 Live from New York, it's theCUBE. Covering theCUBE, New York City, 2018. Brought to you by SiliconANGLE Media and its ecosystem partners. Back to the live CUBE coverage here in New York City for CUBE, NYC, hashtag CUBE NYC. This coverage of all things data, all things cloud, all things machine learning here, in the big data realm. I'm John Furrier, Dave Vellante. We've got two great guests from Cisco. We've got DD's, the Vice President of Data Center and Marketing, and Cisco Han Yang, who's the Senior Product Manager at Cisco. Guys, welcome to theCUBE. Thanks for coming on again. Good to see you. Good to see you. Thanks for having us. So obviously one of the things that's come up this year at the big data show, we used to be called Hadoop World, Strata Data now is called the latest name, and obviously CUBE NYC. We changed from big data NYC to CUBE NYC because there's a lot more going on. I heard hallway conversation around blockchain cryptocurrency. Kubernetes has been set on theCUBE already at least a dozen times here today. Multicloud, so you're seeing the analytical world trying to be in a way kind of brought into the dynamics around IT infrastructure, operations, both cloud and on-premises. So interesting dynamics this year, almost a DevOps kind of culture to analytics. This is a new kind of sign from this community. Yeah, yeah, absolutely. No, absolutely. I think data and analytics is one of those things. It's pervasive. Every industry doesn't matter. Even at Cisco, I know we're going to talk a little more about the new AI and ML workloads, but for the last few years, we've been using AI and ML techniques to improve networking, to improve security, to improve collaboration, so it's everywhere. You mean internally in your own IT? Internally, yeah. Not just in IT, in the way we're designing our network equipment, right? We're storing data that's flowing through the data center, flowing through the, in and out of clouds, and using that data to make better predictions for better networking, application, performance, security, what have you. The first topic I want to talk to you guys about is around the data center. Obviously, you've got data center marketing, is that me? I mean, basically that's where the action is. Cloud, obviously, has been all the buzz, people going into the cloud, but Andy Jassy's announcement at VMworld really is a validation that we're seeing for the first time hybrid and multi-cloud validated. Amazon announced RDS on VMware on-premises. This is the first time Amazon's ever done anything with this magnitude on-premises. So this is a signal from the customers voting with their wallet that on-premises is a dynamic. The data center is where the data is. That's where the main footprint of IT is. That's right. This is important. And what's the impact of that dynamic of data center, where the data is, with the option for cloud, how does that impact data, machine learning, and the things that you guys see as relevant? Sure, so I'll start and Han, feel free to chime in here. So I think those boundaries between this is a data center and this is a cloud and this is a campus and this is the edge, I think those boundaries are going away. Just like you said, data center is where the data is. And it's the ability of our customers to be able to capture that data, process it, curate it, and use it for insights to take decision locally, converts a drone as a data center that flies and a boat as a data center that floats, right? So that- And a cloud as a data center that no one sees. That's right, that's right. So those boundaries are going away. We at Cisco see this as a continuum. It's the edge cloud continuum. And so, and the edge is exploding, right? There's just more and more devices and these devices are cranking out more data than ever before. Like I said, it's the ability of our customers to harness that data to make more meaningful decisions. So Cisco's take on this is the new architectural approach, right? And it starts with the network because the network is the one piece that connects everything, every device, every edge, every individual, every cloud. There's a lot of data within the network which we're using to make better decisions. It's interesting, you know, I've been pretty close to Cisco over the years since 95, time frame. I've had hundreds of meetings, some technical, some kind of business. But I've heard that term edge of the network many times over the years. This is not a new concept of Cisco. I mean, edge of the network actually means something in Cisco parlance. The edge of the network that the packets are moving around. So again, this is not a new idea for Cisco. It's just materialize itself in a new way. It's not, but what's happening is the edge is just now generating so much data. And if you can use that data, you know, convert it into insight and make decisions, that's the exciting thing. And that's why this whole thing about machine learning and artificial intelligence, it's the data that's being generated by these cameras, these sensors, these, you know, so that's what is really, really interesting. Go ahead, please. One