>>Welcome back everybody to AWS reinvent. You're watching the cube, the leader in high tech coverage. My name is Dave Volante with my co-host David Nicholson. We're here all week. We got two sets, 20 plus thousand people here live at AWS reinvent. 21 of course last year was virtual. We got a hybrid event running. We had two studios running before the show running. A lot of pre-records really excited to have ninja Bardelli Walla, who is the chief product officer at data robot. Really interesting AI company. We're going to talk about insights with machine intelligence and then shout. It's great to see you again. It's been awhile. >>Great to see you as well. And I'm so happy to be on the cube. I think eight years since I first came on. >>When you launched the company that you founded back then Peck Sada on the cube, that was part >>Of the inner robot >>Family part of data, robot family. And of course, friend of the cube. Chris Lynch is the executive chairman of data robot. So a lot of connections, I always joke a hundred people in our industry, 99 seats, but tell us about data robot. What's the, what's the scoop these days. >>Thanks. Thanks very much for the opportunity to speak with both of you. Uh, I think we're seeing some very interesting trends. Uh, we've all been in the industry long enough to recognize, uh, that hype cycles they're cycles. They go in waves and, uh, the level of interest in AI has never been higher. Uh, every company in the world is looking for the opportunity to take advantage of AI, to improve their business processes, whether it's to improve their revenue it's to lower their cost profile or it's to lower their risk. What we're seeing that's most interesting is that, uh, we spend a lot of time working with companies on what we consider applied AI. That is how do we solve real business problems, uh, with the technology and not just run a bunch of experiments. You know, it's very tempting for a lot of us, Dave and David, uh, to, to do, uh, you know, spin up a spark cluster with 10,000 nodes and slosh a bunch of data through it. >>But the question we always ask at data robot is what is the business value of doing this? Why are we using these AI techniques and in order to solve what problem? So the biggest trend we see a data robot and one that we feel we're very well positioned to solve is that companies are coming out of that experimental phase. There's still a lot of experimentation going on and they're saying, okay, we, we stood up a cluster. Uh, we got a bunch of Python notebooks running around here, but we haven't really seen a return on our investment yet data robot, can you help us actually make AI real and concrete in terms of achieving a specific business outcome for us? >>Well, and I want to test something on your niche. That's something we've talked about a lot on the cube is a change in the way in which companies are architecting their data. When we first, it was like, okay, create a Hadoop cluster. And that spark came along to make that easier, but it was still this highly technical, highly centralized, hyper specialized roles where the business, people who have a really good understanding of the outcome had to kind of beg to get what they wanted because it was so technical and the success was defined as, Hey, it worked or we ran the experiment and it looks like it has promise. So now it seems like with companies like data robot, you're democratizing AI, allowing organizations to inject AI into their business processes, their applications. And it seems to be more business led. One of you could comment on that. >>I think that is a various dude observation. Uh, we launched this concept a little bit earlier this year of AI cloud. And the idea behind AI cloud is if you want to democratize AI, which is in fact has been DataRobot's vision since 2012, we were the first company on the cloud. The first AI cloud that ever existed was data robots in 2014. And the entire idea was that we knew that data scientists would always play a very important role in an organization, but yet the demand for AI would vastly outstrip the supply. And so in order to solve that challenge, we built AI cloud. We've actually spent over a million engineering hours in building this technology over the, over the last decade and put this together in a way where all of the different personas and the organizations, you have people who create AI applications. >>Those are the folks we usually think about, but those are the data scientists. Those are the analysts, those are the data engineers, but then you actually have to put it into production. You've got to run the system. So you also have to democratize this capability for the folks who are going to operate the system for the folks in risk and compliance. We're actually going to, uh, ensure that the system is operating in accordance with your policies and compliance regimes. And then the third wave of democratization, which we've just embarked on is then how do you bring AI into the hands of the actual business people? How do you put on a mobile device or a web browser, or in context, in an application with the decision, the ability for AI to drive a decision in your organization, which leads to an action, which helps drive you towards the outcome you're trying to optimize for. >>So AI cloud is about this pervasive tapestry, bringing together the creators, the consumers, the individuals who operate these systems into a single system that can lower the barrier to entry for people who don't have the skills, but allow you to plug in and go deep underneath the covers and modify whatever you need to, if you have that level of technical skill and that ability for us to kind of slide, slide the slider in one direction or the other, I could slide it to the right and say, I want all automation, something