>> Narrator: From theCUBE's Studios in Palo Alto and Boston connecting with thought leaders all around the world, this is theCUBE conversation. >> Welcome to this CUBE conversation with Commvault, I'm Lisa Martin, looking forward to having a spirited conversation with my two guests, please welcome Janet Giesen, the VP of Operations and Programs for Metallic, A Commvault Venture. Janet, welcome to theCUBE. >> Yeah, it's happy to be here. >> And joining us from EMEA is Mark Jow, the EMEA VP of Technical Sales at Commvault. Hey Mark, good afternoon to you. >> Good afternoon Lisa, it's great to be here with you. >> So just about a year or so ago, theCUBE had the pleasure of being at Commvault GO 2019 and where Metallic was launched, so happy birthday to Metallic. Some evolution and some recent news. Janet, walk us through what you guys have accomplished recently. >> Absolutely, so last year we launched with three product offerings to Metallic, Office 365 Backup, Endpoint Backup and Backup of Core data like VMs and files. In that year since we started with US only, we're now in Canada and Australia, as well as now in our first set of countries in EMEA which Mark will talk about it a little bit and we've greatly expanded our product offerings. One of the things we did, we just launched the discovery, which is a big deal for folks especially looking for compliance applications and their data protection. So we've had a real journey here and just this quarter, as you see we are doubling our product offerings to Metallic and tripling our country availability. So we're doing a lot and we're a leader in the data protection as a service space. >> A lot accomplished in just a 12 month time period, give me a little bit of a preview Janet, why was metallic launched last year for North America, US expanded to Canada and then I see it was announced... It was launched in Australia, New Zealand in the late summer 2020. I know that the cloud market... Their cloud adoption is quite high but give us a little bit of an overview of the actual go to market sequence from a regional perspective. >> Absolutely, and I'll want Mark to really take this one as well. We started in US only in our initial launch, that's where our first launch event was. That's where a lot of our pilot customers were, and then we expanded to Canada, Australia now EMEA, and this is very thoughtful. You have one chance to really launch in a geography. And we wanted it to take all the steps, whether it was compliance, trademarking, cloud storage availability. We leveraged our partnership with Microsoft and Azure for these launches. And really making sure we had everything lined up to best serve our customers. Mark what would you say about this strategy as well? >> Yeah, I think certainly, I mean the strategy is the right one, it's the right one for following reasons. If you look back to 12 months ago, I think in Colorado, I had a GO user event when we launched Metallic, I was fortunate enough to be hosting a number of EMEA partners and customers, and they were clamoring for the product, they're excited by it, they wanted it. We were (indistinct) some cases pressured to think about releasing it earlier. But all those customers wanted a product that was reversed, secure and coping with specific EMEA requirements that they have for the product in particular GDPR and supporting levels of compliance and data privacy that EMEA has rigorous standards for. And I think if you look at Commvault as a company, you know we take our customer's data extremely seriously. We've got one channels to get this right as Janet said, and I expect our customers absolutely expect and deserve right first time. And so when we launch a product like Metallic with the diversity of workloads, the rigorous high performance and secure environments, we want to make sure it's tested properly, it's compliant in all the jurisdictions. And even in Europe, we think about Europe, it's not one given country, even the EU have different countries with different legal and tax nuances. We want to make sure that when our customers get Metallic, 'cause our customers thankfully first launch in EMEA now can. That purchasing, that user experience is seamless sales and frictionless, and the product stands the promises that we make to those customers. So fully behind half phased release for Metallic as are some of our initial early adopter customers in the geographies that we've launched in already. >> So let's talk about some of the massive changes that we've all experienced since last year, Mark I would stick with you, talk to us about some of the changes that you seen from EMEA customers with respect to data protection and data security 'cause we've seen a lot of things going on globally, ransomware on the rise, every 11 seconds there's a ransomware attack. What are some of the recent challenges that you're hearing from customers that you believe Metallic EMEA is going to resolve? >> Yeah, I mean certainly even before the current COVID crisis, we were seeing a huge increase in uptake of customers wanting to use SaaS applications and to protect SaaS workloads. And the growth thing adoption of Office 365 clearly has driven the need for compelling SaaS based solutions for that market. You overlay on that, the situation that COVID has created for us all. Which in reality is denying our customers with its two most valuable important assets, access to premises and access to staff. And increasingly the staff it does have access to a storing, protecting, generating and creating data, not in the data center, not in the cloud but on laptops. So really for us it's a perfect opportunity and we're seeing an increase in demand from our customers wanting rapid solutions to protecting and managing data, to have low footprint in terms of skills and staff and to reduce the need for them to buy physical infrastructure and to expand an already at capacity set premises. And in many cases they can't even get access to, so it's very much a perfect storm for the solution that Metallic provides. >> Yeah Janet, following onto that and just in terms of when Mark mentioned, you know especially when this first happened, not being able to get access to the premises, this massive pivot to work from home and suddenly millions of endpoints scattered globally. Talk to us about some of the things that you saw here in North America in terms of customer demands changing. >> Oh that's a great question, we absolutely saw changes. I mean I go back to what Satya Nadella said, the CEO of Microsoft. He even said in April and may that what we are seeing is two years of digital transformation happening in a two month period. And that's absolutely what we're seeing, so the interest in fact as Mark mentioned, and then interest in protecting endpoints, your laptops and your desktop, as you have an increasingly remote and distributed workforce has completely changed. I mean when we spoke to you last year ago, we had endpoint backup more for completeness to round out our portfolio. We didn't expect it to be a lead offering and take off the way it has. But now with the changes everyone's seeing and with what IT teams need to do with what security teams and cloud architects need to do, we're absolutely seeing that need for endpoint protection grow. >> Yeah, and just to add to that Lisa is the endpoint potentially is also seeing a change and a shift in the types of markets that are looking to Metallic as a solution, recall that we originally targeted Metallic and SMB mid market, market where people were looking for simple, predictable, low cost but yet still scalable infrastructure. The massive drive to protect endpoints and to maintain compliance and control of data there, is actually driving large enterprise customers to Commvault and Metallic as a solution for protecting not hundreds of endpoints, not thousands, not tens of thousands but hundreds of thousands of endpoints for some of the customers that we're not talking to. >> And that's probably going to be something that we see becomes permanent. You know we're seeing so many leaders, Satya Nadella you mentioned Janet, we've heard other ones, Antonio Neri from HPE saying you know I expect at least 50% of the workforce to stay remote. So this is... Was a big need, it was a big boom and a good amount of this is probably not going to change. How is Metallic positioned to help your customers not just survive this time but be able to thrive and become the winners of tomorrow? >> I think one real advantage of Metallic is the two technologies that it's built on top of, one is Metallic part of Commvault, so what we can do is evolve with the needs of our customers, take all that IP, all those patents decide what workloads are going to help our customers through this times and release those as new offerings delivered as PaaS, it allows us to be agile and to pivot as needed. And that's what you see as I said we're doubling our product offering, we're taking that feedback in real time and that's something we'll be announcing very soon, next month. In addition to that, we're also build on top of Microsoft Azure. So we're leveraging certainly their enterprise scalability, the trust and security that they have because we're really something that flexes from the one terabyte dataset to the 10,000 terabyte as you're looking to scale and protect your infrastructure. So we are poised to take on that agility, that time like these demand. >> Do you think, oh go ahead Mark. >> I think just to add to that as well is if you look at our existing customers that have been traditionally using on-prem Commvault complete software or they bought on a perpetual or subscription basis. A number of those have been looking for Metallic to protect some specific workloads, like endpoint for example, but the way we've done this is, the Metallic solution on the on-prem solution are manageable from a single Commvault interface, a command central interface. So it's not a temporary decision to move to SaaS and then that customer then has to move it back in order to control and manage it in an on-prem environment. They get the best of both worlds from two solutions fit for the purpose they are intended from a company that has a 20 year reputation in designing, building and selling scalable, secure data protection infrastructure. >> Reasonable question in terms of the management console. So for example Mark, the situation that you're talking about customers that may have been using Commvault on-prem for a long time now have had in the last year and now in EMEA the opportunity to leverage SaaS data protection for Office Microsoft 365 for example, endpoints. Talk to me a little bit about the management of that, if a customer, legacy Commvault customer has been using on-prem and now they add Metallic for SaaS, data protection for say Microsoft 365, is that managed by a single console? >> Exactly, it's managed by a command center console. So they can see, manage, control report, all of data that exists within the Metallic SaaS based solution, and that sits within that on-prem or their hybrid cloud environment, giving them that, that total flexibility. And with the recent announcement, the launch earlier in October of MCSS on Microsoft, sorry at Metallic Cloud Storage Solution, that also helps their customers that aren't yet looking to move to metallic, to make the step, to put some of their on-prem data rapidly and easily into cloud as a target, as a metallic cloud storage service. And that's a future stepping stone to a full metallic software as a service solution, should they so choose for a 365 or endpoint? So we're giving customers the ability to move from self-manage to fully managed with a SaaS solution in the middle. >> And for that target market perspective, Mark, some of the things that we've seen globally that are new targets, you mentioned ransomware on the rise, healthcare organizations, schools and governments, are there any specific industries that are going to be leading edge for Metallic in EMEA. >> What we've seen from the initial market data and the market uptake by segment from the America's names that launched is interest from every sector, but a particular interest from the sectors where technology is a key differentiator, particularly finance, banking, insurance, and the telco sector, the tech sector and the retail sector. Interestingly enough, we're also seeing in the government and public services sector from our recent Azure launch and some of the demand and interest in EMEA is validating this, customers in public sector organizations, central and local government who traditionally have been fixated on the CapEx buying model and on-prem solutions, moving and starting to look increasingly at SaaS to get solutions up running, protected and secured rapidly in the cloud. And so we're seeing an encouraging up-taking public sector organizations, which are using SaaS as a way to move from CapEx to OPEX models which is particularly reassuring. >> And Janet question for you if we look at data protection as a service, the fastest growing market segment rather in data protection market, what are some of the things that knowing Metallic's first year in the evolution, the changes that the world has seen, but also this demand for data protection as a service, what are some of the things that we can expect in Metallic's second year? >> Yeah so, first you're absolutely right. Data protection as a service is becoming increasingly popular. You know these are cloud based solutions, also known as backup as a service. And I think what we're finding as we talk to customers is everyone has a cloud based initiative, whether they're starting it or they're well on their way. So having a data protection as a service solution like Metallic can either be your first move into the cloud starting with your backup targets and leveraging MCSS as Mark explained as one way to do that, or it can just be another point in a customer's hybrid story. How they're starting to leverage data protection as a service, SaaS delivery. And there's this whole notion now of SaaS for SaaS. Now you need SaaS backup for your SaaS application to follow how the data moves, and that's what we're doing for Office 365. In the second year, we're certainly aiming to continue increasing our workload, supported the products that... And continuing our geo-expansion as we are right now with the EMEA, this is certainly critical as we continue. We'll also be looking to engage local partners, we work with resellers and distributors today, and we're also going to continue expanding our offerings in Azure marketplace. We went live in Azure marketplace last quarter and we're seeing transactions come through there and we want to continue building out our marketplace model as well. >> Last question Janet, you mentioned SaaS for SaaS and there's been a lot of talk about that recently with customers in every segment. And there was this sort of this a shared responsibility model that Microsoft has in Salesforce right in box. But it's been interesting and a lot of customers I've spoken with in the last few months in salesforce ended support for the data recovery service I think in end of July going, wait we thought it was in the cloud, we have to back it up. So is that another direction in terms of Metallics future of being able to protect more types of SaaS workloads besides Microsoft 365? >> Well that's certainly the idea and starting with Office 365, is how do we compliment what Microsoft already offers. Office 365 Salesforce, all of these tools, they are workflow tools, they're integral in organizations or they're just holding critical data. So how do we compliment that through data backup and protection that give them the controls they need. Whether it's policy customization, smart configurations to help them through this and now E discovery on top to be able to search and manage compliance needs. So we really want to be that kind of extra security blanket for all of these SaaS applications and that's really what we're aiming to do over time but Office 365 is our focus right now. >> Yeah, I think just pick out Lisa on Janet's point about the two points of scale for us about scaling out and launching in new markets and bringing new workloads into the Metallic portfolio. You know one of the things that we understand is we clearly we've seen significant demand for Office 365 and endpoint ussually as for Metallic. But let's also not lose sight of the fact that a number of organizations are coming to us to protect their VMs and their file server environments so being initially in small environments. And they're starting to ask us specifically about our plans to incorporate additional enterprise type on-prem workloads in a Metallic environment. And the fact that we've built 20 years of expertise in IOP in that space, we've been probably the quickest to launch the most innovative and wide this range of workloads in our on-prem and subscription based software makes it far easier for us to pivot and to extend over time rapidly, the workloads that Metallic supports for customers wanting to move traditionally on-prem workloads. That I'll just say 365 endpoint but VMs and other database workloads into the cloud. And that's a unique differentiator for where Metallic can take our customers, not just geographically but in terms of the diversity of workloads that we'll be able to cover. >> Great point Mark, absolutely. >> Well thank you both for explaining the evolution of Metallic, A Commvault Venture in its first year, giving us an insight into some of the recent new announcements and a peek into what's to come. Janet, Mark, we appreciate your time. >> Yeah, thank you. >> That's being a pleasure, thank you. >> For my guests, I'm Lisa Martin, you're watching theCUBE conversation. (upbeat music)

>> Just this quarter alone, Metallic is doubling its product offerings and tripling its country availability. So watch this space.

from the cube studios in Palo Alto in Boston it's the cube covering empowering the autonomous enterprise brought to you by Oracle consulting welcome back everybody to this special digital event coverage that the cube is looking into the rebirth of Oracle consulting Janet George is here she's group vp autonomous for advanced analytics with machine learning and artificial intelligence at oracle and she's joined by grant gibson is a group vp of growth and strategy at oracle folks welcome to the cube thanks so much for coming on thank you thank you great I want to start with you because you get strategy in your title like just start big picture what is the strategy with Oracle specifically as it relates to autonomous and also consulting sure so I think you know Oracle has a deep legacy of strengthened data and over the company's successful history it's evolved what that is from steps along the way if you look at the modern enterprise of Oracle client I think there's no denying that we've entered the age of AI that everyone knows that artificial intelligence and machine learning are a key to their success in the business marketplace going forward and while generally it's acknowledge that it's a transformative technology and people know that they need to take advantage of it it's the how that's really tricky and that most enterprises in order to really get an enterprise level ROI on an AI investment need to engage in projects of significant scope and going from realizing there's an opportunity to realize and there's a threat to mobilizing yourself to capitalize on it is a is a daunting task for an enemy certainly one that's you know anybody that's got any sort of legacy of success has built-in processes that's built in systems has built in skillsets and making that leap to be an autonomous enterprise is is challenging for companies to wrap their heads around so as part of the rebirth of Oracle consulting we've developed a practice around how to both manage the the technology needs for that transformation as well as the human needs as well as the data science needs to it so rather there's about five or six things that I want to followup with you there so there's gonna be good conversations Janet so ever since I've been in the industry we're talking about AI in sort of start stop start stop we had the AI winter and now it seems to be here it's almost feel like that the the technology never lived up to its promise you didn't have the horsepower a compute power you know enough data maybe so we're here today feels like we are entering a new era why is that and and how will the technology perform this time so for AI to perform it's very reliant on the data we entered the age of AI without having the right data for AI so you can imagine that we we just launched into AI without our data being ready to be training sex for AI so we started with bi data or we started the data that was already historically transformed formatted had logical structures physical structures this data was sort of trapped in many different tools and then suddenly AI comes along and we say take this data our historical data we haven't tested to see if this has labels in it this has learning capability in it we just thrust the data to AI and that's why we saw the initial wave of AI sort of failing because it was not ready to fall AI ready for the generation of AI and part of I think the leap that clients are finding success with now is getting the Apple data types and you're moving from the zeros and ones of structured data to image language written language spoken language you're capturing different data sets in ways that prior tools never could and so the classifications that come out of it the insights that come out of it the business process transformation comes out of it is different than what we would have understood under the structured data format so I think it's that combination of really being able to push massive amounts of data through a cloud product to be able to process it at scale that is what I think is the combination that takes it to the next plateau for sure the language that we use today I feel like is going to change and you just started to touch on some of them you know sensing you know they're our senses and you know the visualization and the the the the auditory so it's it's sort of this new experience that customers are saying a lot of this machine intelligence behind them I call it the autonomous enterprise right the journey to be the autonomous enterprise and when you're on this journey to be the autonomous enterprise you need really the platform that can help you be cloud is that platform which can help you get to the autonomous journey but the autonomous journey does not end with the cloud right or doesn't end with the dead lake these are just infrastructures that are basic necessary necessities for being on that on that autonomous journey but at the end it's about how do you train and scale at a very large scale training that needs to happen on this platform for AI to be successful and if you are an autonomous enterprise then you have really figured out how to tap into AI and machine learning in a way that nobody else has to derive business value if you will so you've got the platform you've got the data and now you're actually tapping into the autonomous components AI and machine learning to derive business intelligence and business value so I want to get into a little bit of Oracle's role but to do that I want to talk a little bit more about the industry so if you think about the way this the industry seems to be restructuring around data there historically Industries had their own stack or value chain and if you were in the finance industry you were there for life you know so when you think about banking for example highly regulated industry think about our geek culture these are highly regulated industries they're come it was very difficult to disrupt these industries but now you look at an Amazon right and what does an Amazon or any other tech giant like Apple have they have incredible amounts of data they understand how people use or how they want to do banking and so they've cut off the tap of cash or Amazon pay and these things are starting to eat into the market right so you would have never thought an Amazon could be a competition to your banking industry just because of regulations but they are not hindered by the regulations because they're starting at a different level and so they become an instant threat and an instant destructor to these highly regulated industries that's what data does right then you use data as you DNA for your business and you are sort of born in data or you figured out how to be autonomous if you will capture value from that data in a very significant manner then you can get into industries that are not traditionally your own industry it can be like the food industry it can be the cloud industry the book industry you know different industries so you know that that's what I see happening with the tech giants so great this is a really interesting point that Gina is making that you mentioned you started off with like a couple of industries that are highly regulated harder to disrupt you know music got disrupted publishing got disrupted but you've got these regulated businesses you know defense automotive actually hasn't been truly disrupted yet so I'm Tesla maybes a harbinger and so you've got this spectrum of disruption but is anybody safe from disruption okay I don't think anyone's ever safe from it it's it's changed in evolution right that you whether it's you know swapping horseshoes for cars or TV for movies or Netflix or any sort of evolution of a business you I wouldn't coast on any of them and I think to earlier question around the value that we can help bring to Oracle customers is that you know we have a rich stack of applications and I find that the space between the applications the data that that spans more than one of them is a ripe playground for innovations that where the data already exists inside a company but it's trapped from both a technology and a business perspective and that's where I think really any company can take advantage of knowing its data better and changing itself to take advantage of what's already there yet powerful bit people always throw the bromide out the data is the new oil and we've said no data is far more valuable because you can use it in a lot of different places or you can use once and it's has to follow laws of scarcity data if you can unlock it and so a lot of the incumbents they have built a business around whatever a factory or you know process and people a lot of the the trillion-dollar start in us that they're become trillionaires you know I'm talking about data is at the core their data company so so it seems like a big challenge for you you're incumbent customers clients is to put data hit the core be able to break down those silos how do they do that grading down silos is really super critical for any business it was okay to operate in a silo for example you would think that oh you know I could just be payroll in expense reports and it wouldn't man matter if I get into vendor performance management or purchasing that can operate as a silo but anymore we are finding that there are tremendous insights between vendor performance management I expensive all these things are all connected so you can't afford to have your data set in silos so grading down that silo actually gives the business very good performance right insights that they didn't have before so that's one way to go but but another phenomena happens when you start to great down the silos you start to recognize what data you don't have to take your business to the next level right that awareness will not happen when you're working with existing data so that awareness comes into form when you great the silos and you start to figure out you need to go after different set of data to get you to new product creation what would that look like new test insights or new capex avoidance then that data is just you have to go through the eye tration to be able to figure that out which takes is what you're saying happy so this notion of the autonomous under president help me here because I get kind of autonomous and automation coming into IT IT ops I'm interested in how you see customers taking that beyond the technology organization into the enterprise I think when AI is a technology problem the company is it at a loss ai has to be a business problem ai has to inform the business strategy ai has two main companies the successful companies that have done so 90 percent of our investments are going towards data we know that and and most of it going towards AI data out there about this right and so we looked at what are these ninety cup ninety percent of the company's investments where are these going and who is doing this right and who's not doing this right one of the things we are seeing as results is that the companies that are doing it right have brought data into their business strategy they've changed their business model right so it's not like making a better taxi but coming up with uber right so it's not like saying okay I'm going to have all these I'm going to be the drug manufacturing company I'm going to put drugs out there in the market versus I'm going to do connected health right and so how does data serve the business model of being connected health rather than being a drug company selling drugs to my customers right it's a completely different way of looking at it and so now I is informing drug discovery AI is not helping you just put more drugs to the market rather it's helping you come up with new drugs that will help the process of connected game there's a lot of discussion in the press about you know the ethics of AI and how far should we take AI and how far can we take it from a technology standpoint long roadmap there but how far should we take it do you feel as though public policy will take care of that a lot of that narrative is just kind of journalists looking for you know the negative story well that's sort itself out how much time do you spend with your customers talking about that we in Oracle we're building our data science platform with an explicit feature called explain ability off the model on how the model came up with the features what features it picked we can rearrange the features that the model picked so I think explain ability is very important for ordinary people to trust AI because we can't trust AI even even data scientists contrast AI right to a large extent so for us to get to that level where we can really trust what AI is picking in terms of a model we need to have explained ability and I think a lot of the companies right now are starting to make that as part of their platform well we're definitely entering a new era the the age of AI of the autonomous enterprise folks thanks very much for a great segment really appreciate it yeah our pleasure thank you for having us thank you alright and thank you and keep it right there we're right back with our next guest for this short break you're watching the cubes coverage of the rebirth of Oracle consulting right back you [Music]

