>>from around the globe. It's the Cube >>with digital coverage of VM World 2020 brought to you by VM Ware and its ecosystem partners. Hello and welcome back to the VM World 2020 Virtual coverage with the Cube Virtual I'm John for day. Volonte your hosts our 11th year covering VM. We'll get a great guest Greg Lavender, SBP and the CTO of VM. Where, uh, welcome to the Cube. Virtual for VM World 2020 Virtual Great. Thanks for coming on. >>Privileged to be here. Thank you. >>Um, really. You know, one of the things Dave and I were commenting with Pat on just in general start 11th year covering VM world. Uh, a little difference not face to face. But it's always been a technical conference. Always a lot of technical innovation. Project Monterey's out there. It's pretty nerdy, but it's a it's called the catnip of the future. Right? People get excited by it, right? So there's really ah lot of awareness to it because it kinda it smells like a systems overhaul. It smells like an operating system. Feels like a, you know, a lot of moving parts that are, quite frankly, what distributed computing geeks and software geeks love to hear about and to end distributed software intelligence with new kinds of hardware innovations from and video and whatnot. Where's that innovation coming from? Can you share your thoughts on this direction? >>Yeah, I think first I should say this isn't like, you know, something that just, you know, we decided to do, you know, six months ago, actually, in the office of C T 04 years ago, we actually had a project. Um, you know, future looking project to get our core hyper visor technology running on arm processors and that incubated in the office of the CTO for three years. And then last December, move the engineering team that had done that research and advanced development work in the office of the CTO over to our cloud platforms business unit, you know, and smart Knicks, you know, kind of converged with that. And so we were already, you know, well along the innovation path there, and it's really now about building the partnerships we have with smart nick vendors and driving this technology out to the benefit of our customers who don't want to leverage it. >>You get >>Greg, I want if you could clarify something for me on that. So Pat talked about Monterey, a complete re architect ing of the i o Stack. And he talked about it affecting in video. Uh, intel, melon, ox and Sandoz part of that when he talks about the Iot stack, you know, specifically what are we talking about there? >>So you know any any computing server in the data center, you know, in a cola facility or even even in the cloud, you know? Ah, large portion of the, you know CPU resource is, and even some memory resource is can get consumed by just processing. You know, the high volumes of Iot that's going out, you know, storage devices, you know, communicating between the different parts of multi tiered applications. And so there's there's a there's an overhead that that gets consumed in the course server CPU, even if its multi core multi socket. And so by offloading that a lot of that I owe work onto the arm core and taking advantage of the of the hardware offloads there in the smart Knicks, you can You can offload that processing and free up even as much as 30% of the CPU of a server, multi socket, multicourse server, and give that back to the application so that the application gets the benefit of that extra compute and memory resource is >>So what about a single sort of low cost flash tear to avoid the complexities of tearing? Is that part of the equation? >>Well, you know, you can you can, um you know, much storage now is network attached. And so you could if it's all flash storage, you know, using something like envy me fabric over over Ethernet, you can essentially build large scale storage networks more efficiently, you know more cheaply and take advantage of that offload processing, uh, to begin to reduce the Iot Leighton. See, that's required taxes. That network attached storage and not just storage. But, you know, other devices, you know, that you can use you could better network attached. So disaggregated architectures is term. >>Uh, is that a yes? Or is that a stay tuned? >>Yeah, Yeah, yeah, yeah, yes. I mean the storage. You know, more efficient use of different classes of storage and storage. Tearing is definitely a prime use case there. >>Yeah, great. Thank you. Thanks for that. John, >>How could people think about the edge now? Because one of the things that's in this end to end is the edge. Pat brought it up multi cloud and edge or two areas that are extending off cloud and hybrid. What should people think about the innovation equation around those things? Is that these offload techniques? What specifically in the systems architecture? Er, do you guys see as the key keys there? >>So so, you know, edges very diversified, heterogeneous place, Uh, in the architectures of multi cloud services. So one thing we do know is, you know, workload. I would like to say workload follows data, and a lot of the data will be analyzed, the process at the edge. So the more that you can accelerate that data processing at the edge and apply some machine learning referencing at the edge were almost certainly gonna have kubernetes everywhere, including the edge. So I think you're seeing a convergence of the hardware architectures er the kubernetes control plane and services and machine learning workloads. You know, traveling to the edge where the where the data is going to be processed and actions could be taken autonomously at the edge. So I think we're in this convergence point in the industry where all that comes together. >>How important do you >>do you see that? Okay, John, >>how important is the intelligence piece? Because again, the potatoes at the edge. How do you guys see the data architecture being built out there? >>Um, well, again, it's depending on the other. The thick edge of the thin edge. You know, you're gonna have different, different types of data, and and again, a lot of the the inference thing that could happen at the edges. Going to, I think, for mawr, you know, again to take action at the edges, opposed to calling home to a cloud, you know, to decide what to do. So, depending on, you know, the computational power and the problem with its video processing or monitoring, you know, sensors, Aaron, oil. Well, the kind of interesting that will happen at the edge will will be dependent on that data type and what kind of decisions you want to make. So I think data will be moving, you know, from the edge to the cloud for historical analytics and maybe transitional training mechanisms. But, you know, the five G is gonna play heavily into this is well right for the network connectivity. So we read This unique point is often occurs in the industry every few years of all these technology innovations converging to open up an entirely new platform in a new way of computing that happens at the edge, not just in your data center at the cloud. >>So, Greg, you did a fairly major stint at a large bank. What would something you mentioned? You know, like an oil rig. But what would something like these changes mean for a new industry like banking or financial? Uh, will it have an impact there and put on your customer hat for a minute and take us through that >>e? You know, eight machines, you know, branches, chaos. You know, there's all make banks always been a very distributed computing platform. And so, you know, people want to deliver mawr user experience, services, more video services. You know all these things at the edge to interact positively with the customer without using the people in the loop. And so the banking industry has already gone through the SD when, and I want transformation to deliver the bandwidth more capably to the edge. And I just think that they'll just now be able to deliver Mawr Edge services that happened can happen more autonomously at the edge is opposed that having the hairpin home run everything back to the data center. >>Awesome. Well, Pat talks about the modern platform, the modern companies. Greg, I wanna ask you because we're seeing with Kovar, there's to use cases, you know, the people who don't have a tailwind, Um, companies that are, you know, not doing well because there's no business that you have there modernizing their business while they have some downtime. Other ones have a tailwind. They have a modern app that that takes advantage, this covert situation. So that brings up this idea of what is a modern app look like? Because now, if you're talking about a distributed architecture, some of things you're mentioning around inference, data edge. People are starting to think about these modern naps, and they are changing the game for the business. Now you have vertical industries. You mentioned oil and gas, you got financial services. It used to be you had industry solution. It worked like that and was siloed. Now you have a little bit of a different architectures. If we believe that we're looking up, not down. Does it matter by industry? How should people think about a modern application, how they move faster? Can you share your insights into into some of this conceptual? What is a modern approach and does it doesn't matter by vertical or industry. >>Yes, I mean, certainly over the course of my career, I mean, there's there's a massive diversity of applications. And of course, you know, the explosion of mobile and edge computing is just another sort of sort of use cases that will put demands on the infrastructure in the architecture and the networking. So a modern, a modern app I mean, we historically built sort of these monolithic app. So we sort of built these sort of three tier apse with, you know, sort of the