>>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

>>from Santa Clara, California. In the heart of Silicon Valley, it's the queue covering altitude 2020. Brought to you by aviatrix. >>Next panel is the aviatrix certified engineers, also known as Aces. This is the folks that are certified their engineering. They're building these new solutions. Please welcome Toby Foster Informatica Stacy Linear from terror data. And Jennifer read with Victor Davis to the stage. >>So we're gonna show you a jacket. Yeah, I get it. >>I was just gonna I was just gonna really rib you guys. See? Where's your jackets? And Jen's got the jacket on. Okay. >>Good. Love. The aviators. Aces, Pilot gear. They're above the clouds. Storage to new heights. So guys, aviatrix pace is love the name. I think it's great. Certified. This is all about getting things engineered. So that level of certification I want to get into that. But first take us through the day in the life on a SAS. And just to point out, Stacy's a squad leader. So he's He's like Squadron leader, quadrant leader, quadrant leader. So it's got a bunch of pieces underneath him, but share your perspective day in the life. We'll start with you. >>Sure, So I have actually a whole team that works for me both in the in the North America, both in the U. S. And in Mexico. And so I'm eagerly working to get them certified as well, so I can become a squad leader myself. But it's important because one of the critical gaps that we found is people having the networking background. Because there you graduate from college and you have a lot of computer science background. You can program. We've got python, but now working and packets they just don't get. And so just taking them through all of the processes that it's really necessary to understand when you're troubleshooting is really critical. And, um, because you're going to get an issue where you need to figure out where, exactly is that happening on the network, you know, is by my issue just in a vpc is on the instant side is a security group or is it going on prim? And is this something actually embedded within Amazon itself? I mean, I trouble shot an issue for about six months going back and forth with Amazon, and it was the VW VPN because they were auto scaling on two sides, and we ended up having to pull out the Cisco's and put in aviatrix so I could just say OK, it's fixed and actually actually help the application teams get to that and get it solved. But I'm taking a lot of junior people and getting them through that certification process so they can understand and see the network The way I see the network, I mean, look, I've been doing this for 25 years, but I got out when I went in the Marine Corps. That's what I did and coming out The network is still the network, but people don't get the same training they get. They got >>just so he just write some software that takes care of itself, but we'll come back to that. I want to come back to that problem solve with Amazon, but I think the only thing I have to >>add to that is that it's always the network as long as I've been in. Networking has always been the network's fault. If you're in the I'm even to this day, you know, still, networks fault, and part of being a network guy is that you need to prove when it is and when It's not your fault. And that means you need to know a little bit about 100 different things. Make that >>And now you got a full stack. Dev Ops, you know, a lot more time. Another 100 times are changing your squadron leader. I get that right. What is? What is the squadron leader first? Could you describe what it is? I think probably just leading off with network components of it. But they from my perspective when you think about what you asked them was it's about no issues and the escalations off my days like that. That's a good outcome. That's a good day. Is a good day's a good day for you. Mention the Amazon. This brings up a good point when you have these new waves come in. You have a lot of new things. New use cases, a lot of the finger point against that guy's problem that girls problems. So what? How do you solve that? And how do you get the young guns up to speed? Is there training is that this with a certification comes in, >>whatever the certification is really going to come in I know when we, uh, we got together at reinvent one of the questions that that we had with with Steven the team was What? What should our certification look like? You know, she would just be teaching about what aviatrix troubleshooting brings to bear. But what should that be like? And I think Toby and I was like, No, no, no, no, that's going a little too high. We need to get really low because the better someone can get actually understanding what's actually happening in the network and where to actually troubleshoot the problem, how to step back each of those processes. Because without that, it's just a big black box and they don't know, you know, because everything is abstracted in Amazon and a Net and Azure and Google, it's abstracted in there. These virtual gateways they have VPN is that you just don't have the logs on is you just don't know. And so then what tools can you put in front of them of where they can look because there are full logs? Well, as long as they turned on the flow logs when I built it, you know, and there's like each one of those little things that well, if they had decided to do that when they built it, it's there. But if you can come in later to really supplement that with training to actual troubleshoot and do a packet capture here as it's going through the teaching them how to read act. Even >>so, we were talking before we came on up on stage about your career. You've been networking all your time, and then, you know, you're no mentoring a lot of younger people. How is that going? Because the people who come in fresh, they don't have all the old war stories they don't talk about, You know, it's never fall. I walk in bare feet in the snow when I was your age, so easy now, right? They say, What's your take on how you train the young piece? >>So I've noticed two things. One is that they are up to speed a lot faster in generalities of networking. They can tell you what network is in high school level now where I didn't learn that until midway through my career, and they're learning it faster, but they don't necessarily understand why it's that way here, you know, everybody