>>Yeah, yeah, >>yeah! >>Welcome back, everybody. To this special digital event coverage, the Cube is looking into the rebirth of Oracle Consulting. Janet George is here. She's group VP Autonomous for Advanced Analytics with machine learning and artificial intelligence at Oracle. And she's joined by Grant Gibson Group VP of growth and strategy at Oracle. Folks, welcome to the Cube. Thanks so much for coming on. Great. I want to start with you because you get strategy in your title like this. Start big picture. What is the strategy with Oracle specifically as it relates to autonomous and also consulting? >>Sure. So I think you know, Oracle has a deep legacy of strength and data and, uh uh, over the company's successful history. It's evolved what that is from steps along the way. And if you look at the modern enterprise Oracle client, I think there's no denying that we've entered the age of AI, that everyone knows that artificial intelligence and machine learning are a key to their success in the business marketplace going forward. And while generally it's acknowledged that it's a transformative technology and people know that they need to take advantage of it, it's the how that's really tricky and that most enterprises, in order to really get an enterprise level, are rely on AI investment. Need to engage in projects of significant scope, and going from realizing there's an opportunity of realizing there's a threat to mobilize yourself to capitalize on it is a daunting task or certainly one that's, you know, Anybody that's got any sort of legacy of success has built in processes as building systems has built in skill sets, and making that leap to be an autonomous enterprise is challenging for companies to wrap their heads around. So as part of the rebirth of Oracle Consulting, we've developed a practice around how to both manage the technology needs for that transformation as well as the human needs as well as the data science needs. >>So there's about five or six things that I want to follow up with you there. So this is a good conversation. Ever since I've been in the industry, we were talking about a sort of start stop start stop at the Ai Winter, and now it seems to be here is almost feel like the technology never lived up to its promise. If you didn't have the horsepower compute power data may be so we're here today. It feels like we are entering a new era. Why is that? And how will the technology perform this time? >>So for AI to perform it's very remind on the data we entered the age of Ai without having the right data for AI. So you can imagine that we just launched into Ai without our data being ready to be training sex for AI. So we started with B I data or we started the data that was already historically transformed. Formatted had logical structures, physical structures. This data was sort of trapped in many different tools. And then suddenly Ai comes along and we see Take this data, our historical data we haven't tested to see if this has labels in it. This has learning capability in it. Just trust the data to AI. And that's why we saw the initial wave of ai sort of failing because it was not ready to full ai ready for the generation of Ai, if you will. >>So, to me, this is I always say, this was the contribution that Hadoop left us, right? I mean, the dupe everybody was crazy. It turned into big data. Oracle was never that nuts about it is gonna watch, Setback and wash obviously participated, but it gathered all this data created Chief Data Lakes, which people always joke turns into data swamps. But the data is often times now within organizations least present. Now it's a matter of what? What what's The next step is >>basically about Hadoop did to the world of data. Was her dupe freed data from being stuck in tools it basically brought forth. This concept of a platform and platform is very essential because as we enter the age of AI and be entered, the better wide range of data. We can't have tools handling all of the state of the data needs to scale. The data needs to move, the data needs to grow. And so we need the concept of platforms so we can be elastic for the growth of the data, right, it can be distributed. It can grow based on the growth of the data, and it can learn from that data. So that is that's the reason why Hadoop sort of brought us into the platform board, >>right? A lot of that data ended up in the cloud. I always say, You know, for years we marched to the cadence of Moore's law. That was the innovation engine in this industry and fastest, you could get a chip in, you know, you get a little advantage, and then somebody would leapfrog. Today it's got all this data you apply machine intelligence and cloud gives you scale. It gives you agility of your customers. Are they taking advantage of the new innovation cocktail? First of all, do you buy that? How do you see them taking >>advantage of? Yeah, I think part of what James mentioned makes a lot of sense is that at the beginning, when you know you're taking the existing data in an enterprise and trying to do AI to it, you often get things that look a lot like what you already knew because you're dealing with your existing data set in your existing expertise. And part of I think the leap that clients are finding success with now is getting novel data types, and you're moving from, uh, zeros and ones of structured data, too. Image language, written language, spoken language. You're capturing different data sets in ways that prior tools never could. And so the classifications that come out of it, the insights that come out of it, the business process transformation comes out of it is different than what we would have understood under the structure data format. So I think it's that combination of really being able to push massive amounts of data through a cloud product to be able to process it at scale. That is what I think is the combination that takes it to the next plateau for sure. >>So you talked about sort of. We're entering a new era Age of a AI. You know, a lot of people, you know, kind of focus on the cloud is the current era, but it really does feel like we're moving beyond that. The language that we use today, I feel like it's going to change, and you just started to touch on some of it. Sensing, you know, there are senses and you know the visualization in the the auditory. So it's It's sort of this new experience that customers are seeing a lot of this machine intelligence behind. >>I call it the autonomous and a price right. The journey to be the autonomous enterprise. And then you're on this journey to be the autonomous enterprise you need. Really? The platform that can help you be cloud is that platform which can help you get to the autonomous journey. But the autonomous journey does not end with the cloud or doesn't end with the data lake. These are just infrastructures that are basic necessary necessities for being on that on that autonomous journey. But at the end, it's about how do you train and scale at, um, very large scale training that needs to happen on this platform for AI to be successful. And if you are an autonomous and price, then you have really figured out how to tap into AI and machine learning in a way that nobody else has to derive business value, if you will. So you've got the platform, you've got the data, and now you're actually tapping into the autonomous components ai and machine learning to derive business, intelligence and business value. >>So I want to get into a little bit of Oracle's role. But to do that I want to talk a little bit more about the industry. So if you think about the way that the industry seems to be restructuring around data. Historically, industries had their own stack value chain, and if you were in in in the finance industry, you were there for life. We had your own sales channel distribution, etcetera. But today you see companies traversing industries, which has never happened before. You know, you see apple getting into content and music, and there's so many examples are buying whole foods data is sort of the enabler. There you have a lot of organizations, your customers, that are incumbents that they don't wanna get disrupted your part big party roles to help them become that autonomous and press so they don't get disrupted. I wonder if you could maybe maybe comment on How are you doing? >>Yeah, I'll comment and then grant you China, you know. So when you think about banking, for example, highly regulated industry think about RG culture. These are highly regulated industries there. It was very difficult to destruct these industries. But now you look at an Amazon, right? And what is an Amazon or any other tech giants like Apple have? They have incredible amounts of data. They understand how people use for how they want to do banking. And so they've come up with Apple cash or Amazon pay, and these things are starting to eat into the market, right? So you would have never thought and Amazon could be a competition to a banking industry just because of regulations. But they're not hindered by the regulations because they're starting at a different level. And so they become an instant threat in an instant destructive to these highly regulated industries. That's what data does, right when you use data as your DNA for your business and you are sort of born in data or you figured out how to be autonomous. If you will capture value from that data in a very significant manner, then you can get into industries that are not traditionally your own industry. It can be like the food industry can be the cloud industry, the book industry, you know, different industries. So you know that that's what I see happening with the tech giants. >>So great, there's a really interesting point that the Gina is making that you mentioned. You started off with a couple of industries that are highly regulated, the harder to disrupt use, it got disrupted, publishing got disrupted. But you've got these regulated businesses. Defense or automotive actually hasn't been truly disrupted yet. Some Tesla, maybe a harbinger. And so you've got this spectrum of disruption. But is anybody safe from disruption? >>Kind of. I don't think anyone's ever say from it. It's It's changing evolution, right? That you whether it's, you know, swapping horseshoes for cars are TV for movies or Netflix are any sort of evolution of a business You're I wouldn't coast on any of them. And I think to the earlier question around the value that we can help bring the Oracle customers is that you know, we have a rich stack of applications, and I find that the space between the applications, the data that that spans more than one of them is a ripe playground for innovations that where the data already exists inside a company. But it's trapped from both a technology and a business perspective. Uh, and that's where I think really any company can take advantage of knowing it's data better and changing itself to take advantage of what's already there. >>Yet powerful people always throw the bromide out. The data is the new oil, and we've said. No data is far more valuable because you can use it in a lot of different places. Oil you can use once and it's follow the laws of scarcity data if you can unlock it. And so a lot of the incumbents they have built a business around, whatever a factory or a process and people, a lot of the trillion are starting us that have become billionaires. You know, I'm talking about Data's at the core. They're data companies. So So it seems like a big challenge for your incumbent customers. Clients is to put data at the core, be able to break down those silos. How do they do that? >>Grading down silos is really super critical for any business. It was okay to operate in a silo, for example. You would think that, Oh, you know, I could just be payroll and expense reports and it wouldn't matter matter if I get into vendor performance management or purchasing that can operate as a silo. But any movie of finding that there are tremendous insights between vendor performance management I expensive for these things are all connected, so you can't afford to have your data sits in silos. So grading down that silo actually gives the business very good performance, right? Insights that they didn't have before. So that's one way to go. But but another phenomena happens when you start to great down the silos, you start to recognize what data you don't have to take your business to the next level, right. That awareness will not happen when you're working with existing data so that a Venice comes into form when you great the silos and you start to figure out you need to go after a different set of data to get you to a new product creation. What would that look like? New test insights or new cap ex avoidance that that data is just you have to go through the iteration to be able to figure that out. >>It becomes it becomes a business problem, right? If you got a process now where you can identify 75% of the failures and you know the value of the other 25% of failures, that becomes a simple investment. How much money am I willing to invest to knock down some portion that 25% and it changes it from simply an I t problem or expense management problem to you know, the cash problem. >>But you still need a platform that has AP eyes that allows you to bring in those data sets that you don't have access to this enable an enabler. It's not the answer. It's not the outcome in and of itself, but it enables. And >>I always say, you can't have the best toilet if you're coming, doesn't work. You know what I mean? So you have to have your plumbing. Your plumbing has to be more modern. So you have to bring in modern infrastructure distributed computing that that you cannot. There's no compromise there, right? You have to have the right equal system for you to be able to be technologically advanced on a leader in that >>table. Stakes is what you're saying. And so this notion of the autonomous enterprise I would help me here cause I get kind of autonomous and automation coming into I t I t ops. I'm interested in how you see customers taking that beyond the technology organization into the enterprise. >>Yeah, this is this is such a great question, right? This is what I've been talking about all morning. Um, I think when AI is a technology problem, the company is that at a loss AI has to be a business problem. AI has to inform the business strategy. AI has to been companies. The successful companies that have done so. 90% of my investments are going towards state. We know that and most of it going towards AI. There's data out there about this, right? And so we look at what are these? 90 90% of the company's investments. Where are these going and whose doing this right? Who's not doing this right? One of the things we're seeing as results is that the companies that are doing it right have brought data into their business strategy. They've changed their business model, right? So it's not like making a better taxi, but coming up with a bow, right? So it's not like saying Okay, I'm going to have all these. I'm going to be the drug manufacturing company. I'm gonna put drugs out there in the market forces. I'm going to do connected help, right? And so how does data serve the business model of being connected? Help rather than being a drug company selling drugs to my customers, right? It's a completely different way of looking at it. And so now you guys informing drug discovery is not helping you just put more drugs to the market. Rather, it's helping you come up with new drugs that would help the process of connected games. There's a >>lot of discussion in the press about, you know, the ethics of AI, and how far should we take? A far. Can we take it from a technology standpoint, Long road map there? But how far should we take it? Do you feel as though of public policy will take care of that? A lot of that narrative is just kind of journalists looking for, You know, the negative story. Well, that's sort itself out. How much time do you spend with your customers talking about that and is what's Oracle's role there? I mean, Facebook says, Hey, the government should figure this out. What's your point? >>I think everybody has a role. It's a joint role, and none of us could give up our responsibilities as data scientists. We have heavy responsibility in this area on. We have heavy responsibility to advise the clients on the state area. Also, the data we come from the past has to change. That is inherently biased, right? And we tend to put data signs on biased data with the one dimensional view of the data. So we have to start looking at multiple dimensions of the data. It's got to start examining. I call it a responsible AI when you just simply take one variable or start to do machine learning with that because that's not that's not right. You have to examine the data. You got to understand how much biases in the data are you training a machine learning model with the bias? Is there diversity in the models? Is their diversity in the data? These are conversations we need to have. And we absolutely need policy around this because unless our lawmakers start to understand that we need the source of the data to change. And if we look at this, if we look at the source of the data and the source of the data is inherently biased or the source of the data has only a single representation, we're never going to change that downstream. AI is not going to help us. There so that has to change upstream. That's where the policy makers come into into play. The lawmakers come into play, but at the same time as we're building models, I think we have a responsibility to say can be triangle can be built with multiple models. Can we look at the results of these models? How are these feature's ranked? Are they ranked based on biases, sex, HP II, information? Are we taking the P I information out? Are we really looking at one variable? Somebody fell to pay their bill, but they just felt they they build because they were late, right? Voices that they don't have a bank account and be classified. Them is poor and having no bank account, you know what I mean? So all of this becomes part of response >>that humans are inherently biased, and so humans or building algorithms right there. So you say that through iteration, we can stamp out, the buyers >>can stamp out, or we can confront the bias. >>Let's make it transparent, >>make transparent. So I think that even if we can have the trust to be able to have the discussion on, is this data the right data that we're doing the analysis on On start the conversation day, we start to see the change. >>We'll wait so we could make it transparent. And I'm thinking a lot of AI is black box. Is that a problem? Is the black box you know, syndrome an issue or we actually >>is not a black box. We in Oracle, we're building our data science platform with an explicit feature called Explained Ability. Off the model on how the model came up with the features what features they picked. We can rearrange the features that the model picked, citing Explain ability is very important for ordinary people. Trust ai because we can't trust even even they designed This contrast ai right to a large extent. So for us to get to that level, where we can really trust what ai speaking in terms of a modern, we need to have explain ability. And I think a lot of the companies right now are starting to make that as part of their platform. >>So that's your promise. Toe clients is that your AI will be a that's not everybody's promised. I mean, there's a lot of black box and, you know, >>there is, if you go to open source and you start downloading, you'll get a lot of black boss. The other advantage to open source is sometimes you can just modify the black box. You know they can give you access, and you could modify the black box. But if you get companies that have released to open, source it somewhat of a black box, so you have to figure out the balance between you. Don't really worry too much about the black box. If you can see that the model has done a pretty good job as compared to other models, right if I take if I triangulate the results off the algorithm and the triangulation turns out to be reasonable, the accuracy on our values and the Matrix is show reasonable results. Then I don't really have to brief one model is to bias compared to another moderate. But I worry if if there's only one dimension to it. >>Well, ultimately much too much of the data scientists to make dismay, somebody in the business side is going to ask about cause I think this is what the model says. Why is it saying that? And you know, ethical reasons aside, you're gonna want to understand why the predictions are what they are, and certainly as you're going to examine those things as you look at the factors that are causing the predictions on the outcomes, I think there's any sort of business should be asking those responsibility questions of everything they do, ai included, for sure. >>So we're entering a new era. We kind of all agree on that. So I want to just throw a few questions out, have a little fun here, so feel free to answer in any order. So when do you think machines will be able to make better diagnoses than doctors? >>I think they already are making better diagnosis. And there's so much that I found out recently that most of the very complicated cancel surgeries are done by machines doctors to standing by and making sure that the machines are doing it well, right? And so I think the machines are taking over in some aspects. I wouldn't say all aspects. And then there's the bedside manners. You really need the human doctor and you need the comfort of talking to >>a CIO inside man. Okay, when >>do you >>think that driving and owning your own vehicle is going to be the exception rather than the rule >>that I think it's so far ahead. It's going to be very, very near future, you know, because if you've ever driven in an autonomous car, you'll find that after your initial reservations, you're going to feel a lot more safer in an autonomous car because it's it's got a vision that humans don't. It's got a communication mechanism that humans don't right. It's talking to all the fleets of cars. Richardson Sense of data. It's got a richer sense of vision. It's got a richer sense of ability to react when a kid jumps in front of the car where a human will be terrified, not able to make quick decisions, the car can right. But at the same time we're going to have we're gonna have some startup problems, right? We're going to see a I miss file in certain areas, and junk insurance companies are getting gearing themselves up for that because that's just but the data is showing us that we will have tremendously decreased death rates, right? That's a pretty good start to have AI driving up costs right >>believer. Well, as you're right, there's going to be some startup issues because this car, the vehicle has to decide. Teoh kill the person who jumped in front of me. Or do I kill the driver killing? It's overstating, but those are some of the stories >>and humans you don't. You don't question the judgment system for that. >>There's no you person >>that developed right. It's treated as a one off. But I think if you look back, you look back five years where we're way. You figure the pace of innovation and the speed and the gaps that we're closing now, where we're gonna be in five years, you have to figure it's I mean, I don't I have an eight year old son. My question. If he's ever gonna drive a car, yeah, >>How about retail? Do you think retail stores largely will disappear? >>I think retail. Will there be a customer service element to retail? But it will evolve from where it's at in a very, very high stakes, right, because now, with our if I did, you know we used to be invisible as we want. We still aren't invisible as you walk into a retail store, right, Even if you spend a lot of money in in retail. And you know now with buying patterns and knowing who the customer is and your profile is out there on the Web, you know, just getting a sense of who this person is, what their intent is walking into the store and doing doing responsible ai like bringing value to that intent right, not responsible. That will gain the trust. And as people gain the trust and then verify these, you're in the location. You're nearby. You normally by the sword suits on sale, you know, bring it all together. So I think there's a lot of connective tissue work that needs to happen. But that's all coming. It's coming together, >>not the value and what the what? The proposition of the customers. If it's simply there as a place where you go and buy, pick up something, you already know what you're going to get. That story doesn't add value. But if there's something in the human expertise and the shared felt, that experience of being in the store, that's that's where you'll see retailers differentiate themselves. I >>like, yeah, yeah, yeah, >>you mentioned Apple pay before you think traditional banks will lose control of payment systems, >>They're already losing control of payment systems, right? I mean, if you look at there was no reason for the banks to create Siri like assistance. They're all over right now, right? And we started with Alexa first. So you can see the banks are trying to be a lot more customized customer service, trying to be personalized, trying to really make it connect to them in a way that you have not connected to the bank before. The way we connected to the bank is you know, you knew the person at the bank for 20 years or since when you had your first bank account, right? That's how you connect with the banks. And then you go to a different branch, and then all of a sudden you're invisible, right? Nobody knows you. Nobody knows that you were 20 years with the bank. That's changing, right? They're keeping track of which location you're going to and trying to be a more personalized. So I think ai is is a forcing function in some ways to provide more value. If anything, >>we're definitely entering a new era. The age of of AI of the autonomous enterprise folks, thanks very much for great segment. Really appreciate it. >>Yeah. Pleasure. Thank you for having us. >>All right. And thank you and keep it right there. We'll be back with our next guest right after this short break. You're watching the Cube's coverage of the rebirth of Oracle consulting right back. Yeah, yeah, yeah, yeah.

>> Announcer: From Chicago, it's theCUBE, covering Oracle Transformation Day 2020. Brought to you by Oracle Consulting. >> Welcome back, everybody, to this special digital event coverage that theCUBE is looking into the rebirth of Oracle Consulting. Janet George is here, she's a group VP, autonomous for advanced analytics with machine learning and artificial intelligence at Oracle, and she's joined by Grant Gibson, who's a group VP of growth and strategy at Oracle. Folks, welcome to theCUBE, thanks so much for coming on. >> Thank you. >> Thank you. >> Grant, I want to start with you because you've got strategy in your title. I'd like to start big-picture. What is the strategy with Oracle, specifically as it relates to autonomous, and also consulting? >> Sure, so, I think Oracle has a deep legacy of strength in data, and over the company's successful history, it's evolved what that is from steps along the way. And if you look at the modern enterprise, an Oracle client, I think there's no denying that we've entered the age of AI, that everyone knows that artificial intelligence and machine learning are a key to their success in the business marketplace going forward. And while generally it's acknowledged that it's a transformative technology, and people know that they need to take advantage of it, it's the how that's really tricky, and that most enterprises, in order to really get an enterprise-level ROI on an AI investment, need to engage in projects of significant scope. And going from realizing there's an opportunity or realizing there's a threat to mobilizing yourself to capitalize on it is a daunting task for enterprise. Certainly one that's, anybody that's got any sort of legacy of success has built-in processes, has built-in systems, has built-in skill sets, and making that leap to be an autonomous enterprise is challenging for companies to wrap their heads around. So as part of the rebirth of Oracle Consulting, we've developed a practice around how to both manage the technology needs for that transformation as well as the human needs, as well as the data science needs to it. So there's-- >> So, wow, there's about five or six things that I want to (Grant chuckles) follow up with you there, so this is a good conversation. Janet, ever since I've been in the industry, when you're talking about AI, it's sort of start-stop, start-stop. We had the AI winter, and now it seems to be here. It almost feels like the technology never lived up to its promise, 'cause we didn't have the horsepower, the compute power, it didn't have enough data, maybe. So we're here today, it feels like we are entering a new era. Why is that, and how will the technology perform this time? >> So for AI to perform, it's very reliant on the data. We entered the age of AI without having the right data for AI. So you can imagine that we just launched into AI without our data being ready to be training sets for AI. So we started with BI data, or we started with data that was already historically transformed, formatted, had logical structures, physical structures. This data was sort of trapped in many different tools, and then, suddenly, AI comes along, and we say, take this data, our historical data, we haven't tested it to see if this has labels in it, this has learning capability in it. We just thrust the data to AI. And that's why we saw the initial wave of AI sort of failing, because it was not ready for AI, ready for the generation of AI, if you will. >> So, to me, this is, I always say this was the contribution that Hadoop left us, right? I mean, Hadoop, everybody was crazy, it turned into big data. Oracle was never that nuts about it, they just kind of watched, sat back and watched, obviously participated. But it gathered all this data, it created cheap data lakes, (laughs) which people always joke, turns into data swamps. But the data is oftentimes now within organizations, at least present, right. >> Yes, yes, yes. >> Like now, it's a matter of what? What's the next step for really good value? >> Well, basically, what Hadoop did to the world of data was Hadoop freed data from being stuck in tools. It basically brought forth this concept of platform. And platform is very essential, because as we enter the age of AI and we enter the petabyte range of data, we can't have tools handling all of this data. The data needs to scale. The data needs to move. The data needs to grow. And so, we need the concept of platform so we can be elastic for the growth of the data. It can be distributed. It can grow based on the growth of the data. And it can learn from that data. So that's the reason why Hadoop sort of brought us into the platform world. And-- >> Right, and a lot of that data ended up in the cloud. I always say for years, we marched to the cadence of Moore's law. That was the innovation engine in this industry. As fast as you could get a chip in, you'd get a little advantage, and then somebody would leapfrog. Today, it's, you've got all this data, you apply machine intelligence, and cloud gives you scale, it gives you agility. Your customers, are they taking advantage of that new innovation cocktail? First of all, do you buy that, and how do you see them taking advantage of this? >> Yeah, I think part of what Janet mentioned makes a lot of sense, is that at the beginning, when you're taking the existing data in an enterprise and trying to do AI to it, you often get things that look a lot like what you already knew, because you're dealing with your existing data set and your existing expertise. And part of, I think, the leap that clients are finding success with now is getting novel data types. You're moving from the zeroes and ones of structured data to image, language, written language, spoken language. You're capturing different data sets in ways that prior tools never could, and so, the classifications that come out of it, the insights that come out of it, the business process transformation that comes out of it is different than what we would have understood under the structured data format. So I think it's that combination of really being able to push massive amounts of data through a cloud product to be able to process it at scale. That is what I think is the combination that takes it to the next plateau for sure. >> So you talked about sort of we're entering the new era, age of AI. A lot of people kind of focus on the cloud as sort of the current era, but it really does feel like we're moving beyond that. The language that we use today, I feel like, is going to change, and you just started to touch on some of it, sensing, our senses, and the visualization, and the auditory, so it's sort of this new experience that customers are seeing, and a lot of this machine intelligence behind that. >> I call it the autonomous enterprise, right? >> Okay. >> The journey to be the autonomous enterprise. And when you're on this journey to be the autonomous enterprise, you need, really, the platform that can help you be. Cloud is that platform which can help you get to the autonomous journey. But the autonomous journey does not end with the cloud, or doesn't end with the data lake. These are just infrastructures that are basic, necessary, necessities for being on that autonomous journey. But at the end, it's about, how do you train and scale very large-scale training that needs to happen on this platform for AI to be successful? And if you are an autonomous enterprise, then you have really figured out how to tap into AI and machine learning in a way that nobody else has to derive business value, if you will. So you've got the platform, you've got the data, and now you're actually tapping into the autonomous components, AI and machine learning, to derive business intelligence and business value. >> So I want to get into a little bit of Oracle's role, but to do that, I want to talk a little bit more about the industry. So if you think about the way the industry seems to be restructuring around data, historically, industries had their own stack or value chain, and if you were in the finance industry, you were there for life, you know? >> Yes. >> You had your own sales channel, distribution, et cetera. But today, you see companies traversing industries, which has never happened before. You see Apple getting into content, and music, and there's so many examples, Amazon buying Whole Foods. Data is sort of the enabler there. You have a lot of organizations, your customers, that are incumbents, that they don't want to get disrupted. A big part of your role is to help them become that autonomous enterprise so they don't get disrupted. I wonder if you could maybe comment on how you're doing. >> Yeah, I'll comment, and then, Grant, you can chime in. >> Great. >> So when you think about banking, for example, highly regulated industry, think about agriculture, these are highly regulated industries. It is very difficult to disrupt these industries. But now you're looking at Amazon, and what does an Amazon or any other tech giant like Apple have? They have incredible amounts of data. They understand how people use, or how they want to do, banking. And so, they've come up with Apple Cash, or Amazon Pay, and these things are starting to eat into the market. So you would have never thought an Amazon could be a competition to a banking industry, just because of regulations, but they are not hindered by the regulations because they're starting at a different level, and so, they become an instant threat and an instant disruptor to these highly regulated industries. That's what data does. When you use data as your DNA for your business, and you are sort of born in data, or you've figured out how to be autonomous, if you will, capture value from that data in a very significant manner, then you can get into industries that are not traditionally your own industry. It can be the food industry, it can be the cloud industry, the book industry, you know, different industries. So that's what I see happening with the tech giants. >> So, Grant, this is a really interesting point that Janet is making, that, you mentioned you started off with a couple of industries that are highly regulated and harder to disrupt. You know, music got disrupted, publishing got disrupted, but you've got these regulated businesses, defense. Automotive hasn't been truly disrupted yet, so Tesla maybe is a harbinger. And so, you've got this spectrum of disruption. But is anybody safe from disruption? >> (laughs) I don't think anyone's ever safe from it. It's change and evolution, right? Whether it's swapping horseshoes for cars, or TV for movies, or Netflix, or any sort of evolution of a business, I wouldn't coast on any of it. And I think, to your earlier question around the value that we can help bring to Oracle customers is that we have a rich stack of applications, and I find that the space between the applications, the data that spans more than one of them, is a ripe playground for innovations where the data already exists inside a company but it's trapped from both a technology and a business perspective, and that's where, I think, really, any company can take advantage of knowing its data better and changing itself to take advantage of what's already there. >> The powerful people always throw the bromide out that data is the new oil, and we've said, no, data's far more valuable, 'cause you can use it in a lot of different places. Oil, you can use once and it's all you can do. >> Yeah. >> It has to follow the laws of scarcity. Data, if you can unlock it, and so, a lot of the incumbents, they have built a business around whatever, a factory or process and people. A lot of the trillion-dollar startups, that become trillionaires, you know who I'm talking about, data's at the core, they're data companies. So it seems like a big challenge for your incumbent customers, clients, is to put data at the core, be able to break down those silos. How do they do that? >> Mm, grating down silos is really super critical for any business. If it's okay to operate in a silo, for example, you would think that, "Oh, I could just be payroll and expense reports, "and it wouldn't matter if I get into vendor "performance management or purchasing. "That can operate as a silo." But anymore, we are finding that there are tremendous insights between vendor performance management and expense reports, these things are all connected. So you can't afford to have your data sit in silos. So grating down that silo actually gives the business very good performance, insights that they didn't have before. So that's one way to go. But another phenomena happens. When you start to grate down the silos, you start to recognize what data you don't have to take your business to the next level. That awareness will not happen when you're working with existing data. So that awareness comes into form when you grate the silos and you start to figure out you need to go after a different set of data to get you to new product creation, what would that look like, new test insights, or new capex avoidance, that data is just, you have to go through the iteration to be able to figure that out. >> And then it becomes a business problem, right? If you've got a process now where you can identify 75% of the failures, and you know the value of the other 25% of the failures, it becomes a simple investment. "How much money am I willing to invest "to knock down some portion of that 25%?" And it changes it from simply an IT problem or an expense management problem to the universal cash problem. >> To a business problem. >> But you still need a platform that has APIs, that allows you to bring in-- >> Yes, yes. >> Those data sets that you don't have access to, so it's an enabler. It's not the answer, it's not the outcome, in and of itself, but it enables the outcome. >> Yeah, and-- >> I always say you can't have the best toilet if your plumbing doesn't work, you know what I mean? So you have to have your plumbing. Your plumbing has to be more modern. So you have to bring in modern infrastructure, distributed computing, that, there's no compromise there. You have to have the right ecosystem for you to be able to be technologically advanced and a leader in that space. >> But that's kind of table stakes, is what you're saying. >> Stakes. >> So this notion of the autonomous enterprise, help me here. 'Cause I get kind of autonomous and automation coming into IT, IT ops. I'm interested in how you see customers taking that beyond the technology organization into the enterprise. >> Yeah, this is such a great question. This is what I've been talking about all morning. I think when AI is a technology problem, the company is at a loss. AI has to be a business problem. AI has to inform the business strategy. When companies, the successful companies that have done, so, 90% of our investments are going towards data, we know that, and most of it going towards AI. There's data out there about this. And so, we look at, what are these 90% of the companies' investments, where are these going, and who is doing this right, and who is not doing this right? One of the things we are seeing as results is that the companies that are doing it right have brought data into their business strategy. They've changed their business model. So it's not making a better taxi, but coming up with Uber. So it's not like saying, "Okay, I'm going to be "the drug manufacturing company, "I'm going to put drugs out there in the market," versus, "I'm going to do connected health." And so, how does data serve the business model of being connected health, rather than being a drug company selling drugs to my customers? It's a completely different way of looking at it. And so now, AI's informing drug discovery. AI is not helping you just put more drugs to the market. Rather, it's helping you come up with new drugs that will help the process of connected care. >> There's a lot of discussion in the press about the ethics of AI, and how far should we take AI, and how far can we take it from a technology standpoint, (laughs) long road map, there. But how far should we take it? Do you feel as though public policy will take care of that, a lot of that narrative is just kind of journalists looking for the negative story? Will that sort itself out? How much time do you spend with your customers talking about that, and what's Oracle's role there? Facebook says, "Hey, the government should figure this out." What's your sort of point of view on that? >> I think everybody has a role, it's a joint role, and none of us can give up our responsibilities. As data scientists, we have heavy responsibility in this area, and we have heavy responsibility to advise the clients on this area also. The data we come from, the past, has to change. That is inherently biased. And we tend to put data science on biased data with a one-dimensional view of the data. So we have to start looking at multiple dimensions of the data. We've got to start examining, I call it irresponsible AI, when you just simply take one variable, we'll start to do machine learning with that, 'cause that's not right. You have to examine the data. You've got to understand how much bias is in the data. Are you training a machine learning model with the bias? Is there diversity in the models? Is there diversity in the data? These are conversations we need to have. And we absolutely need policy around this, because unless our lawmakers start to understand that we need the source of the data to change, and if we look at the source of the data, and the source of the data is inherently biased or the source of the data has only a single representation, we're never going to change that downstream. AI's not going to help us there. So that has to change upstream. That's where the policy makers come into play, the lawmakers come into play. But at the same time, as we're building models, I think we have a responsibility to say, "Can we triangulate? "Can we build with multiple models? "Can we look at the results of these models? "How are these features ranked? "Are they ranked based on biases, sex, age, PII information? "Are we taking the PII information out? "Are we really looking at one variable?" Somebody failed to pay their bill, but they just failed to pay their bill because they were late, versus that they don't have a bank account and we classify them as poor on having no bank account, you know what I mean? So all this becomes part of responsible AI. >> But humans are inherently biased, and so, if humans are building algorithms-- >> That's right, that's right. >> There is the bias. >> So you're saying that through iteration, we can stamp out the bias? Is that realistic? >> We can stamp out the bias, or we can confirm the bias. >> Or at least make it transparent. >> Make it transparent. So I think that even if we can have the trust to be able to have the discussion on, "Is this data "the right data that we are doing the analysis on?" and start the conversation there, we start to see the change. >> Well, wait, so we could make it transparent, then I'm thinking, a lot of AI is black box. Is that a problem? Is the black box syndrome an issue, or are we, how would we deal with it? >> Actually, AI is not a black box. We, in Oracle, we are building our data science platform with an explicit feature called explainability of the model, on how the model came up with the features, what features it picked. We can rearrange the features that the model picked. So I think explainability is very important for ordinary people to trust AI. Because we can't trust AI. Even data scientists can't trust AI, to a large extent. So for us to get to that level where we can really trust what AI's picking, in terms of a model, we need to have explainability. And I think a lot of the companies right now are starting to make that as part of their platform. >> So that's your promise to clients, is that your AI will not be a black box. >> Absolutely, absolutely. >> 'Cause that's not everybody's promise. >> Yes. >> I mean, there's a lot of black box in AI, as you well know. >> Yes, yes, there is. If you go to open source and you start downloading, you'll get a lot of black box. The other advantage to open source is sometimes you can just modify the black box. They can give you access and you can modify the black box. But if you get companies that have released to open source, it's somewhat of a black box, so you have to figure out the balance between. You don't really have to worry too much about the black box if you can see that the model has done a pretty good job as compared to other models. If I triangulate the results of the algorithm, and the triangulation turns out to be reasonable, the accuracy and the r values and the matrixes show reasonable results, then I don't really have to worry if one model is too biased compared to another model. But I worry if there's only one dimension to it. >> Mm-hm, well, ultimately, to much of the data scientists' dismay, somebody on the business side is going to ask about causality. >> That's right. >> "Well, this is what "the model says, why is it saying that?" >> Yeah, right. >> Yeah. >> And, ethical reasons aside, you're going to want to understand why the predictions are what they are, and certainly, as you go in to examine those things, as you look at the factors that are causing the predictions and the outcomes, I think any sort of business should be asking those responsibility questions of everything they do, AI included, for sure. >> So, we're entering a new era, we kind of all agree on that. So I just want to throw a few questions out and have a little fun here, so feel free to answer in any order. So when do you think machines will be able to make better diagnoses than doctors? >> I think they already are making better diagnoses. I mean, there's so much, like, I found out recently that most of the very complicated cancer surgeries are done by machines, doctors just standing by and making sure that the machines are doing it well. And so, I think the machines are taking over in some aspects, I wouldn't say all aspects. And then there's the bedside manners, where you (laughs) really need the human doctor, and you need the comfort of talking to the doctor. >> Smiley face, please! (Janet laughs) >> That's advanced AI, to give it a better bedside manner. >> Okay, when do you think that driving and owning your own vehicle is going to be the exception rather than the rule? >> That, I think, is so far ahead, it's going to be very, very near future, because if you've ever driven in an autonomous car, you'll find that after your initial reservations, you're going to feel a lot more safer in an autonomous car. Because it's got a vision that humans don't. It's got a communication mechanism that humans don't. It's talking to all the fleets of cars. >> It's got a richer sense of data. >> It's got a richer sense of data, it's got a richer sense of vision, it's got a richer sense of ability to (snaps) react when a kid jumps in front of the car. Where a human will be terrified and not able to make quick decisions, the car can. But at the same time, we're going to have some startup problems. We're going to see AI misfire in certain areas, and insurance companies are gearing themselves up for that, 'cause that's just, but the data's showing us that we will have tremendously decreased death rates. That's a pretty good start to have AI driving our cars. >> You're a believer, well, and you're right, there's going to be some startup issues, because this car, the vehicle has to decide, "Do I kill that person who jumped in front of me, "or do I kill the driver?" Not kill, I mean, that's overstating-- >> Yeah. >> But those are some of the startup things, and there will be others. >> And humans, you don't question the judgment system for that. >> Yes. >> There's no-- >> Dave: Right, they're yelling at humans. >> Person that developed, right. It's treated as a one-off. But I think if you look back five years, where were we? You figure, the pace of innovation and the speed and the gaps that we're closing now, where are we going to be in five years? >> Yeah. >> You have to figure it's, I have an eight-year-old son, and I question if he's ever going to drive a car. >> Yeah. >> Yeah. >> How about retail? Do you think retail stores largely will disappear? >> Oh, I think retail, there will be a customer service element to retail, but it will evolve from where it's at in a very, very high-stakes rate, because now, with RFID, you know who's, we used to be invisible as we walked, we still are invisible as you walk into a retail store, even if you spend a lot of money in retail. And now, with buying patterns and knowing who the customer is, and your profile is out there on the Web, just getting a sense of who this person is, what their intent is walking into the store, and doing responsible AI, bringing value to that intent, not irresponsibly, that will gain the trust, and as people gain the trust. And then RFIDs, you're in the location, you're nearby, you'd normally buy the suit, the suit's on sale, bring it all together. So I think there's a lot of connective tissue work that needs to happen, but that's all coming together. >> Yeah, it's about the value-add and what the proposition to the customer is. If it's simply there as a place where you go and pick out something you already know what you're going to get, that store doesn't add value, but if there's something in the human expertise, or in the shared, felt sudden experience of being in the store, that's where you'll see retailers differentiate themselves. >> I like to shop still. (laughs) >> Yeah, yeah. >> You mentioned Apple Pay before. Well, you think traditional banks will lose control of the payment systems? >> They're already losing control of payment systems. If you look at, there was no reason for the banks to create Siri-like assistants. They're all over right now. And we started with Alexa first. So you can see the banks are trying to be a lot more customized, customer service, trying to be personalized, trying to really make you connect to them in a way that you have not connected to the bank before. The way that you connected to the bank is you knew the person at the bank for 20 years, or since when you had your first bank account. That's how you connected with the banks. And then you go to a different branch, and then, all of a sudden, you're invisible. Nobody knows you, nobody knows that you were 20 years with the bank. That's changing. They're keeping track of which location you're going to, and trying to be a more personalized. So I think AI is a forcing function, in some ways, to provide more value, if anything. >> Well, we're definitely entering a new era, the age of AI, the autonomous enterprise. Folks, thanks very much for a great segment, really appreciate it. >> Yeah, our pleasure, thank you for having us. >> Thank you for having us. >> You're welcome, all right, and thank you. And keep it right there, we'll be right back with our next guest right after this short break. You're watching theCUBE's coverage of the rebirth of Oracle Consulting. We'll be right back. (upbeat electronic music)