data robot has pioneered and is absolutely the leader in, but we can also, especially in these last couple of years, say, I want to be able to use as much code as I want to bring in. And the beauty of the model is that customers can choose how much they want to let the machine drive or how much they want to let the human being drive. David. I love that, >>That idea of a slider, because now you're talking about generalists getting access to really powerful tools. >>Yeah, no, exactly. And I, I'm curious, what's your view on where we are culturally with AI at this point? And what I mean by culturally is the idea that, okay, that's great. You put powerful tools in the hands of business users. Um, do most of us still need to have a lot of visibility under the covers to understand the inner workings so that we trust what we're being told? You know, I'm fine pulling a lever and having a little biscuit come out of SWOT as long as I've gotten a tour of the kitchen at some point in time. Yes. I mean, where are we with that? Where where's the level of >>Absolutely fantastic question and it's one that's, it's actually pervasive to the way data robot operates. So trust gets, uh, engendered by multiple different capabilities that you build throughout the platform. The first one is around, uh, explainability. So when you get a prediction from a system, just like you mentioned, you know, if, if the stakes are not very high, you know, you, uh, we're here in Las Vegas, of course I'm thinking of slot machines. If you get a biscuit at the end of it and it tastes pretty good. Hey, great. Right? When you're making a mission critical business decision, you don't want to be in the position where you don't understand why the system is making the decision. It does. So we have historically invested an enormous amount of effort in explainability tools, having the system actually at a prediction level, explain to you, why is it making the recommendation it's making? >>For example, the system says this customer has a high likelihood of churn. Why? Because their account balance has been declining over the last five months. Uh, number two, because their credit score has been going down. And what gives you the trust is actually the machine and the human able to communicate in the same language and same vernacular about the business value. So that's one part of it. The second part is about transparency, right? So one of the things that the automated machine learning movement, that data robot pioneered, uh, has been, I'd say rightfully criticized for frankly, is that it's too much of a black box. It's too much magic. I load my dataset. I press the start button and data robot does everything else for me. Well, that's not very satisfying when you have a 10 or a hundred million dollar decision coming on the other side, even if the technology is actually doing the job correctly, which data robot usually does. >>So where we've morphed and evolved our position in the market and where I have driven our technology portfolio at data robot is to say, you know what? There is a very important aspect of trust that needs to be brought to bear here, which is that if somebody wants to see code, let them see code. And in fact, the beauty of AI cloud is that on the same platform, the people who don't like code, but are, are very good at understanding the business domain con uh, the business domain knowledge and the context. They now have the ability to do that. But when they're at the stage before they're going to deploy anything to production. Now you can raise your hand at data robot and actually use our workflow and say, I need a coder to review this. I want the professional data scientist who has all this knowledge who understands and has read up on the latest advances in hyper parameter tuning to look at the model and tell me that this is going to be okay. And so we allow both the less technical folks and the very deep technical data scientists, the ability to collaborate on the same environment, which allows you to build trust in terms of the human side of, Hey, I don't want to just let anybody throw a model into production. I like, >>I mean, I see those, the transparency and the explainability is almost two sides of the same coin, right? Because you know, if you're gonna be accused of gender bias, you can say, no, here's how the system may, it's not like, you know, you think about the internet. It tells you it's a cat, but you don't really know how the machine determined that you're breaking apart, blowing away that black box. And the other thing I like what you said was you have data producers and data consumers, and you also talked about context because a lot of times the data producers, they don't necessarily care about the context or the PI data pipeline. People necessarily care about the context. So, okay. So now we're at the point where you're democratizing data, you're doing some great work. What are some of the blockers that you see today that you're obliterating with data robot? Maybe you could talk about that a little bit. Sure. >>So, so I think, uh, you know, one very important concept is that, uh, in a democracy, we talked about democratization. You still have rules, you still have governance. It's not a free for all the free for all version of that is called NRG. That's not what any company wants, right? So we have to blend the freedom and flexibility that we want businesses to have with the compliance and regulatory observability that we need in order to be successful. So what we're seeing in, in our, in our customer base and what companies are coming to data robot to discuss is, okay, we've tried these experiments. Now we want to actually get to real business value. And one of the things that's really unique about data robot is that we have put, uh, we have, we've