>>Hey everybody. Welcome back. Good afternoon. Lisa Martin here with John Feer live in Detroit, Michigan. We are at Coon Cloud Native Con 2020s North America. John Thank is who. This is nearing the end of our second day of coverage and one of the things that has been breaking all day on this show is news. News. We have more news to >>Break next. Yeah, this next segment is a company we've been following. They got some news we're gonna get into. Managing Kubernetes life cycle has been a huge challenge when you've got large organizations, whether you're spinning up and scaling scale is the big story. Kubernetes is the center of the conversation. This next segment's gonna be great. It >>Is. We've got two guests from Specter Cloud here. Please welcome. It's CEO Chenery Fu and co-founder and it's c g a co-founder Sta Mallek. Guys, great to have you on the program. Thank >>You for having us. My pleasure. >>So Timary, what's going on? What's the big news? >>Yeah, so we just announced our Palace three this morning. So we add a bunch, a new functionality. So first of all we have a Nest cluster. So enable enterprise to easily provide Kubernete service even on top of their existing clusters. And secondly, we also support seamlessly migration for their existing cluster. We enable them to be able to migrate their cluster into our CNC for upstream Kubernete distro called Pallet extended Kubernetes, GX K without any downtime. And lastly, we also add a lot of focus on developer experience. Those additional capability enable developer to easily onboard and and deploy the application for. They have test and troubleshooting without, they have to have a steep Kubernetes lending curve. >>So big breaking news this morning, pallet 3.0. So you got the, you got the product. This is a big theme here. Developer productivity, ease of use is the top story here. As developers are gonna increase their code velocity cuz they're under a lot of pressure. This infrastructure's getting smarter. This is a big part of managing it. So the toil is now moving to the ops. Steves are now dev teams. Security, you gotta enable faster deployment of apps and code. This is what you guys solve while you getting this right. Is that, take us through that specific value proposition. What's the, what are the key things on in this news release? Yeah, >>You're exactly right. Right. So we basically provide our solution to platform engineering ship so that they can use our platform to enable Kubernetes service to serve their developers and their application ship. And then in the meantime, the developers will be able to easily use Kubernetes or without, They have to learn a lot of what Kubernetes specific things like. So maybe you can get in some >>Detail. Yeah. And absolutely the detail about it is there's a big separation between what operations team does and the development teams that are using the actual capabilities. The development teams don't necessarily to know the internals of Kubernetes. There's so much complexity when it comes, comes into it. How do I do things like deployment pause manifests just too much. So what our platform does, it makes it really simple for them to say, I have a containerized application, I wanna be able to model it. It's a really simple profile and from there, being able to say, I have a database service. I wanna attach to it. I have a specific service. Go run it behind the scenes. Does it run inside of a Nest cluster? Which we'll talk into a little bit. Does it run into a host cluster? Those are happen transparently for >>The developer. You know what I love about this? What you guys are doing in the news, it really points out what I love about DevOps. Because cloud, let's face a cloud early adopters, we're all the hardcore cloud folks as it goes mainstream. With Kubernetes, you start to see like words like platform engineering. I mean I love that term. That means as a platform, it's been around for a while. For people who are building their own stuff, that means it's gonna scale and enable people to enable value, build on top of it, move faster. This platform engineering is becoming now standard in enterprises. It wasn't like that before. What's your eyes reactions that, How do you see that evolving faster? Or do you believe that or what's your take on >>It? Yeah, so I think it's starting from the DevOps op team, right? That every application team, they all try to deploy and manage their application under their own ING infrastructure. But very soon all these each application team, they start realize they have to repeatedly do the same thing. So these will need to have a platform engineering team to basically bring some of common practice to >>That. >>And some people call them SREs like and that's really platform >>Engineering. It is, it is. I mean, you think about like Esther ability to deploy your applications at scale and monitoring and observability. I think what platform engineering does is codify all those best practices. Everything when it comes about how you monitor the actual applications. How do you do c i CD your backups? Instead of not having every single individual development team figuring how to do it themselves. Platform engineer is saying, why don't we actually build policy that we can provide as a service to different development teams so that they can operate their own applications at scale. >>So launching Pellet 3.0 today, you also had a launch in September, so just a few weeks ago. Talk about what these two announcements mean from Specter Cloud's perspective in terms of proof points, what you're delivering to the end users and the value that they're getting from that. >>Yeah, so our goal is really to help enterprise to deploy and around Kubernetes anywhere, right? Whether it's in cloud data center or even at Edge locations. So in September we also announce our HV two capabilities, which enable very easy deployment of Edge Kubernetes, right at at at any any location, like a retail stores restaurant, so on and so forth. So as you know, at Edge location, there's no cloud endpoint there. It's not easy to directly deploy and manage Kubernetes. And also at Edge location there's not, it's not as secure as as cloud or data center environment. So how to make the end to end system more secure, right? That it's temper proof, that is also very, very important. >>Right. Great, great take there. Thanks for explaining that. I gotta ask cuz I'm curious, what's the secret sauce? Is it nested clusters? What's, what's the core under the hood here on 3.0 that people should know about it's news? It's what's, what's the, what's that post important >>To? To be honest, it's about enabling developer velocity. Now how do you enable developer velocity? It's gonna be able for them to think about deploying applications without worrying about Kubernetes being able to build this application profiles. This NEA cluster that we're talking about enables them, they get access to it in complete cluster within seconds. They're essentially having access to be able to add any operations, any capabilities without having the ability to provision a cluster on inside of infrastructure. Whether it's Amazon, Google, or OnPrem. >>So, and you get the dev engine too, right? That that, that's a self-service provisioning in for environments. Is that, Yeah, >>So the dev engine itself are the capabilities that we offer to developers so that they can build these application profiles. What the application profiles, again they define aspects about, my application is gonna be a container, it's gonna be a database service, it's gonna be a helm chart. They define that entire structure inside of it. From there they can choose to say, I wanna deploy this. The target environment, whether it becomes an actual host cluster or a cluster itself is irrelevant to them. For them it's complete transparent. >>So transparency, enabling developer velocity. What's been some of the feedback so far? >>Oh, all developer love that. And also same for all >>The ops team. If it's easy and goods faster and the steps >>Win-win team. Yeah, Ops team, they need a consistency. They need a governance, they need visibility, but in the meantime, developers, they need the flexibility then theys or without a steep learning curve. So this really, >>So So I hear a lot of people say, I got a lot of sprawl, cluster sprawl. Yeah, let's get outta hand does, let's solve that. How do you guys solve that problem? Yeah, >>So the Neste cluster is a profit answer for that. So before you nest cluster, for a lot of enterprise to serving developers, they have to either create a very large TED cluster and then isolated by namespace, which not ideal for a lot of situation because name stay namespace is not a hard isolation and also a lot of global resource like CID and operator does not work in space. But the other way is you give each developer a separate, a separate ADE cluster, but that very quickly become too costly. Cause not every developer is working for four, seven, and half of the time your, your cluster is is a sit there idol and that costs a lot of money. So you cluster, you'll be able to basically do all these inside the your wholesale cluster, bring the >>Efficiency there. That is huge. Yeah. Saves a lot of time. Reduces the steps it takes. So I take, take a minute, my last question to you to explain what's in it for the developer, if they work with Spec Cloud, what is your value? What's the pitch? Not the sales pitch, but like what's the value pitch that >>You give them? Yeah, yeah. And the value for us is again, develop their number of different services and teams people are using today are so many, there are so many different languages or so many different libraries there so many different capabilities. It's too hard for developers to have to understand not only the internal development tools, but also the Kubernetes, the containers of technologies. There's too much for it. Our value prop is making it really easy for them to get access to all these different integrations and tooling without having to learn it. Right? And then being able to very easily say, I wanna deploy this into a cluster. Again, whether it's a Nest cluster or a host cluster. But the next layer on top of that is how do we also share those abilities with other teams. If I build my application profile, I'm developing an application, I should be able to share it with my team members. But Henry saying, Hey Tanner, why don't you also take a look at my app profile and let's build and collaborate together on that. So it's about collaboration and be able to move >>Really fast. I mean, more develops gotta be more productive. That's number one. Number one hit here. Great job. >>Exactly. Last question before we run out Time. Is this ga now? Can folks get their hands on it where >>Yes. Yeah. It is GA and available both as a, as a SaaS and also the store. >>Awesome guys, thank you so much for joining us. Congratulations on the announcement and the momentum that Specter Cloud is empowering itself with. We appreciate your insights on your time. >>Thank you. Thank you so much. Right, pleasure. >>Thanks for having us. For our guest and John Furrier, Lisa Martin here live in Michigan at Co con Cloud native PON 22. Our next guests join us in just a minute. So stick around.