thinks that it's always slash 24 for a submit, and they don't understand why you can break it down. Smaller. What? It's really necessary. So the ramp up speed is much faster for these guys that are coming in, but they don't understand why. And they need some of that background knowledge to see where it's coming from. And why is it important? And that's old guys. That's where we thrive. >>Jennifer, you mentioned you got in from the Marines helps. But when you got into networking, how what was it like that? And compare it now? Almost like we heard earlier. Static versus Dynamic. Don't be static. And then you just set the network. You got a perimeter? >>Yeah. No, there was no such thing. Yeah, no. So, back in the day, I mean, yeah, I mean, we had banyan vines for email, you know, we had token ring and I had to set up token ring networks and figure out why that didn't work. Because how many of things were actually sharing it, But then actually, just cutting fiber and running fiber cables and dropping them over, you know, shelters to plug them in, and Oh, crap. They swung it too hard and shattered. And I got a great polish this thing and actually shoot like to see if it works. I mean, that was the network crypt. Five cat, five cables to run an Ethernet, you know? And then from that to set network switches. Dumb switches like those were the most common ones you had then, actually configuring routers and, you know, logging into a Cisco router and actually knowing how to configure that. And it was funny because I had gone all the way up. It was a software product manager for a while, So I've gone all the way up the stack. And then, ah, two and 1/2 3 years ago, I came across, too, to work with NTT Group that became Victor Davis. But we went to help one of our customers, Avis, and it was like, Okay, so we need to fix the network. Okay, I haven't done this in 20 years, but all right, let's get to it, you know, because it really fundamentally does not change. It's still the network. I mean, I've had people tell me Well, you know, when we go to containers, we will not have to worry about the network. And I'm like, Yeah, you don't I >>dio. And then with this with program ability is really interesting. So I think this brings up the certification. What are some of the new things that people should be aware of that come in with the aviatrix? A certification? What are some of the highlights? Can you guys share some of the highlights around certifications? >>I think some of the importance is that its it doesn't need to be vendor specific for network generality or basic networking knowledge. And instead of learning how Cisco does something or how Palo Alto does something, we need to understand how and why it works as a basic model and then understand how each vendor has gone about that problem and solve it in a general. That's true in Multi Cloud as well. You can't learn how cloud networking works without understanding how AWS and Measure and GC P r. All slightly the same, but slightly different and some things work and some things don't. I think that's probably the number one take. >>I think having a certification across clouds is really valuable because we heard the global outside of the business issues. What does it mean to do? That code is that networking is the configurations that aviatrix what is the state matrix is a certification, but what is it about the multi cloud that makes it multi networking and multi vendor? But the >>easy answer is yes, >>yes, it's >>all got to be a general. Let's get your hands and you have to be >>right. And it takes experience because it's every every cloud vendor has their own certification. Um, whether that stops and, um, advanced networking and events, security or whatever it might be. Yeah, they can take the test, but they have no idea how to figure out what's wrong with that system in the same thing with any certification. But it's really getting your hands in there. And actually having to troubleshoot the problems, you know, actually work the problem, you know, and calm down. It's going to be OK because I don't know how many calls I've been on or even had aviatrix join me on. It's like, Okay, so everyone calm down. Let's figure out what's happening. It's like we've looked at that screen three times looking at it again. It's not going to solve that problem, right, But at the same time, remaining calm. But knowing that it really is, I'm getting a packet from here to go over here. It's not working. So what could be the problem, you know, and actually stepping them through those scenarios. But that's like, you only get that by having to do it, you know, and and seeing it and going through it. And >>I have a question. So, you know, I just see it. We started this program maybe six months ago. We're seeing a huge amount of interest. I mean, where oversubscribed on all the training sessions, we've got people flying from around the country, even with Corona virus flying to go to Seattle to go to these events were over >>subscribed. Good is that originally they would put their Yeah. Is >>that something that you see in your organizations? Are you recommending that two people do you see? I mean, I'm just I guess I'm surprised. I'm not surprised, but I'm really surprised by the demand, if you would of this multi cloud network certification. Is there really isn't anything like that? Is that something you guys could comment on? That do you see the same things in your organization I see from >>my side Because we operate in a multi cloud environments that really helps. It's beneficial. Yeah, >>I think I would add that, um, networking guys have always needed to use certifications to prove that they know what they know. It's not good enough to say. Yeah, I know. I p addresses are I know how the network works and a couple little check marks. Our little letters by your helps give you validity. So even in our team, we can say, Hey, you know, we're using these certifications to know that you know enough of the basics and enough of the understandings that you have the tools necessary, >>right? So I guess my final question for you guys is why and a certification is relevant. And then second part is share with Livestream folks who aren't yet a certified or might want to jump in to be aviator certified engineers. Why is it important? So why is it relevant? And why should someone want to be a certified engineer? >>I think my V is a little