client side, the middleware side. The database back in is the system of record. I mean, this is even being more disaggregated in terms of, you know, the the consumer edges both not just web here, but mobile tear. And, you know, we'll see what emerges out of that. The one thing for sure that is that, um they're becoming less monolithic and mawr a conglomeration of sass and other services that are being brought together, whether it's from the cloud services or whether it's s, you know, SBS delivering, you know, bring your own software. Um, and they're becoming more distributed because people need operated higher degrees of scale. There's a limit to Virgil vertical scaling, so you have to go to horizontal scaling, which is what the cloud is really good at. So I think all these things were driving a whole new set of technologies like next generation AP gateways. Message Busses, service mesh. We're announcing Tanzi's service message being world. Um, you know, this is just allowing allowing that application to be disaggregated and then integrated with other APS assassin services that allow you to get faster time to market. So speed of delivery is everything. So modern C I. C d. Modern software, technology and ability to deploy and run that workload anywhere at the edge of the core in the data center in the cloud. >>So when you do in your re architecture like this, Greg, I mean you've seen over the course of history in our industry you've seen so many companies have hit a wall and in VM, whereas it's just amazing engineering culture. How are you able toe, you know, change the engine mid flight here and avoid like, serious technical debt. And I mean, it took, you know, you said started four years ago, but can you give us a peek inside? You know, that sort of transformation and how you're pulling that off? >>Well, I mean, we're providing were delivered the platform and, you know, spring Buddhas a key, you know, technology that's used widely across the industry already, which is what we've got is part of our pivotal acquisition. And so what we're just trying to do is just keep keep delivering the technology and the platform that allows people to go faster with quality security and safety and resiliency. That's what we do really well at VM ware. So I think you're seeing more people building these APS Cloud native is opposed to, you know, taking an existing legacy app In trying to re factor it, they might do what it called e think somebody's called two speed architectures. Take the user front, end the consumer front in, and put that cloud native in the cloud. But the back end system of record still runs in the private cloud in a highly resilient you know, backed up disaster recovered way. So you're having, I think, brand new cloud native APS we're seeing. And then you're seeing people very carefully because there's a cost to it of looking at How do I basically modernized the front end but maintain the reliability of the scalability of security and the reliability of that sort of system of record back in? So either way, it's it's winning for the companies because they could do faster delivery to their businesses and their clients and their partners. But you have to have the resiliency and reliability that were known for for running those mission critical workloads, >>right? So the scenario is that back end stays on premise on the last earnings call, I think, Pat said, or somebody said that, that I think I just they said on Prem or maybe the man hybrid 30 to 40% cheaper, then doing it in the cloud. I presume they were talking about those kind of back end systems that you know you don't wanna migrate. Can you add some color that again from your customer perspective That the economics? >>Yeah. You know, um, somebody asked me one time what's really a cloud. Greg and I said, automation, automation, automation you can take you can take You can take your current environments and highly automate the release. Lifecycle management develop more agile software delivery methods. And so therefore, you could you could get sort of cloud benefits, you know, from your existing applications by just highly optimizing them and, you know, on the cost of goods and services. And then again, the hybrid cloud model just gives customers more choice, which is okay. I want to reduce the number of data centers I have, but I need to maintain reliability, scalability, etcetera. Take advantage of, you know, the hybrid cloud that we offer. But you'll still run things. Cloud natives. I think you're seeing this true multi cloud technology and paradigm, you know, grow out as people have these choices. And then the question is okay. If you have those choices, how do you maintain security? How do you maintain reliability? How do you maintain up time yet be able to move quickly. And so I think there's different speeds in which those platforms will evolve. And our goal is to give you the ability to basically make those choices and and optimize for economics as well as technical. You know, capability. >>Great. I want to ask you a question with Cove it we're seeing and we've been reporting the Cube virtual evolve because we used to be it at events, but we're not there anymore. But the as everyone has realized with cove it it's exposed some projects that you might not want to double down on or highlighted some gaps in architecture. Er, I mean, certainly who would have forecast of the disruption of 100% work from home VP and provisioning to access and access management security, and it really is exposed. What kind of who's where in the journey, Right in digital transformation. So I gotta ask you, what's the most important story or thing to pay attention, Thio as the smart money and smart customers go, Hey, you know what? I'm gonna double down on that. I'm gonna kill that project or sunset. That or I'm not gonna re factor that I'm gonna contain Arise it and there's probably there's a lot of that going on. In our conversations with customers, they're like it's pretty obvious. It's critical path. It's like we stay in business. We build a modern app, but I'm doubling down. I'm transitioning. It's a whole nother ballgame. What >>is >>the most important thing that you see that people should pay attention to around maintaining an innovation and coming out on the other side? >>Yeah, well, I think I think it just generally goes to the whole thesis of software defined. I mean, you know the idea of taking an appliance physical, You know, you have to order the hardware, get it on your loading dock, install in your data center. You know, go configure it, mapping into the rest of your environment. You know, whereas or you could just spend up new, softer instances of load balancers, firewalls, etcetera. So I think you know what's What's really helped in the covert era is the maturity of software to find everything. Compute storage, networking. Lan really allowed customers and many of our customers toe, you know, rapidly make that pivot. And so you know what? It's the you know, the workspace, the remote workspace. You gotta secure it. That's a key part of it, and you've got to give it. You know, you gotta have the scalability back in your data centers or, if you don't have it, be able to run those virtual desktops you know, in the cloud. And I think so. This ability again to take your current environment and, more importantly, your operating model, which, you know the technology could be agile and fast. But if you're operating models not agile, you know you can't executed Well, One of the best comments I heard from a customer CEO was, you know, for six months we debated, you know, the virtual networking architecture and how to deploy the virtual network. And, you know, when covet hit. We made the decision that did it all in one week. So the question the CEO asked now is like Well, why do we Why do we have to operate in that six month model going forward? Let's operate in the one week model going forward. E. I think that that z yeah, that's e think that's the big That's a big inflection point is the operating model has to be agile. We got all kinds of agile technology and choices I mentioned it's like, How do you make your organization agile to take advantage of those technological offerings? That's really what I've been doing the last six months, helping our customers achieve. >>I think that's a key point worth calling out and doubling down on day because, you know, whether you talk about our q Q virtual, our operating model has changed and we're doing new things. But it's not bad. It's actually beneficial. We could talk to more people. This idea of virtual ization. I mean pun intended virtual izing workforces face to face interactions air now remote. This is a software defined operating business. This is the rial innovation. I think this is the exposure. As companies wake up and going. Why didn't we do that before? Reminds me of the old mainframe days. Days? You know, why do we have that mainframe? Because they're still clutching and grabbing onto it. They got a transition. So this is the new the new reality. >>We were joking earlier that you know it ain't broke, don't fix it. And all of a sudden Covic broke everything. And so you know, virtualization becomes a fundamental component of of of how you respond. But and I wonder if Greg you could talk about the security. Peace? How how that fits in. You know everybody you know, the bromide, of course, is security can't be a bolt on. It's gotta be designed in from the start, Pat Gelsinger said years ago in the Cube. Security is a do over. You guys have purchased many different security components you've built in. Security comes. So how should we think about? And how are you thinking about designing insecurity across that entire stack without