>>law from Las Vegas. It's the Q covering a ws re invent 2019. Brought to you by Amazon Web service is and in along with its ecosystem partners. >>Hey, welcome back. Everyone's two cubes. Live coverage I'm John for with the Cube were here reinvent date, too, as it winds down Walter Wall interviews two sets here. We want to think Intel, big sponsor of this, said we without Intel, we wouldn't have this great content. They support our mission at the Q. We really appreciate it. We're here and strengthen the signal the noise on our seventh reinvent of the eight years that they've been here. We've been documenting history, and we got a great panel lined up here. They got Sebastian to holler Who's the CEO? Sale Drone. Henry Stalls, Stool The VP of Science and Technology and Bowery Farming. Great use case around the food supply and Janet his era space weather scientists at NASA. The Kilo Physics division. We got a great lineup here. Great panel. Welcome to the Cube. Thanks for coming. Thank you. Okay. We'll start with you, Jen. And you're doing some super cool space exploration. You're looking at super storms in space. What's your story? >>Yeah, I work at NASA and NASA has in its mandate to understand how to protect life on Earth and in space from events like space, weather and other things. And I'm working with Amazon right now to understand how storms in space get amplified into super storms in space, which now people understand, can have major impacts on infrastructures head earth like power grits. >>So there's impact. >>There's a >>guy's measuring that, not like a supernova critical thing like >>that >>of, like, practical space. >>Actually, the idea that the perception of the world of the other risks of space weather changed dramatically in 1989 when Superstorm actually caused the collapse of a power grid in Canada and the currents flowing in the ground from the storm entered the power grid and it collapsed in 90 seconds. It couldn't even intervene. >>Wow, some serious issues. We want to get into the machine learning and how you guys are applying. But let's get through here, and we're doing some pretty cool stuff that's really important. Mission. Food supply and global food supply something that you're doing. What I think it might explain. >>Yeah, Bowery were growing food for a better future by revolutionizing agriculture. And to do that, we're building these ah network of large warehouse scale indoor farms where we go all sorts of produce indoors 365 days a year, using zero pesticides using hydroponic systems and led technology. So it's really exciting. And at the core of it is some technology we call the Bowery operating system, which is how we leverage software hardware in a I tow, operate and learn from our farm. >>I'm looking forward to digging into that Sebastian sale drone. You're doing some stuff you're sailing around the world. You got nice chance that you now tell your story. >>Sadly, no way. Use wind powered robots to study the 20% of the planet that's currently really data scarce. And that's the oceans on. So we measure things like biomass, which is how many fish down in the ocean. We measure the input of energy, which impacts weather and climate. We mapped the seabed on. We do all kinds of different tasks which are very, very expensive to do with few ships >>and to report now that climate change is on everyone's agenda, understanding potentially blind spots. Super important, right? >>That's what I'm trying to, You know, this whole question of if it's a question of what? When and what and how much. And so, you know, the ice is melting, the Gulf Stream is changing, and Nina is wrecking havoc. But we just do not understand this because we just don't have the data. In city, we use satellites where they have very low resolution. They cannot see through the water where you ships. No, has 16 ships he in the U. S. So we have to do better. We have to translate this into a big data problem. So that's what we're doing. We have 1000 sale drones on our plan with 100 water right now. And so we're trying to instrument old oceans all the time, >>you know, and data scales your friend because you don't want more data. Yes. Talk about what you're working on. What kind of a I in machine learning are you doing? You just gathering day. Then you're pumping it up to the cloud via satellites or what's going on there? >>One of the one of the use cases trying to understand you know who's out there. What are they doing? Another doing anything illegal. So to do this, you need to use cameras and look at the horizon and detect. You know whether you have vessels. And if those vessels are not transmitting the position, it means that they're trying to stay hidden on the ocean. And so we use machine learning and I that we train on on AWS to try to understand what where those things are. It's hard enough on land at sea. It's very hard because every pixel is moving. You have waves. The horizon is moving, the skies moving, the ship is moving. And so trying to solve this problem is a completely new thing that's called maritime domain awareness on, and it's something that has never been done before. >>And what's the current status of the project? >>So wave been live for about four years now we have 100 sail drones were building one a day towards the goal of having 1000 which we covered all the planet in a six by six degrees squares on. We are operationally active in the Arctic in the tropical Pacific. In the Atlantic. We just circumnavigated Antarctica, So it's the thing. That's really it's out there. But it's very far from from from land, >>So the spirit of cloud and agility static buoy goes away. You want to put the sale drones out there to gather and move around and capture. >>That's what the buoy is. You know, a massive steel thing, which has a full mile long cable, and it's it's headed to the silo in a fix stations one point and the ocean goes by. You having and robots means that you can go where you know something interesting is happening where you have a hurricane where you might have an atmospheric river where you might have a natural catastrophe or man made catastrophe. So this intelligence of the platform is really important in the navigation. That platform requires intelligence. And on the other side, getting 1000 times more data allows you to understand things better, just like Michael is doing. >>It isn't a non profit of four profit venture. >>It's a for profit company. So we said raw data a fraction of the cost of existing solution to try to create this kind of transformative impact on understanding what's happening >>that's super exciting for all the maritime folks out there because I love the ocean myself. Henry, you you're tackling real big mission. How using technology. I can almost imagine the instrumentation must be off the charts. What's your opportunity? Looked like? A tech perspective >>s o The level of control we have in our farms is really unparalleled. Weaken tune Just about every parameter that goes into growing our plans from temperature humidity Co Two light intensity day night cycles list keeps going on. And so to do Maur with fewer resource is to grow Maurin our farms. We're doing something called science a scale where we can pull different levers and make changes to recipes in real time. And we're using a I tow, understand the impact that those changes have and to guide us going from millions of different permutations. Trillions of permutations, really too. The perfect outdone >>converging. You jittery? Look at the product outcome. You circle that dated back is all on Amazon >>way. Do operate on Amazon. Yeah, and we're using deep learning technology to analyze pictures that come from cameras all over our farms. So we actually have eyes on every single crop that grows in our facilities and So we process those, learn from the data and and funnel that back into the >>like, Maybe put more light on this or do that kind of make a just a conditions. Is that that thing? That's >>exactly it. And we grow lots of different types of plants. We grow butter, head lettuce, romaine, kale, spinach, arugula, basil, cilantro. So there's a lot of different things we grow, and each of them require different, different little tweaks here and there. Toe produced over the best tasting and most nutritious product. >>That's cool, Janet Space. Lastly, on one inspection, we're gonna live on Mars someday. So you might be a weather forecaster for what route to take to Mars. But right now, the practical matter is Israel correlation between these storms. What kind of data problem are you looking at? What is the machine learning? What are some of the cool things you're working on? >>It? We have a big date, a problem because storms of that magnitude are very rare. So it's hard for us to find enough data to train a I we can't actually train a we have to use, you know, learning that doesn't require us to train it, but we've decided to take the approach that these super storms are like anomalies on the normal weather patterns. So we're trying to use the kind of a I that you used to detect anomalies like people who are trying to break into to do bank fraud or, you know, do a Web server tax. We use that same kind of software to tryto identify anomalies that are the space weather and look at the patterns between sort of a normal, more of a normal storm and a space with a huge space weather event to see how they patterns. Comparing how you're amplifying the regular storm into this big Superstorm activity. >>So it sounds like you have to be prepared for identifying the anomaly. See you looking at anomalies to figure out where the anomaly might be ready to be ready to get the anomaly. >>Yeah, you look at the background, and then what sticks out of the background that doesn't look like the background is is identified as the anomaly. And that's the storms that air happening, which are quite rare, >>all three of you guys to do some real cutting edge cool projects. I guess my question would be for the folks that are putting their toe in the water for machine learning. They tend to be new use cases like what you guys are doing, whether it's just a company tryingto read, factor themselves or we become reborn in the cloud ran legacy stuff. When you hear it, Amazon reinvent. This is the big question for these folks that are here. You guys are on the front end of a really cool projects. What's your advice that the people are trying to get in that mindset? >>So I think I think you know the way the way to think about this is if you're good at something and if you think you have the solution for something, how can you make that a 1,000,000 times more efficient? And so the problem is, there's just not enough capacity in the world, usually to treat data sets that a 1,000,000 times larger. And this is where machine learning should be thought about it as an extension of what humans really good at using a pair of eyes, ears or whatever or the sense. And so in our case. For example, counting fish acoustician, train acoustician, look at sonar data and understand schools of fish and can recognize them. And by using this knowledge base, we can train machines to do this on a much grander scale. And when you're doing a much grander scale, you derive. Ah, holding tight to >>your point is that humans are critical. I'm the process. So scaling the human capabilities and maybe filling in another scale issues or >>that's what a machine learning is. It's the greatest enabler of our time. It enables us to do things which are impossible to do before because we just didn't have enough people to do them at scale. >>AKI is being able to ask questions, right? And so if you have the questions to ask, you can apply this technology in a way that's never really been before possible. >>You're Jake. >>Yeah, I am actually someone who didn't know anything about a Ira ml when I started. I'm on. I'm a research scientist. That space weather. So coming into this, I'm working with E m L Solutions Lab here and putting a I experts with with experts and space brother we're getting we're doing things that are gonna give us new advances. I mean, We're already seeing things we didn't know before. So I think that if you partner with people who really have strong a I knowledge, you can use your knowledge of science to really get to the really important issues. >>Okay, I have to ask the final lightning round question. What is the coolest thing that you've done with your project that you've either observed implemented? That is super cool. Super cool. What's the coolest thing >>well in in terms of us were using anomaly detection to identify storms and in the first round through it actually identified every single Superstorm, which was not the major super storms, but it did. But it also started identifying other anomalous events, and when you went looked at him, they were anomalous events. So we're seeing things. It's picking out the weird things that are happening in space weather. It's kind of exciting and interesting. >>I worked for a day with you. I would love to just leave these anomalies every what's the coolest thing that you've seen or done with your project? >>I think the fact that we've built our own custom hardware own camera systems, uh, and that we feed those through algorithms that tell us something about what's happening minute by minute with plans as they grow to see pictures of plants minute by minute, they dance and it's truly it's It's remarkable. >>Wow! Fascinating Machin >>We've counted every single fish on the West Coast, the United States, every single air from Canada to Mexico. I thought I >>was pretty >>good. I didn't think it was possible. >>Very cool. But what's the number? >>Yeah, If I could tell you, I would. But I'm not allowed to tell you the jam. >>And you know where the salmon are, where they're running all that good stuff. Awesome. Well, congratulations, You guys doing some amazing work is pioneering a great example of just what's coming. And I love this angle of making larger human impact using technology. Where you guys a shaping technology for good things. Really, really exciting. Thanks for coming on, John Kerry. We're here live in Vegas for re invent 2019. Stay with more coverage. Day three coming tomorrow back with more After this break, when a fake intel for making it all happened presented by Intel Without their sponsorship, we wouldn't be able to bring this great content. Thanks for watching

>> live from Barcelona, Spain. It's the key covering KubeCon Cloud, Native Con Europe twenty nineteen by Red Hat, the Cloud, Native Computing Foundation and Ecosystem Partners. >> Welcome back to Barcelona, Spain >> were here of the era, and seventy seven hundred people are here for the KubeCon Cloud NativeCon, twenty, nineteen, Off student. My co host for the two days of coverage is Corey Quinn, and joining Me are the two co chairs of this CNC event. Janet Cooper, who is also thie, suffer engineer with Google and having done the wrap up on stage in the keynote this morning, find Lyle's a senior staff engineer with BM where thank you both for joining us, >> Thank you. >> Thanks for having me. >> So let's start. We're celebrating five years of Kubernetes as damn calm laid out this morning. You know, of course, you know came from Google board in over a decade of experience there. So it just helps out the state for us. >> Um, so I started working on communities since before the 1.4 release and then steal a project Montana today. And I feel so proud to see, uh, the progress off this project and its has grown exponentially. And today we have already thirty one thousand contributors and expect it to grow even more if you can. >> All right. So, Brian, you work with some of the original people that helped create who Burnett ease because you came to be and where, by way of the FTO acquisition, seventy seven hundred people here we said it. So it's, you know, just about the size of us feel that we had in Seattle a few months ago Way Expect that San Diego is going to be massive when we get there in the fall. But you know, talk to us is the co chair, you know, What's it mean to, you know, put something like this together? >> Well, so as ah is a long time open source person and seeing you know, all these companies move around for, you know, decades. Now it's nice to be a part of something that I saw from the sidelines for so, so long. I'm actually... it's kind of surreal because I didn't do anything special to get here. I just did what I was doing. And you know, Jan and I just wound up here together, so it's a great feeling, and it's the best part about it is whenever I get off stage and I walked outside and I walked back. It's like a ten minute walk each way. So many people are like, Yeah, you really made my morning And that's that's super special. >> Yeah. I mean, look, you know, we're we're huge fans of open source in general and, you know, communities, especially here. So look, there was no, you know, you both have full time jobs, and you're giving your time to support this. So thank you for what you did. And, you know, we know it takes an army to put together in a community. Some of these people, we're Brian, you know, you got upstate talk about all the various project. There's so many pieces here. We've only have a few minutes. Any kind of major highlights You wanna pull from the keynote? >> So the biggest. Actually, I I've only highlight won the open census open. Tracing merge is great, because not only because it's going to make a better product, but he had two pretty good pieces of software. One from Google, actually, literally both from Google. Ultimately, But they realize that. Hey, we have the same goals. We have similar interfaces. And instead of going through this arms race, what they did is sable. This is what we'LL do. We'LL create a new project and will merge them. That is, you know, that is one of the best things about open source. You know, you want to see this in a lot of places, but people are mature enough to say, Hey, we're going to actually make something bigger and better for everyone. And that was my favorite update. >> Yeah, well, I tell you, and I'm doing my job well, because literally like during the keynote, I reached out to Ben. And Ben and Morgan are going to come on the program to talk about that merging later today. That was interested. >> I've often been accused of having that first language being snark, and I guess in that light, something that I'm not particularly clear on, and this is not the setup for a joke. But one announcement that was made on stage today was that Tiller is no longer included in the current version of Wasn't Helm. Yes, yes, And everyone clapped and applauded, and my immediate response was first off. Wow, if you were the person that wrote Tiller, that probably didn't feel so good given. Everyone was copping and happy about it. But it seems that that was big and transformative and revelatory for a lot of the audience. What is Tiller and why is it perceived as being less than awesome? >> All right, so I will give you a disclaimer, >> please. >> The disclaimer is I do not work on the helm project... Wonderful >> ...so anything that I say should be fact checked. >> Excellent. >> So Well, so here's the big deal. When Tiller, when Helm was introduced, they had this thing called Tiller. And what tiller did was it ran at a basically a cluster wide level to make sure that it could coordinate software being installed and Kubernetes named Spaces or groups how Kubernetes applications are distributed. So what happens is is that that was the best vector for security problems. Basically, you had this root level piece of software running, and people were figuring out ways to get around it. And it was a big security hole. What >> they've done Just a component. It's an attack platform. It >> was one hundred percent. I mean, I remember bit. Nami actually wrote a block post. You know, disclaimer of'em were just bought that bit na me. >> Yes, I insisted It's called Bitten, am I? But we'LL get to that >> another. This's a disclaimer, You know, There Now you know there now my co workers But they wrote they were with very good article about a year and a half ago about just all the attack vectors, but and then also gave us solution around that. Now you don't need that solution. What you get by default. Now something is much more secure. And that's the most important piece. And I think the community really loves Helm, and now they have helm with better defaults. >> So, Janet, a lot of people at the show you talk about, you know, tens of thousands of contributors to it. But that being said, there's still a lot of the world that is just getting started. Part of the key note. And I knew you wrote something running workloads and cover Netease talk a little bit about how we're helping you know, those that aren't yet, you know, on board with you getting into the community ship. >> So I work on the C gaps. So she grabs one of the sub fracture that own is the work wells AP Eyes. That's why I had that. What post? About running for closing covered alleys. So basically, you you're using coronaries clarity, baby eyes to run a different type of application, and we call it were close. So you have stay full state wears or jobs and demons and you have different guys to run those clothes in the communities. And then for those who are just getting started, maybe start with, uh, stay last were close. That's the easiest one. And then for people who are looking Teo, contribute war I. I encouraged you to start with maybe small fixes, maybe take some documents or do some small P R's and you're reputations from there and star from small contributions and then feel all the way up. >> Yeah, so you know, one of one of the things when I look out there, you know, it's a complex ecosystem now, and, you know, there's a lot of pieces in there, you know, you know, trend we see is a lot of customers looking for manage services. A lot of you know, you know, I need opinions to help get me through all of these various pieces. You know what? What do you say to those people? And they're coming in And there's that, you know, paradox of choice When they, you know, come, come looking. You know, all the options out there. >> So I would say, Start with something simple that works. And then you can always ask others for advice for what works, What doesn't work. And you can hear from their success stories or failure stories. And then I think I recently he saw Block post about Some people in the community is collecting a potential failure stories. There is also a talk about humanity's fellow, the stories. So maybe you can go there and learn from the old those mistakes and then how to build a better system from there. >> I'd love that. We have to celebrate those failures that we hopefully can learn from them. Find anything on that, You know, from your viewpoint. >> Eso Actually, it's something I research is developer experience for you. Bernetti. So my communities is this whole big ping. I look on top of it and I'm looking at the outside in howto developers interact with Burnett, ese. And what we're seeing is that there's lots of room for opportunities and Mohr tools outside of the main community space that will help people actually interact with it because that's not really communities. Developers responsibility, you know, so one anything that I think that we're doing now is we're looking and this is something that we're doing and be aware that I can talk about is that we're looking at a P ice we're looking at. We realize that client go, which is the way that you burnett ese talks with sapi eyes, and a lot of people are using out externally were looking at. But what does it actually mean for human to use this and a lot of my work is just really around. Well, that's cool for computers. Now, what if a human has to use it? So what we're finding is that no. And I'm going to talk about this in my keynote tomorrow. You know, we're on this journey, and Kubernetes is not the destination. Coover Netease is the vehicle that is getting us to the destination that we don't even know what it is. So there's lots of spaces that we can look around to improve Kubernetes without even touching Cooper Netease itself, because actually, it's pretty good and it's fairly stable in a lot of cases. But it's hard, and that's the best part. So that's, you know, lots of work for us, the salt >> from my perspective. One of the turning points in Kou Burnett is a success. Story was when it got beyond just Google. Well, folks working on it. For better or worse, Google has a certain step of coding standards, and then you bring it to the real world, where there are people who are, Let's be honest, like me, where my coding standard is. I should try to right some some days, and not everything winds up having the same constraints. Not everything has the same approach. To some extent, it really feels like a tipping point for all of it was when you wind up getting to a position where people are bringing their real world workload that don't look like anything, anyone would be able to write a googol and keep their job. But still having to work with this, there was a wound up being sort of blossoming effect really accelerating the project. Conversely, other large infrastructure projects we need not mention when they had that tipping point in getting more people involved, they sort of imploded on themselves. I'm curious. Do you have any thoughts as to why you Burnett? He started thriving where other projects and failed trying to do the same things. >> I have something you go first. And >> I think the biggest thing about cybernetics is the really strong community and the ecosystem and also communities has the extensive bility for you to build on top of communities. We've seen people building from works, and then the platform is different platforms. Open source platforms on top of you. Burnett is so other people can use on other layers. Hyah. Layers off stacks on top of fraternities. Just use those open source. So, for example, we have the CRD. It's an A P I that allows you to feel your own customized, overnighted style FBI, so they're using some custom for couple databases. You could just create your own carbonated style FBI and call out your database or other stuffs, and then you can combine them into your own platform. And that's very powerful because everywhere. I can just use the same FBI, the Carbonari style idea to manage almost everything and that enables a Teo be able to, you know, on communities being adopted in different industry, such as I o t. A and Lord. >> So actually, this is perfect because the sleaze and so what I was going to say The secret of community is that we don't talk about actually job, Ada says. It's a lot, but it's a communities is a platform for creating platforms. So Kubernetes really is almost built on itself. You can extend Cooper. Netease like communities extends itself with the same semantics that it lets users extended. So Janet was talking about >> becoming the software that is eating the world. Yeah, it >> literally is. So Janet talked about the CRD sees custom resource definitions. It's the same. It's the same mechanism that Kubernetes uses to add new features. So whenever you're using these mechanisms, you're using Kou Burnett. He's basically the Cooper Nate's infrastructure to create. So really, what it is is that this is the tool kit for creating your solutions. What is why I say that Kubernetes is not an end point its its journey. >> So the cloud native system. >> So you know what? Yeah, and I like I like the limits analogy that people talk about. Like Coburn. Eighties is is like clinics. If you think about how Lennox you know little l. Lennox. Yeah. You know, I'm saying little l olynyk sub Let's put together. Yeah, you Burnett. He's like parts of communities would be system. And it's it's all these components come together the creature operating system, and that's the best part about it. >> Okay, so for me, the people that are not the seventy seven hundred that air here give them a little bit of, you know, walk around the show and some of the nooks and crannies that they might not know, like, you know, for myself having been to a number of these like Boy, there were so many half day and full day workshops yesterday there were, like, at least, like fifteen or seventeen or something like that that I saw, You know, obviously there's some of the big keynote. The Expo Hall is sprawling it, you know, I've been toe, you know, fifteen twenty thousand people show here This sex Bohol feels is bustling ahs that one is and well as tons of breakout session. So, you know, give us some of the things that people would have been missing if they didn't come to the show here. >> So just for the record, if you missed the show, you can still watch all the videos online. And then you can also watch the lifestream for keynotes so on. I personally love the applicant the different ways for a customizing covered at ease. So there's Ah, customizing overnight is track. And also there's the apple that applications track and I personally love that. And also I like the color case studies So you can't go to the case studies track to see on different users and users off Cooper, Natty shared. There were war stories, >> Yes, So I think that she will miss. There's a few things that you'll miss if you if you're not here in Barcelona right now, the first thing is that this convention center is huge. It's a ten minute walk from the door to where we're sitting right now, but more seriously, one. The things you'LL miss is that before the conference starts, there are there are a whole bunch of summits, Red had had a summit and fewer people had some. It's yesterday where they talk about things. There's the training sessions, which a lot of cases aren't recorded. And then another thing is that the special interest groups, the cigs. So Cooper ninety six, they all get together and they have faced the face discussions and then generally one from yesterday We're not. We're not recorded. So what you're missing is the people who actually make this big machine turn. They get together face to face and they first of all, they built from a rotary. But they get to discuss items that have require high bit of bandwith that you really can't do over again of issue or email, or even even a slack call like you can actually get this thing solved. And the best thing is watching these people. And then you watch the great ideas that in, you know, three, six months to a year become like, really big thing. So I bet yesterday, so something was discussed. Actually, I know of some things that we discussed yesterday that might fundamentally change how we deal with communities. So that's that is the value of being here and then the third thing is like when you come to a conference like this, where there's almost a thousand people, there's a lot of conversations that happened between, you know, the Expo Hall and the session rooms. And there's, um there's, you know, people are getting jobs here, People are finding new friends and people are learning. And before thing and I'll end with This is that I walk around looking for people who come in on the on the diversity scholarships, and I would not hear their stories if I did not come. So I met two people. I met a young lady from New Zealand who got the scholarship and flew here, you know, and super smart, but is in New Zealand and university, and I get to hear her insights with life. And then I get to share how you could be better in the same thing. I met a gentleman from Zimbabwe yesterday was going to school and take down, and what I hear is that there's so many smart people without opportunities, so if you're looking for opportunities, it's in these halls. There's a lot of people who have either money for you or they have re sources were really doesn't have a job or just you know what? Maybe there's someone you can call whenever you're stuck. So there is a lot of benefit to come into these. If you can get here, >> talent is evenly distributed. Opportunity is not. So I think the diversity scholarship program is one of the most inspirational things I saw mentioned out of a number of inspirational things that >> I know. It's It's my favorite part of communities. You know, I am super lucky that I haven't employees that our employer that can afford to send me here. Then I'm also super lucky that I probably couldn't afford to send myself here if I wanted to. And I do as much as I can to get people >> here. Well, Brian and Janet thank you so much for all you did to put this and sharing it with our community here. I'Ll repeat something that I said in Seattle. Actually, there was a lot of cloud shows out there. But if you're looking for you know, that independent cloud show that you know, lives in this multi hybrid cloud, whatever you wanna call it world you know this is one of the best out there. And the people? Absolutely. If you don't come with networking opportunities, we had into it on earlier, and they talked about how you know, this is the kind of place you come and you find a few people that you could hire to train the hundreds of people inside on all of the latest cloud native pieces. >> Can I say one thing, please? Brian S O, this is This is significant and it's significant for Janet and I. We are in the United States. We are, you know, Janet is a minority and I am a minority. This is the largest open source conference in the world. Siri's This is the largest open source conference in Europe. When we do, when we do, it ended a year. Whenever we do San Diego, it'Ll be the largest open source conference in the world. And look who's running it. You know, my new co chair is also a minority. This is amazing. And I love that. It shows that people who look like us we can come up here and do these things because like you said, opportunity is is, you know, opportunities the hard thing. Talent is everywhere. It's all over the place. And I'm glad we had a chance to do this. >> All right. Well, Brian, Janet, thank you so much for all of that. And Cory and I will be back with more coverage after this brief break. Thank you for watching the cues.

