(upbeat music) >> Live from Barcelona Spain, it's theCUBE. Covering Cisco Live! Europe. Brought to you by Cisco and its ecosystem partners. >> Hi everyone welcome back to the theCUBE's live coverage here in Barcelona, Spain for Cisco Live! Europe 2019. I'm John Furrier and my co-host Stu Miniman, Dave Vellante is out there as well co-hosting this week. Our next guest is John Apostolopoulos who's the VP and CTO for the Enterprise Networking Business, Unit Lab Director for the Innovation Labs. Here to talk with us about AI and some great innovations. John thanks for coming on theCUBE, great to see you. >> Thank you for inviting me, pleasure to be here. >> So, Cisco has some big announcements, the messages coming together certainly the bridge for the future, bridge for tomorrow, whatever the phrase is. You know, kind of looking at that new world connecting on premise, cloud, ACI anywhere, hyper-flex anywhere, lot of complexity, being mis-tracked the way with software, separate from the V-Comp from the hardware, lot of scale in the cloud and IoT and all around the edge. So software is a big part of this. >> Oh yes. >> So can't help but think, okay complexity, scale, you see Facebook using machine learning. Machine learning and AI operations now, a real conversation for Cisco. >> Yeah. >> Talk about what that is, how are you guys looking at AI, and machine learning in particular, it's been around for a while. What's your thoughts on Cisco's position and opportunity? >> Sure, yeah. Cisco's been investing or using AI for many, many years. What happens to Cisco, like most companies, we haven't really talked about the machine learning as a term because machine learning is a tool used to solve different problems. So you talk about, what are the customer problems we have? And then we saw, no matter how good our solution is, but we haven't really talked about the details about the how but, we've using at Cisco, like myself from past careers and so forth for many many years some machine learning. Security has been using it for multiple decades for example. >> And where's the use case for machine learnering, because it's one of things where there's different versions and flavors of machine learning. Machine learning we know powers AI and data feeds machine learning, so do you have all these dependencies and all these things going on, how do you...how should someone think about sorting through machine learning? >> Well machine learner itself that term is a very broad term, it's almost as big as computer science, right? So that's where a lot of the confusion comes in. But what happens is you can look up what types of problems we want to solve, and when you try to look at what types of problems we want to solve, some of them...for example some problems you can exploit the fact that the laws of physics that apply and if the laws of physics apply, you should use those laws. We can either figure out that if we drop this, this will fall at some speed by measuring it and using a machine learning or we have gravitational force and friction with the air and re-account for that and figure it out. So the many ways to solve these problems and we want to choose the best method for solving each one of them. >> And when the people think about Cisco, the first reaction isn't "Oh machine learning... innovator." What are you guys using machine learning for? Where has it been successful? What are you investing in? Where's the innovation? >> Sure sure, so there's a lot of problems here that come into play. If you look at...if you look at a customer problems, one example is all the digital disruption. We have on the order of a million devices, new devices coming on to the network every hour throughout the world. Now, what are those devices? How should you treat them? With machine learning we're able to identify what the devices are and then figure out what the network caches should be. For instance when IoT device you want to protect it, protect it from others. Another big topic is operations. As you know people spend, I think it was The Gardner identified that people spend about sixty-billion dollars per year on operations costs, why is it so much? Because most of the operations are manual, about 95% manual, which also means that these changes are slow and error-prone. What we do there is we basically use machine learning to do intelligent automation and we get a whole bunch of insights about what's happening and use that to drive intelligent automation. You may have heard about Assurance, which was announced at Cisco Live, one year ago at Barcelona and both in the campus with DNA Center we announced Cisco DNA Center Assurance and the data center went out, network and network analytic engine. And what both of these do is they look at what's happened to the network, they apply machine learning to identify patterns and from those patterns, identify, is there a problem, where's the problem? How can we...what's the root cause and then how can we solve that problem quickly? >> John, can you help us connect where this fits in a multi-cloud environment? Because what we've seen the past couple of years is when we talk about managing the network, a lot of what I might be in charge of managing, is really outside of my purview and therefore I could imagine something like ML is going to be critically important because I'm not going to be touching it but therefore I still need to have data about it and a lot of that needs to happen. >> Yeah, well one of the places ML helps with multi-cloud is the fact you need to figure out which...where to send your packets, and this comes with SD-WAN. So with SD-WAN we often have multiple paths available to us and let's say with the move to Office365, people are using the SaaS service and they want to have very good interactivity. One of the things we realized is that by carefully selecting which path we can use, at the branch and the campus too, we could get a 40% reduction in the latency. So that's a way we choose which colo or which region or which side of Office365 to send the packets to, to dramatically reduce latency. >> What's the role of data? Because when you think about it, you know, moving a packet from point A to point B, that's