of our own studies pointed out that by 2021, there'd be 847 Zetabytes of information out there. But only 1.3 Zetabytes would actually ever make it back to the data center. That just means an opportunity for analytics at the edge to make sense of that information before it ever makes it home. Wait, were those numbers again? It was like, I think it's like 847 Zetabytes of information. And how much makes it back? You know, 1.3. Yeah, there you go. So a huge compression. That confirms your research data. Well, yeah, I mean, we've been saying for a while now that most of the data is going to stay at the edge. There's no reason to move it back. The economics aren't supported, the latency doesn't make sense. The network costs the loan, it's going to kill you. That's right. And I think you really want to make, you want to collect it, you want to clean it and you want to correlate it before you ever sending it back. Otherwise, sending that information of, useless information that status is wonderful. Well, that's not very valuable, right? And 99.9%, things are going well. Temperature hasn't changed. You hope that it's going well, right? If it really goes wrong, well, that's when you want to alert or sending more information. Well, how did it go bad? Why did it go bad? Those are the more insightful things that you want to send back. And you know, this is just not just for IoT. I mean, cat pictures moving between campuses cost money too. So you want to just keep them local, right? So, but these are concepts of networking. This is what I want to get into my point too. You guys got some new announcements around UCS and some of the hardware and the gear and the software. What are some of the new announcements that you're announcing here in New York? And what does it mean for customers? Because they want to know not only speeds and feeds, it's a software driven world. How does the software relate? Is it, how does the gear work? What's the management look like? Where's the control playing? Where's the management playing? Give us all the data. I think the biggest issue starts from this, right? Data scientists, they have, their task is to explore different data sources, find out the value, right? But at the same time, IT is somewhat lagging behind, right? Because as the data scientists go from data source A to data source B, it could be three petabytes of difference. Well, IT is like three petabytes. That's only from Monday through Wednesday. Well, that's a huge infrastructure requirement change. So Cisco's way to help the customer is to make sure that we're able to come out blueprints. Blueprints enabling the IT team to scale so that the data scientists can work beyond their own laptop. As they've worked through the petabytes of data that's coming in from all these different sources, they're able to collaborate well together and make sense of that information. And only by scaling, by with IT helping the data scientists to work to scale, that's the only way they can succeed. So that's why we announced a new server. It's called a C480ML. Happens to have eight GPUs from NVIDIA inside, helping customer that want to do that deep learning kind of capabilities. What are some of the use cases of this product? So it's got new data capabilities. What are some of the impacts? Well, so, I mean, some of the things that Han just mentioned, for me, I think the biggest differentiation in our solution is things that we've put around the box, right? So the management layer, right? I mean, this is not going to be one server in one data center. It's going to be multiple of them. You're never going to have one data center. You're going to have multiple data centers. And we've got a really cool management tool called InterSight. And this is supported in InterSight day one. And InterSight also uses machine learning techniques to look at data from multiple data centers. And that's really where the innovation is. Because honestly, I think every vendor is going to bend sheet metal around the latest chipsets. And we've done the same, but the real differentiation is sort of how we manage it, how we use that data for more meaningful insights. I think that's where some of our magic is. Can you add some color to that in terms of just infrastructure for AI and ML? How is it different than sort of traditional infrastructures? So is the management different? Does the sheet metal is not different, you're saying? But what are some of those nuances that we should understand? I think especially for deep learning, multiple scientists around the world have pointed out that if you're able to use GPUs, they're able to run the deep learning frameworks faster by roughly two orders of magnitude. So that's part of the reason why from infrastructure perspective, we wanted to bring in that GPUs. But for the IT teams, we didn't want them to just add yet another infrastructure silo just to support AI or ML. So therefore, we wanted to make sure that it fits in within a UCS managed unified architecture enabling the IT team to scale, but without adding more infrastructures and silos just for that new workload. By