>>From around the globe. It's the cube covering data citizens. 21 brought to you by Colibra >>Welcome to the cubes coverage of Collibra data citizens 21. I'm Lisa Martin. I have three guests with me here today. Colibra customer Freddie Mac, please welcome JAG chief data officer and vice president of single family data and decisions. Jog. Welcome to the cube. >>Thank you, Lisa. Look forward to be, >>Uh, excellent on Kiko LSU as well. Vice president data transformation and analytics solution on Kay. Good to have you on the program. >>Thank you, Lisa. Great to be here and >>A teeth Malik senior director from the single family division at Freddie Mac is here as well. A team welcome. So we have big congratulations in order. Uh, pretty Mac was just announced at data citizens as the winners of the Colibra excellence award for data program of the year. Congratulations on that. We're going to unpack that. Talk about what that means, but I'd love to get familiar with the 3d Jack. Start with you. Talk to me a little bit about your background, your current role as chief data officer. >>Appreciate it, Lisa, thank you for the opportunity to share our story. Uh, my name is Arvind calls me Jack. And as you said, I'm just single-family chief data officer at Freddie Mac, but those that don't know, Freddie Mac is a Garland sponsored entity that supports the U S housing finance system and single family deals with the residential side of the marketplace, as CDO are responsible for our managed content data lineage, data governance, business architecture, which Cleaver plays a integral role, uh, in, in depth, that function as well as, uh, support our shared assets across the enterprise and our data monetization efforts, data, product execution, decision modeling, as well as our business intelligence capabilities, including AI and ML for various use cases as a background, starting my career in New York and then moved to Boston and last 20 years of living in the Northern Virginia DC area and fortunate to have been responsible for business operations, as well as led and, um, executed large transformation efforts. That background has reinforced the power of data and how, how it's so critical to meeting our business objectives. Look forward to our dialogue today, Lisa, once again. >>Excellent. You have a great background and clearly not a dull moment in your job with Freddy, Matt. And tell me a little bit about your background, your role, what you're doing at Freddie >>Mac. Definitely. Um, hi everyone. I'm,, I'm vice president of data transformation and analytics solutions. And I worked for JAG. I'm responsible for many of the things he said, including leading our transformation to the cloud and migrating all our existing data assets front of that transformation journey. I'm also responsible for our business information and business data architecture, decision modeling, business intelligence, and some of the analytics and artificial intelligence. I started my career back in the day as a computer engineer, but I've always been in the financial industry up in New York. And now in the Northern Virginia area, I called myself that bridge between business and technology. And I would say, I think over the last six years with data found that perfect spot where business and technology actually come together to solve real problems and, and really lead, um, you know, businesses to the next stage of, so thank you Lisa for the opportunity today. Excellent. >>And we're going to unpack you call yourself the bridge between business and it that's always such an important bridge. We're going to talk about that in just a minute, but I want to get your background, tell our audience about you. >>Uh, I'm Alec Malek, I'm senior director of business, data architecture, data transformation, and Freddie Mac. Uh, I'm responsible for the overall business data architecture and transformation of the existing data onto the cloud data lake. Uh, my team is responsible for the Kleberg platform and the business analysts that are using and maintaining the data in Libra and also driving the data architecture in close collaboration with our engineering teams. My background is I'm a engineer at heart. I still do a lot of development. This is my first time as of crossing over onto the bridge onto business side of maintaining data and working with data teams. >>Jan, let's talk about digital transformation. Freddie Mac is a 50 year old and growing company. I always love talking with established businesses about digital transformation. It's pretty challenging. Talk to me about your initial plan and what some of the main challenges were that you were looking to solve. >>Uh, great question, Lisa, and, uh, it's definitely pertinent as you say, in our digital world or figuring out how we need to accomplish it. If I look at our data, modernization is it is a major program and, uh, effort, uh, in, in our, in our division, what started as a reducing cost or looking at an infrastructure play, moving from physical data assets to the cloud, as well as enhancing our resiliency as quickly morphed into meeting business demand and objectives, whether it be for sourcing, servicing or securitization of our loan products. So where are we as we think about creating this digital data marketplace, we are, we are basically forming, empowering a new data ecosystem, which Columbia is definitely playing a major role. It's more than just a cloud native data lake, but it's bringing in some of our current assets and capabilities into this new data landscape. >>So as we think about creating an information hub, part of the challenges, as you say, 50 years of having millions of loans and millions of data across multiple assets, it's frigging out that you still have to care and feed legacy while you're building the new highway and figuring out how you best have to transform and translate and move data and assets to this new platform. What we've been striving for is looking at what is the business demand or what is the business use case, and what's the value to help prioritize that transformation. Exciting part is, as you think about new uses of acquiring and distribution of data, as well as news new use cases for prescriptive and predictive analytics, the power of what we're building in our daily, this new data ecosystem, we're feeling comfortable, we'll meet the business demand, but as any CTO will tell you demand is always, uh, outpaces our capacity. And that's why we want to be very diligent in terms of our execution plan. So we're very excited as to what we've accomplished so far this year and looking forward as we offered a remainder year. And as you go into 2022. Excellent, >>Thanks JAG. Uh, two books go to you. As I mentioned in the intro of that Freddie Mac has won the Culebra excellence award for data program of the year. Again, congratulations on that, but I'd love to understand the Kleber center of excellence that you're building at Freddie Mac. First of all, define what a center of excellence is to Freddie Mac and then what you're specifically building. Yeah, sure. >>So the Cleaver center of excellence provides us the overall framework from a people and process standpoint to focus in on our use of Colibra and for adopting best practices. Uh, we can have teams that are focused just on developing best practices and implementing workflows and lineage within Collibra and implementing and adopting a number of different aspects of Libra. It provides the central hub of people being domain experts on the tool that can then be leveraged by different groups within the organization to maintain, uh, the tool. >>Put another follow on question a T for you. How does Freddie Mac define, uh, dated citizens as anybody in finance or sales or marketing or operations? What does that definition of data citizen? >>It's really everyone it's within the organization. They all consume data in different ways and we provide a way of governing data and for them to get a better understanding of data from Collibra itself. So it's really everyone within the organization that way. >>Excellent. Okay. Let's go over to you a big topic at data citizens. 21 is collaboration. That's probably a word that we used a ton in the last 15 plus months or so it was every business really pivoted quickly to figure out how do we best collaborate. But something that you talked about in your intro is being the bridge between business and it, I want to understand from your perspective, how can data teams help to drive improved collaboration between business and it, >>The collaboration between business and technology have been a key focus area for us over the last few years, we actually started an agile transformation journey two years ago that we called modern delivery. And that was about moving away from project teams to persistent product teams that brought business and technology together. And we've really been able to pioneer that in the data space within Freddie Mac, where we have now teams with product owners coming from the data team and then full stack ID developers with them creating these combined teams to meet the business needs. We found that bringing these teams together really remove the barriers that were there in the interaction and the employee satisfaction has been high. And like you said, over the last 16 months with the pandemic, we've actually seen the productivity stay same or even go up because the teams were all working together, they work as a unit and they all have the sense of ownership versus working on a project that has a finite end date to fail. So we've, um, you know, we've been really lucky with having started this two years ago. Well, and >>That's great. And congratulations about either maintaining productivity or having it go up during the last 16 months, which had been incredibly challenging. Jack. I want to ask you what does winning this award from Collibra what does this mean to you and your team and does this signify that you're really establishing a data first culture? >>Great question, Lisa again. Um, I think winning the award, uh, just from a team standpoint, it's a great honor. Uh, Kleber has been a fantastic partner. And when I think about the journey of going from spread sheets, right, that all of us had in the past to now having all our business class returns lineage, and really being at the forefront of our data monetization. So as we think about moving to the cloud Beliebers step in step with us in terms of our integral part of that holistic delivery model, when I ultimately, as a CDO, it's really the team's honor and effort, cause this has been a multi-year journey to get here. And it's great that Libra as a, as a partner has helped