>> Live from Stanford University. It's the Cube covering global Women in Data Science conference brought to you by Silicon Angle media. >> Welcome back to the key. We air live at Stanford University for the fourth annual Women in Data Science Conference. The Cube has had the pleasure of being here all four years on I'm welcoming Back to the Cube, one of our distinguished alumni Janet George, the fellow chief data officer, scientists, big data and cognitive computing at Western Digital. Janet, it's great to see you. Thank you. Thank you so much. So I mentioned yes. Fourth, Annie will women in data science. And it's been, I think I met you here a couple of years ago, and we look at the impact. It had a chance to speak with Margo Garrett's in a about an hour ago, one of the co founders of Woods saying, We're expecting twenty thousand people to be engaging today with the Livestream. There are wigs events in one hundred and fifty locations this year, fifty plus countries expecting about one hundred thousand people to engage the attention. The focus that they have on data science and the opportunities that it has is really palpable. Tell us a little bit about Western Digital's continued sponsorship and what makes this important to you? >> So Western distal has recently transformed itself as a company, and we are a data driven company, so we are very much data infrastructure company, and I think that this momentum off A is phenomenal. It's just it's a foundational shift in the way we do business, and this foundational shift is just gaining tremendous momentum. Businesses are realizing that they're going to be in two categories the have and have not. And in order to be in the half category, you have started to embrace a You've got to start to embrace data. You've got to start to embrace scale and you've got to be in the transformation process. You have to transform yourself to put yourself in a competitive position. And that's why Vest Initial is here, where the leaders in storage worldwide and we'd like to be at the heart of their data is. >> So how has Western Digital transform? Because if we look at the evolution of a I and I know you're give you're on a panel tan, you're also giving a breakout on deep learning. But some of the importance it's not just the technical expertise. There's other really important skills. Communication, collaboration, empathy. How has Western digital transformed to really, I guess, maybe transform the human capital to be able to really become broad enough to be ableto tow harness. Aye, aye, for good. >> So we're not just a company that focuses on business for a We're doing a number of initiatives One of the initiatives were doing is a I for good, and we're doing data for good. This is related to working with the U. N. We've been focusing on trying to figure out how climate change the data that impacts climate change, collecting data and providing infrastructure to store massive amounts of species data in the environment that we've never actually collected before. So climate change is a huge area for us. Education is a huge area for us. Diversity is a huge area for us. We're using all of these areas as launching pad for data for good and trying to use data to better mankind and use a eye to better mankind. >> One of the things that is going on at this year's with second annual data fun. And when you talk about data for good, I think this year's Predictive Analytics Challenge was to look at satellite imagery to train the model to evaluate which images air likely tohave oil palm plantations. And we know that there's a tremendous social impact that palm oil and oil palm plantations in that can can impact, such as I think in Borneo and eighty percent reduction in the Oregon ten population. So it's interesting that they're also taking this opportunity to look at data for good. And how can they look at predictive Analytics to understand how to reduce deforestation like you talked about climate and the impact in the potential that a I and data for good have is astronomical? >> That's right. We could not build predictive models. We didn't have the data to put predictive boats predictive models. Now we have the data to put put out massively predictive models that can help us understand what change would look like twenty five years from now and then take corrective action. So we know carbon emissions are causing very significant damage to our environment. And there's something we can do about it. Data is helping us do that. We have the infrastructure, economies of scale. We can build massive platforms that can store this data, and then we can. Alan, it's the state at scale. We have enough technology now to adapt to our ecosystem, to look at disappearing grillers, you know, to look at disappearing insects, to look at just equal system that be living, how, how the ecosystem is going to survive and be better in the next ten years. There's a >> tremendous amount of power that data for good has, when often times whether the Cube is that technology conferences or events like this. The word trust issues yes, a lot in some pretty significant ways. And we often hear that data is not just the life blood of an organization, whether it's in just industry or academia. To have that trust is essential without it. That's right. No, go. >> That's right. So the data we have to be able to be discriminated. That's where the trust comes into factor, right? Because you can create a very good eh? I'm odder, or you can create a bad air more so a lot depends on who is creating the modern. The authorship of the model the creator of the modern is pretty significant to what the model actually does. Now we're getting a lot of this new area ofthe eyes coming in, which is the adversarial neural networks. And these areas are really just springing up because it can be creators to stop and block bad that's being done in the world next. So, for example, if you have malicious attacks on your website or hear militias, data collection on that data is being used against you. These adversarial networks and had built the trust in the data and in the so that is a whole new effort that has started in the latest world, which is >> critical because you mentioned everybody. I think, regardless of what generation you're in that's on. The planet today is aware of cybersecurity issues, whether it's H vac systems with DDOS attacks or it's ah baby boomer, who was part of the fifty million Facebook users whose data was used without their knowledge. It's becoming, I won't say accepted, but very much commonplace, Yes, so training the A I to be used for good is one thing. But I'm curious in terms of the potential that individuals have. What are your thoughts on some of these practices or concepts that we're hearing about data scientists taking something like a Hippocratic oath to start owning accountability for the data that they're working with. I'm just curious. What's >> more, I have a strong opinion on this because I think that data scientists are hugely responsible for what they are creating. We need a diversity of data scientists to have multiple models that are completely divorce, and we have to be very responsible when we start to create. Creators are by default, have to be responsible for their creation. Now where we get into tricky areas off, then you are the human auto or the creator ofthe Anay I model. And now the marshal has self created because it a self learned who owns the patent, who owns the copyright to those when I becomes the creator and whether it's malicious or non malicious right. And that's also ownership for the data scientist. So the group of people that are responsible for creating the environment, creating the morals the question comes into how do we protect the authors, the uses, the producers and the new creators off the original piece of art? Because at the end of the day, when you think about algorithms and I, it's just art its creation and you can use the creation for good or bad. And as the creation recreates itself like a learning on its own with massive amounts of data after an original data scientist has created the model well, how we how to be a confident. So that's a very interesting area that we haven't even touched upon because now the laws have to change. Policies have to change, but we can't stop innovation. Innovation has to go, and at the same time we have to be responsible about what we innovate >> and where do you think we are? Is a society in terms of catching As you mentioned, we can't. We have to continue innovation. Where are we A society and society and starting to understand the different principles of practices that have to be implemented in order for proper management of data, too. Enable innovation to continue at the pace that it needs. >> June. I would say that UK and other countries that kind of better than us, US is still catching up. But we're having great conversations. This is very important, right? We're debating the issues. We're coming together as a community. We're having so many discussions with experts. I'm sitting in so many panels contributing as an Aye aye expert in what we're creating. What? We see its scale when we deploy an aye aye, modern in production. What have we seen as the longevity of that? A marker in a business setting in a non business setting. How does the I perform and were now able to see sustained performance of the model? So let's say you deploy and am are in production. You're able inform yourself watching the sustained performance of that a model and how it is behaving, how it is learning how it's growing, what is its track record. And this knowledge is to come back and be part of discussions and part of being informed so we can change the regulations and be prepared for where this is going. Otherwise will be surprised. And I think that we have started a lot of discussions. The community's air coming together. The experts are coming together. So this is very good news. >> Theologian is's there? The moment of Edward is building. These conversations are happening. >> Yes, and policy makers are actively participating. This is very good for us because we don't want innovators to innovate without the participation of policymakers. We want the policymakers hand in hand with the innovators to lead the charter. So we have the checks and balances in place, and we feel safe because safety is so important. We need psychological safety for anything we do even to have a conversation. We need psychological safety. So imagine having a >> I >> systems run our lives without having that psychological safety. That's bad news for all of us, right? And so we really need to focus on the trust. And we need to focus on our ability to trust the data or a right to help us trust the data or surface the issues that are causing the trust. >> Janet, what a pleasure to have you back on the Cube. I wish we had more time to keep talking, but it's I can't wait till we talk to you next year because what you guys are doing and also your pact, true passion for data science for trust and a I for good is palpable. So thank you so much for carving out some time to stop by the program. Thank you. It's my pleasure. We want to thank you for watching the Cuba and Lisa Martin live at Stanford for the fourth annual Women in Data Science conference. We back after a short break.

(spirited orchestral music) >> Hello and I'm John Furrier, cohost of theCUBE, founder of SiliconANGLE Media. I'm here at Palo Alto studios for CUBE Conversation as a preview for upcoming, the CNCF-sponsored KubeCon event coming up in Shanghai and in Seattle. I'm here with Janet Kuo, who is a software engineer at Google and recently named the co-chair of KubeCon, the main event around Kubernetes, multi-cloud, all the things happening in cloud-native. Janet, thanks for joining me today. >> Thanks for having me. So you were recently named co-chair, Kelsey was previously the co-chair and he always had those good demos but the program has been changing a lot and you're the new co-chair, what's it like? What's happening? What's the focus this year? What's the content going to look like? Tell us what's happening >> So we get a lot of overwhelming number of submissions, much more than last year, and I see a lot of interesting case studies and also I see that because Kubernetes is actually help you extract the infrastructure away and it runs anywhere so I see a lot of people are actually deploying it everywhere, multi-cloud, hybrid, and even in Edge. For example, I see Chick-Fil-A, they are going to talk about how they deploy Kubernetes in their Edge restaurants and the store owners, they are not tech expert, as you can expect. >> Yeah, I mean that's the edge of the network, a Chick-Fil-A, and you know, great retail example. We run a lot of Chick-Fil-A certainly out here in California it's like In-N-Out Burger, they go hand in hand. But this is a good use case of Edge and this is real world, so Kubernetes has certainly grown up. We know from the growth of KubeCon, the event itself has gotten to be pretty massive, the number of people involved has been great, how has Kubernetes grown up? Because we're seeing the conversation move from we love containers, Kubernetes is great for orchestrating everything, but now people are starting to really start really cranking it up a notch, is that the trend that you're seeing as well, and is that some of the content you'll be focused on? >> So I see, I took a lot at the Google trend for search for Kubernetes and it's still going way up since the beginning and also I look at a recent CNCF survey and I realize that about 40% of people who'll respond to their survey and they work in a enterprise and they said they run Kubernetes in production so that's a huge number. >> That's awesome. Well, now that you're the new co-chair, tell us a little bit about yourself, how, what's your background, how did you get there? >> I started working at Google in 2015 and that's before Kubernetes 1.0 was released and before CNCF and before the first KubeCon and when I joined Google, it's Kubernetes is a way, very new concept and not like it's fixed and it's already adopted by everyone so we work very hard to get the ease of use and get more people adoption and we get a lot of feedback from people and then Kubernetes is getting more and more popular, so after that I decided that I want to submit my first ever conference talk to KubeCon and I got selected and then I start to feel like I enjoy this and I did, and other CNCF hosted events, for example, a panel in San Francisco and I think that might be how I was selected. >> What was your first talk about, that you talked about? >> So I talk about running workloads in Kubernetes and I did an overview of the workloads API because I am the developer of that workloads API. >> So that's also, you got hooked on Kubernetes like everybody else, it's like the Kubernetes drug. So how did you get involved in open source? Were you always developing with open source? How did you get involved in the open source community? >> So Kubernetes is actually my first open source project and before that, I had a phone call with Tim Hawkins, he's the principal engineer at Google and he sold me the idea of Kubernetes and we need to be open and let people choose the best technology for them and he sold me the idea and I think Kubernetes is the future and also I want to work on open source but I just didn't have the chance to work on it yet. >> So we had a good fun time in Copenhagen for the last KubeCon, and we, theCUBE, has been at all the KubeCons as you know. We love this community, we think it's really special, not only because we've been there from the beginning, but we've gotten to see the people involved and the people have been very close-knit but yet so open and inclusive, we're seeing a lot of input, and then at the same time, so that's always great, open source, inclusive, and fun, but then the companies are coming in in waves, a massive amount of waves of commercial vendors jumping in, and I think this foundation's done a great job of balancing being a good upstream and good project but that dynamic is very interesting. It's probably the fastest open source kind of commercial, yet good vibes, commercial open source, how does that change or affect you guys as you pick and look at the data, 'cause you get surveys, you see what people want, vendors, users, industry participants, developers, what is the data telling you? What's all this data coming from the different KubeCons and how is that changing the selections and what's the trend I guess, what's the trends coming from the community? >> So from selecting talks, because we want to focus on make Kubernetes, make KubeCon, still community-focused conference so when we pick talks, we pick the ones that not just doing vendor pitch or sales pitch but we pick the ones that we think the community is going to benefit from and especially when they are talking about a solution that others could adopt or is it open source or not, then that affect our choice and then we also see a lot of people start customizing Kubernetes for their own needs and a lot of people are starting using Kubernetes API to managing resources outside of Kubernetes and that's a very interesting trend because with that, you can have Kubernetes to manage everything your infrastructure, lot of things running on Kubernetes. >> So what are some of those examples that are outside Kubernetes? So for example, you can use, so Kubernetes has a concept called custom resource that you can register a custom API in Kubernetes and so you can use that, you can register an API and you can implement a controller to manage anything you want, for example, different cloud resources or VMs, I even saw people use Kubernetes API to manage robots. >> Wow, so this is real world, so you mentioned you were working workload API at Google, the big trend that we're seeing on theCUBE and that crosses all the different events, not just cloud-native, is workload management, managing workloads and workloads are changing and it's very dynamic, it's not a static world anymore. So managing workloads to the infrastructure is where we see this nice activity happening from containers, Kubernetes, to service meshes, so there's a lot of activity going on there and some of the stuff is straightforward, I won't say straightforward, but containers and Kubernetes is easy to work with but services meshes are difficult. Istio, for instance, Kubeflow or Hot Projects, there's a real focus of stateless has been there, but stateful is hard, is there going to be talks about stateful applications, are you guys looking at some of the Istio, is service mesh going to be a focus this year? >> Yeah, we still see a lot of submissions from service meshes and so you can use service mesh to manage your service easily and secure them easily and we also see a lot of talks for stateful workloads, for example, how you customize something that manage your stateful workloads or what that best practice is and there is a pattern that's popular in the community which is called operator and the concept is that you write a controller, use the custom API that I just mentioned, and you just embed the knowledge of a human operator into that controller and let the controller do the automation for you. >> So it's putting intelligence, like an operator, into the software and letting that ride? >> Yeah and it will do all the work for you and you only need to write it once. >> And automation's a big trend, so if you could stack or rank the top three trends that we expect to see at KubeCon this year, what would they be? >> In the top three, I would say customize and multi-cloud and then service mesh or serverless they're both pretty popular, yeah. >> Is storageless coming? So if we have serverless, will there be storageless (laughs) I made that up, I tweeted that the other day, if there's servers, there's no servers, there's going to be no storage. I mean, service and storage go together so again, this is where the fun action is, the infrastructure is being programmable. And I think one of the things I like about what KubeCon has done is they've really enabled developers to be more efficient with DevOps, the DevOps trend, which is the cloud-native trend. The question I want to ask you is specifically kind of a Google question because I think this is important and Google cloud, I really love the trend of how application developers are being modernized, that's so cool, I love that, but the SRE concept that Google pioneered is becoming more of a trend as more of an operator role, not in the sense of what we just talked about but like an SRE, businesses are starting to look at that kind of scale out infrastructure where there's a need for kind of like an SRE, does that come up at KubeCon at all or is that too operator-oriented? Is that on the agenda? Does that come up in the KubeCon selection criteria, the notion of having operators or SRE-like roles? >> So we have a track called operations, so some of the operator, human operator, talks are submitting through that, to that topic, but we didn't see... >> Might be too early. >> Yeah, too early. >> It might be a little bit too early, that's what I think, alright and then since I brought up some of the tracks, we're always interested in knowing about startups 'cause there seems to be a lot of startup activity, doing a lot of AI stuff or applications, AI ops, and some new things going on, is there a startup activity involved that you're seeing, is there features of startups at all, do you guys look at that, is there going to be an emphasis of emerging companies and startups involved or is it mostly coming from the community? >> We definitely see a lot of startups and something in talks and also you just mentioned mission learning, we also see several talks on and about mission learning and AI submitting to both the Shanghai event and Seattle event. So projects like Kubeflow and Spark, that's being used a lot and we still, we see a lot of submissions from those. >> So those are the popular ones? >> Yeah, the popular ones and those are from Shanghai, I saw some AI submissions and I'm excited about those. >> Okay, so now back to the popular question, everyone wants to know where the popular parties are, what's the popular projects if you had to, in terms of contributors, activity, do you guys have like a rating like here's the most popular project? Do you guys look at just number of contributors? How do you rank the popularity of the projects? >> Or how would you rank them? >> We didn't actually look at the popularity of the projects because are you talking about CNCF projects or any projects? >> CNCF and KubeCon, let me ask the question differently... If I go to Shanghai or Seattle, what's going on? What do I engage, what should I pay attention to, what can I expect if I'm a user and I come to the event, what's going to happen at Shanghai and Seattle? What's the format? >> We separate all the talks in tracks so you can look up the track that you are interested in, for example, do you want to know all the case studies, then you can go to case studies and if you're interested in observability then you go to the observability track and they'll be a lot of different projects, they are presenting their own solutions and you can go and figure out which one fits you the best. >> And so multi-cloud's high, I'll ask you a multi-cloud question 'cause one of the things that we're tracking is what is multi-cloud and how is that different from hybrid? How would you describe that 'cause there are people that talk about hybrid cloud all the time but multi-cloud seems to have different definitions. Is there a different definition to hybrid cloud versus multi-cloud? >> So I think hybrid includes things that's not cloud, for example, your on-prem versus you have your on-premise solutions and you also use some cloud solutions and that's hybrid... >> And multi-cloud is multiple clouds so workloads on different clouds or sharing workloads across clouds? >> Workloads on different clouds. >> Yeah, so Office 365, that's Azure, a TensorFlow on Google and something, okay. I always want to know, comparing running workloads between clouds, that would be the ideal scenario. Here's the tough question for you, put you on the spot here, what is your favorite open source project in the CNCF and favorite track at KubeCon? >> My favorite project is of course Kubernetes and my favorite track would be case studies because I care a lot about user experience and I love to hear user stories. So for Seattle we picked a lot of user stories that we think are interesting and we also pick some keynote speakers that are going to talk about their large-scale usage of Kubernetes and that's very exciting for me, I can't wait to hear their story. >> Yeah, we love the end user stories too, 'cause it really puts the real world scenario around it. Okay, final question for you Janet, I wanted to ask you about diversity at KubeCon, what's going on and what can you share around that program? >> Yeah, we care about diversity a lot. We look at that when we select talks to accept and also we have a diversity scholarship that allows people to apply for a scholarship, we're going to cover the ticket to conference and also the travel to conference and also we have a diversity luncheon on December 12 and that will be sponsored by both Google and Heptio. >> So December 12 in Seattle? And that was a great, by the way, you did a great job last year, the program with scholarship got I think a standing ovation, so that's awesome. Thanks for doing that. >> Thank you, thanks. For the folks watching that might not be really deep on Kubernetes, in your opinion, why is Kubernetes so important and why should IT leaders, developers, and people in mainstream tech who are now new to Kubernetes and seeing the trends, why should they pay attention to Kubernetes, what's the relevance, what's the impact, why should they pay attention to Kubernetes? >> Because Kubernetes allows you to easily adopt cloud, because it's extract every infrastructure the infrastructure level away and allows you to easily run your infrastructure anywhere and most importantly, because a lot of people on different cloud and different stack of development, for example, CICD service mesh, they put a lot energy to integrate with Kubernetes so if you have Kubernetes you have everything. >> You have Kubernetes, you have everything. We love the work you're doing, thanks for co-chairing the KubeCon event, we love going there, CNCF's been very successful, been a great relationship, we love working with them, obviously it's a content-rich environment and I think everyone who is interested in cloud-native should go to the CNCF, there's a lot of sponsors, and more and more logos come on every day, so you guys are doing a good job. Thanks for doing that, appreciate it. Maybe we'll do two cubes this year. Janet Kuo, who is a software engineer at Google is joining me here at theCUBE. She's also the co-chair for KubeCon, the event put on by the CNCF and the industry around cloud-native and all things Kubernetes, multi-cloud, and really applications' workloads for a cloud environment. I'm John Furrier here in theCUBE studios in Palo Alto, thanks for watching. (spirited orchestral music)