really bolting in, You know, pieces, whether it's carbon, carbon, black or other acquisitions that you've made? >>Yeah, I mean, I think that's that's the key. Inflection point we're in is an industry. I mean, getting back to my banking experience, I was responsible for cybersecurity, engineering the platforms that we engineered and deployed across the bank globally. And the challenge, the challenge. You know, that's I had, you know, 150 plus security products, and you go to bed at night wondering what? Which one did I forget to deploy or what did I get that gap? Do you think you think you're safe by the sheer number, but when you really boil down to it is like, you know, because you have to sort of like both all this stuff together to create a secure environment, you know, on a global level. And so really, our philosophy of VM where is Okay? Well, let's kind of break that model. That's what we call it intrinsic security, which is just, you know, we have the hyper visor. If you're running, the hyper visor is running on most of the service in your data center. If we have your if you have our network virtualization, we see all the traffic going between all those hyper visors and out to the cloud as well hybrid cloud or public cloud with our NSX technology. And then, you know, then you sort of bring into that the load balancers and the software to find firewalls. And pretty soon you have realized Okay, look, we have we have most of the estate. Therefore we could see everything and bring some intelligent machine learning to that and get proactive as opposed to reactive. Because our whole model now is we. All this technology and some alert pops and we get reactive. How about proactively telling me that something nasty is going on. >>I need to ask you a >>question. May be remediated. Sorry, John. It may be remediated at some point anyway. Bring in some machine intelligence tow. So instead of like you said, getting an alert actually tells me what what happened and how it was fixed, you know? Or at least recommending what I should dio, right? >>Yeah. I mean, part of the problem in the historic architectures is it was all these little silos. You know, every business unit had its own sort of technology. And Aziz, you make things virtualized. You you sort of do the virtual networking. The virtual stories of virtual compute all the software. You know, all of a sudden you have you have a different platform, you have lots of standardization. Therefore you don't have your operating model simplifies right and amount of and then it's about just collecting all the data and then making sense of the data. So you're not overwhelming the human's capacity to respond to it. And so I think that's really the fundamental thing we're all trying to get to. But the surface area is enlarged outside the data centers we've discussed out to the edge, whatever the edges, you know, into the cloud hybrid or public. So now you've got this big surface area where you've gotta have all that telemetry and all that visibility again, Back to getting proactive. So you got to do it in Band is opposed out of band. >>Great. I want to ask you a question on cyber security. We have an event on October 4th, the virtual event that Cuba is hosting with Cal Poly around this space and cybersecurity, symposiums, intersection of space and cyber. I noticed VM Ware recently announced last month that the United States Space Force has committed to the Tan Xue platform for for Continuous Dev ops operation for agility. I interviewed Lieutenant General John Thompson, Space Force, and we talked about that. He said quote, it's hard to do break fix in space. Uh, illustrating, really? Just can't send someone to swap out something in space. Not yet, at least. So they're looking at software defined as a key operating reality. Okay, so again, talk about the edge of space Isas edges. You're gonna get it. Need to be completely mad and talk about payloads and data. This >>is kind >>of interesting data point because you have security issues because space is gonna be contested and congested as an edge device. So it's actually the government's interested in that. But fundamentally, the death hops problem that you're you guys are involved in This >>is a >>reality. It's kind of connects this reality idea of operating models based in reality have to be software. What's >>your name? Yeah. I mean, I think the term we use now is def sec ops because you can't just do Dev ops. You have to have the security component in there, So, uh, yeah, the interesting. You know, like, there's a lot of interesting things happen just in fundamental networking, right? I mean, you know, the StarLink, you know, satellites at Testa. His launched Elon musk has launched and, you know, bringing sort of, you know, higher band with laurel agency to those. Yeah, we'll call it near space the and then again, just opens up all new opportunities for what we can dio. And so, Yeah, I think that's the software that the whole the whole saw for development ecosystem again, back to this idea. I think of three things. You gotta have speed. You gotta have scale and you gotta have security. And so that's really the emerging platform, whether it's a terrestrial or in near space, Uh, that's giving us the opportunity, Thio Do new architectures create service measures of services, some terrestrial, some some you know, far remote. And as you bring these new application architectures and system platform architectures together with all the underlying hardware and networking innovations that are occurring, you mentioned flash. But even getting into pmm persistent memory, right? So this this is so much happening that is converging. What's exciting to me about being a TV? Where is the CTO and we partner with all the hardware vendors? We partner with all the system providers, like in video and others. You know, the smart nick vendors. And then we get to come up with software architectures that sort of bring that together holistically and give people a platform. We can run your workloads to get work done wherever you need to land those workloads. And that's really the excitement about >>the candy store. And yet you've got problems hard problems to work on to solve. I mean, this really brings the whole project moderate, full circle because we think about space and networks and all these things you're talking about, You need to have smart everything. I mean, isn't that software? It's a complete tie into the Monterey. >>Yeah, yeah, yeah, Exactly. You're right. It's not just it's not just connecting everything and pushing data around its than having the intelligence to do it efficiently, economically, insecurely. And that's you know. So I see that you don't want to over hype machine learning. I did not to use the term AI, but use the machine learning technologies, you know, properly trained with the proper data sets, you know, and then the proper algorithms. You know that you can then a employee, you know, at the edge small edge, thick edge, you know, in the data center at the cloud is really Then you give the visibility so that we get to that proactive world I was talking about. >>Yeah, great stuff, Greg. Great insight, great conversation. Looking forward to talking mawr Tech with you. Obviously you are in the right spot was in the center of all the action across the board final point. If you could just close it out for us. What is the most important story at VM World 2020 this year. >>Um, well, I think you know, I like to say that I have the best job. I think you know that I've had in my career. I've had some great ones is you know, we get to be disruptive innovators, and we have a culture of perpetual innovation and really being world for us, Aly employees and all the people that work together to put it together is we get to showcase. You know, some of that obviously have more up our sleeves for the future. But, you know, being world is are, you know, coming coming out out show of the latest set of innovations and technologies. So there's going to be so much I have, ah, vision and innovation. Keynote kickoff, right. Do some lightning demos. And actually, I talk about work we're doing in sustainability, and we're putting a micro grid on our campus in Palo Alto and partnership with City of Palo Alto so that when the wildfires come through or there is power outages, you know we're in oasis of power generating capacity with our solar in our batteries. And so the city of Palo Alto could take their emergency command vehicles and plug into our batteries when the power is out in Palo Alto and operate city services and city emergency services. So we're not just innovating, you know, in cortex we're innovating to become a more, you know, sustainable company and provide sustainable, you know, carbon neutral technology for our customers to adopt. And I think that's an area we wanna talk about me. We talk about it next time, but I think you know our innovations. We're gonna basically help change the world with regard to climate as well. >>Let's definitely do that. Let's follow up for another in depth conversation on the societal impact. Of course, VM Ware VM Ware's VM World's 2020 is virtual is a ton of sessions. There's a Cloud City portion. Check out the 60 solution demos. Of course, they ask the expert, Greg, you're in there with Joe Beta Raghu, all the experts, um, engage and check it out. Thank you so much for the insight here on the Cube. Virtual. Thanks for coming on. >>Appreciate the opportunity. Great conversation and good questions. >>Great stuff. Thank you very much. Innovation that vm where it's the heart of their missions always has been, but they're doing well on the business side, Dave. Okay. The cube coverage. They're not there in person. Virtual. I'm John for day. Volonte. Thanks for watching.