us achieve some of these goals, but also recognized, um, where we are in terms of, uh, as looking at data as a product and some of our, um, leading forefront and using that holistic delivery, uh, to, uh, to meet our business objectives. So overall poorly jazzed when, uh, we've been found that we wanted the data program here at Collibra and very honored, um, uh, to, to win this award. That's >>Where we got to bring back I'm jazzed. I liked that jug sticking with you, let's unpack a little bit, some of those positive results, those business outcomes that you've seen so far from the data program. What are those? >>Yeah. So again, if you were thinking about a traditional CDO model, what were the terms that would have been used few years ago? It was around governance and may have been viewed as an oversight. Um, maybe less talking, um, monetization of what it was, the business values that you needed to accomplish collectively. It's really those three building blocks managing content. You got to trust the source, but ultimately it's empowering the business. So the best success that I could say at Freddy, as you're moving to this digital world, it's really empowering the business to figure out the new capabilities and demand and objectives that we're meeting. We're not going to be able to transform the mortgage industry. We're not going to be able or any, any industry, if we're still stuck in old world thinking, and ultimately data is going to be the blood that has to enable those capabilities. >>So if you tell me the business best success, we're no longer talking a okay, I got my data governance, what do we have to do? It's all embedded together. And as I alluded to that partnership between business and it informing that data is a product where you now you're delivering capabilities holistically from program teams all across data. It's no longer an afterthought. As I said, a few minutes ago, you're able to then meet the demand what's current. And how do we want to think about going forward? So it's no longer buzzwords of digital data marketplace. What is the value of that? And that's what the success, I think if our group collectively working across the organization, it's just not one team it's across the organization. Um, and we have our partners, our operations, everyone from business owners, all swimming in the same direction with, and I would say critical management support. So top of the house, our, our head of business, my, my boss was the COO full supportive in terms of how we're trying to execute and I've makes us, um, it's critical because when there is a potential, trade-offs, we're all looking at it collectively as an organization, >>Right. And that's the best viewpoint to have is that sort of centralized unified vision. And as you say, JAG, the support from, from up top, uh, I'd see if I want to ask you, you establish the Culebra center of excellence. What are you focused on now? >>So we really focused in allowing our users to consume data and understand data and really democratizing data so that they can really get a better understanding of that. So that's a lot of our focus and engaging with Collibra and getting them to start to define things in Colibra law form. That's a lot of focus right now. >>Excellent. Want to stay with you one more question and take that I'm gonna ask to all of you, what are you most excited about a lot of success that you've talked about transforming a legacy institution? What are you most excited about and what are the next steps for the data program? Uh, teak what's are your thoughts? >>Yeah, so really modernizing onto, uh, onto a cloud data lake and allowing all of the users and, uh, Freddie Mac to consume data with the level of governance that we need around. It is a exciting proposition for me. >>What would you say is most exciting to you? >>I'm really looking forward to the opportunities that artificial intelligence has to offer, not just in the augmented analytics space, but in the overall data management life cycle. There's still a lot of things that are manual in the data management space. And, uh, I personally believe, uh, artificial intelligence has a huge role to play there. And Jackson >>Question to you, it seems like you have a really strong collaborative team. You have a very collaborative relationship with management and with Collibra, what are you excited about? What's coming down the pipe. >>So Lisa, if I look at it, you know, we sit back here June, 2021, where were we a year ago? And you think about a lot of the capabilities and some of the advancements that we may just in a year sitting virtually using that word jazzed or induced or feeling really great about. We made a lot of accomplishments. I'm excited what we're going to be doing for the next year. So there's other use cases, and I could talk about AIML and OCHA talks about, you know, our new ecosystem. Seeing those use cases come to fruition so that we're, we are contributing to value from a business standpoint. The organization is what really keeps me up. Uh, keeps me up at night. It gets me up in the morning and I'm really feeling dues for the entire division. Excellent. >>Well, thank you. I want to thank all three of you for joining me today. Talking about the successes that Freddie Mac has had transforming in partnership with Colibra again, congratulations on the Culebra excellence award for the data program. It's been a pleasure talking to all three of you. I'm Lisa Martin. You're watching the cubes coverage of Collibra data citizens 21.

>> Narrator: Live from San Diego, California. It's theCUBE. covering Cisco Live US 2019. Brought to you by Cisco and its ecosystem partners. >> Welcome back to San Diego, everybody. You're watching Cisco Live 2019. This is theCUBE, the leader in live tech coverage. This is day three of our wall-to-wall coverage. We go out to the events, we extract the signal from the noise. My name is Dave Vellante. Stu Miniman is here. Our third host, Lisa Martin is also in the house. Dave Malik is here. He's a fellow and Chief Architect at Cisco. David, good to see you. >> Oh, glad to be here. >> Thanks for coming on. First of all, congratulations on being a fellow. What does that mean, a Cisco Fellow? What do you got to go through to achieve that status? >> It's pretty arduous task. It's one of the most highest technical designations in Cisco, but we work across multiple architectures in technologies, as well as our partners, as well, to drive corporate-wide strategy. >> So you've been talking to customers here, you've been presenting. I think you said you gave three presentations here? Multi-cloud, blockchain, and some stuff on machine intelligence, ML. >> Yes. >> Let's hit those. Kind of summarize the overall themes, and then we'll maybe get into each, and then we got a zillion questions for you. >> Sure, excellent. So multi-cloud, I think one of the customers, we're clearly hearing from them is around, how do we get a universal policy model and connectivity model, and how do you orchestrate workloads seamlessly? And those are some of the challenges that we trying to address at this conference. On blockchain, a lot of buzz out there. We're not talking about Bitcoin or cryptocurrency, it's really about leveraging blockchain from a networking perspective, or an identity and encryption, and providing a uniform ledger that everything is pervasive across infrastructure. And then ML, I think it's the heart of every conversation. How do we take pervasive analytics and bring it into the network so we can drive actionable insights into automation? >> So let's start with the third one. When you talk about ML, was your talk on machine learning? Did it spill into artificial intelligence? What's the difference to you from a technology perspective? >> Machine learning is really getting a lot of the data and looking at repetitive patterns in a very common fashion, and doing a massive correlation across multiple domains. So you may have some things happening in the branch, the data set, or a WAN in cloud, but the whole idea is how do you put them together to drive insight? And through artificial intelligence and algorithms, we can try to take those insights and automate them and push them back into the infrastructure or to the application layer. So now you're driving intelligence for not just consumers or devices, but also humans as well to drive insight. >> All right. So Dave, I wonder if you'd help connect with us what you were talking about there, and we'll get to the multicloud piece because I was at an Amazon show last week from Amazon, talking about how when they look at all the technologies that they use to get packages, their fulfillment centers, everything that they do as a business, ML and AI, they said, is underneath that, and AWS is what's driving that technology from that standpoint. Now, multicloud, AWS is a partner of yours. >> Yes. >> Can you give us how you work in multicloud and does ML and IA, is that a Cisco specific? Are you working with some of the standards out there to connect all those pieces? Help us look at some of the big picture of those items. >> So we believe we're agnostic, whether you connect to Amazon, Azure, Google, et cetera, we believe in a uniform policy model and connectivity model, which is very, very arduous today. So you shouldn't have to have a specific policy model, connectivity model, security model for that matter, for each provider. So we're normalizing that plane completely, which is awesome. Then, at a workload level, regardless of whether your workload is spun up or spun down, it should have the same security posture and visibility. We have certain customers that are running as single applications across multiple clouds, so your data is going to be obviously on-prem, you may be running analytics in TenserFlow, compute in EC2, and connecting to O365, that's one app. And where we're seeing the models go is are you leveraging technology such as this? Do you offer service mesh? How do we tie a lot of these micro-services together and then be able to layer workload orchestration on top? So regardless of where your workload sits, and one key point that we keep hearing from our customers is their ungovernance. How we provide cloud-based governance regardless of where their workload is, and that's something we're doing in a very large fashion with customers that have a multicloud strategy. >> So Stu, I think there's still some confusion around multicloud generally, and maybe Cisco's strategy. I wonder if we could maybe clear it up a little bit. >> Dave, it's that big elephant in the room, and I always feel like everybody describes multicloud from a different angle. >> So