>> Announcer: Live from Copenhagen, Denmark, it's theCUBE. Covering KubeCon and CloudNativeCon Europe 2018. Brought to you by the Cloud Native Computing Foundation and its ecosystem partners. >> Hello, everyone. Welcome back to theCUBE's exclusive coverage here in Copenhagen, Denmark for KubeCon 2018, part of the CNCF Cloud Native Compute Foundation, which is part of the Linux Foundation. I'm John Furrier, your host. We've got two great guests here, we've got Liz Rice, the co-chair of KubeCon and CloudNativeCon, kind of a dual naming because it's Kubernetes and it's Cloud Native and also technology evangelist at Aqua Security. She's co-chairing with Kelsey Hightower who will be on later today, and CUBE alumni as well, and Janet Kuo who is a software engineer at Google. Welcome to theCUBE, thanks for coming on. >> Yeah, thanks for inviting us. >> Super excited, we have a lot of energy even though we've got interviews all day and it's kind of, we're holding the line here. It's almost a celebration but also not a celebration because there's more work to do with Kubernetes. Just the growth of the CNCF continues to hit some interesting good performance KPIs on metrics. Growth's up on the membership, satisfaction is high, Kubernetes is being called a de facto standard. So by all kind of general qualitative metrics and quantitative, it's doing well. >> Lauren: It's doing great. >> But it's just the beginning. >> Yeah, yeah. I talked yesterday a little bit in, in the keynote, about project updates, about how Kubernetes has graduated. That's a real signal of maturity. It's a signal to the end-user companies out there that you know, the risk, nothing is ever risk-free, but you know, Kubernetes is here to stay. It's stable, it's got stable governance model, you know, it's not going away. >> John: It's working. >> It's going to continue to evolve and improve. But it's really working, and we've got end users, you know, not only happy and using it, they're prepared to come to this conference and share their stories, share their learnings, it's brilliant. >> Yeah, and Janet also, you know, you talk about China, we have announcement that, I don't know if it's formally announced, but Shanghai, is it out there now? >> Lauren: It is. >> Okay, so Shanghai in, I think November 14th, let me get the dates here, 14th and 15th in Shanghai, China. >> Janet: Yeah. >> Where it's going to be presented in either English or in Chinese, so it's going to be fully translated. Give us the update. >> Yeah, it will be fully translated, and we'll have a CFP coming soon, and people will be submitting their talks in English but they can choose to present either in English or Chinese. >> Can you help us get a CUBE host that can translate theCUBE for us? We need some, if you're out there watching, we need some hosts in China. But in all seriousness, this is a global framework, and this is again the theme of Cloud Native, you know. Being my age, I've seen the lift and shift IT world go from awesome greatness to consolidation to VMwares. I've seen the waves. But this is a different phenomenon with Cloud Native. Take a minute to share your perspectives on the global phenomenon of Cloud Native. It's a global platform, it's not just IT, it's a global platform, the cloud, and what that brings to the table for end users. >> I think for end users, if we're talking about consumers, it actually is, well what it's doing is allowing businesses to develop applications more quickly, to respond to their market needs more quickly. And end users are seeing that in more responsive applications, more responsive services, improved delivery of tech. >> And the businesses, too, have engineers on the front lines now. >> Absolutely, and there's a lot of work going on here, I think, to basically, we were talking earlier about making technology boring, you know, this Kubernetes level is really an abstraction that most application developers don't really need to know about. And making their experience easier, they can just write their code and it runs. >> So if it's invisible to the application developer, that's the success. >> That's a really helpful thing. They shouldn't have to worry about where their code is running. >> John: That's DevOps. >> Yeah, yeah. >> I think the container in Kubernetes technology or this Cloud Native technology that brings developer the ability to, you know, move fast and give them the agility to react to the business needs very quickly. And also users benefit from that and operators also, you know, can manage their applications much more easily. >> Yeah, when you have that abstraction layer, when you have that infrastructure as code, or even this new abstraction layer which is not just infrastructure, it's services, micro-services, growth has been phenomenal. You're bringing the application developer into an efficiency productivity mode where they're dictating the business model through software of the companies. So it's not just, "Hey build me something "and let's go sell it." They're on the front lines, writing the business logic of businesses and their customers. So you're seeing it's super important for them to have that ability to either double down or abandon quickly. This is what agile is. Now it's going from software to business. This, to me, I think is the highlight for me on this show. You see the dots connecting where the developers are truly in charge of actually being a business impact because they now have more capability. As you guys put this together and do the co-chair, do you and Kelsey, what do you guys do in the room, the secret room, you like, "Well let's do this on the content." I mean, 'cause there's so much to do. Take us through the process. >> So, a little bit of insight into how that whole process works. So we had well over 1,000 submissions, which, you know, there's no, I think there's like 150 slots, something like that. So that's a pretty small percentage that we can actually accept. We had an amazing program committee, I think there were around 60 people who reviewed, every individual reviewer looked at a subset. We didn't ask them to look at all thousand, that would be crazy. They scored them, that gave us a kind of first pass, like a sort of an ability to say, "Well, anything that was below average, "we can only take the top 15%, "so anything that's below average "is not going to make the cut." And then we could start looking at trying to balance, say, for example, there's been a lot of talk about were there too many Istio talks? Well, there were a lot of Istio talks because there were a lot of Istio submissions. And that says to us that the community wants to talk about Istio. >> And then number of stars, that's the number one project on the new list. I mean, Kubeflow and Istio are super hot. >> Yeah, yeah, Kubeflow's another great example, there are lots of submissions around it. We can't take them all but we can use the ratings and the advice from the program committee to try and assemble, you know, the best talks to try and bring different voices in, you know, we want to have subject matter experts and new voices. We want to have the big name companies and start-ups, we wanted to try and get a mix, you know. A diversity of opinion, really. >> And you're a membership organization so you have to balance the membership needs with the content program so, challenging with given the growth. I mean, I can only imagine. >> Yeah, so as program co-chairs, we actually have a really free hand over the content, so it's one of the really, I think, nice things about this conference. You know, sponsors do get to stand on stage and deliver their message, but they don't get to influence the actual program. The program is put together for the community, and by doing things like looking at the number of submissions, using those signals that the community want to talk about, I hope we can carry on giving the attendees that format. >> I would just say from an outsider perspective, I think that's something you want to preserve because if you look at the success of the CNCF, one thing I'm impressed by is they've really allowed a commercial environment to be fostered and enabled. But they didn't compromise the technical. >> Lauren: Yeah. >> And the content to me, content and technical tracks are super important because content, they all work together, right? So as long as there's no meddling, stay in your swim lane, whatever, whatever it is. Content is really important. >> Absolutely, yeah. >> Because that's the learning. >> Yeah, yeah. >> Okay, so what's on the cut list that you wish you could have put back on stage? Or is that too risque? You'll come back to that. >> Yeah. >> China, talk about China. Because obviously, we were super impressed last year when we went to go visit Alibaba just to the order of magnitude to the cultural mindset for their thinking around Cloud Native. And what I was most impressed with was Dr. Wong was talking about artistry. They just don't look at it as just technology, although they are nerdy and geeky like us in Silicon Valley. But they really were thinking about the artistry 'cause the app side of it has kind of a, not just design element to the user perspective. And they're very mobile-centric in China, so they're like, they were like, "This is what we want to do." So they were very advanced in my mind on this. Does that change the program in China vis a vis Seattle and here, is there any stark differences between Shanghai and Copenhagen and Seattle in terms of the program? Is there a certain focus? What's the insight into China? >> I think it's a little early to say 'cause we haven't yet opened the CFP. It'll be opening soon but I'm fully expecting that there will be, you know, some differences. I think the, you know, we're hoping to have speakers, a lot more speakers from China, from Asia, because it's local to them. So, like here, we tried to have a European flavor. You'll see a lot of innovators from Europe, like Spotify and the Financial Times, Monzo Bank. You know, they've all been able to share their stories with us. And I think we're hoping to get the same kind of thing in China, hear local stories as well. >> I mean that's a good call. I think conferences that do the rinse and repeat from North America and just slap it down in different regions aren't as effective as making it localized, in a way. >> Yeah. >> That's super important. >> I know that a lot of China companies, they are pretty invested pretty heavily into Kubernetes and Cloud Native technology and they are very innovative. So I actually joined a project in 2015 and I've been collaborating with a lot of Chinese contributors from China remotely on GitHub. For example, the contributors from Huawei and they've been invested a lot in this. >> And they have some contributors in the core. >> Yeah, so we are expecting to see submissions from those contributors and companies and users. >> Well, that's super exciting. We look forward to being there, and it should be excellent. We always have a fun time. The question that I want to ask you guys now, just to switch gears is, for the people watching who couldn't make it or might watch it on YouTube on Demand who didn't make the trip. What surprised you here? What's new, I'm asking, you have a view as the co-chair, you've seen it. But was there anything that surprised you, or did it go right? Nothing goes perfect. I mean, it's like my wedding, everything happens, didn't happen the way you planned it. There's always a surprise. Any wild cards, any x-factors, anything that stands out to you guys? >> So what I see from, so I attend, I think around five KubeCons. So from the first one it's only 550 people, only the small community, the contributors from Google and Red Hat and CoreOS and growing from now. We are growing from the inner circle to the outside circle, from the just contributors to also the users of it, like and also the ecosystem. Everyone that's building the technology around Cloud Native, and I see that growth and it's very surprising to me. We have a keynote yesterday from CERN and everyone is talking about their keynote, like they have I think 200 clusters, and that's amazing. And they said because of Kubernetes they can just focus on physics. >> Yeah, and that's a testimonial right there. >> Yeah. >> That was really good stories to hear, and I think maybe one of the things that surprises me, it sort of continues to surprise me is how collaborative, it's something about this kind of organization, this conference, this whole kind of movement, if you like. Where companies are coming in and sharing their learnings, and we've seen that, we've seen that a lot through the keynotes. And I think we see it on the conference floor, we see it in the hallway chat. And I think we see it in the way that the different SIGs and working groups and projects are all, kind of, collaborating on problem solving. And that's really exciting. >> That's why I was saying earlier in the beginning that there's a celebration amongst ourselves and the community. But also a realization that this is just the beginning, it's not a, it's kind of like when you get venture funding if you're a start-up. That's really when it begins, you don't celebrate, but you take a little bit of a pause. Now my personal take only to all of the hundreds of events we do a year is that I that think this community here has fought the hard DevOps battle. If you go back to 2008 timeframe, and '08, '09, '10, '11, '12, those years were, those were hyper scale years. Look at Google, Facebook, all the original DevOps engineers, they were eating glass and spitting nails. It was hard work. And it was really build your own, a lot of engineering, not just software development. So I think this, kind of like, camaraderie amongst the DevOps community saying, "Look, this is a really big "step up function with Kubernetes." Everyone's had some scar tissue. >> Yeah, I think a lot of people have learned from previous, you know, even other open source projects that they've worked on. And you see some of the amazing work that goes into the kind of, like, community governance side. The things that, you know, Paris Pittman does around contributor experience. It's so good to see people who are experts in helping developers engage, helping engineers engage, really getting to play that role. >> There's a lot of common experiences for people who have never met each other because there's people who have seen the hard work pay with scale and leverage and benefits. They see it, this is amazing. We had Sheryl from Google on saying, "When I left Google and I went out into the real world, "I was like, oh my God, "they don't actually use Borg," like, what? "What do they, how do they actually write software?" I mean, so she's a fish out of water and that, it's like, so again I think there's a lot of commonality, and it's a super great opportunity and a great community and you guys have done a great job, CNCF. And we hope to nurture that, the principles, and looking forward to China. Thanks for coming on theCUBE, we appreciate it. >> Yeah. >> Okay we're here at CNCF's KubeCon 2018, I'm John Furrier, more live coverage. Stay with us, day two of two days of CUBE coverage. Go to thecube.net, siliconangle.com for all the coverage. We'll be back, stay with us after this short break.

>> Announcer: Live from San Jose, California, it's theCUBE, covering Innovating to Fuel the Next Decade of Big Data, brought to you by Western Digital. >> Hey welcome back everybody, Jeff Frick here with theCUBE. We're at Western Digital at their global headquarters in San Jose, California, it's the Almaden campus. This campus has a long history of innovation, and we're excited to be here, and probably have the smartest person in the building, if not the county, area code and zip code. I love to embarrass here, Janet George, she is the Fellow and Chief Data Scientist for Western Digital. We saw you at Women in Data Science, you were just at Grace Hopper, you're everywhere and get to get a chance to sit down again. >> Thank you Jeff, I appreciate it very much. >> So as a data scientist, today's announcement about MAMR, how does that make you feel, why is this exciting, how is this going to make you be more successful in your job and more importantly, the areas in which you study? >> So today's announcement is actually a breakthrough announcement, both in the field of machine learning and AI, because we've been on this data journey, and we have been very selectively storing data on our storage devices, and the selection is actually coming from the preconstructed queries that we do with business data, and now we no longer have to preconstruct these queries. We can store the data at scale in raw form. We don't even have to worry about the format or the schema of the data. We can look at the schema dynamically as the data grows within the storage and within the applications. >> Right, cause there's been two things, right. Before data was bad 'cause it was expensive to store >> Yes. >> Now suddenly we want to store it 'cause we know data is good, but even then, it still can be expensive, but you know, we've got this concept of data lakes and data swamps and data all kind of oceans, pick your favorite metaphor, but we want the data 'cause we're not really sure what we're going to do with it, and I think what's interesting that you said earlier today, is it was schema on write, then we evolved to schema on read, which was all the rage at Hadoop Summit a couple years ago, but you're talking about the whole next generation, which is an evolving dynamic schema >> Exactly. >> Based whatever happens to drive that query at the time. >> Exactly, exactly. So as we go through this journey, we are now getting independent of schema, we are decoupled from schema, and what we are finding out is we can capture data at its raw form, and we can do the learning at the raw form without human interference, in terms of transformation of the data and assigning a schema to that data. We got to understand the fidelity of the data, but we can train at scale from that data. So with massive amounts of training, the models already know to train itself from raw data. So now we are only talking about incremental learning, as the train model goes out into the field in production, and actually performs, now we are talking about how does the model learn, and this is where fast data plays a very big role. >> So that's interesting, 'cause you talked about that also earlier in your part of the presentation, kind of the fast data versus big data, which kind of maps the flash versus hard drive, and the two are not, it's not either or, but it's really both, because within the storage of the big data, you build the base foundations of the models, and then you can adapt, learn and grow, change with the fast data, with the streaming data on the front end, >> Exactly >> It's a whole new world. >> Exactly, so the fast data actually helps us after the training phase, right, and these are evolving architectures. This is part of your journey. As you come through the big data journey you experience this. But for fast data, what we are seeing is, these architectures like Lambda and Kappa are evolving, and especially the Lambda architecture is very interesting, because it allows for batch processing of historical data, and then it allows for what we call a high latency layer or a speed layer, where this data can then be promoted up the stack for serving purposes. And then Kappa architecture's where the data is being streamed near real time, bounded and unbounded streams of data. So this is again very important when we build machine learning and AI applications, because evolution is happening on the fly, learning is happening on the fly. Also, if you think about the learning, we are mimicking more and more on how humans learn. We don't really learn with very large chunks of data all at once, right? That's important for initially model training and model learning, but on a regular basis, we are learning with small chunks of data that are streamed to us near real time. >> Right, learning on the Delta. >> Learning on the Delta. >> So what is the bound versus the unbound? Unpack that a little bit. What does that mean? >> So what is bounded is basically saying, hey we are going to get certain amounts of data, so you're sizing the data for example. Unbounded is infinite streams of data coming to you. And so if your architecture can absorb infinite streams of data, like for example, the sensors constantly transmitting data to you, right? At that point you're not worried about whether you can store that data, you're simply worried about the fidelity of that data. But bounded would be saying, I'm going to send the data in chunks. You could also do bounded where you basically say, I'm going to pre-process the data a little bit just to see if the data's healthy, or if there is signal in the data. You don't want to find that out later as you're training, right? You're trying to figure that out up front. >> But it's funny, everything is ultimately bounded, it just depends on how you define the unit of time, right, 'cause you take it down to infinite zero, everything is frozen. But I love the example of the autonomous cars. We were at the event with, just talking about navigation just for autonomous cars. Goldman Sachs says it's going to be a seven billion dollar industry, and the great example that you used of the two systems working well together, 'cause is it the car centers or is it the map? >> Janet: That's right. >> And he says, well you know, you want to use the map, and the data from the map as much as you can to set the stage for the car driving down the road to give it some level of intelligence, but if today we happen to be paving lane number two on 101, and there's cones, now it's the real time data that's going to train the system. But the two have to work together, and the two are not autonomous and really can't work independent of each other. >> Yes. >> Pretty interesting. >> It makes perfect sense, right. And why it makes perfect sense is because first the autonomous cars have to learn to drive. Then the autonomous cars have to become an experienced driver. And the experience cannot be learned. It comes on the road. So one of the things I was watching was how insurance companies were doing testing on these cars, and they had a human, a human driving a car, and then an autonomous car. And the autonomous car, with the sensors, were predicting the behavior, every permutation and combination of how a bicycle would react to that car. It was almost predicting what the human on the bicycle would do, like jump in front of the car, and it got it right 80% of the cases. But a human driving a car, we're not sure how the bicycle is going to perform. We don't have peripheral vision, and we can't predict how the bicycle is going to perform, so we get it wrong. Now, we can't transmit that knowledge. If I'm a driver and I just encountered a bicycle, I can't transmit that knowledge to you. But a driverless car can learn, it can predict the behavior of the bicycle, and then it can transfer that information to a fleet of cars. So it's very powerful in where the learning can scale. >> Such a big part of the autonomous vehicle story that most people don't understand, that not only is the car driving down the road, but it's constantly measuring and modeling everything that's happening around it, including bikes, including pedestrians, including everything else, and whether it gets in a crash or not, it's still gathering that data and building the model and advancing the models, and I think that's, you know, people just don't talk about that enough. I want follow up on another topic. So we were both at Grace Hopper last week, which is a phenomenal experience, if you haven't been, go. Ill just leave it at that. But Dr. Fei-Fei Li gave one of the keynotes, and she made a really deep statement at the end of her keynote, and we were both talking about it before we turned the cameras on, which is, there's no question that AI is going to change the world, and it's changing the world today. The real question is, who are the people that are going to build the algorithms that train the AI? So you sit in your position here, with the power, both in the data and the tools and the compute that are available today, and this brand new world of AI and ML. How do you think about that? How does that make you feel about the opportunity to define the systems that drive the cars, et cetera. >> I think not just the diversity in data, but the diversity in the representation of that data are equally powerful. We need both. Because we cannot tackle diverse data, diverse experiences with only a single representation. We need multiple representation to be able to tackle that data. And this is how we will overcome bias of every sort. So it's not the question of who is going to build the AI models, it is a question of who is going to build the models, but not the question of will the AI models be built, because the AI models are already being built, but some of the models have biases into it from any kind of lack of representation. Like who's building the model, right? So I think it's very important. I think we have a powerful moment in history to change that, to make real impact. >> Because the trick is we all have bias. You can't do anything about it. We grew up in the world in which we grew up, we saw what we saw, we went to our schools, we had our family relationships et cetera. So everyone is locked into who they are. That's not the problem. The problem is the acceptance of bring in some other, (chuckles) and the combination will provide better outcomes, it's a proven scientific fact. >> I very much agree with that. I also think that having the freedom, having the choice to hear another person's conditioning, another person's experiences is very powerful, because that enriches our own experiences. Even if we are constrained, even if we are like that storage that has been structured and processed, we know that there's this other storage, and we can figure out how to get the freedom between the two point of views, right? And we have the freedom to choose. So that's very, very powerful, just having that freedom. >> So as we get ready to turn the calendar on 2017, which is hard to imagine it's true, it is. You look to 2018, what are some of your personal and professional priorities, what are you looking forward to, what are you working on, what's top of mind for Janet George? >> So right now I'm thinking about genetic algorithms, genetic machine learning algorithms. This has been around for a while, but I'll tell you where the power of genetic algorithms is, especially when you're creating powerful new technology memory cell. So when you start out trying to create a new technology memory cell, you have materials, material deformations, you have process, you have hundred permutation combination, and the genetic algorithms, we can quickly assign a cause function, and we can kill all the survival of the fittest, all that won't fit we can kill, arriving to the fastest, quickest new technology node, and then from there, we can scale that in mass production. So we can use these survival of the fittest mechanisms that evolution has used for a long period of time. So this is biology inspired. And using a cause function we can figure out how to get the best of every process, every technology, all the coupling effects, all the master effects of introducing a program voltage on a particular cell, reducing the program voltage on a particular cell, resetting and setting, and the neighboring effects, we can pull all that together, so 600, 700 permutation combination that we've been struggling on and not trying to figure out how to quickly narrow down to that perfect cell, which is the new technology node that we can then scale out into tens of millions of vehicles, right? >> Right, you're going to have to >> Getting to that spot. >> You're going to have to get me on the whiteboard on that one, Janet. That is amazing. Smart lady. >> Thank you. >> Thanks for taking a few minutes out of your time. Always great to catch up, and it was terrific to see you at Grace Hopper as well. >> Thank you, I really appreciate it, I appreciate it very much. >> All right, Janet George, I'm Jeff Frick. You are watching theCUBE. We're at Western Digital headquarters at Innovating to Fuel the Next Generation of Big Data. Thanks for watching.

(upbeat electronic music) >> Narrator: From the Fairmont Hotel in the heart of Silicon Valley, it's theCUBE. Covering when IoT met AI, The Intelligence of Things. Brought to you by Western Digital. >> Welcome back here everybody, Jeff Frick here with theCUBE. We are at downtown San Jose at the Fairmont Hotel. When IoT met AI it happened right here, you saw it first. The Intelligence of Things, a really interesting event put on by readwrite and Western Digital and we are really excited to welcome back a many time CUBE alumni and always a fan favorite, she's Janet George. She's Fellow & Chief Data Officer of Western Digital. Janet, great to see you. >> Thank you, thank you. >> So, as I asked you when you sat down, you're always working on cool things. You're always kind of at the cutting edge. So, what have you been playing with lately? >> Lately I have been working on neural networks and TensorFlow. So really trying to study and understand the behaviors and patterns of neural networks, how they work and then unleashing our data at it. So trying to figure out how it's training through our data, how many nets there are, and then trying to figure out what results it's coming with. What are the predictions? Looking at how the predictions are, whether the predictions are accurate or less accurate and then validating the predictions to make it more accurate, and so on and so forth. >> So it's interesting. It's a different tool, so you're learning the tool itself. >> Yes. >> And you're learning the underlying technology behind the tool. >> Yes. >> And then testing it actually against some of the other tools that you guys have, I mean obviously you guys have been doing- >> That's right. >> Mean time between failure analysis for a long long time. >> That's right, that's right. >> So, first off, kind of experience with the tool, how is it different? >> So with machine learning, fundamentally we have to go into feature extraction. So you have to figure out all the features and then you use the features for predictions. With neural networks you can throw all the raw data at it. It's in fact data-agnostic. So you don't have to spend enormous amounts of time trying to detect the features. Like for example, If you throw hundreds of cat images at the neural network, the neural network will figure out image features of the cat; the nose, the eyes, the ears and so on and so forth. And once it trains itself through a series of iterations, you can throw a lot of deranged cats at the neural network and it's still going to figure out what the features of a real cat is. >> Right. >> And it will predict the cat correctly. >> Right. So then, how does that apply to, you know, the more specific use case in terms of your failure analysis? >> Yeah. So we have failures and we have multiple failures. Some failures through through the human eye, it's very obvious, right? But humans get tired, and over a period of time we can't endure looking at hundreds and millions of failures, right? And some failures are interconnected. So there is a relationship between these failure patterns or there is a correlation between two failures, right? It could be an edge failure. It could a radial failure, eye pattern type failure. It could be a radial failure. So these failures, for us as humans, we can't escape. >> Right. >> And we used to be able to take these failures and train them at scale and then predict. Now with neural networks, we don't have to take and do all that. We don't have to extract these labels and try to show them what these failures look like. Training is almost like throwing a lot of data at the neural networks. >> So it almost sounds like kind of the promise of the data lake if you will. >> Yes. >> If you have heard about, from the Hadoop Summit- >> Yes, yes, yes. >> For ever and ever and ever. Right? You dump it all in and insights will flow. But we found, often, that that's not true. You need hypothesis. >> Yes, yes. >> You need to structure and get it going. But what you're describing though, sounds much more along kind of that vision. >> Yes, very much so. Now, the only caveat is you need some labels, right? If there is no label on the failure data, it's very difficult for the neural networks to figure out what the failure is. >> Jeff: Right. >> So you have to give it some labels to understand what patterns it should learn. >> Right. >> Right, and that is where the domain experts come in. So we train it with labeled data. So if you are training with a cat, you know the features of a cat, right? In the industrial world, cat is really what's in the heads of people. The domain knowledge is not so authoritative. Like the sky or the animals or the cat. >> Jeff: Right. >> The domain knowledge is much more embedded in the brains of the people who are working. And so we have to extract that domain knowledge into labels. And then you're able to scale the domain. >> Jeff: Right. >> Through the neural network. >> So okay so then how does it then compare with the other tools that you've used in the past? In terms of, obviously the process is very different, but in terms of just pure performance? What are you finding? >> So we are finding very good performance and actually we are finding very good accuracy. Right? So once it's trained, and it's doing very well on the failure patterns, it's getting it right 90% of the time, right? >> Really? >> Yes, but in a machine learning program, what happens is sometimes the model is over-fitted or it's under-fitted or there is bias in the model and you got to remove the bias in the model or you got to figure out, well, is the model false-positive or false-negative? You got to optimize for something, right? >> Right, right. >> Because we are really dealing with mathematical approximation, we are not dealing with preciseness, we are not dealing with exactness. >> Right, right. >> In neural networks, actually, it's pretty good, because it's actually always dealing with accuracy. It's not dealing with precision, right? So it's accurate most of the time. >> Interesting, because that's often what's common about the kind of difference between computer science and statistics, right? >> Yes. >> Computers is binary. Statistics always has a kind of a confidence interval. But what you're describing, it sounds like the confidence is tightening up to such a degree that it's almost reaching binary. >> Yeah, yeah, exactly. And see, brute force is good when your traditional computing programing paradigm is very brute force type paradigm, right? The traditional paradigm is very good when the problems are simpler. But when the problems are of scale, like you're talking 70 petabytes of data or you're talking 70 billion roles, right? Find all these patterns in that, right? >> Jeff: Right. >> I mean you just, the scale at which that operates and at the scale at which traditional machine learning even works is quite different from how neural networks work. >> Jeff: Okay. >> Right? Traditional machine learning you still have to do some feature extraction. You still have to say "Oh I can't." Otherwise you are going to have dimensionality issues, right? It's too broad to get the prediction anywhere close. >> Right. >> Right? And so you want to reduce the dimensionality to get a better prediction. But here you don't have to worry about dimensionality. You just have to make sure the labels are right. >> Right, right. So as you dig deeper into this tool and expose all these new capabilities, what do you look forward to? What can you do that you couldn't do before? >> It's interesting because it's grossly underestimating the human brain, right? The human brain is supremely powerful in all aspects, right? And there is a great deal of difficulty in trying to code the human brain, right? But with neural networks and because of the various propagation layers and the ability to move through these networks we are coming closer and closer, right? So one example: When you think about driving, recently, Google driverless car got into an accident, right? And where it got into an accident was the driverless car was merging into a lane and there was a bus and it collided with the bus. So where did A.I. go wrong? Now if you train an A.I., birds can fly, and then you say penguin is a bird, it is going to assume penguin can fly. >> Jeff: Right, right. >> We as humans know penguin is a bird but it can't fly like other birds, right? >> Jeff: Right. >> It's that anomaly thing, right? Naturally when are driving and a bus shows up, even if it's yield, the bus goes. >> Jeff: Right, right. >> We yield to the bus because it's bigger and we know that. >> A.I. doesn't know that. It was taught that yield is yield. >> Right, right. >> So it collided with the bus. But the beauty is now large fleets of cars can learn very quickly based on what it just got from that one car. >> Right, right. >> So now there are pros and cons. So think about you driving down Highway 85 and there is a collision, it's Sunday morning, you don't know about the collision. You're coming down on the hill, right? Blind corner and boom that's how these crashes happen and so many people died, right? If you were driving a driverless car, you would have knowledge from the fleet and from everywhere else. >> Right. >> So you know ahead of time. We don't talk to each other when we are in cars. We don't have universal knowledge, right? >> Car-to-car communication. >> Car-to-car communications and A.I. has that so directly it can save accidents. It can save people from dying, right? But people still feel, it's a psychology thing, people still feel very unsafe in a driverless car, right? So we have to get over- >> Well they will get over that. They feel plenty safe in a driverless airplane, right? >> That's right. Or in a driveless light rail. >> Jeff: Right. >> Or, you know, when somebody else is driving they're fine with the driver who's driving. You just sit in the driver's car. >> But there's that one pesky autonomous car problem, when the pedestrian won't go. >> Yeah. >> And the car is stopped it's like a friendly battle-lock. >> That's right, that's right. >> Well good stuff Janet and always great to see you. I'm sure we will see you very shortly 'cause you are at all the great big data conferences. >> Thank you. >> Thanks for taking a few minutes out of your day. >> Thank you. >> Alright she is Janet George, she is the smartest lady at Western Digital, perhaps in Silicon Valley. We're not sure but we feel pretty confident. I am Jeff Frick and you're watching theCUBE from When IoT meets AI: The Intelligence of Things. We will be right back after this short break. Thanks for watching. (upbeat electronic music)