>>from the Cube Studios >>in Palo Alto and >>Boston connecting with thought leaders all around the world. This is a cube conversation. >>Hey, welcome back here. Ready? Jeff Frick here with the Cube. We're doing a special presentation today really talking about AI and making ai really with two companies that are right in the heart of the Dell EMC as well as Intel. So we're excited to have a couple Cube alumni back on the program. Haven't seen him in a little while. First off from Intel. Lisa Spelman. She is the corporate VP and GM for the Xeon Group in Jersey on and Memory Group. Great to see you, Lisa. >>Good to see you again, too. >>And we've got Ravi Pinter. Conte. He is the SBP server product management, also from Dell Technologies. Ravi, great to see you as well. >>Good to see you on beast. Of course, >>yes. So let's jump into it. So, yesterday, Robbie, you guys announced a bunch of new kind of ai based solutions where if you can take us through that >>Absolutely so one of the things we did Jeff was we said it's not good enough for us to have a point product. But we talked about hope, the tour of products, more importantly, everything from our workstation side to the server to these storage elements and things that we're doing with VM Ware, for example. Beyond that, we're also obviously pleased with everything we're doing on bringing the right set off validated configurations and reference architectures and ready solutions so that the customer really doesn't have to go ahead and do the due diligence. Are figuring out how the various integration points are coming for us in making a solution possible. Obviously, all this is based on the great partnership we have with Intel on using not just their, you know, super cues, but FPG's as well. >>That's great. So, Lisa, I wonder, you know, I think a lot of people you know, obviously everybody knows Intel for your CPU is, but I don't think they recognize kind of all the other stuff that can wrap around the core CPU to add value around a particular solution. Set or problems. That's what If you could tell us a little bit more about Z on family and what you guys are doing in the data center with this kind of new interesting thing called AI and machine learning. >>Yeah. Um, so thanks, Jeff and Ravi. It's, um, amazing. The way to see that artificial intelligence applications are just growing in their pervasiveness. And you see it taking it out across all sorts of industries. And it's actually being built into just about every application that is coming down the pipe. And so if you think about meeting toe, have your hardware foundation able to support that. That's where we're seeing a lot of the customer interest come in. And not just a first Xeon, but, like Robbie said on the whole portfolio and how the system and solution configuration come together. So we're approaching it from a total view of being able to move all that data, store all of that data and cross us all of that data and providing options along that entire pipeline that move, um, and within that on Z on. Specifically, we've really set that as our cornerstone foundation for AI. If it's the most deployed solution and data center CPU around the world and every single application is going to have artificial intelligence in it, it makes sense that you would have artificial intelligence acceleration built into the actual hardware so that customers get a better experience right out of the box, regardless of which industry they're in or which specialized function they might be focusing on. >>It's really it's really wild, right? Cause in process, right, you always move through your next point of failure. So, you know, having all these kind of accelerants and the ways that you can carve off parts of the workload part of the intelligence that you can optimize betters is so important as you said Lisa and also Rocket and the solution side. Nobody wants General Ai just for ai sake. It's a nice word. Interesting science experiment. But it's really in the applied. A world is. We're starting to see the value in the application of this stuff, and I wonder you have a customer. You want to highlight Absalon, tell us a little bit about their journey and what you guys did with them. >>Great, sure. I mean, if you didn't start looking at Epsilon there in the market in the marketing business, and one of the crucial things for them is to ensure that they're able to provide the right data. Based on that analysis, there run on? What is it that the customer is looking for? And they can't wait for a period of time, but they need to be doing that in the near real time basis, and that's what excellent does. And what really blew my mind was the fact that they actually service are send out close to 100 billion messages. Again, it's 100 billion messages a year. And so you can imagine the amount of data that they're analyzing, which is in petabytes of data, and they need to do real time. And that's all possible because of the kind of analytics we have driven into the power It silver's, you know, using the latest of the Intel Intel Xeon processor couple with some of the technologies from the BGS side, which again I love them to go back in and analyze this data and service to the customers very rapidly. >>You know, it's funny. I think Mark Tech is kind of an under appreciated ah world of ai and, you know, in machine to machine execution, right, That's the amount of transactions go through when you load a webpage on your site that actually ideas who you are you know, puts puts a marketplace together, sells time on that or a spot on that ad and then lets people in is a really sophisticated, as you said in massive amounts of data going through the interesting stuff. If it's done right, it's magic. And if it's done, not right, then people get pissed off. You gotta have. You gotta have use our tools. >>You got it. I mean, this is where I talked about, you know, it can be garbage in garbage out if you don't really act on the right data. Right. So that is where I think it becomes important. But also, if you don't do it in a timely fashion, but you don't service up the right content at the right time. You miss the opportunity to go ahead and grab attention, >>right? Right. Lisa kind of back to you. Um, you know, there's all kinds of open source stuff that's happening also in the in the AI and machine learning world. So we hear things about tense or flow and and all these different libraries. How are you guys, you know, kind of embracing that world as you look at ai and kind of the development. We've been at it for a while. You guys are involved in everything from autonomous vehicles to the Mar Tech. Is we discussed? How are you making sure that these things were using all the available resources to optimize the solutions? >>Yeah, I think you and Robbie we're just hitting on some of those examples of how many ways people have figured out how to apply AI now. So maybe at first it was really driven by just image recognition and image tagging. But now you see so much work being driven in recommendation engines and an object detection for much more industrial use cases, not just consumer enjoyment and also those things you mentioned and hit on where the personalization is a really fine line you walk between. How do you make an experience feel good? Personalized versus creepy personalized is a real challenge and opportunity across so many industries. And so open source like you mentioned, is a great place for that foundation because it gives people the tools to build upon. And I think our strategy is really a stack strategy that starts first with delivering the best hardware for artificial intelligence and again the other is the foundation for that. But we also have, you know, Milat type processing for out of the Edge. And then we have all the way through to very custom specific accelerators into the data center, then on top about the optimized software, which is going into each of those frameworks and doing the work so that the framework recognizes the specific acceleration we built into the CPU. Whether that steel boost or recognizes the capabilities that sit in that accelerator silicon, and then once we've done that software layer and this is where we have the opportunity for a lot of partnership is the ecosystem and the solutions work that Robbie started off by talking about. So Ai isn't, um, it's not easy for everyone. It has a lot of value, but it takes work to extract that value. And so partnerships within the ecosystem to make sure that I see these are taking those optimization is building them in and fundamentally can deliver to customers. Reliable solution is the last leg of that of that strategy, but it really is one of the most important because without it you get a lot of really good benchmark results but not a lot of good, happy customer, >>right? I'm just curious, Lee says, because you kind of sit in the catbird seat. You guys at the core, you know, kind of under all the layers running data centers run these workloads. How >>do you see >>kind of the evolution of machine learning and ai from kind of the early days, where with science projects and and really smart people on mahogany row versus now people are talking about trying to get it to, like a citizen developer, but really a citizen data science and, you know, in exposing in the power of AI to business leaders or business executioners. Analysts, if you will, so they can apply it to their day to day world in their day to day life. How do you see that kind of evolving? Because you not only in it early, but you get to see some of the stuff coming down the road in design, find wins and reference architectures. How should people think about this evolution? >>It