let's dig into this a little bit, and let's hear from Cisco's perspective. So you got, to my count, five companies really going after this space. You got Cisco, VMware, IBM Red Hat, Microsoft, and Google with Anthos. Probably all those guys are partners of yours. >> Yes. >> Okay, but you guys want to provide the bromide or the single pane of glass, okay. I'm hearing open and agnostic. That's a differentiator. Security, you're in a good position to make an argument that you're in a good position to make things secure. You got the network and so forth. High-performance network, and cost-effective. Everybody's going to make that argument relative to having multiple stovepipes, but that's part of your story as well. So the question. Why Cisco? What's the key differentiator and what gives you confidence that you can really help win in this marketplace? >> So our core competencies are our networking and security. Whether it's cloud-based security or on-prem security, it's uniform. From a security perspective, we have a universal architecture. Whether it's the endpoint, the edge, the cloud, they're all sharing information and intelligence. That's really important. Instead of having bespoke products, these products and solutions need to communicate with each other, so if someone's sick in one area, we're informing the other one. So threat intelligence and network intelligence is huge. Then more importantly, after working with Google, Microsoft, and Amazon, we have on-prem solutions as well, so as customers are going on their multicloud journey, and eventually the workload will transition, you have the same management experience and security experience. So Anthos was a recent announcement, AWS as well, where you can run on-prem Kubernetes, and you can take the same workload and move it to AWS or GCP, but the management model and the control pane model, they are extremely similar and you don't have to learn anything new from a training perspective. >> Okay, but I used the term agnostic, oh, no. You did agnostic, I said open. But you don't care if it's Anthos or VMware, or OpenShift, you don't care. >> Don't care. >> And, architecturally, how is it that you can successfully not care? >> Because the underlying, fundamental principles is you can load any workload you want with this, bare metal, virtualized, or Kubernetes-based containers, they all need the same. For example, everyone needs bread and water. It's not different. So why should you be able to discriminate against a workload or OpenShare if they're using Pivotal Cloud Foundry, for example? The same model, all applications still need security, visibility, networking, and management, but they should not be different across all clouds, and that's traditionally what you're seeing from the other vendors in the market. They're very unique to their stovepipe, and we want to break down those stovepipes across the board, regardless of what app and what workload you have. >> Dave, talk a little bit about the automation that Cisco's delivering to help enable this because there's skill set challenges, just the scale of these environments are more than humans alone can take care of, so how does that automation, I know you're heavily involved in the CX beast of Cisco. How does that all tie together? >> So we're working on a lot of automation projects with our large enterprises and SPs, I mean, you see Rakuten being fairly prominent in the show, but more importantly, we understand not everyone's building a greenfield environment, not everything is purely public cloud. We have to deal with brownfield, we have to deal with third-party ecosystem partners, so you can't have a vertically tight single-vendor solution. So again, to your point, it's completely open. Then we have frameworks, meaning you have orchestrators that can talk down to the device through programmatic interfaces. That's why we see DevNet surrounding us, but then more importantly, we're looking at services that have workflows that could span on-prem, off-prem, third-party, it doesn't really matter. And we stitch a lot of those workloads southbound, but more importantly, northbound to security at ITSM Systems. So those frameworks are coming into life, whether you're a telecom cloud provider or you're a large enterprise. And they slowly fall into those workflows as they become more multi-domain. You saw David Goeckeler the other day, talking about SD-WAN, ECI, and campus wired and wireless. These domains are coming together and that's where we're driving a lot of the automation work. >> So automation is a linchpin to what business outcome? Ultimately, what are customers trying to achieve through automation? >> There's a couple of things. Mean time to value. So if you're a service provider, to your internal customers or external, time to value and speed and agility are key. The other ones are mean time to repair and mean time to detect. If I can shorten the time to detect and shorten time to react, then I can take proactive and preemptive action in situations that may happen. So time to value is really, really important. Cost is a play, obviously, 'cause when you have more and more machines doing your work, your OPEX will come down, but it's really not purely a cost play. Agility and speed are really driving automation to that scale as we're working with folks like Rakuten and others. >> What do you see, Dave, as the big challenges of achieving automation when customers, first of all, I was talking like, 10, 15 years ago people, they were afraid of automation. Some still are. But they I think understand as part of a digital transformation, they got to automate. So what are the challenges that they're having and how are you helping them solve them? >> So typically, what people have thought about automation has been more network-centric, but as we just discussed multicloud, automation is extending all the way to the public cloud, at the workload or at the functional level, if you're running in Lambda, for example. And then more importantly, traditionally, customers have been leveraging Python scripts and things of that nature, but the days of scripters are there, but they cannot scale. You need a model-driven framework, you need model-driven telemetry to get insight. So I think the learning curve of customers moving to a model-driven mindset is extremely important, and it's not just about the network alone, it's also about the application. So that's why we're driving a lot of our frameworks and education and training. And talent's a big gap that we're helping with with our training programs. >> Okay, so you're talking about insights. There's a lot of data. The saying goes, "data is plentiful, insights aren't." So how do you get from data to insights? Is that where the machine intelligence comes in? Maybe you can explain that. >> There's a combination. Machines can process much faster than humans can, but more importantly, somebody has to drive the 30 or 40 years of experience that Cisco has from our tech, our architects and CX, and our customers and the community that we're developing through DevNet. So taking trusted expertise from humans, from all that knowledge base, combining that with machine learning so we get the best of both worlds. 'Cause you need that experience. And that is driving insight so we can filter the signal from the noise, and then more importantly, how do you take that signal and then, in an automated fashion, push that down to an intent-based architecture across the board. >> Dave, can you take us inside a little bit of your touchpoints into customers? In the old days, it was a CCIE, his job, his title, it was equipment that he would touch, and today, talking about this multicloud and the automation, it's very dispersed as to who owns it, most of what I am managing is not something that's under their purview, so the touchpoints you have into the company and the relationship you have changed a lot in the last three, five years or so. >> Absolutely, 'cause the buying center's also changing, because folks are getting more and more centric around the line of business and want the outcome we want to drive for their clients. So the cloud architecture teams that are being built, they're more horizontal now. You'll have a security person, an application, networking, operations, for example, and what we're actually pioneering, a lot of the enterprises and SPs, is building the site reliability engineering teams, or SRE, which Google has obviously pioneered, and we're bringing those concepts and teams through a CX framework, through telecos, and some of their high-end enterprises initially, and you'll see more around that over the coming months. Our SRE jobs, if you go on LinkedIn, you'll probably see hundreds of them out there now. >> One of the other things we've been watching is Cisco has a very broad portfolio. This whole CX piece has to make sure that, from a customer's standpoint, no matter where the portfolio, whether core, edge, IOT, all these various devices, I should have a simplified experience today, which isn't necessarily, my words, Cisco's legacy. How do you make sure, is software a unifying factor inside the company? Give us a little bit about those dynamics inside. >> Absolutely, so we take a life cycle approach. It's not one and done. From the time there's a concept where you want to build out a blueprint, but there's no transformation journey, we have to make sure we walk the client through preparation, planning, design, architecture optimization, but then making sure they actually adopt, and get the true value. So we're working with our customers to make sure that they go around the entire life cycle, from end to end, from cradle to grave, and be able to constantly optimize. You're hearing the word continuous pretty much everywhere. It's kind of the fundamental of CICD, so we believe in a continuous life cycle approach that we're walking the customers end to end to make sure from the point of purchase to the point of decommissioning, making sure they're getting the most value out of the solutions they're getting from Cisco. >> All right Dave, we'll give you the last word on Cisco Live 2019. Thoughts? Takeaways? >> I think there's just amazing energy here, and there's a lot more to come. Come down to the CX booth and we'll have to show you some more gadgets and solutions where we're taking our forward customers. >> Great. David, thank you very much for coming to The Cube. >> Pleasure, thank you. >> All right, 28,000 people and The Cube bringing it to you live. This is Dave Vellante with Stu Miniman. Lisa Martin is also in the house. We'll be right back from Cisco Live San Diego 2019, Day 3. You're watching The Cube.