worked in our system on over 1 million projects, training models, inside data robot. >>We have seen every type of use case across different industries, whether it's healthcare or manufacturing, uh, or, or retail, uh, we have the ability to understand those different data sets and actually to come up with models. So we have that breadth of information there if you aggregate that over time, right? So again, we did not come to AI. This is not a fad for us. We didn't start as one kind of company than slap the AI label on and say, Hey, we're an AI company now, right? We have been AI native since day one. And in that process, what we have found is working on these, this million plus projects on these data sets across these industries, we have a very good sense of which projects will actually deliver value and which don't. And that gets to a previous point that you were making, which is that you have to know and partner with an organization who it's not just about the technology. So we have fantastic people who we call our customer facing data scientists who will tell the customer, look, I know you think this is a really high value use case, but we've tried it at other customers. And unfortunately it didn't work very well. Let's steer you, cause you need with a, with a technology that is largely at the early stage and the maturity that organizations have with it, you need to help them in order to deliver success. And no vendor has delivered more successful production deployment of AI than data road. >>No, don't go down that path. It's a dead end as a cul-de-sac. So just avoid it. So we talked about transparency, explainability governance. Can you get that to the point where it's self-serve as you, as you put data in the hands of business, people where the context lives, the domain experts, can you get to self-serve and federate that governance? Yes. >>So you can, uh, that's one of the key principles of what we, what we do at data robot. And it comes back to a concept that I learned, uh, you, you both will remember. We were in the Sarbanes-Oxley crazy world of, I dunno, was that 15 years of saved data warehousing. >>Everybody wanted to talk about socks. You know, my wife would hear me on the phone. She'd be like, what is your sudden obsession with socks? I'm like, no, no, it's not what you fit. And so, um, but what came from Sarbanes Oxley are, are these, uh, longstanding principles around the segregation of duties and segregation of responsibilities. You can have democracy democratization with governance, if you have the right segregation of duties. So for example, I have somebody who can generate lots of different models, right? But I don't allow them to, to, uh, in a self-service way, just deploy into production. I actually have a workflow system which will go through multiple rigorous approvals and say, these three people have signed off, they've done an audit, uh, an, an audit assessment of this model. It's good to go, let's go and drop it into production. So the way that you get to self-service with governance is to have the right controls and policies and frameworks that surround the self-service model with the right checks and balances that implement the segregation of duties I'm talking >>And you get that right. And then you can automate it and then you can really scale, right? You gotta have your back because it's such a great topic. We, we barely scratched the surface. It was great to see you again, congratulations on all the success. And, uh, as I say any time, let's do this again. Fantastic. Thank >>You so much. All right, you're welcome. And thank you for watching you watching the cubes coverage of AWS reinvent 2021, Dave Volante for David Nicholson. Keep it right there. You're watching the cube, the leader in high-tech coverage.

>> Announcer: Live from Barcelona Spain, it's theCUBE! Covering Cisco Live 2020, brought to you by Cisco and its ecosystem partners. >> Welcome back, this is theCUBE's live coverage of Cisco Live 2020 here in Barcelona, Spain. I'm Stu Miniman, my cohost for this segment is Dave Vellante, John Furrier is also in the house. We're doing about three and a half days, wall-to-wall coverage. The surface area that we are covering here is rather broad and I use that term, my guest is laughing, Vikas Butaney, who is the Vice President of IoT, of course. Extending the network to the edge, to the devices, and beyond with Cisco. Thank you so much for joining us. >> It's great to be here. >> All right, the IoT thing. I've worked with Cisco my entire career, I've watched through the fog computing era for a couple of years. Edge of course, one of the hottest conversations, something that I bought up in many of the conversations, the across the portfolio but Liz Centoni was up on the main stage for the day one keynote talking a lot about IoT and IT and OT and your customers of the like. So let's start there, what's new, and how does IoT fit into the overall Cisco Story? >> Absolutely. So as Liz was on the main stage and David talked about the cross domain and multi-domain architecture; Now, IoT and our operational environment is one of the key domains within that environment. And what Liz announce yesterday are two pieces of news that we are releasing at Cisco Live. First of them is an IoT security architecture which ties together the capabilities with cyber vision and then integrates it within the rest of our IT security portfolio and the second part that I'm also excited to talk about is Edge Intelligence. It's about how we are helping our customers extract the data at the edge, then deploy and move it to wherever the applications are in the multicloud environment. >> You know, we definitely want to dig into those pieces, but IoT is such a diverse solution set so it's often helpful to talk about specific industries, any