>> Male Voiceover: Live from Stanford University, it's The Cube covering the Women in Data Science Conference 2017. >> Hi, welcome back to The Cube, I'm Lisa Martin and we are live at Stanford University at the second annual Women in Data Science Technical Conference. It's a one day event here, incredibly inspiring morning we've had. We're joined by Janet George, who is the chief data scientist at Western Digital. Janet, welcome to the show. >> Thank you very much. >> You're a speaker at-- >> Very happy to be here. >> We're very happy to have you. You're a speaker at this event and we want to talk about what you're going to be talking about. Industrialized data science. What is that? >> Industrialized data science is mostly about how data science is applied in the industry. It's less about more research work, but it's more about practical application of industry use cases in which we actually apply machine learning and artificial intelligence. >> What are some of the use cases at Western Digital for that application? >> One of the use case that we use is, we are in the business of creating new technology nodes and for creating new technology nodes we actually create a lot of data. And with that data, we actually look at, can we understand pattern recognition at very large scale? We're talking millions of wafers. Can we understand memory holes? The shape, the type, the curvature, circularity, radius, can we detect these patterns at scale? And then how can we detect if the memory hole is warped or deformed and how can we have machine learning do that for us? We also look at things like correlations during the manufacturing process. Strong correlations, weak correlations, and we try to figure out interactions between different correlations. >> Fantastic. So if we look at big data, it's probably applicable across every industry. How has it helped to transform Western Digital, that's been an institution here in Silicon Valley for a while? >> We in Western Digital we move mountains of data. That's just part of our job, right? And so we are the leaders in storage technology, people store data in Western Digital products, and so data's inherently very familiar to us. We actually deal with data on a regular basis. And now we've started confronting our data with data science. And we started confronting our data with machine learning because we are very aware that artificial intelligence, machine learning can bring a different value to that data. We can look at the insides, we can develop intelligence about how we build our storage products. What we do with our storage. Failure analysis is a huge area for us. So we're really tapping into our data to figure out how can we make artificial intelligence and machine learning ingrained in the way we do work. >> So from a cultural perspective, you've really done a lot to evolve the culture of Western Digital to apply the learnings, to improve the values that you deliver to all of your customers. >> Yes, believe it or not, we've become a data-driven company. That's amazing, because we've invested in our own data, and we've said "Hey, if we are going to store the world's data, we need to lead, from a data perspective" and so we've sort of embraced machine learning and artificial intelligence. We've embraced new algorithms, technologies that's out there we can tap into to look at our data. >> So from a machine learning, human perspective, in storage manufacturing, is there still a dependence on human insight where storage manufacturing devices are concerned, or are you seeing the machine learning really, in this case, take more of a lead? >> No, I think humans play a huge role, right? Because these are domain experts. We're talking about Ph.D.'s in material science and device physics areas so what I see is the augmentation between machine learning and humans, and the domain experts. Domain experts will not be able to scale. When the scale of wafer production becomes very large. So let's talk about 3 million wafers. How is a machine going to physically look at all the failure patterns on those wafers? We're not going to be able to scale just having domain expertise. But taking our core domain expertise and using that as training data to build intelligence models that can inform the domain expert and be smart and come up with all the ideas, that's where we want to be. >> Excellent. So you talked a little bit about the manufacturing process. Who are some of the other constituents that you collaborate with as chief data scientist at Western Digital that are demanding access to data, marketing, etcetera, what are some of those key collaborators for your group? >> Many of our marketing department, as well as our customer service department, we also have collaborations going on with universities, but one of the things we found out was when a drive fails, and it goes to our customer, it's much better for us to figure out the failure. So we've started modeling out all the customer returns that we've received, and look at that and see "How can we predict the life cycle of our storage?" And get to those return possibilities or potential issues before it lands in the hands of customers. >> That's excellent. >> So that's one area we've been focusing quite a bit on, to look at the whole life cycle of failures. >> You also talked about collaborating with universities. Share a little bit about that in terms of, is there a program for internships for example? How are you helping to shape the next generation of computer scientists? >> We are very strongly embedded in universities. We usually have a very good internship program. Six to eight weeks, to 12 weeks in the summer, the interns come in. Ours is a little different where we treat our interns as real value add. They come in, and they're given a hypothesis, or problem domain that they need to go after. And within six to eight weeks, and they have access to tremendous amounts of data, so they get to play with all this industry data that they would never get to play with. They can quickly bring their academic background, or their academic learning to that data. We also take really hard research-ended problems or further out problems and we collaborate with universities on that, especially Stanford University, we've been doing great collaborations with them. I'm super encouraged with Feliz's work on computer vision, and we've been looking into things around deep neural networks. This is an area of great passion for me. I think the cognitive computing space is just started to open up and we have a lot to learn from neural networks and how they work and where the value can be added. >> Looking at, just want to explore the internship topic for a second. And we're at the second annual Women in Data Science Conference. There's a lot of young minds here, not just here in person, but in many cities across the globe. What are you seeing with some of the interns that come in? Are they confident enough to say "I'm getting access to real world data I wouldn't have access to in school", are they confident to play around with that, test out a hypothesis and fail? Or do they fear, "I need to get this right right away, this is my career at stake?" >> It's an interesting dichotomy because they have a really short time frame. That's an issue because of the time frame, and they have to quickly discover. Failing fast and learning fast is part of data science and I really think that we have to get to that point where we're really comfortable with failure, and the learning we get from the failure. Remember the light bulb was invented with 99% negative knowledge, so we have to get to that negative knowledge and treat that as learning. So we encourage a culture, we encourage a style of different learning cycles so we say, "What did we learn in the first learning cycle?" "What discoveries, what hypothesis did we figure out in the first learning cycle, which will then prepare our second learning cycle?" And we don't see it as a one-stop, rather more iterative form of work. Also with the internships, I think sometimes it's really essential to have critical thinking. And so the interns get that environment to learn critical thinking in the industry space. >> Tell us about, from a skills perspective, these are, you can share with us, presumably young people studying computer science, maybe engineering topics, what are some of the traditional data science skills that you think are still absolutely there? Maybe it's a hybrid of a hacker and someone who's got, great statistician background. What about the creative side and the ability to communicate? What's your ideal data scientist today? What are the embodiments of those? >> So this is a fantastic question, because I've been thinking about this a lot. I think the ideal data scientist is at the intersection of three circles. The first circle is really somebody who's very comfortable with data, mathematics, statistics, machine learning, that sort of thing. The second circle is in the intersection of implementation, engineering, computer science, electrical engineering, those backgrounds where they've had discipline. They understand that they can take complex math or complex algorithms and then actually implement them to get business value out of them. And the third circle is around business acumen, program management, critical thinking, really going deeper, asking the questions, explaining the results, very complex charts. The ability to visualize that data and understand the trends in that data. So it's the intersection of these very diverse disciplines, and somebody who has deep critical thinking and never gives up. (laughs) >> That's a great one, that never gives up. But looking at it, in that way, have you seen this, we're really here at a revolution, right? Have you seen that data science traditionalist role evolve into these three, the intersection of these three elements? >> Yeah, traditionally, if you did a lot of computer science, or you did a lot of math, you'd be considered a great data scientist. But if you don't have that business acumen, how do you look at the critical problems? How do you communicate what you found? How do you communicate that what you found actually matters in the scheme of things? Sometimes people talk about anomalies, and I always say "is the anomaly structured enough that I need to care about?" Is it systematic? Why should I care about this anomaly? Why is it different from an alert? If you have modeled all the behaviors, and you understand that this is a different anomaly than I've normally seen, and you must care about it. So you need to have business acumen to ask the right business questions and understand why that matters. >> So your background in computer science, your bachelor's Ph.D.? >> Bachelor's and master's in computer science, mathematics, and statistics, so I've got a combination of all of those and then my business experience comes from being in the field. >> Lisa: I was going to ask you that, how did you get that business acumen? Sounds like it was by in-field training, basically on-the-job? >> It was in the industry, it was on-the-job, I put myself in positions where I've had great opportunities and tackled great business problems that I had to go out and solve, very unique set of business problems that I had to dig deep into figuring out what the solutions were, and so then gained the experience from that. >> So going back to Western Digital, how you're leveraging data science to really evolve the company. You talked about the cultural evolution there, which we both were mentioning off-camera, is quite a feat because it's very challenging. Data from many angles, security, usage, is a board level, boardroom conversation. I'd love to understand, and you also talked about collaboration, so talk to us a little bit about how, and some of the ways, tangible ways, that data science and your team have helped evolve Western Digital. Improving products, improving services, improving revenue. >> I think of it as when an algorithm or a machine learning model is smart, it cannot be a threat. There's a difference between being smart and being a threat. It's smart when it actually provides value. It's a threat when it takes away or does something you would be wanting to do, and here I see that initially there's a lot of fear in the industry, and I think the fear is related to "oh, here's a new technology," and we've seen technologies come in and disrupt in a major way. And machine learning will make a lot of disruptions in the industry for sure. But I think that will cause a shift, or a change. Look at our phone industry, and how much the phone industry has gone through. We never complain that the smart phone is smarter than us. (laughs) We love the fact that the smartphone can show us maps and it can send us in the right, of course, it sends us in the wrong direction sometimes, most of the time it's pretty good. We've grown to rely on our cell phones. We've grown to rely on the smartness. I look at when technology becomes your partner, when technology becomes your ally, and when it actually becomes useful to you, there is a shift in culture. We start by saying "how do we earn the value of the humans?" How can machine learning, how can the algorithms we built, actually show you the difference? How can it come up with things you didn't see? How can it discover new things for you that will create a wow factor for you? And when it does create a wow factor for you, you will want more of it, so it's more, to me, it's most an intent-based progress, in terms of a culture change. You can't push any new technology on people. People will be reluctant to adapt. The only way you can, that people adopt to new technologies is when they the value of the technology instantly and then they become believers. It's a very grassroots-level change, if you will. >> For the foreseeable future, that from a fear perspective and maybe job security, that at least in the storage and manufacturing industry, people aren't going to be replaced by machines. You think it's going to maybe live together for a very long, long time? >> I totally agree. I think that it's going to augment the humans for a long, long time. I think that we will get over our fear, we worry that the humans, I think humans are incredibly powerful. We give way too little credit to ourselves. I think we have huge creative capacity. Machines do have processing capacity, they have very large scale processing capacity, and humans and machines can augment each other. I do believe that the time when we had computers and we relied on our computers for data processing. We're going to rely on computers for machine learning. We're going to get smarter, so we don't have to do all the automation and the daily grind of stuff. If you can predict, and that prediction can help you, and you can feed that prediction model some learning mechanism by reinforced learning or reading or ranking. Look at spam industry. We just taught the Spam-a-Guccis to become so good at catching spam, and we don't worry about the fact that they do the cleansing of that level of data for us and so we'll get to that stage first, and then we'll get better and better and better. I think humans have a natural tendency to step up, they always do. We've always, through many generations, we have always stepped up higher than where we were before, so this is going to make us step up further. We're going to demand more, we're going to invent more, we're going to create more. But it's not going to be, I don't see it as a real threat. The places where I see it as a threat is when the data has bias, or the data is manipulated, which exists even without machine learning. >> I love though, that the analogy that you're making is as technology is evolving, it's kind of a natural catalyst >> Janet: It is a natural catalyst. >> For us humans to evolve and learn and progress and that's a great cycle that you're-- >> Yeah, imagine how we did farming ten years ago, twenty years ago. Imagine how we drive our cars today than we did many years ago. Imagine the role of maps in our lives. Imagine the role of autonomous cars. This is a natural progression of the human race, that's how I see it, and you can see the younger, young people now are so natural for them, technology is so natural for them. They can tweet, and swipe, and that's the natural progression of the human race. I don't think we can stop that, I think we have to embrace that it's a gift. >> That's a great message, embracing it. It is a gift. Well, we wish you the best of luck this year at Western Digital, and thank you for inspiring us and probably many that are here and those that are watching the livestream. Janet George, thanks so much for being on The Cube. >> Thank you. >> Thank you for watching The Cube. We are again live from the second annual Women in Data Science conference at Stanford, I'm Lisa Martin, don't go away. We'll be right back. (upbeat electronic music)

foreign welcome back to re invent 2022 we're wrapping up four days well one evening and three solid days wall-to-wall of cube coverage I'm Dave vellante John furrier's birthday is today he's on a plane to London to go see his nephew get married his his great Sister Janet awesome family the furriers uh spanning the globe and uh and John I know you wanted to be here you're watching in Newark or you were waiting to uh to get in the plane so all the best to you happy birthday one year the Amazon PR people brought a cake out to celebrate John's birthday because he's always here at AWS re invented his birthday so I'm really pleased to have two really special guests uh former Cube host Cube Alum great wikibon contributor Stu miniman now with red hat still good to see you again great to be here Dave yeah I was here for that cake uh the twitterverse uh was uh really helping to celebrate John's birthday today and uh you know always great to be here with you and then with this you know Awesome event this week and friend of the cube of many time Cube often Cube contributor as here's a cube analyst this week as his own consultancy sarbj johal great to see you thanks for coming on good to see you Dave uh great to see you stu I'm always happy to participate in these discussions and um I enjoy the discussion every time so this is kind of cool because you know usually the last day is a getaway day and this is a getaway day but this place is still packed I mean it's I mean yeah it's definitely lighter you can at least walk and not get slammed but I subjit I'm going to start with you I I wanted to have you as the the tail end here because cause you participated in the analyst sessions you've been watching this event from from the first moment and now you've got four days of the Kool-Aid injection but you're also talking to customers developers Partners the ecosystem where do you want to go what's your big takeaways I think big takeaways that Amazon sort of innovation machine is chugging along they are I was listening to some of the accessions and when I was back to my room at nine so they're filling the holes in some areas but in some areas they're moving forward there's a lot to fix still it doesn't seem like that it seems like we are done with the cloud or The Innovation is done now we are building at the millisecond level so where do you go next there's a lot of room to grow on the storage side on the network side uh the improvements we need and and also making sure that the software which is you know which fits the hardware like there's a specialized software um sorry specialized hardware for certain software you know so there was a lot of talk around that and I attended some of those sessions where I asked the questions around like we have a specialized database for each kind of workload specialized processes processors for each kind of workload yeah the graviton section and actually the the one interesting before I forget that the arbitration was I asked that like why there are so many so many databases and IRS for the egress costs and all that stuff can you are you guys thinking about reducing that you know um the answer was no egress cost is not a big big sort of uh um show stopper for many of the customers but but the from all that sort of little discussion with with the folks sitting who build these products over there was that the plethora of choice is given to the customers to to make them feel that there's no vendor lock-in so if you are using some open source you know um soft software it can be on the you know platform side or can be database side you have database site you have that option at AWS so this is a lot there because I always thought that that AWS is the mother of all lock-ins but it's got an ecosystem and we're going to talk about exactly we'll talk about Stu what's working within AWS when you talk to customers and where are the challenges yeah I I got a comment on open source Dave of course there because I mean look we criticized to Amazon for years about their lack of contribution they've gotten better they're doing more in open source but is Amazon the mother of all lock-ins many times absolutely there's certain people inside Amazon I'm saying you know many of us talk Cloud native they're like well let's do Amazon native which means you're like full stack is things from Amazon and do things the way that we want to do things and you know I talk to a lot of customers they use more than one Cloud Dave and therefore certain things absolutely I want to Leverage The Innovation that Amazon has brought I do think we're past building all the main building blocks in many ways we are like in day two yes Amazon is fanatically customer focused and will always stay that way but you know there wasn't anything that jumped out at me last year or this year that was like Wow new category whole new way of thinking about something we're in a vocals last year Dave said you know we have over 200 services and if we listen to you the customer we'd have over two thousand his session this week actually got some great buzz from my friends in the serverless ecosystem they love some of the things tying together we're using data the next flywheel that we're going to see for the next 10 years Amazon's at the center of the cloud ecosystem in the IT world so you know there's a lot of good things here and to your point Dave the ecosystem one of the things I always look at is you know was there a booth that they're all going to be crying in their beer after Amazon made an announcement there was not a tech vendor that I saw this week that was like oh gosh there was an announcement and all of a sudden our business is gone where I did hear some rumbling is Amazon might be the next GSI to really move forward and we've seen all the gsis pushing really deep into supporting Cloud bringing workloads to the cloud and there's a little bit of rumbling as to that balance between what Amazon will do and their uh their go to market so a couple things so I think I think we all agree that a lot of the the announcements here today were taping seams right I call it and as it relates to the mother of all lock-in the reason why I say that it's it's obviously very much a pejorative compare Oracle company you know really well with Amazon's lock-in for Amazon's lock-in is about bringing this ecosystem together so that you actually have Choice Within the the house so you don't have to leave you know there's a there's a lot to eat at the table yeah you look at oracle's ecosystem it's like yeah you know oracle is oracle's ecosystem so so that is how I think they do lock in customers by incenting them not to leave because there's so much Choice Dave I agree with you a thousand I mean I'm here I'm a I'm a good partner of AWS and all of the partners here want to be successful with Amazon and Amazon is open to that it's not our way or get out which Oracle tries how much do you extract from the overall I.T budget you know are you a YouTube where you give the people that help you create a large sum of the money YouTube hasn't been all that profitable Amazon I think is doing a good balance of the ecosystem makes money you know we used to talk Dave about you know how much dollars does VMware make versus there um I think you know Amazon is a much bigger you know VMware 2.0 we used to think talk about all the time that VMware for every dollar spent on VMware licenses 15 or or 12 or 20 were spent in the ecosystem I would think the ratio is even higher here sarbji and an Oracle I would say it's I don't know yeah actually 1 to 0.5 maybe I don't know but I want to pick on your discussion about the the ecosystem the the partner ecosystem is so it's it's robust strong because it's wider I was I was not saying that there's no lock-in with with Amazon right AWS there's lock-in there's lock-in with everything there's lock-in with open source as well but but the point is that they're they're the the circle is so big you don't feel like locked in but they're playing smart as well they're bringing in the software the the platforms from the open source they're picking up those packages and saying we'll bring it in and cater that to you through AWS make it better perform better and also throw in their custom chips on top of that hey this MySQL runs better here so like what do you do I said oh Oracle because it's oracle's product if you will right so they are I think think they're filing or not slenders from their go to market strategy from their engineering and they listen to they're listening to customers like very closely and that has sort of side effects as well listening to customers creates a sprawl of services they have so many services and I criticized them last year for calling everything a new service I said don't call it a new service it's a feature of a existing service sure a lot of features a lot of features this is egress our egress costs a real problem or is it just the the on-prem guys picking at the the scab I mean what do you hear from customers so I mean Dave you know I I look at what Corey Quinn talks about all the time and Amazon charges on that are more expensive than any other Cloud the cloud providers and partly because Amazon is you know probably not a word they'd use they are dominant when it comes to the infrastructure space and therefore they do want to make it a little bit harder to do that they can get away with it um because um yeah you know we've seen some of the cloud providers have special Partnerships where you can actually you know leave and you're not going to be charged and Amazon they've been a little bit more flexible but absolutely I've heard customers say that they wish some good tunning and tongue-in-cheek stuff what else you got we lay it on us so do our players okay this year I think the focus was on the upside it's shifting gradually this was more focused on offside there were less talk of of developers from the main stage from from all sort of quadrants if you will from all Keynotes right so even Werner this morning he had a little bit for he was talking about he he was talking he he's job is to Rally up the builders right yeah so he talks about the go build right AWS pipes I thought was kind of cool then I said like I'm making glue easier I thought that was good you know I know some folks don't use that I I couldn't attend the whole session but but I heard in between right so it is really adopt or die you know I am Cloud Pro for last you know 10 years and I think it's the best model for a technology consumption right um because of economies of scale but more importantly because of division of labor because of specialization because you can't afford to hire the best security people the best you know the arm chip designers uh you can't you know there's one actually I came up with a bumper sticker you guys talked about bumper sticker I came up with that like last couple of weeks The Innovation favorite scale they have scale they have Innovation so that's where the Innovation is and it's it's not there again they actually say the market sets the price Market you as a customer don't set the price the vendor doesn't set the price Market sets the price so if somebody's complaining about their margins or egress and all that I think that's BS um yeah I I have a few more notes on the the partner if you you concur yeah Dave you know with just coming back to some of this commentary about like can Amazon actually enable something we used to call like Community clouds uh your companies like you know Goldman and NASDAQ and the like where Industries will actually be able to share data uh and you know expand the usage and you know Amazon's going to help drive that API economy forward some so it's good to see those things because you know we all know you know all of us are smarter than just any uh single company together so again some of that's open source but some of that is you know I think Amazon is is you know allowing Innovation to thrive I think the word you're looking for is super cloud there well yeah I mean it it's uh Dave if you want to go there with the super cloud because you know there's a metaphor for exactly what you described NASDAQ Goldman Sachs we you know and and you know a number of other companies that are few weeks at the Berkeley Sky Computing paper yeah you know that's a former supercloud Dave Linthicum calls it metacloud I'm not really careful I mean you know I go back to the the challenge we've been you know working at for a decade is the distributed architecture you know if you talk about AI architectures you know what lives in the cloud what lives at the edge where do we train things where do we do inferences um locations should matter a lot less Amazon you know I I didn't hear a lot about it this show but when they came out with like local zones and oh my gosh out you know all the things that Amazon is building to push out to the edge and also enabling that technology and software and the partner ecosystem helps expand that and Pull It in it's no longer you know Dave it was Hotel California all of the data eventually is going to end up in the public cloud and lock it in it's like I don't think that's going to be the case we know that there will be so much data out at the edge Amazon absolutely is super important um there some of those examples we're giving it's not necessarily multi-cloud but there's collaboration happening like in the healthcare world you know universities and hospitals can all share what they're doing uh regardless of you know where they live well Stephen Armstrong in the analyst session did say that you know we're going to talk about multi-cloud we're not going to lead with it necessarily but we are going to actually talk about it and that's different to your points too than in the fullness of time all the data will be in the cloud that's a new narrative but go ahead yeah actually Amazon is a leader in the cloud so if they push the cloud even if they don't say AWS or Amazon with it they benefit from it right and and the narrative is that way there's the proof is there right so again Innovation favorite scale there are chips which are being made for high scale their software being tweaked for high scale you as a Bank of America or for the Chrysler as a typical Enterprise you cannot afford to do those things in-house what cloud providers can I'm not saying just AWS Google cloud is there Azure guys are there and few others who are behind them and and you guys are there as well so IBM has IBM by the way congratulations to your red hat I know but IBM won the award um right you know very good partner and yeah but yeah people are dragging their feet people usually do on the change and they are in denial denial they they drag their feet and they came in IBM director feed the cave Den Dell drag their feed the cave in yeah you mean by Dragon vs cloud deniers cloud deniers right so server Huggers I call them but they they actually are sitting in Amazon Cloud Marketplace everybody is buying stuff from there the marketplace is the new model OKAY Amazon created the marketplace for b2c they are leading the marketplace of B2B as well on the technology side and other people are copying it so there are multiple marketplaces now so now actually it's like if you're in in a mobile app development there are two main platforms Android and Apple you first write the application for Apple right then for Android hex same here as a technology provider as and I I and and I actually you put your stuff to AWS first then you go anywhere else yeah they are later yeah the Enterprise app store is what we've wanted for a long time the question is is Amazon alone the Enterprise app store or are they partner of a of a larger portfolio because there's a lot of SAS companies out there uh that that play into yeah what we need well and this is what you're talking about the future but I just want to make a point about the past you talking about dragging their feet because the Cube's been following this and Stu you remember this in 2013 IBM actually you know got in a big fight with with Amazon over the CIA deal you know and it all became public judge wheeler eviscerated you know IBM and it ended up IBM ended up buying you know soft layer and then we know what happened there and it Joe Tucci thought the cloud was Mosey right so it's just amazing to see we have booksellers you know VMware called them books I wasn't not all of them are like talking about how great Partnerships they are it's amazing like you said sub GC and IBM uh with the the GSI you know Partnership of the year but what you guys were just talking about was the future and that's what I wanted to get to is because you know Amazon's been leading the way I I was listening to Werner this morning and that just reminded me of back in the days when we used to listen to IBM educate us give us a master class on system design and decoupled systems and and IO and everything else now Amazon is you know the master educator and it got me thinking how long will that last you know will they go the way of you know the other you know incumbents will they be disrupted or will they you know keep innovating maybe it's going to take 10 or 20 years I don't know yeah I mean Dave you actually you did some research I believe it was a year or so ago yeah but what will stop Amazon and the one thing that worries me a little bit um is the two Pizza teams when you have over 202 Pizza teams the amount of things that each one of those groups needs to take care of was more than any human could take care of people burn out they run out of people how many amazonians only last two or three years and then leave because it is tough I bumped into plenty of friends of mine that have been you know six ten years at Amazon and love it but it is a tough culture and they are driving werner's keynote I thought did look to from a product standpoint you could say tape over some of the seams some of those solutions to bring Beyond just a single product and bring them together and leverage data so there are some signs that they might be able to get past some of those limitations but I still worry structurally culturally there could be some challenges for Amazon to keep the momentum going especially with the global economic impact that we are likely to see in the next year bring us home I think the future side like we could talk about the vendors all day right to serve the community out there I think we should talk about how what's the future of technology consumption from the consumer side so from the supplier side just a quick note I think the only danger AWS has has that that you know Fred's going after them you know too big you know like we will break you up and that can cause some disruption there other than that I think they they have some more steam to go for a few more years at least before we start thinking about like oh this thing is falling apart or anything like that so they have a lot more they have momentum and it's continuing so okay from the I think game is on retail by the way is going to get disrupted before AWS yeah go ahead from the buyer's side I think um the the future of the sort of Technology consumption is based on the paper uh use and they actually are turning all their services to uh they are sort of becoming serverless behind the scenes right all analytics service they had one service left they they did that this year so every service is serverless so that means you pay exactly for the amount you use the compute the iops the the storage so all these three layers of course Network we talked about the egress stuff and that's a problem there because of the network design mainly because Google has a flatter design and they have lower cost so so they are actually squeezing the their their designing this their services in a way that you don't waste any resources as a buyer so for example very simple example when early earlier In This Cloud you will get a VM right in Cloud that's how we started so and you can get 20 use 20 percent of the VM 80 is getting wasted that's not happening now that that has been reduced to the most extent so now your VM grows as you grow the usage and if you go higher than the tier you picked they will charge you otherwise they will not charge you extra so that's why there's still a lot of instances like many different types you have to pick one I think the future is that those instances will go away the the instance will be formed for you on the fly so that is the future serverless all right give us bumper sticker Stu and then Serb G I'll give you my quick one and then we'll wrap yeah so just Dave to play off of sharp G and to wrap it up you actually wrote about it on your preview post for here uh serverless we're talking about how developers think about things um and you know Amazon in many ways you know is the new default server uh you know for the cloud um and containerization fits into the whole serverless Paradigm uh it's the space that I live in uh you know every day here and you know I was happy to see the last few years serverless and containers there's a blurring a line and you know subject we're still going to see VMS for a long time yeah yeah we will see that so give us give us your book Instagram my number six is innovation favorite scale that's my bumper sticker and and Amazon has that but also I I want everybody else to like the viewers to take a look at the the Google Cloud as well as well as IBM with others like maybe you have a better price to Performance there for certain workloads and by the way one vendor cannot do it alone we know that for sure the market is so big there's a lot of room for uh Red Hats of the world and and and Microsoft's the world to innovate so keep an eye on them they we need the competition actually and that's why competition Will Keep Us to a place where Market sets the price one vendor doesn't so the only only danger is if if AWS is a monopoly then I will be worried I think ecosystems are the Hallmark of a great Cloud company and Amazon's got the the biggest and baddest ecosystem and I think the other thing to watch for is Industries building on top of the cloud you mentioned the Goldman Sachs NASDAQ Capital One and Warner media these all these industries are building their own clouds and that's where the real money is going to be made in the latter half of the 2020s all right we're a wrap this is Dave Valente I want to first of all thank thanks to our great sponsors AWS for for having us here this is our 10th year at the cube AMD you know sponsoring as well the the the cube here Accenture sponsor to third set upstairs upstairs on the fifth floor all the ecosystem partners that came on the cube this week and supported our mission for free content our content is always free we try to give more to the community and we we take back so go to thecube.net and you'll see all these videos go to siliconangle com for all the news wikibon.com I publish weekly a breaking analysis series I want to thank our amazing crew here you guys we have probably 30 35 people unbelievable our awesome last session John Walls uh Paul Gillen Lisa Martin Savannah Peterson John Furrier who's on a plane we appreciate Andrew and Leonard in our ear and all of our our crew Palo Alto Boston and across the country thank you so much really appreciate it all right we are a wrap AWS re invent 2022 we'll see you in two weeks we'll see you two weeks at Palo Alto ignite back here in Vegas thanks for watching thecube the leader in Enterprise and emerging Tech coverage [Music]