different. I think certification comes from proving that you have the knowledge not proving that you get a certification to get. I mean, they're backwards. So when you've got the training and the understanding in the you use that to prove and you can, like, grow your certification list with it versus studying for a test to get a certification and have no understanding of >>that. So that who is the right person that look at this and saying I'm qualified is a network engineer. Is that a Dev ops person? What your view? Is it a certain >>you know, I think Cloud is really the answer. It's the as we talked like the edge is getting eroded. So is the network definition getting eroded? We're getting more and more of some network. Some develop some security lots and lots of security. Because network is so involved in so many of them, that's just the next progression. >>You want to add something there, I would say expand that to more automation engineers because we have those now, so I'm probably extended >>Well, I think the training classes themselves are helpful, especially the entry level ones for people who maybe quote unquote cloud architects. But I've never done anything in networking for them to understand why we need those things to really work, Whether or not they go through it. Eventually get a certification is something different. But I really think fundamentally understanding how these things work. It makes them a better architect. Make some better application developer, but even more so as you deploy more of your applications into the cloud. Really getting an understanding even from our people have tradition down on Prem networking. They can understand how that's gonna work in the cloud. >>I know we've got just under 30 seconds left. I want to get one more question than just one more for the folks watching that are maybe younger than I don't have. The networking training from your experience is, each of you can answer. Why should they know about networking? What's the benefit? What's in it for them? Motivate them, share some insights and why they should go with the deeper and networking space we'll start with. You know, I would say it's probably fundamental right after delivery solutions networking. Use the very top. I >>would say. If you fundamental of an operating system running on a machine, how those machines talk together, um, is a fundamental change is something that starts from the base and work your way up. >>Well, I think it's a challenge because you've come from top down. Now you're going to start looking from bottom up, and you want those different systems to cross, communicate and say you built something and your overlapping eyepiece space. Not that that doesn't happen. But how can I actually make that still operate without having to re? I re platform? It's like those challenges, like those younger developers or Cisco engineers can really start to get their hands around and understand those complexities and bring that forward in their careers. >>And, you know, the pipes are working plumbing. >>That's right. >>And they know how it works. How to code it. >>That's right. >>Awesome. Thank you, guys for great insights. Ace certified engineers, also known as aces, give a round of applause. >>Yeah, Yeah, that's great. Thank you. Okay, alright, that >>concludes my portion. Thank you, Steve. Thanks for having >>on. Thank you very much. That was fantastic. Everybody >>running with John Furrier. Yeah. So Great event. Great event. I'm >>not gonna take along with that. We got lunch outside for the people here. Just a couple of things. I just called action, right? So we saw the aces. You know, for those of you out of the stream here, become a certified. It's great for your career. Is great for not knowledge is is fantastic. It's not just an aviatrix thing. It's going to teach you about cloud networking, multi cloud networking with a little bit of aviatrix, exactly what the Cisco CC IE program was for I p Network. That type of the thing that's number one second thing is, is is learn, right? So there's a There's a link up there for the for to join the community, get like I started this. This is a community. This is the kickoff to this community, and it's a movement. So go to what may be community dot IBM dot com. Starting a community of multi cloud. So you get trained learn. I'd say the next thing is we're doing over 100 seminars in across the United States and also starting into Europe. Soon we will come out and we'll actually spend a couple hours and talk about architecture and talk about those beginning things. For those of you on the you know, on the live stream in here as well. You know, we're coming to a city near you. Go to one of those events. It's a great way to network with other people that are in the industry as well is to start to learn and get on that multi cloud journey. And then I'd say the last thing is, you know, we haven't talked a lot about what aviatrix does here, and that's intentional. We want you, you know, leaving with wanting to gnome or and schedule get with us and schedule a multi our architecture workshop sessions. So we we sit down with customers and we talk about where they're at in that journey and, more importantly, where they're going and define that end state architecture from networking, compute storage, everything and everything you've heard. Today. Every panel kept talking about architecture, talking about operations. Those are the types of things that we saw. We help. You could define that canonical architecture that system architecture, that's yours. So for so many of our customers, they have three by five plotted lucid charts, architecture, drawings, and it's the customer name slash aviatrix our network architecture, and they put it on the whiteboard that's what we and that's the most valuable thing they get from us. So this becomes there 20 year network architecture, drawing that. They don't do anything without talking us. And look at that architecture. That's what we do in these multi hour workshop sessions with customers. And that's super super powerful. So if you're interested, definitely call us. And let's schedule that with our team. So anyway, I just want to thank everybody on the livestream. Thank everybody here. Hopefully it was It was very useful. I think it waas and join the movement. And for those of you here, join us for lunch and thank you very much. >>Yeah, >>yeah, yeah.