customer examples so what can you share with us there to help illuminate where Cisco's helping the customers love the security angles and edge? >> That's right. Just a level set, when we think about industrial IoT we're really talking about the heavier industries, plant environments for a manufacturing company. We're thinking about roadways for a public sector customer. We're thinking the grid for utility environments. We're thinking refineries and oil extraction upstream environments, right. So this is the kind of spectrum in which we are working in, where customers have real businesses, real assets where the operations is the heart of the enterprise that they are running. And the technology can really be a revolutionary change for them to help them connect and then extract the data and then make sense of the data to improve their business practice so industrial IoT, whether you're a roadway in Austria like Asfinag, you're a utility in Germany like NRG, or EDF in France as an example. Enel in turn in Italy, all of these industries and all of these customers are using industrial IoT technologies in running their businesses better today. >> Where are we in terms of that critical infrastructure being both connected and instrumented? Where are we on the adoption curve? >> Sure, look and many of these industries we have talked about SCADA systems, right, that have been here for thirty plus years for our customers and most of those is really a one-way flow of information, right. And typically customers stood up separate side load networks which weren't really connected to the rest of the enterprise so, Rockwell has a saying from the shop floor to the top floor, right like how the digital enterprise where all of these environments are coming together is where customers are. Critical infrastructure, as you said, in this day and age with security and other kind of threats, customers are a little hesitant about how they connect it all together. But Cisco is working with these customers and helping them think through the benefits they can get but also make sure, from a cyber security point of view, that you're helping protect assets, manage these environments because you can't just arbitrarily connect them because IT tool sets just are not ready to manage these environments. >> I love that all the examples you gave were European, of course, being here in Europe. I'm curious, there's some technologies where North America might take the lead or Asia might take the lead. Is IoT relatively distributed? Is Europe kind of on-par or with the rest of the world when it comes to general adoption? >> What we have found in Europe, because of many countries like Germany leading in the renewable energy effort, and the climate is a big focus here. Data privacy and concerns around data sharing are much more top-of-mind in Europe, so we find those kind of use cases getting adopted much much faster. In Germany, as an example, NRG which is one of our customers, and they were here with us last year at Cisco live and we launched a capability with them. They are trying to manage the real time flow of energy in their grid environment, such that make sure there are no outages, no brownouts in these environments. So utilities and customers like that across Europe are adopting technology faster. Manufacturing, as always, is a leading use case. There we see some of the automotives in US are leading a little bit more in getting environments connected to their environment but overall, IoT is a global market. We work, we have over 70,000 enterprise IoT customers today at Cisco so we are fortunate to be able to serve these customers on a global basis across the range of industries I talked about earlier. >> In a lot of respects too, I would say the US is behind, right, when you look at public policy from a federal standpoint, the US doesn't really have a digital strategy from an overall perspective whereas certainly India does and countries in Europe. You look at the railway systems in Europe. >> Vikas: Much more advanced, yeah. >> Beautiful and shiny and advanced. So I would say the US has a little bit of work to do here, in my perspective. >> That's right, in India Prime Minister Modi started the effort around One Hundred Smart Cities, right, and Cisco is working with many of those smart cities with our Cisco Kinetic for Cities to kind of create, connect all of the sensor networks. Video surveillance, safety, environmental sensors, managing the flow of that data and digitizing those environments, right, and in Europe we've been working in France, Germany, Italy, UK. I think we are seeing much more adoption in these specific industries but it's a global market and again, like I said, 70,000 customers, we get to see quite a bit of the landscape around the globe. >> What should we know about the architecture? Can you give us kind of a high-level summary? What are the basics? >> Sure, so in the comprehensive IoT security architecture we released this week, it really starts with, you have to be able to identify the devices, right. In IT environments, you know, to your laptop and to your PC, they have been managed by MDM technologies for years but in the industrial environment I might have a programmable logic controller that I deployed 15 years ago. It's not ready for modern capabilities so what you really have to start with is identifying all of these assets in the communication baselines that are happening there, that's step one. Step number two is really, now that I know that this is a PLC or that's a controller, I need to come up with a policy, a security policy which says this cell in a plant environment can only talk to the other cell but doesn't need to talk to a paint zone. So I'll give you an example in automotive, if I'm welding a car, I'm building a car, the welding