>>Okay, welcome back everyone to super cloud 22 here in our live studio performance. You're on stage in Palo Alto. I'm Sean fur. You're host with the queue with Dave ante. My co it's got a great industry ecosystem panel to discuss whether it's realer hype, David MC Janet CEO of Hashi Corp, hugely successful company as will LA forest field CTO, Colu and Victoria over yourgo from VMware guys. Thanks for coming on the queue. Appreciate it. Thanks for having us. So realer, hype, super cloud David. >>Well, I think it depends on the definition. >>Okay. How do you define super cloud start there? So I think we have a, >>I think we have a, like an inherently pragmatic view of super cloud of the idea of super cloud as you talk about it, which is, you know, for those of us that have been in the infrastructure world for a long time, we know there are really only six or seven categories of infrastructure. There's sort of the infrastructure security, networking databases, middleware, and, and, and, and really the message queuing aspects. And I think our view is that if the steady state of the world is multi-cloud, what you've seen is sort of some modicum of standardization across those different elements, you know, take, you know, take confluent. You know, I, I worked in the middleware world years ago, MQ series, and typical multicast was how you did message queuing. Well, you don't do that anymore. All the different cloud providers have their own message, queuing tech, there's, Google pub sub, and the equivalents across the different, different clouds. Kafka has provided a consistent way to do that. And they're not trying to project that. You can run everything connected. They're saying, Hey, you should standardize on Kafka for message cuing is that way you can have operational consistency. So I think to me, that's more how we think about it is sort of, there is sort of layer by layer of sort of de facto standardization for the lingo Franco. >>So a streaming super cloud is how you would think of it, or no, I just, or a component of >>Cloud that could be a super cloud. >>I just, I just think that there are like, if I'm gonna build an application message, queuing is gonna be a necessary element of it. I'm gonna use Kafka, not, you know, a native pub sub engine on one of the clouds, because operationally that's just the only way I can do it. So I think that's more, our view's much more pragmatic rather than trying to create like a single platform that you can run everywhere and deal with the networking realities of like network, you know, hops missing across those different worlds and have that be our responsibility. It's much more around, Hey, let's standardize each layer, operational >>Standardized layer that you can use to build a super cloud if that's in your, your intent or, yeah. Okay. >>And it reminds me of the web services days. You kind of go throwback there. I mean, we're kind of living the next gen of web services, the dream of that next level, because DevOps dev SecOps now is now gone mainstream. That's the big challenge we're hearing devs are doing great. Yep. But the ops teams and screen, they gotta go faster. This seems to be a core, I won't say blocker, but more of a drag to the innovation. >>Well, I I'll just get off, I'll hand it off to, to you guys. But I think the idea that like, you know, if I'm gonna have an app that's running on Amazon that needs to connect to a database that's running on, on the private data center, that's essentially the SOA notion, you know, w large that we're all trying to solve 20 years ago, but is much more complicated because you're brokering different identity models, different networking models. They're just much more complex. So that's where the ops bit is the constraint, you know, for me to build that app, not that complicated for the ops person to let it see traffic is another thing altogether. I think that's, that's the break point for so much of what looks easier to a developer is the operational reality of how you do that. And the good news is those are actually really well solved problems. They're just not broadly understood. >>Well, what's your take, you talk to customers all the time, field CTO, confluent, really doing well, streaming data. I mean, everyone's doing it now. They have to, yeah. These are new things that pop up that need solutions. You guys step up and doing more. What's your take on super cloud? >>Well, I mean, the way we address it honestly is we don't, it's gonna be honest. We don't think about super cloud much less is the fact that SAS is really being pushed down. Like if we rely on seven years ago and you took a look at SAS, like it was obvious if you were gonna build a product for an end consumer or business user, you'd do SAS. You'd be crazy not to. Right. But seven years ago, if you look at your average software company producing something for a developer that people building those apps, chances are you had an open source model. Yeah. Or, you know, self-managed, I think with the success of a lot of the companies that are here today, you know, snowflake data, bricks, Colu, it's, it's obvious that SaaS is the way to deliver software to the developers as well. And as such, because our product is provided that way to the developers across the clouds. That's, that's how they have a unifying data layer, right. They don't necessarily, you know, developers like many people don't necessarily wanna deal with the infrastructure. They just wanna consume cloud data services. Right. So that's how we help our customers span cloud. >>So we evenly that SAS was gonna be either built on a single cloud or in the case of service. Now they built their own cloud. Right. So increasingly we're seeing opportunities to build a Salesforce as well across clouds tap different, different, different services. So, so how does that evolve? Do you, some clouds have, you know, better capabilities in other clouds. So how does that all get sort of adjudicated, do you, do you devolve to the lowest common denominator? Or can you take the best of all of each? >>The whole point to that I think is that when you move from the business user and the personal consumer to the developer, you, you can no longer be on a cloud, right. There has to be locality to where applications are being developed. So we can't just deploy on a single cloud and have people send their data to that cloud. We have to be where the developer is. And our job is to make the most of each, an individual cloud to provide the same experience to them. Right. So yes, we're using the capabilities of each cloud, but we're hiding that to the developer. They don't shouldn't need to know or care. Right. >>Okay. And you're hiding that with the abstraction layer. We talked about this before Victoria, and that, that layer has what, some intelligence that has metadata knowledge that can adjudicate what, what, the best, where the best, you know, service is, or function of latency or data sovereignty. How do you see that? >>Well, I think as the, you need to instrument these applications so that you, you, you can get that data and then make the intelligent decision of where, where, where this, the deploy application. I think what Dave said is, is right. You know, the level of super cloud that they talking about is the standardization across messaging. And, and are you what's happening within the application, right? So you don't, you are not too dependent on the underlying, but then the application say that it takes the form of a, of a microservice, right. And you deploy that. There has to be a way for operator to say, okay, I see all these microservices running across clouds, and I can factor out how they're performing, how I, I, life lifecycle managed and all that. And so I think there is, there is, to me, there's the next level of the super cloud is how you factor this out. So an operator can actually keep up with the developers and make sense of all that and manage it. Like >>You guys that's time. Like its also like that's what Datadog does. So Datadog basically in allows you to instrument all those services, on-prem private data center, you know, all the different clouds to have a consistent view. I think that that's not a good example of a vendor that's created a, a sort of a level of standardization across a layer. And I think that's, that's more how we think about it. I think the notion of like a developer building an application, they can deploy and not have to worry where it exists. Yeah. Is more of a PAs kind of construct, you know, things like cloud Foundry have done a great job of, of doing that. But underneath that there's still infrastructure. There's still security. There's still networking underneath it. And I think that's where, you know, things like confluent and perhaps at the infrastructure layer have standardized, but >>You have off the shelf PAs, if I can call it that. Yeah. Kind of plain. And then, and then you have PAs and I think about, you mentioned snowflake, snowflake is with snow park, seems to be developing a PAs layer that's purpose built for their specific purpose of sharing data and governing data across multiple clouds call super paths. Is, is that a prerequisite of a super cloud you're building blocks. I'm hearing yeah. For super cloud. Is that a prerequisite for super cloud? That's different than PAs of 10 years ago. No, but I, >>But I think this is, there's just different layers. So it's like, I don't know how that the, the snowflake offering is built built, but I would guess it's probably built on Terraform and vault and cons underneath it. Cuz those are the ingredients with respect to how you would build a composite application that runs across multiple. And >>That's how Oracle that town that's how Oracle with the Microsoft announcement. They just, they just made if you saw that that was built on Terraform. Right. But, but they would claim that they, they did some special things within their past that were purpose built for, for sure. Low latency, for example, they're not gonna build that on, you know, open shift as an, as an example, they're gonna, you know, do their own little, you know, >>For sure, for sure. So I think what you're, you're pointing at and what Victoria was talking about is, Hey, can a vendor provided consistent experience across the application layer across these multiple clouds? And I would say, sure, just like, you know, you might build a mobile banking application that has a front end on Amazon in the back end running on vSphere on your private data center. Sure. But the ingredients you use to do that have to be, they can't be the cloud native aspects for how you do that. How do you think about, you know, the connectivity of, of like networking between that thing to this thing? Is it different on Amazon? Is it different on Azure? Is it different on, on Google? And so the, the, the, the companies that we all serve, that's what they're building, they're building composited applications. Snowflake is just an example of a company that we serve this building >>Composite. And, but, but, but don't those don't, you have to hide the complexity of that, those, those cloud native primitives that's your job, right. Is to actually it creates simplicity across clouds. Is it not? >>Why? Go ahead. You. >>Yeah, absolutely. I mean that in fact is what we're doing for developers that need to do event streaming, right. That need to process this data in real time. Now we're, we're doing the sort of things that Victoria was just talking about, like underneath the covers, of course, you know, we're using Kubernetes and we're managing the differences between the clouds, but we're hiding the, that, and we've become sort of a defacto standard across the cloud. So if I'm developing an app in any of those cloud, and I think we all know, and you were mentioning earlier every significant company's multi-cloud now all the large enterprises, I just got back from Brazil and like every single one of 'em have multiple clouds and on-prem right. So they need something that can span those. >>What's the challenge there. If you talk to those customers, because we're seeing the same thing, they have multiple clouds. Yeah. But it was kind of by default or they had some use case, either.net developers there with Azure, they'll do whatever cloud. And it kind of seems specialty relative to the cloud native that they're on what problems do they have because the complexity to run infrastructure risk code across clouds is hard. Right? So the trade up between native cloud and have better integration to complexity of multiple clouds seems to be a topic around super cloud. What are you seeing for, for issues that they might have or concerns? >>Yeah. I mean, honestly it is, it is hard to actually, so here's the thing that I think is kind of interesting though, by the way, is that I, I think we tend to, you know, if you're, if you're from a technical background, you tend to think of multicloud as a problem for the it organization. Like how do we solve this? How do we save money? But actually it's a business problem now, too, because every single one of these companies that have multiple clouds, they want to integrate their data, their products across these, and it it's inhibiting their innovation. It's hard to do, but that's where something like, you know, Hatchie Corp comes in right. Is to help solve that. So you can instrument it. It has to happen at each of these layers. And I suppose if it does happen at every single layer, then voila, we organically have something that amounts to Supercloud. Right. >>I love how you guys are representing each other's firms. And, but, but, and they also correct me if I'm a very similar, your customers want to, it is very similar, but your customers want to monetize, right. They want bring their tools, their software, their particular IP and their data and create, you know, every, every company's a software company, as you know, Andreesen says every company's becoming a cloud company to, to monetize in, in the future. Is that, is that a reasonable premise of super cloud? >>Yeah. I think, think everyone's trying to build composite applications to, to generate revenue. Like that's, that's why they're building applications. So yeah. One, 100%. I'm just gonna make it point cuz we see it as well. Like it's actually quite different by geography weirdly. So if you go to like different geographies, you see actually different cloud providers, more represented than others. So like in north America, Amazon's pretty dominant Japan. Amazon's pretty dominant. You go to Southeast Asia actually. It's not necessarily that way. Like it might be Google for, for whatever reason more hourly Bob. So this notion of multi's just the reality of one's everybody's dealing with. But yeah, I think everyone, everyone goes through the same process. What we've observed, they kind of go, there's like there's cloud V one and there's cloud V two. Yeah. Cloud V one is sort of the very tactical let's go build something on cloud cloud V two is like, whoa, whoa, whoa, whoa. And I have some stuff on Amazon, some stuff on Azure, some stuff on, on vSphere and I need some operational consistency. How do I think about zero trust across that way in a consistent way. And that's where this conversation comes into being. It's sort of, it's not like the first version of cloud it's actually when people step back and say, Hey, Hey, I wanna build composite applications to monetize. How am I gonna do that in an industrialized way? And that's the problem that you were for. It's >>Not, it's not as, it's not a no brainer like it was with cloud, go to the cloud, write an app. You're good here. It's architectural systems thinking, you gotta think about regions. What's the latency, you know, >>It's step back and go. Like, how are we gonna do this, this exactly. Like it's wanted to do one app, but how we do this at scale >>Zero trust is a great example. I mean, Amazon kind of had, was forced to get into the zero trust, you know, discussion that, that wasn't, you know, even a term that they used and now sort of, they're starting to talk about it, but within their domain. And so how do you do zero trust trust across cost to your point? >>I, I wonder if we're limiting our conversation too much to the, the very technical set of developers, cuz I'm thinking back at again, my example of C plus plus libraries C plus plus libraries makes it easier. And then visual BA visual basic. Right. And right now we don't have enough developers to build the software that we want to build. And so I want, and we are like now debating, oh, can we, do we hide that AI API from Google versus that SQL server API from, from Microsoft. I wonder at some point who cares? Right. You know, we, I think if we want to get really economy scale, we need to get to a level of abstraction for developers that really allows them to say, I don't need, for most of most of the procedural application that I need to build as a developer, as a, as a procedural developer, I don't care about this. Some, some propeller had, has done that for me. I just like plug it in my ID and, and I use it. And so I don't, I don't know how far we are from that, but if we don't get to that level, it fits me that we never gonna get really the, the economy or the cost of building application to the level. >>I was gonna ask you in the previous segment about low code, no code expanding the number of developers out there and you talking about propel heads. That's, that's what you guys all do. Yeah. You're the technical geniuses, right. To solve that problem so that, so you can have low code development is that I >>Don't think we have the right here. Cause I, we, we are still, you know, trying to solve that problem at that level. But, but >>That problem has to be solved first, right before we can address what you're talking about. >>Yeah. I, I worked very closely with one of my biggest mentors was Adam Bosworth that built, you know, all the APIs for visual basics and, and the SQL API to visual basic and all that stuff. And he always was on that front. In fact that his last job was at my, at AWS building that no code environment. So I'm a little detached from that. It just hit me as we were discussing this. It's like, maybe we're just like >>Creating, but I would, I would argue that you kind of gotta separate the two layers. So you think about the application platform layer that a developer interfaces to, you know, Victoria and I worked together years ago and one of the products we created was cloud Foundry, right? So this is the idea of like just, you know, CF push, just push this app artifact and I don't care. That's how you get the developer community written large to adopt something complicated by hiding all the complexity. And I think that that is one model. Yeah. Turns out Kubernetes is actually become a peer to that and perhaps become more popular. And that's what folks like Tanza are trying to do. But there's another layer underneath that, which is the infrastructure that supports it. Right? Yeah. Cause that's only needs to run on something. And I think that's, that's the separation we have to do. Yes. We're talking a little bit about the plumbing, but you know, we just easily be talking about the app layer. You need, both of them. Our point of view is you need to standardize at this layer just like you need standardize at this layer. >>Well, this is, this is infrastructure. This is DevOps V two >>Dev >>Ops. Yeah. And this is where I think the ops piece with open source, I would argue that open source is blooming more than ever. So I think there's plenty of developers coming. The automation question becomes interesting because I think what we're seeing is shift left is proving that there's app developers out there that wanna stay in their pipelining. They don't want to get in under the hood. They just want infrastructure as code, but then you got supply chain software issues there. We talked about the Docker on big time. So developers at the top, I think are gonna be fine. The question is what's the blocker. What's holding them back. And I don't see the devs piece Victoria as much. What do you guys think? Is it, is the, is the blocker ops or is it the developer experience? That's the blocker. >>It's both. There are enough people truthfully. >>That's true. Yeah. I mean, I think I sort of view the developer as sort of the engine of the digital innovation. So, you know, if you talk about creative destruction, that's, that was the economic equivalent of softwares, eating the world. The developers are the ones that are doing that innovation. It's absolutely essential that you make it super easy for them to consume. Right. So I think, you know, they're nerds, they want to deal with infrastructure to some degree, but I think they understand the value of getting a bag of Legos that they can construct something new around. And I think that's the key because honestly, I mean, no code may help for some things. Maybe I'm just old >>School, >>But I, I went through this before with like Delphy and there were some other ones and, and I hated it. Like I just wanted a code. Yeah. Right. So I think making them more efficient is, is absolutely good. >>But I think what, where you're going with that question is that the, the developers, they tend to stay ahead. They, they just, they're just gear, you know, wired that way. Right. So I think right now where there is a big bottleneck in developers, I think the operation team needs to catch up. Cuz I, I talk to these, these, these people like our customers all the time and I see them still stuck in the old world. Right. Gimme a bunch of VMs and I'll, I know how to manage well that world, you know, although as lag is gonna be there forever, so managing mainframe. But so if they, the world is all about microservices and containers and if the operation team doesn't get on top of it and the security team that then that they're gonna be a bottleneck. >>Okay. I want to ask you guys if the, if the companies can get through that knothole of having their ops teams and the dev teams work well together, what's the benefits of a Supercloud. How do you see the, the outcome if you kind of architect it, right? You think the big picture you zoom as saying what's the end game look like for Supercloud? Is that >>What I would >>Say? Or what's the Nirvana >>To me Nirvana is that you don't care. You just don't don't care. You know, you just think when you running building application, let's go back to the on-prem days. You don't care if it runs on HP or Dell or, you know, I'm gonna make some enemies here with my old, old family, but you know, you don't really care, right. What you want is the application is up and running and people can use it. Right. And so I think that Nirvana is that, you know, there is some, some computing power out there, some pass layer that allows me to deploy, build application. And I just like build code and I deploy it and I get value at a reasonable cost. I think one of the things that the super cloud for as far as we're concerned is cost. How do you manage monitor the cost across all this cloud? >>Make sure that you don't, the economics don't get outta whack. Right? How many companies we know that have gone to the cloud only to realize that holy crap, now I, I got the bill and, and you know, I, as a vendor, when I was in my previous company, you know, we had a whole team figuring out how to lower our cost on the one hyperscaler that we were using. So these are, you know, the, once you have in the super cloud, you don't care just you, you, you go with the path of least the best economics is. >>So what about the open versus closed debate will you were mentioning that we had snowflake here and data bricks is both ends of the spectrum. Yeah. You guys are building open standards across clouds. Clearly even the CLO, the walled gardens are using O open standards, but historically de facto standards have emerged and solved these problems. So the super cloud as a defacto standard, versus what data bricks is trying to do super cloud kind of as an, as an open platform, what are you, what are your thoughts on that? Can you actually have an, an open set of standards that can be a super cloud for a specific purpose, or will it just be built on open source technologies? >>Well, I mean, I, I think open source continues to be an important part of innovation, but I will say from a business model perspective, like the days, like when we started off, we were an open source company. I think that's really done in my opinion, because if you wanna be successful nowadays, you need to provide a cloud native SAS oriented product. It doesn't matter. What's running underneath the covers could be commercial closed source, open source. They just wanna service and they want to use it quite frankly. Now it's nice to have open source cuz the developers can download it and run on their laptop. But I, I can imagine in 10 years time actually, and you see most companies that are in the cloud providing SAS, you know, free $500 credit, they may not even be doing that. They'll just, you know, go whatever cloud provider that their company is telling them to use. They'll spin up their SAS product, they'll start playing with it. And that's how adoption will grow. Right? >>Yeah. I, I think, I mean my personal view is that it's, that it's infrastructure is pervasive enough. It exists at the bottom of everything that the standards emerge out of open source in my view. And you think about how something like Terraform is built, just, just pick one of the layers there's Terraform core. And then there's a plugin for everything you integrate with all of those are open source. There are over 2000 of these. We don't build them. Right. That's and it's the same way that drove Linux standardization years ago, like someone had to build the drivers for every piece of hardware in the world. The market does not do that twice. The market does that once. And so I, I I'm deeply convicted that opensource is the only way that this works at the infrastructure layer, because everybody relies on it at the application layer, you may have different kinds of databases. You may have different kind of runtime environments. And that's just the nature of it. You can't to have two different ways of doing network, >>Right? Because the stakes are so high, basically. >>Yeah. Cuz there's, there's an infinite number of the surface areas are so large. So I actually worked in product development years ago for middleware. And the biggest challenge was how do you keep the adapter ecosystem up to date to integrate with everything in the world? And the only way to do it in our view is through open source. And I think that's a fundamental philosophical view that it we're just, you know, grounded in. I think when people are making infrastructure decisions that span 20 years at the customer base, this is what they think about. They go which standard it will emerge based on the model of the vendor. And I don't think my personal view is, is it's not possible to do in a, in >>A, do you think that's a defacto standard kind of psychological perspective or is there actual material work being done or both in >>There it's, it's, it's a network effect thing. Right? So, so, you know, before Google releases a new service service on Google cloud, as part of the release checklist is does it support Terraform? They do that work, not us. Why? Because every one of their customers uses Terraform to interface with them and that's how it works. So see, so the philosophical view of, of the customers, okay, what am I making a standardize on for this layer for the next 30 years? It's kind of a no brainer. Philosophically. >>I tend, >>I think the standards are organically created based upon adoption. I mean, for instance, Terraform, we have a provider we're again, we're at the data layer that we created for you. So like, I don't think there's a board out there. I mean there are that creating standards. I think those days are kind of done to be honest, >>The, the Terraform provider for vSphere has been downloaded five and a half million times this year. Yeah. Right. Like, so, I >>Mean, these are unifying moments. This are like the de facto standards are really important process in these structural changes. I think that's something that we're looking at here at Supercloud is what's next? What has to unify look what Kubernetes has done? I mean, that's essentially the easy thing to orchestra, but people get behind it. So I see this is a big part of this next, the two. Totally. What do you guys see that's needed? What's the rallying unification point? Is it the past layer? Is it more infrastructure? I guess that's the question we're trying to, >>I think every layer will need that open source or a major traction from one of the proprietary vendor. But I, I agree with David, it's gonna be open source for the most part, but you know, going back to the original question of the whole panel, if I may, if this is reality of hype, look at the roster of companies that are presenting or participating today, these are all companies that have some sort of multi-cloud cross cloud, super cloud play. They're either public have real revenue or about to go public. So the answer to the question. Yeah, it's real. Yeah. >>And so, and there's more too, we had couldn't fit him in, but we, >>We chose super cloud on purpose cuz it kind of fun, John and I kind came up with it and, and but, but do you think it's, it hurts the industry to have this, try to put forth this new term or is it helpful to actually try to push the industry to define this new term? Or should it just be multi-cloud 2.0, >>I mean, conceptually it's different than multi-cloud right. I mean, in my opinion, right? So in that, in that respect, it has value, right? Because it's talking about something greater than just multi-cloud everyone's got multi-cloud well, >>To me multi-cloud is the, the problem I should say the opportunity. Yeah. Super cloud or we call it cross cloud is the solution to that channel. Let's >>Not call again. And we're debating that we're debating that in our cloud already panel where we're talking about is multi-cloud a problem yet that needs to get solved or is it not yet ready for a market to your point? Is it, are we, are we in the front end of coming into the true problem set, >>Give you definitely answer to that. The answer is yes. If you look at the customers that are there, they won, they have gone through the euphoria phase. They're all like, holy something, what, what are we gonna do about this? Right. >>And, but they don't know what to do. >>Yeah. And the more advanced ones as the vendor look at the end of the day, markets are created by vendors that build ed that customers wanna buy. Yeah. Because they get value >>And it's nuance. David, we were sort talking about before, but Goldman Sachs has announced they're analysis software vendor, right? Capital one is a software vendor. I've been really interested Liberty what Cerner does with what Oracle does with Cerner and in terms of them becoming super cloud vendors and monetizing that to me is that is their digital transformation. Do you guys, do you guys see that in the customer base? Am I way too far out of my, of my skis there or >>I think it's two different things. I think, I think basically it's the idea of building applications. If they monetize yeah. There and Cerner's gonna build those. And you know, I think about like, you know, IOT companies that sell that sell or, or you think people that sell like, you know, thermostats, they sell an application that monetizes those thermostats. Some of that runs on Amazon. Some of that runs a private data center. So they're basically in composite applications and monetize monetizing them for the particular vertical. I think that's what we ation every day. That's what, >>Yeah. You can, you can argue. That's not, not anything new, but what's new is they're doing that on the cloud and taking across multiple clouds. Multiple. Exactly. That's what makes >>Edge. And I think what we all participate in is, Hey, in order to do that, you need to drive standardization of how you do provisioning, how you do networking, how you do security to underpin those applications. I think that's what we're all >>Talking about, guys. It's great stuff. And I really appreciate you taking the time outta your day to help us continue the conversation to put out in the open. We wanna keep it out in the open. So in the last minute we have left, let's go down the line from a hash core perspective, confluent and VMware. What's your position on super cloud? What's the outcome that you would like to see from your standpoint, going out five years, what's it look like they will start with you? >>I just think people like sort under understanding that there is a layer by layer of view of how to interact across cloud, to provide operational consistency and decomposing it that way. Thinking about that way is the best way to enable people to build and run apps. >>We wanna help our customers work with their data in real time, regardless of where they're on primer in the cloud and super cloud can enable them to build applications that do that more effectively. That's that's great for us >>For tour you. >>I, my Niana for us is customers don't care, just that's computing out there. And it's a, it's a, it's a tool that allows me to grow my business and we make it all, all the differences and all the, the challenges, you know, >>Disappear, dial up, compute utility infrastructure, ISN >>Code. I open up the thought there's this water coming out? Yeah, I don't care. I got how I got here. I don't wanna care. Well, >>Thank you guys so much and congratulations on all your success in the marketplace, both of you guys and VMware and your new journey, and it's gonna be great to watch. Thanks for participating. Really appreciate it. Thank you, sir. Okay. This is super cloud 22, our events, a pilot. We're gonna get it out there in the open. We're gonna get the data we're gonna share with everyone out in the open on Silicon angle.com in the cube.net. We'll be back with more live coverage here in Palo Alto. After this short break.