really is one of those things where if you step back from the fundamentals of AI, they've actually been around for 50 or more years. It's just that the changes in the amount of computing capability that's available, the network capacity that's available and the fundamental efficiency that I t and infrastructure managers and get out of their cloud architectures as allowed for this pervasiveness to evolve. And I think that's been the big tipping point that pushed people over this fear. Of course, I went through the same thing that cloud did where you had maybe every business leader or CEO saying Hey, get me a cloud and I'll figure out what for later give me some AI will get a week and make it work, But we're through those initial use pieces and starting to see a business value derived from from those deployments. And I think some of the most exciting areas are in the medical services field and just the amount, especially if you think of the environment we're in right now. The amount of efficiency and in some cases, reduction in human contact that you could require for diagnostics and just customer tracking and ability, ability to follow their entire patient History is really powerful and represents the next wave and care and how we scale our limited resource of doctors nurses technician. And the point we're making of what's coming next is where you start to see even more mass personalization and recommendations in that way that feel very not spooky to people but actually comforting. And they take value from them because it allows them to immediately act. Robbie reference to the speed at which you have to utilize the data. When people get immediately act more efficiently. They're generally happier with the service. So we see so much opportunity and we're continuing to address across, you know, again that hardware, software and solution stack so we can stay a step ahead of our customers, >>Right? That's great, Ravi. I want to give you the final word because you guys have to put the solutions together, it actually delivering to the customer. So not only, you know the hardware and the software, but any other kind of ecosystem components that you have to bring together. So I wonder if you can talk about that approach and how you know it's it's really the solution. At the end of the day, not specs, not speeds and feeds. That's not really what people care about. It's really a good solution. >>Yeah, three like Jeff, because end of the day I mean, it's like this. Most of us probably use the A team to retry money, but we really don't know what really sits behind 80 and my point being that you really care at that particular point in time to be able to put a radio do machine and get your dollar bills out, for example. Likewise, when you start looking at what the customer really needs to know, what Lisa hit upon is actually right. I mean what they're looking for. And you said this on the whole solution side house. To our our mantra to this is very simple. We want to make sure that we use the right basic building blocks, ensuring that we bring the right solutions using three things the right products which essentially means that we need to use the right partners to get the right processes in GPU Xen. But then >>we get >>to the next level by ensuring that we can actually do things we can either provide no ready solutions are validated reference architectures being that you have the sausage making process that you now don't need to have the customer go through, right? In a way. We have done the cooking and we provide a recipe book and you just go through the ingredient process of peering does and then off your off right to go get your solution done. And finally, the final stages there might be helped that customers still need in terms of services. That's something else Dell technology provides. And the whole idea is that customers want to go out and have them help deploying the solutions. We can also do that we're services. So that's probably the way we approach our data. The way we approach, you know, providing the building blocks are using the right technologies from our partners, then making sure that we have the right solutions that our customers can look at. And finally, they need deployment. Help weaken due their services. >>Well, Robbie, Lisa, thanks for taking a few minutes. That was a great tee up, Rob, because I think we're gonna go to a customer a couple of customer interviews enjoying that nice meal that you prepared with that combination of hardware, software, services and support. So thank you for your time and a great to catch up. All right, let's go and run the tape. Hi, Jeff. I wanted to talk about two examples of collaboration that we have with the partners that have yielded Ah, really examples of ah put through HPC and AI activities. So the first example that I wanted to cover is within your AHMAD team up in Canada with that team. We collaborated with Intel on a tuning of algorithm and code in order to accelerate the mapping of the human brain. So we have a cluster down here in Texas called Zenith based on Z on and obtain memory on. And we were able to that customer with the three of us are friends and Intel the norm, our team on the Dell HPC on data innovation, injuring team to go and accelerate the mapping of the human brain. So imagine patients playing video games or doing all sorts of activities that help understand how the brain sends the signal in order to trigger a response of the nervous system. And it's not only good, good way to map the human brain, but think about what you can get with that type of information in order to help cure Alzheimer's or dementia down the road. So this is really something I'm passionate about. Is using technology to help all of us on all of those that are suffering from those really tough diseases? Yeah, yeah, way >>boil. I'm a project manager for the project, and the idea is actually to scan six participants really intensively in both the memory scanner and the G scanner and see if we can use human brain data to get closer to something called Generalized Intelligence. What we have in the AI world, the systems that are mathematically computational, built often they do one task really, really well, but they struggle with other tasks. Really good example. This is video games. Artificial neural nets can often outperform humans and video games, but they don't really play in a natural way. Artificial neural net. Playing Mario Brothers The way that it beats the system is by actually kind of gliding its way through as quickly as possible. And it doesn't like collect pennies. For example, if you play Mary Brothers as a child, you know that collecting those coins is part of your game. And so the idea is to get artificial neural nets to behave more like humans. So like we have Transfer of knowledge is just something that humans do really, really well and very naturally. It doesn't take 50,000 examples for a child to know the difference between a dog and a hot dog when you eat when you play with. But an artificial neural net can often take massive computational power and many examples before it understands >>that video games are awesome, because when you do video game, you're doing a vision task instant. You're also doing a >>lot of planning and strategy thinking, but >>you're also taking decisions you several times a second, and we record that we try to see. Can we from brain activity predict >>what people were doing? We can break almost 90% accuracy with this type of architecture. >>Yeah, yeah, >>Use I was the lead posts. Talk on this collaboration with Dell and Intel. She's trying to work on a model called Graph Convolution Neural nets. >>We have being involved like two computing systems to compare it, like how the performance >>was voting for The lab relies on both servers that we have internally here, so I have a GPU server, but what we really rely on is compute Canada and Compute Canada is just not powerful enough to be able to run the models that he was trying to run so it would take her days. Weeks it would crash, would have to wait in line. Dell was visiting, and I was invited into the meeting very kindly, and they >>told us that they started working with a new >>type of hardware to train our neural nets. >>Dell's using traditional CPU use, pairing it with a new >>type off memory developed by Intel. Which thing? They also >>their new CPU architectures and really optimized to do deep learning. So all of that sounds great because we had this problem. We run out of memory, >>the innovation lab having access to experts to help answer questions immediately. That's not something to gate. >>We were able to train the attic snatch within 20 minutes. But before we do the same thing, all the GPU we need to wait almost three hours to each one simple way we >>were able to train the short original neural net. Dell has been really great cause anytime we need more memory, we send an email, Dell says. Yeah, sure, no problem. We'll extended how much memory do you need? It's been really simple from our end, and I think it's really great to be at the edge of science and technology. We're not just doing the same old. We're pushing the boundaries. Like often. We don't know where we're going to be in six months. In the big data world computing power makes a big difference. >>Yeah, yeah, yeah, yeah. The second example I'd like to cover is the one that will call the data accelerator. That's a publisher that we have with the University of Cambridge, England. There we partnered with Intel on Cambridge, and we built up at the time the number one Io 500 storage solution on. And it's pretty amazing because it was built on standard building blocks, power edge servers until Xeon processors some envy me drives from