robots need to be communicating with each other. There's no real reason that the welding robot needs to talk to the paint shop, as an example. So you can come up with a set of policies like that to keep these environments separate because if you don't, then if there is one infection, one malware, one security, then it just traverses your whole factory. And we know customers in Europe that their networks have gone down and they've impacted 150 to 200 million dollars of downtime impact. >> Well we had a real world use case 10 years ago or so with Stuxnet with Siemens PLC and boom it went all over the world, I mean it was amazing. >> Exactly right, so again back to identification then I create the policy, then I implement the policy within our switching or a firewall network but you're never done so you have to keep monitoring on a real time basis as the landscape changes. What's happening, how do I keep up with it? And that's where things like anomaly detection are super important, right, so those are the four steps off the architecture that I want to talk about. >> So it sounds like something like cyber security is both a threat and an opportunity of bringing together IT and OT. Bring us inside a little bit those dynamics, we know it's one of the bigger challenges in the IoT space. >> Yeah, I mean I think, look, both parties whether I'm an operational person or an IT person, both of us, both audiences have their own care-abouts. If I'm a plant manager, I'm measured on number of units I'm producing, the quality, the reliability of my products. If I'm in IT I really am measured on downtime of the network or the cyber security threat. There aren't really common measurable capabilities but cyber and security, it kind of brings both the parties together. So when we use our cyber vision product, we're able to provide to that plant manager visibility to what's happening, how are their PLC's performing, did anybody change my program, is my recipe for my given product I'm making secure and safe? So you have to appeal to the operational user with what they care about. IT really cares about to manage the threat surface, don't let that threat kind of propigate. Now at the board level because the board sees both sides of it, they're asking these teams to work together because they have a complimentary skill set. >> Well I think that's critical because, rhetorical question, who's bigger control freaks? Network engineers or operation technology engineers? They both, you know, keep that operation going and are very protective of their infrastructure. So it's got to come from top down and it is a board level discussion, right? >> Yeah that's right, we have customers where, you know, the board, the CEO has mandated to say listen, whether it's for the national threat actors or other corporate espionage, I need to protect the corporate intellectual property. Because it's not just a process, it's also about safety of employees and safety of their assets that comes into play, right. So when some of the customers we're working with, where the CEO has kind of dictated that the IT teams help the operational environments, but it is a two-way street, like, there has to be value for both parties to come together to solve these challenges. >> Okay so we talked a little bit about the threat, also when we're talking IoT, there's all that data involved. What's the opportunity there for customers with data, how's Cisco involved? >> Absolutely, look, I think one of the reasons customers are doing digitization projects is because they're trying to use the data to make better business decisions. It has to improve, yield, and meet their KPI's of their industry. So far what we have seen is that all of the data is really trapped in all of these distributed environments. Gartner tells you that 75% of the data will be produced at the IoT edge. But our customers to date have not had the tool set to be able to get access to the data, cleanse the data at the edge of the network, bring the right data that they can create insights with, and improve their businesses so it's been a heterogeneous environment, lots of protocols, lots of legacy, so that's kind of what our customers are struggling with today. >> Yeah, absolutely and most of that data is going to stay at the edge so I need to be able to process the edge. Heck I even went to a conference last year, talked about satellites that are collecting all of the data, I need to be able to have the storage, the processing, the compute there because I can't send all of the data back, as fast as it is. So it's a changing architecture as to where I collect data, where I process data. We think it is very much additive to traditional cloud and data center environments today, it's just yet another challenge that enterprises need to deal with. >> That's right, so the work that Cisco is doing in the IoT edge environment is we are enabling these customers to connect their remote terminal units, their machines, and their robots and providing them the tool set with four capabilities. First, extract the data. So we have a set of protocols like Modbus, like OPC UA where they can extract the data from their machine so that's step number one. Second is to transform the data, as you said, over an LTE circuit or over a connection, I'm not going to be able to send all of the data back so how do I transform the circuit, transform the data where I maybe take an average over the last five minutes or I kind of put some functions, and we are providing, as we are in the Devnet zone, we are providing developers the capability such that they can use visual studio, they can use Javascript to write logic that can run right at the edge of the network so now you have extracted the data, you have transformed the data. Governance is a key topic, who should have