(upbeat music) >> Welcome back. Day two of theCUBE's coverage of re:MARS in Las Vegas. Amazon re:MARS, it's part of the Re Series they call it at Amazon. re:Invent is their big show, re:Inforce is a security show, re:MARS is the new emerging machine learning automation, robotics, and space. The confluence of machine learning powering a new industrial age and inflection point. I'm John Furrier, host of theCUBE. We're here to break it down for another wall to wall coverage. We've got a great guest here, CUBE alumni from our AWS startup showcase, Krishna Gade, founder and CEO of fiddler.ai. Welcome back to theCUBE. Good to see you. >> Great to see you, John. >> In person. We did the remote one before. >> Absolutely, great to be here, and I always love to be part of these interviews and love to talk more about what we're doing. >> Well, you guys have a lot of good street cred, a lot of good word of mouth around the quality of your product, the work you're doing. I know a lot of folks that I admire and trust in the AI machine learning area say great things about you. A lot going on, you guys are growing companies. So you're kind of like a startup on a rocket ship, getting ready to go, pun intended here at the space event. What's going on with you guys? You're here. Machine learning is the centerpiece of it. Swami gave the keynote here at day two and it really is an inflection point. Machine learning is now ready, it's scaling, and some of the examples that they were showing with the workloads and the data sets that they're tapping into, you know, you've got CodeWhisperer, which they announced, you've got trust and bias now being addressed, we're hitting a level, a new level in ML, ML operations, ML modeling, ML workloads for developers. >> Yep, yep, absolutely. You know, I think machine learning now has become an operational software, right? Like you know a lot of companies are investing millions and billions of dollars and creating teams to operationalize machine learning based products. And that's the exciting part. I think the thing that that is very exciting for us is like we are helping those teams to observe how those machine learning applications are working so that they can build trust into it. Because I believe as Swami was alluding to this today, without actually building trust into AI, it's really hard to actually have your business users use it in their business workflows. And that's where we are excited about bringing their trust and visibility factor into machine learning. >> You know, a lot of us all know what you guys are doing here in the ecosystem of AWS. And now extending here, take a minute to explain what Fiddler is doing for the folks that are in the space, that are in discovery mode, trying to understand who's got what, because like Swami said on stage, it's a full-time job to keep up on all the machine learning activities and tool sets and platforms. Take a minute to explain what Fiddler's doing, then we can get into some, some good questions. >> Absolutely. As the enterprise is taking on operationalization of machine learning models, one of the key problems that they run into is lack of visibility into how those models perform. You know, for example, let's say if I'm a bank, I'm trying to introduce credit risk scoring models using machine learning. You know, how do I know when my model is rejecting someone's loan? You know, when my model is accepting someone's loan? And why is it doing it? And I think this is basically what makes machine learning a complex thing to implement and operationalize. Without this visibility, you cannot build trust and actually use it in your business. With Fiddler, what we provide is we actually open up this black box and we help our customers to really understand how those models work. You know, for example, how is my model doing? Is it accurately working or not? You know, why is it actually rejecting someone's loan application? We provide these both fine grain as well as coarse grain insights. So our customers can actually deploy machine learning in a safe and trustworthy manner. >> Who is your customer? Who you're targeting? What persona is it, the data engineer, is it data science, is it the CSO, is it all the above? >> Yeah, our customer is the data scientist and the machine learning engineer, right? And we usually talk to teams that have a few models running in production, that's basically our sweet spot, where they're trying to look for a single pane of glass to see like what models are running in their production, how they're performing, how they're affecting their business metrics. So we typically engage with like head of data science or head of machine learning that has a few machine learning engineers and data scientists. >> Okay, so those people that are watching, you're into this, you can go check it out. It's good to learn. I want to get your thoughts on some trends that I see emerging, and I want to get your reaction to those. Number one, we're seeing the cloud scale now and integration a big part of things. So the time to value was brought up on stage today, Swami kind of mentioned time to value, showed some benchmark where they got four hours, some other teams were doing eight weeks. Where are we on the progression of value, time to value, and on the scale side. Can you scope that for me? >> I mean, it depends, right? You know, depending upon the company. So for example, when we work with banks, for them to time to operationalize a model can take months actually, because of all the regulatory procedures that they have to go through. You know, they have to get the models reviewed by model validators, model risk management teams, and then they audit those models, they have to then ship those models and constantly monitor them. So it's a very long process for them. And even for non-regulated sectors, if you do not have the right tools and processes in place, operationalizing machine learning models can take a long time. You know, with tools like Fiddler, what we are enabling is we are basically compressing that life cycle. We are helping them automate like model monitoring and explainability so that they can actually ship models more faster. Like you get like velocity in terms of shipping models. For example, one of the growing fintech companies that started with us last year started with six models in production, now they're running about 36 models in production. So it's within a year, they were able to like grow like 10x. So that is basically what we are trying to do. >> At other things, we at re:MARS, so first of all, you got a great product and a lot of markets that grow onto, but here you got space. I mean, anyone who's coming out of college or university PhD program, and if they're into aero, they're going to be here, right? This is where they are. Now you have a new core companies with machine learning, not just the engineering that you see in the space or aerospace area, you have a new engineering. Now I go back to the old days where my parents, there was Fortran, you used Fortran was Lingua Franca to manage the equipment. Little throwback to the old school. But now machine learning is companion, first class citizen, to the hardware. And in fact, and some will say more important. >> Yep, I mean, machine learning model is the new software artifact. It is going into production in a big way. And I think it has two different things that compare to traditional software. Number one, unlike traditional software, it's a black box. You cannot read up a machine learning model score and see why it's making those predictions. Number two, it's a stochastic entity. What that means is it's predictive power can wane over time. So it needs to be constantly monitored and then constantly refreshed so that it's actually working in tech. So those are the two main things you need to take care. And if you can do that, then machine learning can give you a huge amount of ROI. >> There is some practitioner kind of like craft to it. >> Correct. >> As you said, you got to know when to refresh, what data sets to bring in, which to stay away from, certainly when you get to the bias, but I'll get to that in a second. My next question is really along the lines of software. So if you believe that open source will dominate the software business, which I do, I mean, most people won't argue. I think you would agree with that, right? Open source is driving everything. If everything's open source, where's the differentiation coming from? So if I'm a startup entrepreneur or I'm a project manager working on the next Artemis mission, I got to open source. Okay, there's definitely security issues here. I don't want to talk about shift left right now, but like, okay, open source is everything. Where's the differentiation, where do I have the proprietary edge? >> It's a great question, right? So I used to work in tech companies before Fiddler. You know, when I used to work at Facebook, we would build everything in house. We would not even use a lot of open source software. So there are companies like that that build everything in house. And then I also worked at companies like Twitter and Pinterest, which are actually used a lot of open source, right? So now, like the thing is, it depends on the maturity of the organization. So if you're a Facebook or a Google, you can build a lot of things in house. Then if you're like a modern tech company, you would probably leverage open source, but there are lots of other companies in the world that still don't have the talent pool to actually build, take things from open source and productionize it. And that's where the opportunity for startups comes in so that we can commercialize these things, create a great enterprise experience, so actually operationalize things for them so that they don't have to do it in house for them. And that's the advantage working with startups. >> I don't want to get all operating systems with you on theory here on the stage here, but I will have to ask you the next question, which I totally agree with you, by the way, that's the way to go. There's not a lot of people out there that are peaked. And that's just statistical and it'll get better. Data engineering is really narrow. That is like the SRE of data. That's a new role emerging. Okay, all the things are happening. So if open source is there, integration is a huge deal. And you start to see the rise of a lot of MSPs, managed service providers. I run Kubernetes clusters, I do this, that, and the other thing. So what's your reaction to the growth of the integration side of the business and this role of new services coming from third parties? >> Yeah, absolutely. I think one of the big challenges for a chief data officer or someone like a CTO is how do they devise this infrastructure architecture and with components, either homegrown components or open source components or some vendor components, and how do they integrate? You know, when I used to run data engineering at Pinterest, we had to devise a data architecture combining all of these things and create something that actually flows very nicely, right? >> If you didn't do it right, it would break. >> Absolutely. And this is why it's important for us, like at Fiddler, to really make sure that Fiddler can integrate to all varies of ML platforms. Today, a lot of our customers use machine learning, build machine learning models on SageMaker. So Fiddler nicely integrate with SageMaker so that data, they get a seamless experience to monitor their models. >> Yeah, I mean, this might not be the right words for it, but I think data engineering as a service is really what I see you guys doing, as well other things, you're providing all that. >> And ML engineering as a service. >> ML engineering as a- Well it's hard. I mean, it's like the hard stuff. >> Yeah, yeah. >> Hear, hear. But that has to enable. So you as a business entrepreneur, you have to create a multiple of value proposition to your customers. What's your vision on that? What is that value? It has to be a multiple, at least 5 to 10. >> I mean, the value is simple, right? You know, if you have to operationize machine learning, you need visibility into how these things work. You know, if you're CTO or like chief data officer is asking how is my model working and how is it affecting my business? You need to be able to show them a dashboard, how it's working, right? And so like a data scientist today struggles to do this. They have to manually