our partners and Intel. And what we did is we. Both of this system with a very, very smart and elaborate suffering code that gives an ultra fast performance for our customers, are looking for a front and fast scratch to their HPC storage solutions. We're also very mindful that this innovation is great for others to leverage, so the suffering Could will soon be available on Get Hub on. And, as I said, this was number one on the Iot 500 was initially released >>within Cambridge with always out of focus on opening up our technologies to UK industry, where we can encourage UK companies to take advantage of advanced research computing technologies way have many customers in the fields of automotive gas life sciences find our systems really help them accelerate their product development process. Manage Poor Khalidiya. I'm the director of research computing at Cambridge University. Yeah, we are a research computing cloud provider, but the emphasis is on the consulting on the processes around how to exploit that technology rather than the better results. Our value is in how we help businesses use advanced computing resources rather than the provision. Those results we see increasingly more and more data being produced across a wide range of verticals, life sciences, astronomy, manufacturing. So the data accelerators that was created as a component within our data center compute environment. Data processing is becoming more and more central element within research computing. We're getting very large data sets, traditional spinning disk file systems can't keep up and we find applications being slowed down due to a lack of data, So the data accelerator was born to take advantage of new solid state storage devices. I tried to work out how we can have a a staging mechanism for keeping your data on spinning disk when it's not required pre staging it on fast envy any stories? Devices so that can feed the applications at the rate quiet for maximum performance. So we have the highest AI capability available anywhere in the UK, where we match II compute performance Very high stories performance Because for AI, high performance storage is a key element to get the performance up. Currently, the data accelerated is the fastest HPC storage system in the world way are able to obtain 500 gigabytes a second read write with AI ops up in the 20 million range. We provide advanced computing technologies allow some of the brightest minds in the world really pushed scientific and medical research. We enable some of the greatest academics in the world to make tomorrow's discoveries. Yeah, yeah, yeah. >>Alright, Welcome back, Jeff Frick here and we're excited for this next segment. We're joined by Jeremy Raider. He is the GM digital transformation and scale solutions for Intel Corporation. Jeremy, great to see you. Hey, thanks for having me. I love I love the flowers in the backyard. I thought maybe you ran over to the Japanese, the Japanese garden or the Rose Garden, Right To very beautiful places to visit in Portland. >>Yeah. You know, you only get him for a couple. Ah, couple weeks here, so we get the timing just right. >>Excellent. All right, so let's jump into it. Really? And in this conversation really is all about making Ai Riel. Um, and you guys are working with Dell and you're working with not only Dell, right? There's the hardware and software, but a lot of these smaller a solution provider. So what is some of the key attributes that that needs to make ai riel for your customers out there? >>Yeah, so, you know, it's a it's a complex space. So when you can bring the best of the intel portfolio, which is which is expanding a lot, you know, it's not just the few anymore you're getting into Memory technologies, network technologies and kind of a little less known as how many resources we have focused on the software side of things optimizing frameworks and optimizing, and in these key ingredients and libraries that you can stitch into that portfolio to really get more performance in value, out of your machine learning and deep learning space. And so you know what we've really done here with Dell? It has started to bring a bunch of that portfolio together with Dell's capabilities, and then bring in that ai's V partner, that software vendor where we can really take and stitch and bring the most value out of that broad portfolio, ultimately using using the complexity of what it takes to deploy an AI capability. So a lot going on. They're bringing kind of the three legged stool of the software vendor hardware vendor dental into the mix, and you get a really strong outcome, >>right? So before we get to the solutions piece, let's stick a little bit into the Intel world. And I don't know if a lot of people are aware that obviously you guys make CPUs and you've been making great CPIs forever. But there's a whole lot more stuff that you've added, you know, kind of around the core CPU. If you will in terms of of actual libraries and ways to really optimize the seond processors to operate in an AI world. I wonder if you can kind of take us a little bit below the surface on how that works. What are some of the examples of things you can do to get more from your Gambira Intel processors for ai specific applications of workloads? >>Yeah, well, you know, there's a ton of software optimization that goes into this. You know that having the great CPU is definitely step one. But ultimately you want to get down into the libraries like tensor flow. We have data analytics, acceleration libraries. You know, that really allows you to get kind of again under the covers a little bit and look at it. How do we have to get the most out of the kinds of capabilities that are ultimately used in machine learning in deep learning capabilities, and then bring that forward and trying and enable that with our software vendors so that they can take advantage of those acceleration components and ultimately, you know, move from, you know, less training time or could be a the cost factor. But those are the kind of capabilities we want to expose to software vendors do these kinds of partnerships. >>Okay. Ah, and that's terrific. And I do think that's a big part of the story that a lot of people are probably not as aware of that. There are a lot of these optimization opportunities that you guys have been leveraging for a while. So shifting gears a little bit, right? AI and machine learning is all about the data. And in doing a little research for this, I found actually you on stage talking about some company that had, like, 350 of road off, 315 petabytes of data, 140,000 sources of those data. And I think probably not great quote of six months access time to get that's right and actually work with it. And the company you're referencing was intel. So you guys know a lot about debt data, managing data, everything from your manufacturing, and obviously supporting a global organization for I t and run and ah, a lot of complexity and secrets and good stuff. So you know what have you guys leveraged as intel in the way you work with data and getting a good data pipeline. That's enabling you to kind of put that into these other solutions that you're providing to the customers, >>right? Well, it is, You know, it's absolutely a journey, and it doesn't happen overnight, and that's what we've you know. We've seen it at Intel on We see it with many of our customers that are on the same journey that we've been on. And so you know, this idea of building that pipeline it really starts with what kind of problems that you're trying to solve. What are the big issues that are holding you back that company where you see that competitive advantage that you're trying to get to? And then ultimately, how do you build the structure to enable the right kind of pipeline of that data? Because that's that's what machine learning and deep learning is that data journey. So really a lot of focus around you know how we can understand those business challenges bring forward those kinds of capabilities along the way through to where we structure our entire company around those assets and then ultimately some of the partnerships that we're gonna be talking about these companies that are out there to help us really squeeze the most out of that data as quickly as possible because otherwise it goes stale real fast, sits on the shelf and you're not getting that value out of right. So, yeah, we've been on the journey. It's Ah, it's a long journey, but ultimately we could take a lot of those those kind of learnings and we can apply them to our silicon technology. The software optimization is that we're doing and ultimately, how we talk to our enterprise customers about how they can solve overcome some of the same challenges that we did. >>Well, let's talk about some of those challenges specifically because, you know, I think part of the the challenge is that kind of knocked big data, if you will in Hadoop, if you will kind of off the rails. Little bit was there's a whole lot that goes into it. Besides just doing the analysis, there's a lot of data practice data collection, data organization, a whole bunch of things that have to happen before. You can actually start to do the sexy stuff of AI. So you know, what are some of those challenges. How are you helping people get over kind of these baby steps before they can really get into the deep end of the pool? >>Yeah, well, you know, one is you have to have the resource is so you know, do you even have the resource is if you can acquire those Resource is can you keep them interested in the kind of work that you're doing? So that's a big challenge on and actually will talk about how that fits into some of the partnerships that we've been establishing in the ecosystem. It's also you