access to my data, especially here in Europe where we're concerned about privacy, we're concerned about data governance. We are enabling our customers to come up with the right logic by which if there's a machine data and you are the supplier, I'm only going to give you the data, the temperature, the vibration, the pressure that you need to support the machine, but I'm not going to give you the number of units I produce. I'm not going to give you the data about my intellectual property. And then you have to integrate to where the data is going, right. So what we're doing is we are working with the public cloud providers, we are working with software ISVs, and we are giving them the integration capability and the benefit of this for the customer is we have done pre-integration on the extraction part and we have done pre-integrations on the delivery part, which allows the projects to go faster and they can deliver their IoT efforts. >> So how do you envision the compute model at the edge, I mean, probably not going to throw a zillion cores so maybe lighter weight components, and I have some follow up on that as well. >> Sure, absolutely. Look, Moore's law is a friend of ours here, right, like with every cycle, every generation of CPU technology, you get more and more compute capabilities. So the IoT gateways that we provide to our customers today have four ARM cores in them. We are using a couple, two of those ARM cores for the networking function but those cores are available for our customers. We have designed an extra memory for them to be able to process these applications and we give them SSD and some storage at that so we can provide up to sixty gigs or one hundred gigs of storage so now that gateway, that communication device, a router, a switch that's at the edge of the network can kind of do a dual purpose. It can not only process and provide you security for the communications but is now an edge processing node so we call them IoT gateways and I can tell you, we are deploying these kind of products on buses. You know, in a mass transit bus, we all ride these buses, there are over six systems that are on that bus. A video surveillance system, I'm going to monitor the tire pressure, I want to monitor if the driver is going over the speed limit. We have now connected all of these systems and we are running logic at the edge such that the riders have a safer experience and then they can get real time visibility to where the bus is as well. >> Yeah and my follow up was on persisting, so you mentioned storage, you know, flash storage at the edge and then you also referred to earlier the challenges this data today is locked in silos or maybe it's not even persisted, it's analog data sometimes. So do you envision, if you think about successful digital companies, kind of born digital, data's at the core and traditionally big manufacturing firms, large infrastructure, the manufacturing plant is the center of the universe and data sort of sits around it. Do you envision a period where that data is somehow virtualized and we have access to it, we could really build digital businesses around that data, what are your thoughts? >> Absolutely. So we have been working with a customer, it's a steel manufacturer in Austria, the heartland of Europe as an example. And they make high quality steel, right, and when they're building the high quality steel, they have two hundred different machine types and like you're saying, the data is trapped in there. This customer is trying to digitize and trying to do that but they have been struggling for the last two years or so to be able to get the data because it's a variety of machines and they want to use our IoT services but they haven't been able to pipeline the data all the way to their cloud environments so that was one of our lighthouse customers and we worked with them like, you know, roll up your sleeves and kind of designed the system with them. And we worked to get that data such that now, they're not quite a born-digital company but they are a hard manufacturing company, they can get the best of the tool sets and analytics and all of the things that contemporary tech companies use and they can bridge them into this digital environment. >> Yeah and this is how the incumbents can compete with the sort of digital natives, right I mean it's an equilibrium that occurs. >> That's right, I mean look we love the digital companies but they're not really, they don't have physical assets there or out there working. They're working in a more physical or more of the real economy whether if you are an oil company and you're getting, extracting oil from a pumpjack, right, well you need to still have the capability to do that better. So that's what we're doing, whether you're a transportation, like the bus example I gave you, an oil and gas company whose trying to extract oil from the ground or you are a manufacturer or you're a utility, if we improve use of our digital technologies and operate, improve the efficiency of the business, a 0.1%, a 1%, that has got a much much bigger implication for us as a society and the world at large. But just making them better and more efficient. >> Huge productivity gains. >> Exactly right, that's right, right. >> Massive, yeah. >> So I think that technology and IoT technologies can benefit all of these industries and you know Cisco is kind of invested and kind of helping our 70,000 customers to get better with all of these capabilities. >> Awesome, congratulations. 70,000 customers, big number, rolling out IoT solutions. Look forward to keeping track of Cisco's IoT solutions. >> Super excited to be here, thanks again. >> For Dave Vellante, I'm Stu Miniman, back with lots more wall-to-wall coverage here at Cisco Live 2020 in Barcelona. Thanks for watching theCUBE. (upbeat music)