a moving data center, some say it's also a human interface to the world, as they move around in transportation. So it kind of brings out the AI question, and I want to ask you specifically. Intel talks about this a lot in their super demos. What actually is Intel doing with the compute and what are you guys doing to make that accelerate faster and create a good safe environment? Is it just more chips, is it software? Can you explain, take a minute to explain what Intel's doing specifically? >> Intel is uniquely positioned in this space, 'cause it's a great example of a full end to end problem. We have in-car compute, we have software, we have interfaces, we have actuators. That's maybe not Intel's suite. Then we have connectivity, and then we have cloud. Intel is every one of those things, and so we're extremely well positioned to drive this field forward. Now you ask what are we doing in terms of hardware and software, yes, it's all of it. This is a big focus area for Intel now. We see autonomous vehicles as being one of the major ways that people interact with the world, like locality between cars and interaction through social networks and these kinds of things. This is a big focus area, we are working on the in-car compute actively, we're going to lead that, 5G is a huge focus for Intel, as you might've seen in other, Mobile World Congress, other places. And then the data center. And so we own the data center today, and we're going to continue to do that with new technologies and actually enable these solutions, not just from a pure hardware primitives perspective, but from the software-hardware interaction in full stack. >> So for those people who think of Intel as a chip company, obviously you guys abstract away complexities and put it into silicon, I obviously get that. Google Next this week, one thing I was really impressed by was the TensorFlow machine learning algorithms in open source, you guys are optimizing the Xeon processor to offload, not offload, but kind of take on... Is this kind of the paradigm that Intel looks at, that you guys will optimize the highest performance in the chip where possible, and then to let the software be more functional? Is that a guiding principle, is that a one off? >> I would say that Intel is not just a chip company. We make chips, but we're a platform solutions company. So we sell primitives to various levels, and so, in certain cases, yes, we do optimize for software that's out there because that drives adoption of our solutions, of course. But in new areas, like the car for instance, we are driving the whole stack, it's not just the chip, it's the entire package end to end. And so with TensorFlow, definitely. Google is a very strong partner of ours, and we continue to team up on activities like that. >> We are talking with Naveene Rao, vice president general manager Intel's AI solutions. Breaking it down for us. This end to end thing is really interesting to me. So I want to get just double click on that a little bit. It requires a community to do that, right? So it's not just Intel, right? Intel's always had a great rising tide floats all boats kind of concept over the life of the company, but now, more than ever, it's an API world, you see integration points between companies. This becomes an interesting part. Can you talk up to that point about how you guys are enabling partners to work with, and if people want to work with Intel, how do they work, from a developer to whoever? How do you guys view this community aspect? I mean, sure you'd agree with that, right? >> Yeah, absolutely. Working with Intel can take on many different forms. We're very active in the open source community. The Intel Nervana AI solutions are completely open source. We're very happy to enable people in the open source, help them develop their solutions on our hardware, but also, the open source is there to form that community and actually give us feedback on what to build. The next piece is kind of one quick down, if you're actually trying to build an end to end solution, like you're saying, you got a camera. We're not building cameras. But these interfaces are pretty well defined. Generally what we'll do is, we like to select some partners that we think are high value add. And we work with them very closely, and we build stuff that our customers can rely on. Intel stands for quality. We're not going to put Intel branding on something, unless it sort of conforms to some really high standard. And so that's I think a big power here. It doesn't mean we're not going to enable the people that aren't our channel partners or whatever, they're going to have to be enabled through a more of a standard set of interfaces, software or hardware. >> Naveene, I'll ask you, in the final couple minutes we have left, to kind of zoom out and look at the coolness of the industry right now. So you're exposed, your background, we got your PhD, and then you topic wise now heading up the AI solutions. You probably see a lot of stuff. Go down the what's cool to you scene, share with the audience some of the cool things that you can point to that we should pay attention to or even things that are cool that we should be aware that we might not be aware of. What are some of the coolest things that are out there that you could share? >> To share new things, we'll get to that in a second. Things