get stuck in this poc do loop, right? You finally get those resource is and they start to get access to that data that we talked about. It start to play out some scenarios, a theorize a little bit. Maybe they show you some really interesting value, but it never seems to make its way into a full production mode. And I think that is a challenge that has faced so many enterprises that are stuck in that loop. And so that's where we look at who's out there in the ecosystem that can help more readily move through that whole process of the evaluation that proved the r a y, the POC and ultimately move that thing that capability into production mode as quickly as possible that you know that to me is one of those fundamental aspects of if you're stuck in the POC. Nothing's happening from this. This is not helping your company. We want to move things more quickly, >>right? Right. And let's just talk about some of these companies that you guys are working with that you've got some reference architectures is data robot a Grid dynamics H 20 just down the road in Antigua. So a lot of the companies we've worked with with Cube and I think you know another part that's interesting. It again we can learn from kind of old days of big data is kind of generalized. Ai versus solution specific. Ai and I think you know where there's a real opportunity is not AI for a sake, but really it's got to be applied to a specific solution, a specific problem so that you have, you know, better chatbots, better customer service experience, you know, better something. So when you were working with these folks and trying to design solutions or some of the opportunities that you saw to work with some of these folks to now have an applied a application slash solution versus just kind of AI for ai's sake. >>Yeah. I mean, that could be anything from fraud, detection and financial services, or even taking a step back and looking more horizontally like back to that data challenge. If if you're stuck at the AI built a fantastic Data lake, but I haven't been able to pull anything back out of it, who are some of the companies that are out there that can help overcome some of those big data challenges and ultimately get you to where you know, you don't have a data scientist spending 60% of their time on data acquisition pre processing? That's not where we want them, right? We want them on building out that next theory. We want them on looking at the next business challenge. We want them on selecting the right models, but ultimately they have to do that as quickly as possible so that they can move that that capability forward into the next phase. So, really, it's about that that connection of looking at those those problems or challenges in the whole pipeline. And these companies like data robot in H 20 quasi. Oh, they're all addressing specific challenges in the end to end. That's why they've kind of bubbled up as ones that we want to continue to collaborate with, because it can help enterprises overcome those issues more fast. You know more readily. >>Great. Well, Jeremy, thanks for taking a few minutes and giving us the Intel side of the story. Um, it's a great company has been around forever. I worked there many, many moons ago. That's Ah, that's a story for another time, but really appreciate it and I'll interview you will go there. Alright, so super. Thanks a lot. So he's Jeremy. I'm Jeff Frick. So now it's time to go ahead and jump into the crowd chat. It's crowdchat dot net slash make ai real. Um, we'll see you in the chat. And thanks for watching

>> Live from Orlando, Florida. It's the Cube. Covering Microsoft Ignite. Brought to you by Cohesity and the Cube's ecosystem partners. >> Welcome back, everyone, to the Cube's live coverage of the Microsoft Ignite here in Orlando. I'm your host Rebecca Night. Co-hosting today with Stu Miniman. We're joined by Calvin Roland. He is the SBP of Business Development at F5. Thanks so much for coming on the Cube. >> Lovely to be here. >> So set the scene for our viewers. What is F5? What are you about? You're based in Seattle. What do you do? >> Based in Seattle. Founded in 1996. Went public in 1999. We were known as the load balancer back then. We were the grandfathers that created that market space. We evolved it to an application centric focus, so now known as an application delivery control, or ADC, market and we're the leader in that space. >> You were $107 million in sales in 2001. Today $2 billion plus company. >> A little bit of growth. Been quite a ride. But we're not satisfied. We're looking to double that and more through the course of the next few years. >> So Calvin, like I said I've got a networking background, so obviously watch the ADC market. I might have been a little bit further down in the layer one through three stuff, but watched layers four through seven. I actually forgot that you guys are based in Seattle. There's been a little bit of activity over the last ten or fifteen years. Maybe you can explain how cloud's been impacting your space. (Inaudible) virtualized and all the Cloud guys are just going to eat your business alive? >> So I'm glad you asked that, actually. So a lot of people have said, gosh, the public cloud. Isn't that a problem for you? Is that going to be a head win at best for you guys? And the answer is well, if we don't continue to innovate the way we have since 1996, well, then yes, of course that's going to be a problem for us. But it's actually also a tremendous opportunity for us, and let me tell you why. So in the past, we were a physical product deployed in a data center. It had a floor. It had a roof. It had air conditioning. We put our product in a rack. And you had to buy all of the services in that box, if you will, and so then even as servers and data centers virtualized and we had virtual editions of our product, big IPEV, you still had to buy every feature that was in the product. But now with the advent of the cloud, we have an opportunity now to dis-aggregate those services and then re-aggregate them in any number of ways that are bespoke or specific for a given implementation construct, so the cloud puts us in a position to get in front of more application workloads, to get to more customers. Different personas like DevOps and ApDev, that we would not have been able to get in front of. So it puts us in a position to deliver on this vision we have, which is supplying applications and services for every application anywhere. >> Well Calvin, it's interesting. There's another Seattle-based company posting a 30,000 (inaudible). Microsoft has been going through their own digital transformation. >> Correct. >> We think about Windows on the PC, Windows on the server. Well, we've talked a lot about Windows 2019 and things like that, but Microsoft's gone through a digital transformation and it sounds like F5's going through a lot of those. Maybe help connect the dots as to the Microsoft ecosystem, how F5 plays into that. >> Okay, sure. Well, we have a long history of going to market together. It's a coincidence, but it doesn't hurt, that we're across Lake Washington from one another. F5 in Seattle, Microsoft in Redmond. But back in the early 2000s, Microsoft and F5 started working together saying hey, server constructs have moved to a three tier architecture being accessed through a web browser. There is a traffic management requirement to make sure that these applications, these servers, are always available, running fast, and then more secure than what it would otherwise be. We should be working with one another to make sure that we have best practice implementation guidance for our customers. And we focus on the enterprise, obviously. So it started there. And as the world started to evolve, server virtualization, data center virtualization, and now the cloud, we've continued to work hand in hand. And so now, regardless of whether or not you're deploying Azure Stack on prem, enabling a private cloud, and it's probably an and statement, it's not an or statement. deploying applications in Azure, you get the same experience as a result of that collaborative posture. >> So working hand in hand for digital transformation, you talked about the best practices. What have you learned? What emerged? What patterns? What behaviors that you have learned that you could also extend to other companies >> Okay, so beautiful thing about the cloud, about digital transformation, is there is now something that can satisfy that insatiable appetite in the marketplace for more and more applications. More complex architectures, as well. The good news: the technology is there. The economy makes sense. But that introduces complexity, right? That can actually be a gating factor for the enjoyment of that digital transformation. So, a best practice is implementing consistent methodologies for application and security services for the apps that you are standing up in this multi-cloud architecture. By having consistent methodologies you actually give yourself an opportunity to continue that pace of innovation. So the beauty is you're deploying more applications than ever before, more capability, more productivity. You're also increasing the opportunity for things to fail. You're also increasing your exposure footprint, if you will. 53% of cyber attacks are focused on the application, for example. Having consistent methodologies for ensuring that you have an appropriate security posture is something that obviously is a table stake. So F5 has been focusing on that as we go forward. >> Calvin, one of the things we look at is it's not just where things live but a lot of times, how do I take advantage of what the new platform can offer. You talked about in the cloud I can choose what features I'd need. As