having that unified architecture, it helps the IT to be more efficient and at the same time, in better support of the data scientists. The other thing I would add is sort of again, the things around the box, right? I mean, look, this industry is still pretty nascent. There's lots of startups, there's lots of different solutions. And when we build a server like this, we don't just build the server and just toss it over the fence to the customer and say, we'll figure it out. No, we've done validated design guides, right? With Googles, with the leading vendors in the space to make sure everything works as we say it would. And so it's all of those integrations, those partnerships all the way through our systems integrators to really understand a customer's AI and ML environment and to fine tune it for the environment. So is that really where a lot of the innovation comes from? Is that sort of doing that hard work to say, yes, it's going to be a solution that's going to work in this environment. Here's what you have to do to ensure best practice, et cetera. Is that right? So I think some of our blueprints or validated designs is basically enabling the IT team to scale. Scale their stores, scale their CPUs, scale their GPU and scale their network. But do it in a way so that we work with partners like Hortonworks or Cloudera so that they're able to take advantage of the data lake, but and adding in the GPU so they do the deep learning with TensorFlow, with PyTorch, or whatever, curated deep learning framework that data scientists need to be able to get value out of those multiple data sources. So these are the kind of solution that we're putting together, making sure our customers are able to get to that business outcome sooner and faster, not just a... Right, so there's innovation at all altitudes, right? So there's the hardware, there's the integrations, there's the management, so it's innovation. So not to go too much into the weeds, but I'm curious, as you introduce these alternate processing units, what is the relationship between sort of traditional CPUs and these GPUs? Are you managing them differently, kind of communicating somehow, or are they sort of fenced off architecturally? I wonder if you could describe it. We actually want it to be integrated because by having it separated and fenced off, well that's a IT infrastructure silo that you're not going to have the same security policy or the storage mechanism. We want it to be unified so that it's easier on IT teams to support the data scientist, right? So therefore, the latest software, Hadoop software, is able to manage both CPUs and GPUs as well as having Hadoop file system. Those are the solutions that we're putting forth so that our IT folks can scale, our data scientists can succeed. So IT is managing a logical block. That's right, and even for things like inventory management or going back and adding patches in the event of some security event, it's so much better to have one integrated system rather than silos of management, which we see in the industry. So the hard news is basically UCS for AI and ML workloads. That's right, right? This is our first server custom build ground up to support these deep learning, machine learning workloads. We partnered with NVIDIA, with Google. We announced it earlier this week and the phone is ringing constantly. I don't want to say God Box, I just said it. This is basically the power tool for deep learning. Absolutely. That's what you guys see it. All right, well great. Thanks for coming on. Appreciate it. Good to see you guys at Cisco. Again, deep learning, dedicated technology around the box, not just the box itself. That's right. Ecosystem, NVIDIA, good call. Those guys really get the hot GPUs out there. So those guys last night, great success they're having. They're a key partner with you guys. Absolutely. Who are the hostess partnering real quick before we end the segment? Just get a quick plug into the partner. We've been partnering with software side, we've partnered with folks like Anaconda, with their Anaconda Enterprise, which data science love to use as their Python, data science framework. We've been working with Google, with their Kubeflow, which is open source project integrating TensorFlow on top of Kubernetes. And of course we've been working with folks like Cloudera, as well as Hortonworks to access the data lake, that phone from a big data perspective. Yeah, I know you guys didn't get a lot of credit at Google Cloud, we were certainly amplifying it. You guys were co-developing the Google Cloud service with Google, I know they were announcing it. You guys had Chuck on stage there with Diane Greene, so it was pretty positive. Good integration with Google, looking pretty good there. Absolutely. Good partner. Great, thanks for coming on theCUBE. Thanks for appreciating the commentary. Just go here on theCUBE. We're in New York City for Kube NYC. This is where the world of data is converging in with IT infrastructure, developers, operators, all running analytics for the future of business. We'll be back with more coverage after this short break.