generate a report, manually do this analysis. What Fiddler is doing them is basically reducing their work so that they can automate these things and they can still focus on the core aspect of model building and data preparation and this boring aspect of monitoring the model and creating reports around the models is automated for them. >> Yeah, you guys got a great business. I think it's a lot of great future there and it's only going to get bigger. Again, the TAM's going to expand as the growth rising tide comes in. I want to ask you on while we're on that topic of rising tides, Dave Malik and I, since re:Invent last year have been kind of kicked down around this term that we made up called supercloud. And supercloud was a word that came out of these clouds that were not Amazon hyperscalers. So Snowflake, Buildman Sachs, Capital One, you name it, they're building massive proprietary value on top of the CapEx of Amazon. Jerry Chen at Greylock calls it castles in the cloud. You can create these moats. >> Yeah, right. >> So this is a phenomenon, right? And you land on one, and then you go to the others. So the strategies, everyone goes to Amazon first, and then hits Azure and GCP. That then creates this kind of multicloud so, okay, so super cloud's kind of happening, it's a thing. Charles Fitzgerald will disagree, he's a platformer, he says he's against the term. I get why, but he's off base a little. We can't wait to debate him on that. So superclouds are happening, but now what do I do about multicloud, because now I understand multicloud, I have this on that cloud, integrating across clouds is a very difficult thing. >> Krishna: Right, right, right. >> If I'm Snowflake or whatever, hey, I'll go to Azure, more TAM expansion, more market. But are people actually working together? Are we there yet? Where it's like, okay, I'm going to re-operationalize this code base over here. >> I mean, the reality of it, enterprise wants optionality, right? I think they don't want to be locked in into one particular cloud vendor on one particular software. And therefore you actually have in a situation where you have a multicloud scenario where they want to have some workloads in Amazon, some workloads in Azure. And this is an opportunity for startups like us because we are cloud agnostic. We can monitor models wherever you have. So this is where a lot of our customers, they have some of their models are running in their data centers and some of their models running in Amazon. And so we can provide a universal single pan of glass, right? So we can basically connect all of those data and actually showcase. I think this is an opportunity for startups to combine the data streams come from various different clouds and give them a single pain of experience. That way, the sort of the where is your data, where are my models running, which cloud are there, is all abstracted out from the customer. Because at the end of the day, enterprises will want optionality. And we are in this multicloud. >> Yeah, I mean, this reminds me of the interoperability days back when I was growing into the business. Everything was interoperability and OSI and the standards came out, but what's your opinion on openness, okay? There's a kneejerk reaction right now in the market to go silo on your data for governance or whatever reasons, but yet machine learning gurus and experts will say, "Hey, you want to horizon horizontal scalability and have the best machine learning models, you've got to have access to data and fast in real time or near real time." And the antithesis is siloing. >> Krishna: Right, right, right. >> So what's the solution? Customers control the data plane and have a control plane that's... What do customers do? It's a big challenge. >> Yeah, absolutely. I think there are multiple different architectures of ML, right, you know? We've seen like where vendors like us used to deploy completely on-prem, right? And they still do it, we still do it in some customers. And then you had this managed cloud experience where you just abstract out the entire operations from the customer. And then now you have this hybrid experience where you split the control plane and data plane. So you preserve the privacy of the customer from the data perspective, but you still control the infrastructure, right? I don't think there's a right answer. It depends on the product that you're trying to solve. You know, Databricks is able to solve this control plane, data plane split really well. I've seen some other tools that have not done this really well. So I think it all depends upon- >> What about Snowflake? I think they a- >> Sorry, correct. They have a managed cloud service, right? So predominantly that's their business. So I think it all depends on what is your go to market? You know, which customers you're talking to? You know, what's your product architecture look like? You know, from Fiddler's perspective today, we actually have chosen, we either go completely on-prem or we basically provide a managed cloud service and that's actually simpler for us instead of splitting- >> John: So it's customer choice. >> Exactly. >> That's your position. >> Exactly. >> Whoever you want to use Fiddler, go on-prem, no problem, or cloud. >> Correct, or cloud, yeah. >> You'll deploy and you'll work across whatever observability space you want to. >> That's right, that's right. >> Okay, yeah. So that's the big challenge, all right. What's the big observation from your standpoint? You've been on the hyperscaler side, your journey, Facebook, Pinterest, so back then you built everything, because no one else had software for you, but now everybody wants to be a hyperscaler, but there's a huge CapEx advantage. What should someone do? If you're a big enterprise, obviously I could be a big insurance, I could be financial services, oil and gas, whatever vertical, I want a supercloud, what do I do? >> I think like the biggest advantage enterprise today have is they have a plethora of tools. You know, when I used to work on machine learning way back in Microsoft on Bing Search, we had to build everything. You know, from like training platforms, deployment platforms, experimentation platforms. You know, how do we monitor those models? You know, everything has to be homegrown, right? A lot of open source also did not exist at the time. Today, the enterprise has this advantage, they're sitting on this gold mine of tools. You know, obviously there's probably a little bit of tool fatigue as well. You know, which tools to select? >> There's plenty of tools available. >> Exactly, right? And then there's like services available for you. So now you need to make like smarter choices to cobble together this, to create like a workflow for your engineers. And you can really get started quite fast, and actually get on par with some of these modern tech companies. And that is the advantage that a lot of enterprises see. >> If you were going to be the CTO or CEO of a big transformation, knowing what you know, 'cause you just brought up the killer point about why it's such a great time right now, you got platform as a service and the tooling essentially reset everything. So if you're going to throw everything out and start fresh, you're basically brewing the system architecture. It's a complete reset. That's doable. How fast do you think you could do that for say a large enterprise? >> See, I think if you set aside the organization processes and whatever kind of comes in the friction, from a technology perspective, it's pretty fast, right? You can devise a data architecture today with like tools like Kafka, Snowflake and Redshift, and you can actually devise a data architecture very clearly right from day one and actually implement it at scale. And then once you have accumulated enough data and you can extract more value from it, you can go and implement your MLOps workflow as well on top of it. And I think this is where tools like Fiddler can help as well. So I would start with looking at data, do we have centralization of data? Do we have like governance around data? Do we have analytics around data? And then kind of get into machine learning operations. >> Krishna, always great to have you on theCUBE. You're great masterclass guest. Obviously great success in your company. Been there, done that, and doing it again. I got to ask you, since you just brought that up about the whole reset, what is the superhero persona right now? Because it used to be the full stack developer, you know? And then it's like, then I call them, it didn't go over very well in theCUBE, the half stack developer, because nobody wants to be a half stack anything, a half sounds bad, worse than full. But cloud is essentially half a stack. I mean, you got infrastructure, you got tools. Now you're talking about a persona that's going to reset, look at tools, make selections, build an architecture, build an operating environment, distributed computing operating. Who is that person? What's that persona look like? >> I mean, I think the superhero persona today is ML engineering. I'm usually surprised how much is put on an ML engineer to do actually these days. You know, when I entered the industry as a software engineer, I had three or four things in my job to do, I write code, I test it, I deploy it, I'm done. Like today as an ML engineer, I need to worry about my data. How do I collect it? I need to clean the data, I need to train my models, I need to experiment with what it is, and to deploy them, I need to make sure that they're working once they're deployed. >> Now you got to do all the DevOps behind it. >> And all the DevOps behind it. And so I'm like working halftime as a data scientist, halftime as a software engineer, halftime as like a DevOps cloud. >> Cloud architect. >> It's like a heroic job. And I think this is why this is why obviously these jobs are like now really hard jobs and people want to be more and more machine learning >> And they get paid. >> engineering. >> Commensurate with the- >> And they're paid commensurately as well. And this is where I think an opportunity for tools like Fiddler exists as well because we can help those ML engineers do their jobs better. >> Thanks for coming on theCUBE. Great to see you. We're here at re:MARS. And great to see you again. And congratulations on being on the AWS startup showcase that we're in year two, episode four, coming up. We'll have to have you back on. Krishna, great to see you. Thanks for coming on. Okay, This is theCUBE's coverage here at re:MARS. I'm John Furrier, bringing all the signal from all the noise here. Not a lot of noise at this event, it's very small, very intimate, a little bit different, but all on point with space, machine learning, robotics, the future of industrial. We'll back with more coverage after the short break. >> Man: Thank you John. (upbeat music)