networking. Storage acts differently 'cause you store data data's got to come back out and be discovered. Now if you have this horizontal scalability for cloud, edge, core coming into the middle, get of the data 'cause machine learning needs the data, good data, not dirty data you need clean data. How do you see that evolving, how should customers then be thinking about preparing for either low-hanging use-cases. Just what's your thoughts and reaction to that? >> Yeah well the example you gave is a very interesting example. You described how you need to get data from one point to another, for instance, for my device to a data center with applications over the cloud. And you also mentioned how the many things between. What we care about, not necessarily the application data, we care about... You know we want to have the best network performance so your applications are working as well as possible. In that case we want to have an understanding of what's happening across a path so we want to pull to telemetry in all kinds of contexts to be able to understand, is there problem, where's the problem, what is it, and how to solve it. And that's what Assurance does. We pull this data from the access points the switches, from the routers, we pour, pull in all kinds of contextual information to get a rich understanding of the situation, and try to identify if there's a problem or not, and then how to solve it. >> Its the classic behavioral, contextual, paradigm of data but now you guys are looking at it from a network perspective and as the patterns changed the applications centric, programmability of the network, the traffic patterns are changing. Hence the announcements here but intent-based networking and hyper-flexed anywhere. This is now a new dynamic. Talk about the impact of that from an AI perspective. How are you guys getting out front on that? It's not just North, South, East, West, it's pretty much everywhere. The patterns are, could be application specific at any given point, on a certain segment of a network, I mean it's complex. >> Yeah, its complex. One of the really nice things about intent-based network and those, it fits in really nicely and that was by design, 'cause what happens with intent-based networking, as you know, a user expresses some intent if it's something they want to do. I want to securely onboard the SIoT device, and then it gets activated in the network, and then we use Assurance to see if it's doing the right thing. But what happens is that Assurance part, that's basically gathering visibility and insight in terms of what's happening. That's using machine learning to understand what's happening in the network across all these different parts that you mentioned. And then, what happens is we take those insights and then we make intelligent actions and that's part of the activation. So this...with intent based network in this feedback loop that we have directly ties with using the data for getting insights and then for activation, for intelligent actions. >> John, always want to get the update on the innovation lab, is there anything particular here at the show or, what's new that you can share? >> So we're looking at extending IBN to the cloud, to multi-cloud, to multiple devices so there's a lot of really fascinating work happening there. I believe you're going to be talking to one of my colleagues later, too, T.K. He's, I think, hopefully going to talk about some of the machine learning that's been done and that's already prioritized as you know in encrypted thread analytics. That's an example of where we use machine learning to identify if there's malware in encrypted traffic. Which is really a fascinating problem. >> That's a hard problem to solve. I'm looking forward to that conversation. >> So some members of Cisco, Dave McGrew, in particular, Cisco Fellow, started working on that problem four and a half years ago. Because of his work with other colleagues, he was able and they were able to come up with a solution. So it was a very complicated problem as you saw but through the use of machine learning and many years of investment, plus the fact that Cisco's access to Talos which has, they know the threats throughout the world. They're a list of data in terms of all kinds of threats that's massive. That's pretty powerful. >> The volume, that's where machine learning shines. I mean you see the amount of volume of data coming in, that's where it could do some heavy lifting. >> Exactly, that's one of Cisco's strengths. The fact that we have this massive view on all the threats throughout the world and we can bring it to bear. >> Network security foundation only just creates so much value for apps. Final question for you, for the folks watching, what's in you opinion the most important story here at Cicso Live Barcelona, that people should be paying attention to? >> I think how we are trying to extend across all these different domains and make it like one network for our customers. This is still a journey and it's going to take time but with intent based networking we can do that. We're going across campus, WAN, data center to multi-cloud. >> How hard is cross domain, just put it in perspective. Cross domains reversal and having visibility into these, from a latency, from a physics standpoint, how hard is it? >> It's quite hard, there's all kinds of technical challenges but there's even other sorts of challenges. This is WiFi, right? IEEE 802.11 defines the QoS standard for wireless and that's completely different than how the internet group ITEF defined it for wired. So even between wireless and wired, there's a lot of work that has to be done and Cisco's leading that effort. >> And having all that data. Great to have you on John, thanks for spending the time and demystifying machine learning and looking forward to this encrypted understanding with machine learning, that's a hard problem, looking forward to digging into that. Again, truly, the breakthroughs are happening with machine learning and adding values with application centric world. It's all about the data, it's theCUBE bringing you the data from Barcelona, I'm John with Stu Mini, stay with us for more coverage after this short break. (upbeat music)