>>The cube presents HPE discover 2022 brought to you by HPE. >>Hello, everybody. Welcome to day three of the Cube's coverage of HPE discover 2022 we're live from Las Vegas and the Venetian convention center. This is I, I counted him up. I think this is the 14th HP HP slash HPE. Discover that we've done really excited to welcome in Alexia Clements. She's the vice president of go to market for HPE GreenLake cloud services. That's all the rage everybody's talking about. Green, all the wood behind the arrow, as the saying goes, welcome to the queue. Good to see >>You. Thank you so much for having me thrilled to be here. >>You walk up Janet Jackson last night, >>Epic. Wow. She killed it. She was awesome. >>I thought the band was super tight, but the other thing was the place was >>Packed. It was >>Nice. You know, what happens is a lot of time they put the band in the getaway day, you know, and nobody stays, but wow, the, the hall was jammed. >>It was great. It was, you could feel the momentum and the excitement. And it was just a great way to, to kind of end the, the HP discover. So it was great. >>Yeah. I mean, I, I mentioned that we've been to a lot of HP slash HPE discovers and, and this one was different in the sense that I think first of all, 8,000 people, yep. People are excited to get back together, but I think, you know, HPE has a spring in its step and the customers are kind of interested. It's much more focused than some of the past HPE discoverers, which was kind of hard to get my hands around. Sometimes the business was sort of an Antonio's pulled that together. So what's changed since the last time we were face to face. >>We're transforming and hope you all saw that on the, on the floor here. So, um, we're absolutely trans going through a transformation and, you know, I, I think we're, you know, we're shifting to an edge to cloud platform company. And with that, it's, it's how we approach our customers differently and our partners and, you know, we're hoping that, uh, we showed this week and that, that we're different and we're transforming. >>So how do you spend your time Mo mostly in front of customers having conversations about what, what their needs are and aligning is that right? >>Yeah. So, um, I, I lead the, the go to market for GreenLake. So that's everything around how we're driving our as a service go to market strategy, how we're driving programs, enablement, how we're really in the end, how we're executing on that as a service strategy from a sales perspective. >>So what do you hear? Of course, a lot of that involves partners. Yep. Right. I mean, that's kind of the route to market. Absolutely. The HPE prefers for obvious reasons, although others don't necessarily share that, but, but, so what are you hearing from the partner ecosystem and the customers that their biggest challenges are now that we're entering the let's call it the post isolation economy? <laugh> >>Yeah. I mean, the reality is, is digital transformations are hard and I think some customers, um, who haven't necessarily moved forward on it or, you know, maybe they move forward and they're realizing, Hey, I'm stuck and I'm not, I'm not getting to where I wanna be and really, you know, driving that end state. So, I mean, I, I would just say overall, I think things are like, customers are, are struggling if they didn't, you know, they're falling behind a little bit. And I think through the conversations that we're having and through HP green, like it gives customers choice. And so really, um, I mean, what, you know, I spend my time with, and, and when we're talking to customers and partners, it's about helping customers on that digital transformation journey and understanding what are they trying to drive? What business outcomes are they trying to drive and how we can help them get there. So >>I, I often call it the force March to digital yep. With the pandemic. Um, and, and I, I was looking at a survey recently, I think it was put on by couch base. And it was probably on a thousand respondents and it was a CIO survey and they asked who's, who's responsible for the digital transformation at the organization and overwhelmingly it was the it organization. And I said, uhoh, that's the problem now. But it made sense to me because when the economy shut down, everybody went to it and said help, right. Make this work somehow. Right. But, but what, that doesn't seem to me to be the right prescription for a successful digital transformation. Do you agree with that? And what do you see as a successful template for DX? >>Well, I think what, what we see is that really the lines of business are desperate to move fast and they're really looking for their it partners to help them in that journey and, and, and drive, you know, whether it be, you know, drive them, you know, drive orders, drive, you know, they need it to help them in that journey. And so really it's gotta be a partnership between the two organizations. And what we're trying to do with HP GreenLake is kind of abstract that almost. So, Hey, we're gonna give it to you in an, as a service and you're gonna get all of these components. And all you have to think about is where do I need to grow and what are the outcomes that I'm looking for? So that's what it's gotta be. There's gotta be tight alignment, I think between the lines of business and it, and sometimes those two don't know how to talk to each other. >>Mm-hmm <affirmative> so that's another way of, of really trying to speak to the business leaders and say, what are you trying to do? Where do you need to go? And what do you need to get? And, and a lot of times they don't even know what they need to get there. So that's where we need to have those different conversations with our customers to, and that's where we look for our partners to help us in that. So really having those different conversations to progress, um, what, you know, what customers are really looking to, to drive, >>How, how does GreenLake specifically accelerate that transformation? Where does it fit? Maybe you can kind of take us through, you know, a, a generic example of how that works. >>Yeah. I mean, a great example is, you know, especially with the pandemic is desktop, Hey, you now need to, you know, everybody's working from different locations. So, you know, desktop as a service VDI as a service, and, you know, you're putting it in a, you know, per whatever, you know, per you can, whatever variable pricing you want, but think about it, you have that one pay as you go. And so the it organization, all they have to think about is that's my, you know, per, per unit price there. So that's a great example of how we saw, like, especially during the pandemic, that was something that was, you know, a huge area of focus organizations. What's >>The spectrum that you see in terms of, you know, the maturity model, if you will, a digital transformation. I mean, if you weren't in a digital business during the pandemic, you were pretty much out of business. Yeah. And with very few exceptions. Um, and so, okay. So on the one end, you have folks that sort of were forced into it. You, my forced March scenario, others were actually moving quite a bit along before the pandemic, others were kind of given at lip service and maybe doing a few projects. What do you see as that spectrum? >>I think if you're not transforming, you're falling behind. And so everybody needs to be, you know, looking to the future and understanding, you know, really trying to get aggressive on that. And that's what we're seeing. We're seeing companies who, you know, aren't moving fast on that or falling behind. >>Do you see a bifurcation? I'm sure you do those that say, yeah, I want as a service and others that say, look, I I'm really well capitalized. I'm gonna gimme the, gimme the CapEx. I'm gonna put it in and run it myself. And is there a relationship between that approach and their digital transformation maturity, or is it kind of just really their preference? >>I, I mean, for us, we're meeting customers where they're at on their journey and their multi-cloud journey. So some, and, and what I'm seeing is that every customer today has multiple clouds, whether that be their, you know, their kind of, MultiGen it, the, the legacy stuff that they've gotta deal with. They've got stuff in public clouds, and they're trying to really transform and figure out how do I work all of that in like, how do I move forward with that new operating model? And so what I'm seeing is, you know, we're gonna meet customers where they're at on their journey. So some are gonna continue to go down that path in a, how they've always purchased their it. And others are really, you know, more often than not, we're seeing, they want that as a service cloudlike to have all the benefits of cloud, but yet still have it on their prem or in a colo or, you know, at the edge. So I do see some of those customers who are thinking differently, right. That, and they're the ones that are more apt to be a little bit more aggressive on their digital transformation. They're, they're open to the possibility if that makes sense. No, >>It does. It makes total sense. I, I, I think, you know, on the one hand they're a lot of customers are trying to build their own cloud. Yep. Um, so you mention multicloud, I'm not gonna go to Amazon to help me with my multicloud strategy. That's not, that's not gonna be my preferr. Yeah. I might talk to Microsoft about it a little bit. Google's got Antos and that's kind of interesting, but you know, Google's not enterprise, they got good data, but so, but there are other choices out there. Why HPE for my cloud hybrid multi-cloud strategy, give us the >>Sticker. It's, it's the best of both worlds for customers. So it enables them to have the security. It enables them to grow, to, to be in their data centers or in colos at the edge. It allows them to not over provision. It allows them to pay as they go and pay as they grow there's. Um, and then it also really is that ease factor. So it it's that thinking about it as I have, I already, I know what my pricing is. I know what that predictability is from a pricing perspective and what my costs are gonna be. So all of those things really re that all those messages resonate with customers, >>Right? L thanks so much for coming on. We got the trains are backing up super tight schedule today. This is wall to wall coverage of HPE. Discover. Thank you. Thank >>You so much for having me appreciate it. >>You're SU very welcome. All right. Keep it right there. Dave ante is here. John furrier, HPE discover 2022 from Las Vegas. We're live. We'll be right back.

>>The cube presents HPE discover 2022 brought to you by HPE. >>Hey everyone. Welcome back to the Cube's continuing coverage of HPE. Discover 22 live from Las Vegas, the Venetian expo center at Lisa Martin and Dave Velante have a very special guest. Next one of our esteemed alumni here on the cube, Antonio Neri, the president and CEO of HPE, Antonio. Thank you so much for joining us this morning. >>Well, thanks for free with us today. >>Great to be back here after three years away. Yeah. Sit on stage yesterday in front of a massive sea of people. The energy here is electric. Yeah. Must have felt great yesterday, but you, you stood on stage three years ago and said buy 20, 22. And here it is. Yeah. We're gonna deliver our entire portfolio as a service. What was it like to be on stage and say we've done that. Here's where we are. We are a new company. >>Well, first of all, as always, I love the cube to cover HP discover, as you said, has been many, many years, and I hope you saw a different company yesterday. I'm really proud of what happened yesterday, because it was a pivotable moment in our journey. If I reflect back in my four years as a CEO, we said the enterprise of the future will be edge centric, cloud enable and data driven in 2018. And I pledged to invest 4 billion over four years. And you see the momentum we have at the edge with our business. And then in 19, to your point, Lisa, we said, by the end of 2022, we will offer everything as a service. When you look at the floor behind us, everything is a as a service experience from the moment you log through IHP GreenLake platform to all the cloud services we offer. So for me, it is a proud moment because our team worked really hard to deliver on that province on the face of a lot of challenges, >>Tremendous challenges, the last couple years that nobody could have predicted or even forecast, how can we tolerate this? Talk to me about your customer conversations and how they have changed and evolved as every company today has to be a data company. >>Well, even this morning, up to this interview, I already met four customers in, in less than an hour and a half. And I will say all of them, first of all, really appreciated bringing HP discover back. And what they really appreciated was the fact that they had the opportunity to meet and greet and talk to people. The energy that comes from that engagement is second to none. And I think says something right about the moment we are at this time, where the return to work and everything else. I think this is a wake up call in many ways, but customers are telling us is that they want to engage with a partner that has a vision that can take them to their journey, whatever that journey is. And we know digital transformation is core to everything, but ultimately they are now more focused on delivering outcomes for the organization they're running in it. And that's why HP GreenLake is incredible well positioned to do so, you >>Know, just picking up on that. I, I, I counted Antonio. I think I've been to 14 HP and HPE discovers when you include Europe. Yeah. I mean, Frankford, London, Barcelona Madrid, of course, you know the us, and I've never seen why I've tweeted this out. I've never seen this type of energy. Right. People are excited to get back. That's part of it. The other big part of it is course the focus. Yeah. So that focus on as a service was a burn, the boat moment for HPV. >>I don't think it was a burn the boat moment. It was a moment that we have to decide how we think about the future and how we become even more relevant for customers. And we are very important to all the customers they buy from us. Right. But I think about the next 3, 5, 10 years, how we position the company, enter the future to be relevant to whatever they need to do. >>Well, what I mean by that is you're not turning back. No, the bridge is gone. You go, you're going forward. And so my question is, did the pandemic accelerate that move or did it, did it hinder it? And, and, and how so >>Actually it was an, a moment for us to think about how we go further and faster to what we call this journey to one, one platform, one experience. And, and we felt as a team, as an organization, this was a unique moment in time to go further, faster. So to us, it was a catalyst to accelerate that transformation. >>Yeah. Now I, I want to ask you a question in your keynote. I love this, cuz you say I'm often asked by customers, what workload should we move to the public cloud and what should stay on prem? I'm like, yeah, I get that question all the time. And I was waiting for the answer. You said, that's the wrong question. And I was like, wait, but that's the question everybody's asking. So it was really interesting that you said that. And I wonder if you could, you could comment. And I think you said basically the world's hybrid is your challenge with, with the customers in this initiative to actually get people to stop asking that question. Right. And not think about that. >>No, I think the challenge we all collectively have is that how we think about data and how we drive what I call a data first modernization, you know, strategy for our customers in an age to cloud architecture, which basically says you are living a hybrid world is not a question which workloads are put in the public cloud, which workloads are put OnPrem. You know, the, all the issues around data gravity and whatnot is a question of how I bring the cloud experience to all your workloads of data, wherever they live. And that's where, you know, the opportunity really exists. And as customers understand more and more about the new environments, how they work, how they enable these new experiences is all driven by that data. And that data has enormous value. So it's not about which cloud use is about how you bring the cloud experience to your data in workloads. >>When you're talking to CIOs, especially transformational CIOs, what's the value pro to those CIOs that wanna transform and need to transform with the power of HPE. >>More and more of them are becoming conscious about the fact that they need to go faster in everything they do. We have done some interesting analysis with the brands that have done a better job or have become way more proficient on extracting insight from the data. They are actually the brands that winning the marketplace, not just with customers driving the preference, but also in the market capitalization because they become where more sophisticated in driving better efficiency, which is a necessity today. Second is the fact that also they need to improve their security aspect of it, but they are creating new experiences and new revenue streams. And those transformational CIOs are transforming their business in the way they run it into more an innovation engine. And so that's why, you know, we love working with them because they are advanced and the push has to think differently in the way we think about the innovation. How >>Do you help customers go from data, rich insight, port to data, rich insight, rich actions, new revenue, streams, new services. >>Well, first of all, you have to deploy the right architecture, which starts with a network, obviously because digital transformation requires an on-ramp and the connectivity is the first step. Second is to make sure you have a true end to end visibility of that data. And that's why we announced yesterday with the data fabric, right? A, a revolutionary way to think about that age to cloud architecture from a data driven perspective. And then the third piece of this is, is the aspect of how we bring that intelligence to that data. And that's where, you know, we are enabling all these amazing services with AI machine learning with, with, you know, HP GreenLake, which is ultimately the way we are gonna enable them. >>What's your favorite announcement from this week? >>I think HP GreenLake, you know, I think I >>Mentioned a lot of GreenLake, >>36 times HP GreenLake. And to me, you know, as I think about what comes next, right, is about how we innovate now on the platform at the pace that customers are demanding. And so for me, there is a lot of things there, but obviously the private cloud enterprise edition was a big moment for us because that's the way we bring the cloud operating experience on-prem and at the edge, but also all the hybrid capabilities that Brian showed during the demo is something that I think customers now say, wow, I didn't know. We can do that. >>And thinking about your business, you know, despite some macro headwinds and, and like you, you reaffirmed your guidance on the, on the last earnings call. Does GreenLake give you better visibility or is it harder to predict? >>No, I think the more we get engaged with customers in running their workloads and data, the more visibility we get, you know, I said, you know, customers voted with the workloads and data. And in, in that context, you know, we already have 65,000 customers more than 120,000 users. And the one interesting stat, which I hope it didn't go lost during that transition was the, the fact that we now have under the GreenLake management over an next bite of data. And so to me, right, that's a unique, a unique opportunity for us to learn and improve the whole cycle. >>So obviously a big pillar of your strategy is the data. And I wanted, if you could talk more about that because I, I would observe, you know, we, the cube started in sort of as big data, you know, started to take off and you saw that had ecosystem and, and that ecosystem has dispersed now. Yeah. So gone into the cloud, it's got snowflakes pulling and some in Mongo. Now you have the opportunity with this ecosystem yeah. To have a data ecosystem. How do you look at the ecosystem and the value that your partners can build on top of GreenLake and specifically monetize? Well, >>If you walk through the floor, one of the things we changed this time is that the partners are actually in the flow of all our solutions, not sitting on a corner of the show floor, right? And, and, and that's because what we have done in the last three years has been together with our partners, but we conceive HP GreenLake with the partners in mind, at the core of everything we do in the platform. And that's why on Monday we announced the new partner one ready vantage program that actually opens the platform through our APIs for allowing them to add their own value on the platform, whether in their own services to the marketplace or the other way around they to use our capabilities in their own solutions. Because some of these cloud operating capabilities are hard to develop, whether it is, you know, metering and billing and all the other services, sometimes you don't don't have to build yourself. So that's why, what we love about our strategies, the partners can decide where to participate in this broad ecosystem and then grow with us and we can grow through them as well. >>So GreenLake as a service, the focus is, is very clear. Hybrid is very clear. What's less clear to me is, is that I'll and I'll ask you to comment, is this, we go a term called super cloud and super cloud is different than multi-cloud multi-cloud oh, I run in AWS or, and, or I run in Azure. I run in, in, in GCP, Supercloud builds a layer above that hides the underlying complexity of the primitives and the APIs, and then builds new value on top of that, out to the edge as well. You guys talk about the edge all the time. You have Aruba a as an asset, you got space space born. You're doing some pretty edge. Like, well, >>We have it here. Yeah. Yeah. We are connected to the ISS. So if you were to that show floor, you can actually see what's being processed today. >>I mean, that's, you don't get more edge than that. So my question is, is, is that part of the vision to actually build that I call super cloud layer? Or is it more to be focused on hybrid and connecting on-prem to the cloud? >>No, I, I don't like to call it super cloud because that means, unless you are a superpower, you can't do what you need to do. I, I think I call it a super straight okay. Right. That we are enabling to our H to cloud architecture. So the customers can build their own experiences and consume the services that they need to compete and win in today's market. So our H to cloud approach is to create that substrate with connectivity, obviously the cloud and the data capability that you need to operate in today's >>Environment. Okay. So they're fair enough. I would say that your customers are gonna build then the super cloud on top of that software. >>Well, actually we want to give it to them. They don't have to build anything. They just need to run the business. Well, they don't have the time to really build stuff. They just need to innovate that's our, our value proposition. So they don't have to waste cycles in doing so if it comes ready to go, why you want to build it? >>Well, when I say build it, I'm talking about building their business on top of it things you're not gonna, I agree with that, bringing their tools, financial services companies with their data, their tools, their ecosystem, connecting OnPrem to the clouds. Yeah. That above that substrate that's their as a digital. >>Yeah. And that's why I said yesterday with our approach, we're actually enabling customers to power the next generation business models that they need. We enable the substrate, they can innovate on the platform, these next gen business models, >>Tap your engineering mind. And I'd like you to talk about how architectures are changing you along with many, many other CEOs signed a letter to, to the us government, you know, urging them to, to, to pass the chips act. As I posted on LinkedIn, there were, there were a few notables missing apple wasn't on there, meta wasn't on there, Tesla wasn't on there. I'd like to see them step up and sign that. Yeah. And so why did you, you know, sign that? Why did you post that? And, and, and why is that important? >>Well, first of all, it's important to customers because obviously they need to get access to technologies in a more ubiquitous way. And second it's important for the United States. We live in a, in a global economy that today is going to a refactoring of sorts where supply chain disruption has caused a lot of consternation and disruption across many industries. And I think, you know, as we think about the next generation supply chains, which are built for resiliency and obviously inclusion, we need to make sure that the United States address this problem. Because once you fall behind, it takes a long time to catch up. Even if we sign the chips act, it's gonna take many years for us, but we need to start now. Otherwise we never get what we need to >>Get. I, I agree. We're late. I think pat Gelsinger has done a very good job laying out the mission, you know, to bring, I mean, to me, it's modest, bring 20% of the manufacturing back to the us by the end of the decade. I mean that that's not gonna be easy, but even so that's, >>That's, we need stuff somewhere. Absolutely. You know, we are great partners with Intel. I really support the vision that path has laid out. And its not just about Intel again, it's about our customers in the United States, >>HP and HPE now cuz H HP labs is part of, of HPE. I believe that's correct state. Well, >>We refocus HP labs as a part of our high performance. Yeah. And AI business. Yes. >>But H HP and, and now HPE possess custom Silicon expertise. We may, we always >>Had. >>Yeah, exactly. And, and you know, with the fabulous world, do you see, I mean, you probably do in some custom Silicon today that I don't really, you know, have visibility on, but do you see getting more into that? Is there a need for >>That? Yeah. Well we already design more than 60 different silicons that are included in our solution. More and more of that. Silicon is actually in support of our other service experience. That's truly programmable for this new way to deploy a cloud or a data fabric or a network in fabric of sorts. When you look our, our age portfolio as a part of green lake through our Aruba set of offerings, we actually have a lot of the Silicon building. Our switching portfolio that's program. Normally give us the ability to drive intelligent routing in the network at the application layer. But also as you know, many years ago, we introduced our own ILO, the lights out technology, the BMC type of support that allows us to provide security to the root of our systems. But now more implement a cloud operating security environment if you will, but there is many more in the analog space for AI at scale. And even the latest introduction with frontier. When you look at frontier that wonderful high performance exit scale system, the, the magic of that is in the Silicon we developed, which is the interconnect fabric. Plus the smart mix at massive massive scale for parallel computing. And then ultimately it's the software environment that we put on top of it. So we can process billion, billion, square transactions per second. >>And when you think about a lot of the AI today is modeling, that's done in the cloud. When you think about the edge actual real time in, you're not gonna send all that back to the cloud. When you have to make a left turn or a right turn, >>Stop sign. I think, you know, people need to realize that 70% of the data today is outside the public cloud and 50% is at the edge. And when you think about the real time use cases, actually 30% of that data will need to be processed real time. So which means you need to establish inference the rate at the edge and at the same time run, you know, the analytics at the edge, whether it's machine learnings or some sort of simulation they need to do at the edge. And so that's why, you know, we can provide inference. We can provide machine learning at the edge on top of the connectivity and the edge compute or cloud computing at the edge. But also we can provide on the other side, AI at scale for massive amount of data analytics. And >>Will that be part of the GreenLake? >>We already offered that experience. We already offered that as a HPC, as a service is one of the key services we provide at scale. And then you also have machine learning operations as a service. So we have both and with the data fabric, now we're gonna take it to one step forward so we can connect the data. And I think one of the most exciting services, I actually, I'm a true believer. That is the capability we develop through HP labs. Since you asked for that early on, which is called the swarm learning technology. Of >>Course. Yeah. I've talked to Dr. GU about there you >>Go. >>So, so he >>Will do a better job than me explaining, >>Hey, I don't know. You're pretty, pretty good at it, but he's awesome. I mean, I have to admit on your keynote, you specifically took the time to mention your support for women's rights. Yes. Will HPE pay for women to leave the state to have a medical procedure? >>Yeah. So what happened last week was a sad moment in a history. I believe we, as a company felt compelled to stand up and take a position on the rights of women to choose. And as a part of that, we already offer as a part of our benefits, the ability to travel and pay all the medical expenses related to their choice. >>Yeah. Well thank you for doing that. I appreciate it. As a, as a father of two daughters who have less rights than, than my wife did when she was their age, I applaud you for your bravery and standing up and, and thank you for doing that. How excited are you for Janet Jackson? >>I think is gonna be a phenomenal rap of the HP discover, I think is gonna be a great moment for people to celebrate the coming together. One of the feedback I got from the meetings early on from customers is that put aside the vision, the strategy, the solutions that they actually can experience themselves is the fact that the, the, the one thing that really appreciated it is that they can be together. They can talk to people, they can learn with each other from each other. That energy is obviously very palpable when you go through it. And I think, you know, the celebration tonight and I want to thank the sponsor for allowing us to do so, is, is the fact that, you know, it's gonna be a moment of reuniting ourselves and look at the Fu at the future with optimism, but have some fun. >>Well, that's great, Antonio, as I said, I've been to a lot of HP and HPE discovers. You've brought a new focus clearly to the company, outstanding job of, of getting people aligned. I mean, it's not easy. It's 60,000, you know, professionals a around the globe and the energy is like I've never seen before. So congratulations. Thank you so much for coming back in the queue. >>Thank you, Dave. And as always, we appreciate you covering the, the event. You, you share the news with all the audiences around the globe here and, and that's, that means us means a lot to us. Thank you. Thank you. >>And thank you for watching. This is Dave Velante for Lisa Martin and John furrier. We'll be right back with our next guest. Live from HPE. Discover 2022 in Las Vegas.