>>from around the globe. It's the Cube with digital coverage of workplace next made possible by Hewlett Packard Enterprise. >>Hello, everyone. We're here covering workplace next on the Cube For years, you know, we've talked about new ways to work, and it was great thought exercise. And then overnight the pandemic heightened the challenges of creating an effective work force. Most of the executives that we talked to in our survey say that productivity actually has improved since the work from Home Mandate was initiative. But, you know, we're talking not just about productivity, but the well being of our associates and managing the unknown. We're going to shift gears a little bit now. We've heard some interesting real world examples of how organizations are dealing with the rapid change in workplace, and we've heard about some lessons to take into the future. But now we're going to get more practical and look at some of the tools that are available to help you navigate. The changes that we've been discussing and with me to talk about these trends related to the future of work are are are Qadoura, who's the vice president of worldwide sales and go to market for Green Lake at HP Sadat Malik is the VP of I O t and Intelligent Edge at HP and Satish Yarra Valley is the global cloud and infrastructure practice Head at Whip Probe guys welcomes. Good to see you. Thanks for coming on. >>Thanks for having us. >>You're very welcome. Let me start with Sadat. You're coming from Austin, Texas here. So thank you. Stay crazy. As they say in Austin, for the uninitiated, maybe you could talk a little bit about h p E point. Next. It's a strategic component of H p. E. And maybe tell us a little bit about those services. >>Thank you so much for taking the time today. Appreciate everybody's participation here. So absolutely so point Next is HP Services on. This is the 23,000 strong organization globally spread out, and we have a very strong ecosystem of partners that be leveraged to deliver services to our customers. Um, our organization differentiates itself in the market by focusing on digital digital transformation journeys for our customers. For customers looking toe move to a different way off, engaging with its customers, transforming the way its employees work, figuring out a different way off producing the products that it sells to. His customers are changing the way it operationalize these things. For example, moving to the cloud going to a hybrid model, we help them achieve any of these four transformation outcomes. So point next job is toe point. What is next in this digital transformation journey and then partner with our customers to make that happen? So that's what we do. >>Thank you for that. I mean, obviously, you're gonna be seeing a lot of activity around workplace with shift from work from home, changes in the network changes in security. I mean the whole deal. What are some of your top takeaways that you can share with our audience? >>Yeah, they're >>so a lot has been happening in the workplace arena lately. So this is not new, right? This is not something that all of a sudden side happening when Kobe 19 hit, uh, the digital workplace was already transforming before over 19 happened. What over 19 has done is that it has massively accelerated the pace at which this change was happening. So, for example, right remote work was already there before over 19. But now everybody is working remotely so, in many ways, the solution that we have for remote work. They have been strained to appoint, never seen before. Networks that support these remote work environments have been pushed to their limits. Security was already there, right? So security was a critical piece off any off the thinking, any of the frameworks that we had. But now security is pivotal and central. Any discussion that we're having about the workplace environment data is being generated all across the all across the environment that we operated, right? So it's no longer being generated. One place being stored. Another. It's all over the place now. So what Kobe, 19 has done is that the transformation that was already underway in the digital workplace, it has taken that and accelerated it massive. The key take away for me is right that we have to make sure that when we're working with our customers, our clients, we don't just look at the technology aspect of things. We have to look at all the other aspect as well the people in the process aspect off this environment. It is critical that we don't assume that just because the technology is there to address these challenges that I just mentioned. Our people and our processes would be able to handle that as well. We need to bring everybody along. Everybody has different needs, and we need to be able to cater to those needs effectively. So that's my biggest take away. Make sure that the process and the people aspect of things was hand in glove with the technology that we were able to bring to bear here. >>Got it. Thank you. So, ah, let's go to San Francisco, bringing our war to the conversation. You're one of your areas of focus is is HP Green Lake. You guys were early on with the as a service model. Clearly, we've seen Mawr interest in cloud and cloud like models. I wonder if you could just start by sharing. What's Green Lake all about? Where does it fit into this whole workplace? Next, Uh, conversation that we're having? >>Yeah, absolutely. Um HP Green lake effectively is the cloud that comes to your data center to your Coehlo or to your edge, right? We saw with Public Cloud. The public cloud brought a ton of innovations, um, into the sort of hyper scale model. Now, with HP. What we've done is we've said, Look, customers need this level of innovation and this level of, you know, pay as you go economics the, you know, management layer the automation layer not just in a public cloud environment, but also in our customers data center or to the other potential edges or Coehlo scenarios. And what we've done is we've brought together Asada just mentioned the best of our point next services our software management layer as well as H. P. E s rich portfolio of hardware to come together to create that cloud experience. Um, of course, we can't do this without the rich ecosystem around us as well. And so everything from you know, some of our big S I partners like we bro, who also have the virtual desktop expertise or virtual desk that then come together to start helping us launch some of these new workloads supported cloud services such as D. D i eso for my perspective, v. D. I is the most important topic for a lot of our customers right now, especially in sectors like financial services, um, advanced engineering scenarios and health care where they need access to those, uh to their data centers in a very secure way and in a highly cost optimized way as well. >>Well, okay. Thank you. And then let's let's bring in, uh, petition talk a little bit about the ecosystem. I mean, we're pro. That's really kind of your wheelhouse. We've been talking a lot on the cube about moving from an industry of point products to platforms and now ecosystem innovation, Uh, are are mentioned VD I we saw that exploding eso teach. Maybe you could weigh in here and and share with us what you're seeing in the market and specifically around ecosystem. >>As we all know, the pandemic has redefined the way we collaborate to support this collaboration. We have set up huge campuses and office infrastructure In summary, our industry has centralized approach. Now, the very premise of the centralization bringing people together for work has changed. This evolving workspace dynamics have triggered the agency to reimagine the workspace strategy. CEO, CEO S and C H R ose are all coming together to redefine the business process and find new ways off engaging with customers and employees as organizations embrace work from home for the foreseeable future. Customer need to create secure by design workspaces for remote working environments. With the pro virtual disk platform, we can help create such seamless distal workspaces and enable customers to connect, collaborate and communicate with ease from anywhere securely. They're consistent user experience. Through this platform led approach, we are able to utter the market demands which are focused on business outcomes. >>Okay, and this is the specifics of this hard news that you're talking about Video on demand and Citrix coming together with your ecosystem. H p E were pro and again, the many partners that you work with is that correct? >>Well, actually, Dave, we see a strong playoff ecosystem partners coming together to achieve transformative business outcomes. As Arbor said earlier, HP and Wipro have long standing partnership, and today's announcement around HP Green Lake is an extension off this collaboration, where we provide leverage HP Green Leg Andre Pro, which elders platform to offer video as a service in a paper user model. Our aim is to enable customers fast track there. It is still works based transformation efforts by eliminating the need to support upfront capital investments and old provisioning costs while allowing customers to enjoy the benefit off compromise, control, security and compliance. Together, we have implemented our solution across various industry segments and deliver exceptional customer experiences by helping customer businesses in their workspace. Transformation journeys by defining their workspace strategy with an intelligent, platform led approach that enables responsiveness, scalability and resilience. It's known that Wipro is recognized as a global leader in the distal workspace and video I, with HP being a technology leader, enabling us with high level of program ability on integration capabilities. We see tremendous potential to jointly address the industry challenges as we move forward. >>Excellent. Uh, sad. I wanna come back to you. We talk a lot about the digital business, the mandate for digital business, especially with the pandemic. Let's talk about data. Earlier this year, HP announced the number of solutions that used data to help organizations work more productively safely. You know, the gamut talk about data and the importance of data and what you guys were doing there specifically, >>Yeah, that's a great question. So that is fundamental to everything that we're doing in the workplace arena, right? So from a technology perspective that provides us with the wherewithal to be able to make all the changes that we want to make happen for the people in the process side of things. So the journey that we've been on this past year is a very interesting one. Let me share with the audience a little bit of what's been going on on the ground with our customers. Um, what's what's been happening in the field? So when the when Kobe 19 hit right, a lot of our customers were subjected to these shutdown, which were very pervasive, and they had to stop their operations. In many cases, they had to send their employees home. So at that point, HB stepped in the point. Next organization stepped in and helped these customers set up remote work out options, which allowed them to keep their businesses going while they handle these shutdowns. Fast forward. Six months and the shutdown. We're starting to get lifted and our customers were coming back to us and saying to us that Hey, we would now like to get a least a portion off our workforce back to the normal place of work. But we're concerned that if we do that, it's gonna jeopardize their safety because off the infection concerned that were there. So what we did was that we built a cities or five solutions using various types of video analytics and data analysis analysis technologies that allowed these customers to make that move. So these five solutions, uh, let me walk, walk our customers and our clients and audience through those. The first two of these solutions are touchless entry and fever detection. So this is the access control off your premise, right? So to make sure that whoever is entering the building that's in a safe manner and any infection concerned, we stop it at the very get go once the employees inside the workplace, the next thing that we have is a set of two solutions. What one is social distance tracing and tracking, and the other one is workplace alerting. What these two solutions do is that they use video analytics and data technology is to figure out if there is a concern with employees adhering to the various guidelines that are in place on alerting the employees and the employers if there is any infringement happening which could risk overall environment. Finally, we realized right that irrespective off how much technology and process we put in place. Not everybody will be able to come into the normal place of work. So what we have done is that the first solution that we have is augmented reality and visual remote guidance. This solution uses a our technologies allow. People were on site to take advantage of the expertise that resides offsite to undertake complex task task, which could be as complex as overhauling a machine on ah factory floor using augmented reality where somebody off site who's an expert in that machine is helping somebody on site data has become central to a lot of the things that we do. But as I said, technology is one aspect of things. So ultimately the people process technology continuum has to come together to make these solutions real for our customers. >>Thank you, Arwa. We just have just about 30 seconds left and I wonder if you could close on. We're talking about cloud hybrid. Uh, everybody's talking about hybrid. We're talking about the hybrid workplace. What do you see for the for the future over the next 2345 years? >>Absolutely. And I think you're right, Dave. It is, ah, hybrid world. It's a multi cloud world. Ultimately, what our customers want is the choice and the flexibility to bring in the capabilities that drive the business outcomes that they need to support. And that has multiple dimensions, right? It's making sure that they are minimizing their egress costs, right. And many of our on Prem solutions do give them that flexibility. It is the paper use economics that we talked about. It is about our collective capability as an ecosystem to come together. You know, with Citrix and NVIDIA with R s I partner we pro and the rich heritage of HP es services as well as hardware to bring together these solutions that are fully managed on behalf of our customers so that they can focus their staff their i t capabilities on the products and services they need to deliver to their customers. >>Awesome. Guys, I wish we had more time. We got to go day volonte for the cube. Keep it right there. Lots of great more content coming your way. >>Yeah,