customers that are building new applications, whether that's micro services, containerized server (inaudible) or the like, what opportunities are there for F5 to get in there more. I don't know if it's new features or the like but, yeah. >> Sure, so the thing that we need to do is, speaking a little philosophically, is we need to meet customers where and when and how they want to be met and with what they want to be met with. I can flip it around and say the same thing for the applications. In this new application capital economy that we have, the application decides where it should be deployed, right? And so we need to do the technology and business model, they both go in hand in hand, innovation to ensure that we do just that. Meet the work load where and when and how it wants to be met and with the features and functionality that it needs to be met with. And so we have iterated our product roadmap portfolios, so we still have our physical big IP product, we still have the VE virtual edition of the product, we now have a cloud specific version, cloud edition. We are developing and will be available in our FY19 a DevOps CICD-focused version of the product. We have a SAS offering that is development being incubated as we speak. So we are looking to attack all of those vectors, so at the moment of ideation and instrumentation and orchestration we can be there to make sure that those personas know that they can take advantage of the application and security services that we provide. >> Calvin I want to have you take us one level deeper on securities. So obviously, critically important. Something we've been talking a lot about trust with Microsoft and how does security play into the product line from F5? >> It has for some time. We're just now shining a brighter light on it. >> Right. >> Because we were the indoor and outdoor for the majority of data centers, I'm dating myself by saying data center, for applications in the past our customers have said, hey, you're providing layer four through seven application services for us. This is an obvious place for you to supply security services like a web application firewall, access services, DDOS services, et cetera. And so we have done that and we've become a leader, for example, in the web application firewall, WAF, space. And so you'll continue to see us now focus on stand-alone security offerings that take advantage of that footprint that we've established in the marketplace, with this multi-cloud construct in mind. >> So you've painted this picture of a landscape. A multi-cloud world. Customers have so much choice. They're also struggling to keep up with the pace of innovation. I'm curious how you at F5 keep up with the pace of innovation and then also how you help customers do the same. >> No problem. It's easy. I'd like to say that we're better at it than everybody else, but we're in the pool swimming as fast as we can with everybody else. I used this phrase before. The market has this insatiable appetite for more and more applications. Now the good news is, well, the bad news is there is not commensurately more human capital to satisfy that insatiable appetite. No different for us. Luckily, technology and the economy for that technology has put us in a position to have a prayer, if you will. So CICD technology, obviously the agility that the cloud brings to us, the notion of being able to spread the tent that is DevOps to envelope the NetOps profession in a way that we now have coined this phrase SuperNetOps. So we've given the traditional NetOps profession the opportunity to partner more effectively with the DevOps persona that is driving a lot of this innovation to say, hey, as you're instrumenting these applications you need to make sure that you're thinking about these layer seven services, be they traffic management or security focused from day zero. And we can help you do so. So there's that on the implementation side and over on the development side, I mean we're just hiring like crazy and changing our methodologies like crazy, as well, just like everybody else. >> So I want to ask you about the hiring. At this point in time so many tech leaders really struggle with finding talent with the right kinds of skills and also the right kind of mindset because it is actually the people that drive the innovation. >> Right. >> So how do you recruit, and how do you retain the talent to make sure that they are there to make F5 the successful organization you want it to be? >> Are you going to make me put on my amateur Chief HR Officer hat? It's a challenge for us just like it is everybody else. Now we're lucky. We're in cloud city. We fell backwards to being in the most amazing spot on this rock that's hurtling through space. And so we benefit from the proximity to us being cloud central, if you will. And so almost through osmosis, we've picked up the ability to have that cloud shining on us to attract talent. But we have to diversify our R&D strategy as well. And so we're not just hiring in Seattle. We're not just hiring in San Jose. We're not just hiring in Spokane and Lowell, Tel Aviv. We have, like many others, we've stood up an F5 innovation center in India as well, for us to help us continue to drive that velocity of hiring for tech talent. We're going to continue to make investments in the R&D centers that we have stateside and in Israel and also in Warsaw, Poland, but for us to be able to continue to drive the R&D for the growth aspirations that we have we're hiring in India, as well. >> Calvin, this is actually the first time we've had the Cube at this event. We've done lots of industry events. The infrastructure side, the operating system side, the server side, the cloud and the like. You've had a large partnership with Microsoft for years, so, maybe help for people that haven't come, give them a little bit about what they're missing by not being at Microsoft Ignite. What kind of the vibe is that you get from customers at the show, meetings you're having, people you're talking to. >> Sure. Well I benefit from getting to be at a Ignite and InVision as well. The business focus sister event, if you will. But specifically to Ignite, all I could say is if you could turn the cameras around you would be able to see the energy that is taking place here. I actually feel like I'm shouting a little bit so hopefully I'm not bursting the ear drum of the listeners right now because it's loud in here. There's a lot of energy. There's a tremendous number of technology companies here, just like F5, that see an opportunity to be drivers of digital transformation. So people are curious about some of the challenges that we've talked about. And you're not here? Well then you've missed an opportunity. >> Anything that you would differentiate Microsoft and its ecosystem in this show? And the Invision, too. The business side compared to some of the other shows of the world? We go to- (crosstalk) >> It's breadth and depth. So either you get a very focused, very deep technology subject that you drill in on at an event like this. Or you get wide and shallow. And what I'd say about here is because of the decades, really, of enterprise focus and innovation and forward thinking of Microsoft, you get the breadth but you also get the depth as well. >> And actually you're the first guest we've actually had that mentioned the sister event. Maybe give us a little bit of color of what goes on there. >> So, I'll over-simplify it. The planners of the events are going to cringe. But I guess the simple differentiation is tech focus at Ignite. Business focus at Invision, if you will. So a lot of business leaders there that are being spoken to with the language that they need to be spoken to with. Helping them understand the breadth and depth of the technology that's happening here at Ignite but translating it into business transformation. So here we're focused a little bit more on technology innovation over at Invision, I don't even know if I'm pointing at the right direction, business model innovation. >> So if F5 were to have its own conference, its own Ignite-like event, what would you want to communicate about the vision and the strategy and the product services that F5 provides? >> So I've touched on it so I'll just reiterate it. We are excited about the phenomenon that is multi-cloud implementation constructs, digital transformation. We're excited about being a driver for that phenomenon. Enabling it to happen at a pace that it otherwise would not be able to happen in. And so the innovation that we're doing from a technology perspective, the product portfolio that I described, big IP, VE, cloud edition, Big IQ, our management and orchestration platform, our CICD-focused cloud specific implementation, our SAS, our managed service offering that is Silver Line. All of that technology and innovation we're tremendously excited about along with business model innovation. Licensing models like enterprise license agreements, subscription, et cetera. All of this puts us in a position within the Venn diagram that is digital transformation to actually achieve that nirvana which is providing application services for every application, anywhere. And so if you come to our event that's what you're going to learn about. >> But actually F5 Agility was in our backyard in Boston. >> Oh, man! >> You just missed it. You just missed it. Yes. >> Excellent, excellent. Well we'll be there next time. >> I'm counting on it. Don't say it if you don't mean it. >> Great. Well Calvin, thank you so much for coming on the show. It was a real pleasure having you here. >> It was a pleasure being here. Thank you. >> I'm Rebecca Night for Stu Miniman. We will have more from Microsoft Ignite in the Cube's live coverage in just a little bit.