I think are one of my favorites, AlphaGo, I know this is like, maybe it's hackneyed. But as an engineering student in CS in the mid-90s, studying artificial intelligence back then or what we called artificial intelligence, Go was just off the table. That was less than 20 years ago. In that time, it looked like such an insurmountable problem, the brain is doing something so special that we're just not going to figure it out in my lifetime, to actually doing it is incredible. So to me, that represents a lot. So that's a big one. Interesting things that you may not be aware of are other use cases of AI, like we see it in farming. This is something we take for granted. We go to the grocery store, we pick up our food and we're happy, but the reality is, that's a whole economy in and of itself, and scaling it as our population scales is an extremely difficult thing to do. And we're actually interacting with companies that are doing this at multiple levels. One is at the farming level itself, automating things, using AI to determine the state of different props and actually taking action in the field automatically. That's huge, this is back-breaking work. Humans don't necessarily-- >> And it's important too, because people are worried about the farming industry in general. >> Absolutely. And what I love about that use case of like applying AI to farming techniques is that, by doing that, we actually get more consistency and you get better yields. And you're doing it without any additional chemicals, no genetic engineering, nothing like that, you're just applying the same principles we know better. And so I think that's where we see a lot of wonderful things happening. It's a solved problem, but just not at scale. How do I scale this problem up? I can't do that in many instances, like I talked about with the legal documents and trying to come up with a summary. You just can't scale it today. But with these techniques, we can. And so that's what I think is extremely exciting, any interaction there, where we start to see scale-- >> And new stuff, and new stuff? >> New stuff. Well, some of it I can't necessarily talk about. In the robot space, there's a lot happening there. I'm seeing a lot in the startup world right now. We have a convergence of the mechanical part of it becoming cheaper and easier to build with 3D printing, the Maker revolution, all these kind of things happening, which our CEO is really big on. So that, combined with these techniques becoming mature, is going to come up with some really cool stuff. We're going to start seeing The Jetsons kind of thing. It's kind of neat to think about, really. I don't want to clean my room, hey robot, go clean my room. >> John: I'd love that. >> I'd love that too. Make me dinner, maybe like a gourmet dinner, that'd be really awesome. So we're actually getting to a point where there's a line of sight. We're not there yet, I can see it in the next 10 years. >> So the fog is lifting. All right, final question, just more of a personal note. Obviously, you have a neuroscience background, you mentioned that Go is cool. But the humanization factor's coming in. And we mentioned ethics, came up, we don't have time to talk about the ethics role, but as societal changes are happening, with these new impacts of technologies, there's real impact. Whether it's solving diseases and farming, or finding missing children, there's some serious stuff that's really being done. But the human aspects of converging with algorithms and software and scale. Your thoughts on that, how do you see that and how would you, a lot of people are trying to really put this in a framework to try to advance more either sociology thinking, how do I bring sociology into computer science in a way that's relevant. What are some of your thought here? Can you share any color commentary? >> I think it's a very difficult thing to comment on, especially because there are these emergent dynamics. But I think what we'll see is, just as like social network have interfered in some ways and actually helped our interaction with each other, we're going to start seeing that more and more. We can have AIs that are filtering interactions for us. A positive of that is that we can actually understand more about what's going on around in our world, and we're more tightly interconnected. You can sort of think of it as a higher bandwidth communication between all of us. When we're in hunter-gatherer societies, we can only talk to so many people in a day. Now we can actually do more, and so we can gather more information. Bad things are maybe that things become more impersonal, or people have to start doing weird things to stand out in other people's view. There's all these weird interactions-- >> It's kind of like Twitter. (laughs) >> A little bit like Twitter. You can say ridiculous things sometimes to get noticed. We're going to continue to see that, we're already starting to see that at this point. And so I think that's really where the social dynamic happened. It's just how it impacts our day to day communication. >> Talk to Naveene Rao, great conversation here inside the Intel AI lounge. These are the kind of conversations that are going to be on more and more kitchen tables across the world, I'm John Furrier with theCUBE. Be right back with more after this short break. >> Thanks, John. (bright music)