>> SiliconANGLE Media presents... theCUBE! Covering AlibabaCloud's annual conference. Brought to you by Intel. Now, here's John Furrier... >> Hello everyone, welcome to Silicon Angle's theCUBE here on the ground, in Hangzhou, China. We're here at the Intel Booth as part of our coverage, exclusive coverage of Alibaba Cloud Conference here in the cloud city. I'm John Furrier, the co-founder of SiliconANGLE, Wikibon and theCUBE. And I'm here with CJ Bruno, who is the Corporate Vice President and General Manager of Global Accounts of the sales and marketing group at Intel. That's a mouthful but basically you run a lot of the major accounts, you bring a lot of value to Intel Supplier to these big clouds. >> I do, John. We look after our top 20 or so largest partners and customers around the world. Amazing like Alibaba, edge to cloud enterprises, deep rich engagements, just an exciting, exciting time to be in the business with these big customers. >> And there's no borders to the cloud so its not as easy as saying PC, like people might think of Intel in the old days. You guys have these major cloud providers, there's a lot of intel inside so to speak but that value is enabling a new kind of functionality. We're hearing it here at the show. >> You are. We work together with partners like Ali, in the area of such big artificial intelligence development, big data analytics and of course, the cloud. We've been working with them for over 12 years now and you can see the advancements and the services that they're providing to their customers, not only domestically, here in China but on a global stage as well. >> Its interesting, Intel, you've been working with these guys for 12 years, what a journey, from an entrepreneurial 12 guys in a dorm room, or an apartment for Jackie Ma, that he talks about all the time, to now the powerhouse. What's it like, because these guys have an interesting formula going on here. They're bringing culture and art, with science, kind of sounds like Steve Jobs, technology meets liberal arts, bringing a cultural aspect. How far have they come? Give us some insight into where they've come from and where you think they're going. >> Its amazing, Jack Ma, yesterday in his keynote, talked about this event eight years ago. 120 people, John, we're standing amongst 60,000 or so, in this event today, just eight short years later. Its amazing what they've been able to do. They're driving innovation, this is not a copy economy, it's an innovation economy. They invest, very high-degree of technical acumen. Willingness to break barriers, try things people have not. Fail fast and correct. Take risks. They're entrepreneurs at heart, they're technologists in their bloodstream and they really invest to win. >> You guys are supplying. We talked to people who talk about Photonics, Deeraj Malik, who's really going deep on these pathways around. Some of the Intel innovations, some of it's like wow, mind-blowing. The other end is just practical stuff, making it easier, faster, simpler to run things. IoT, their big use case, I mean you can't get any more sexier than looking at a city cloud that's actually running the city with traffic and all those IoT devices, so what is the big thing that you guys do for Alibaba? Talk about that journey because its not one thing, what is it? What is the magical formula? >> Sure, of course, first off we deliver, we think, world-class ingredients to their world-class cloud. And enable them to deliver amazing services to their customer, at the base level. But we really work together to solve societal problems. Look at the precision medical cloud that we announced last April together, John. Genome sequencing, solving people's cancer problems, in a matter of days, instead of months. Just one example of the real use case that we bring these technologies to bear on and have an amazing influence. We work on them with the Tenatchi Medical Imaging Competition. 3,000 entrants competing to see who can identify lung cancer quickest, and we have some winners selected, just this week. So these things are real, taking this technology, solving real life problems, and business problems, around the globe. >> And its not just the big, heaving lifting technology that moves the needle, like you were mentioning but its also the micro technologies, like FPGA, you guys have got lot of things. This is like the new Intel, so I'd love to get your thoughts, if you can just take a moment to share the journey that Intel is on right now because you gave a talk yesterday, a kind of a keynote, onstage. What is the Intel journey right now look like? >> We're transforming ourselves from a PC centric company to a company that runs the cloud and powers countless numbers, billions and billions of smart-connected devices. That's a big journey we're on. We've diversified our business significantly in a five year period, John. Driving our data-center business, our IoT business, our programmable logic business as you said, our friends from former Alterra are now two years inside Intel. Our memory business, our NSG technologies, 3D NAND Optane, driving breakthroughs in SSDs and of course new technologies that we're exploring, like drones and neuromorphic computing, making sure we never miss the next big thing. >> I've been following Intel for 30 years of my career and life, as an initial user-developer and now in the media. It's interesting, Intel has never done it alone, it's always been part of the ecosystem. You have brought a lot of goods to the party, so to speak, in technology, Moore's law and the list is endless. Now is an end to end game but you look at 5G for instance, you kind of connect the dots, put a radio frequency cloud over a city and you got to run the IoT devices like a city brain, they're showing here. You got to tie it together with programmable arrays, it's a hardware thing but now the software guys are doing it. You've got cloud native with the Linux Foundation, that's DevOps. You've got data centers that are 10 to one silicon to the edge, this is a wide opportunity, how do you guys make sense of it to customers? Because its a complex story. >> It is John, look, we're the ultimate ingredient supplier. We're bringing forward technologies in artificial intelligence, in 5G, in VR and AR, areas that are just autonomous everything. Autonomous driving in particular. These are big investment areas we're driving into that require an enormous amount to compute, storage, networking, connectivity and we're making the investments to make sure we're critical partners with our customers, in all those huge growth areas. Making us a big growth company now. >> I had a great conversation with Dr. Wong, who's the founder of Alibaba Cloud, he's on the Technology Steering Committee for Alibaba Group and yesterday they just announced a 15 billion dollar investment over three years for FinTech, across the board IoT, AI, collaborate with scientists as well as artisans. This is a big deal. >> It is John, this is exactly an example of what I mentioned earlier. These guys invest to win and they have a will to win. And they want to pioneer and they want to innovate and they put their money where their mouth is, in that announcement, its pretty exciting. >> So the cloud serves quite a market, doing really well. Your global accounts are doing well, certainly in Asia and People's Republic of China, PRC, as you guys call it, extremely well but now there's a Renaissance in cloud in general, so we're expecting to see a lot more cloud service providers, maybe not as big as Alibaba but Alibaba is going to start getting customers that become SaaS companies, that's technically a cloud service provider if you think about it, if they have an application, how do you look at that mark? >> We see what is known as the super seven in the industry, the large folks, both US based and China based but then we've identified the next 60-70 next wave CSPs that are growing vibrantly around the globe and there's a long tail of another 120 that we're interacting with. You're absolutely on point, an exploding area. Significant double-digit growth for years to come and just solving, big, big life and business problems. >> So at SiliconANGLE also silicon is in the name and Wikibon Research is really big in China, here, interesting dynamic that's happening here with the data and the software and was brought up with Dr. Wong about the IoTs, kind of a nuanced point but I want to get it out for the folks watching that you're going to start to see new compute at the edge because data is now the currency of the future. It needs to flow, it's like water but at the edge it can be expensive, low latency that table stakes that everyone wants to get to. You're going to see a lot more compute or silicon at the edge of network. Internet of things coming, your view on that? >> There's no question John, that's exactly the way we see it. The time to get the data back to the long-haul data center, is very expensive and very challenging and requires an absolute redo of the network. We're moving to compute closer and closer to the data, of course, the cloud remains a vital, vital part of that but we move that compute capability closer to where the data is sensed, you can analyze it quicker, you can make faster decisions and you can implement those decisions at the edge. >> CJ, final question for you, obviously Alibaba, big part of their growth strategy is going outside mainland China, obviously doing very well here, not to knock them there but great opportunity to go into the global marketplace, specifically North America. That's going to put more competition, competition was good but it's also going to require more growth. How are you helping Alibaba and how does your relationship at Intel expand with Alibaba? >> We work with Alibaba, not only on the technical front of course but on their go-to-market plans, on ecosystem development plans and even some business models. We do that across our entire customer and partner base, John. We're seeing this explosive growth in cloud and being able to work with our partners on all four of those fronts; technology development, ecosystem development, business model development, are obviously a benefit to both of us. >> Alibaba is going to need some help because you know its competitive, Amazon had a nice run for a while, Microsoft nibbling at the heels, Google and now Alibaba coming in. Competition is good. >> We're proud to call all those innovators our customers and we work hard everyday to earn their business. >> Final, final question, this one just popped in my head. What should folks in America know about this PRC market or China market that they may not know about? Obviously they read what they read in the paper. They see the security hacks, they see the crypto-currency temporarily on hold but blockchain certainly has a lot of promise, but it's a dynamic market here. A lot of of opportunities. What should that audience know about the China market? >> I think the first thing they should know is that if they haven't come to experience it themselves they should. The scale of the opportunity, the scale of the country is like nothing people have ever seen before. As I said, the investments they're making-to innovate, to drive an innovation economy is breakthrough. You take that scale and that investment and this is a market to be reckoned with. >> Congratulations on the 12 year run with Alibaba, and now Alibaba Cloud. Looking really, really, strong, love the culture, got to unique twist; artistry and scientific cultures coming together, looking good. >> Absolutely John, thanks for letting us tell our story. >> CJ Bruno, Group Vice President, General Manager Global Accounts for Intel. I'm John Furrier with SiliconANGLE, thanks for watching.