>> Live from Stanford University. It's the Cube covering global Women in Data Science conference brought to you by Silicon Angle media. >> Welcome back to the key. We air live at Stanford University for the fourth annual Women in Data Science Conference. The Cube has had the pleasure of being here all four years on I'm welcoming Back to the Cube, one of our distinguished alumni Janet George, the fellow chief data officer, scientists, big data and cognitive computing at Western Digital. Janet, it's great to see you. Thank you. Thank you so much. So I mentioned yes. Fourth, Annie will women in data science. And it's been, I think I met you here a couple of years ago, and we look at the impact. It had a chance to speak with Margo Garrett's in a about an hour ago, one of the co founders of Woods saying, We're expecting twenty thousand people to be engaging today with the Livestream. There are wigs events in one hundred and fifty locations this year, fifty plus countries expecting about one hundred thousand people to engage the attention. The focus that they have on data science and the opportunities that it has is really palpable. Tell us a little bit about Western Digital's continued sponsorship and what makes this important to you? >> So Western distal has recently transformed itself as a company, and we are a data driven company, so we are very much data infrastructure company, and I think that this momentum off A is phenomenal. It's just it's a foundational shift in the way we do business, and this foundational shift is just gaining tremendous momentum. Businesses are realizing that they're going to be in two categories the have and have not. And in order to be in the half category, you have started to embrace a You've got to start to embrace data. You've got to start to embrace scale and you've got to be in the transformation process. You have to transform yourself to put yourself in a competitive position. And that's why Vest Initial is here, where the leaders in storage worldwide and we'd like to be at the heart of their data is. >> So how has Western Digital transform? Because if we look at the evolution of a I and I know you're give you're on a panel tan, you're also giving a breakout on deep learning. But some of the importance it's not just the technical expertise. There's other really important skills. Communication, collaboration, empathy. How has Western digital transformed to really, I guess, maybe transform the human capital to be able to really become broad enough to be ableto tow harness. Aye, aye, for good. >> So we're not just a company that focuses on business for a We're doing a number of initiatives One of the initiatives were doing is a I for good, and we're doing data for good. This is related to working with the U. N. We've been focusing on trying to figure out how climate change the data that impacts climate change, collecting data and providing infrastructure to store massive amounts of species data in the environment that we've never actually collected before. So climate change is a huge area for us. Education is a huge area for us. Diversity is a huge area for us. We're using all of these areas as launching pad for data for good and trying to use data to better mankind and use a eye to better mankind. >> One of the things that is going on at this year's with second annual data fun. And when you talk about data for good, I think this year's Predictive Analytics Challenge was to look at satellite imagery to train the model to evaluate which images air likely tohave oil palm plantations. And we know that there's a tremendous social impact that palm oil and oil palm plantations in that can can impact, such as I think in Borneo and eighty percent reduction in the Oregon ten population. So it's interesting that they're also taking this opportunity to look at data for good. And how can they look at predictive Analytics to understand how to reduce deforestation like you talked about climate and the impact in the potential that a I and data for good have is astronomical? >> That's right. We could not build predictive models. We didn't have the data to put predictive boats predictive models. Now we have the data to put put out massively predictive models that can help us understand what change would look like twenty five years from now and then take corrective action. So we know carbon emissions are causing very significant damage to our environment. And there's something we can do about it. Data is helping us do that. We have the infrastructure, economies of scale. We can build massive platforms that can store this data, and then we can. Alan, it's the state at scale. We have enough technology now to adapt to our ecosystem, to look at disappearing grillers, you know, to look at disappearing insects, to look at just equal system that be living, how, how the ecosystem is going to survive and be better in the next ten years. There's a >> tremendous amount of power that data for good has, when often times whether the Cube is that technology conferences or events like this. The word trust issues yes, a lot in some pretty significant ways. And we often hear that data is not just the life blood of an organization, whether it's in just industry or academia. To have that trust is essential without it. That's right. No, go. >> That's right. So the data we have to be able to be discriminated. That's where the trust comes into factor, right? Because you can create a very good eh? I'm odder, or you can create a bad air more so a lot depends on who is creating the modern. The authorship of the model the creator of the modern is pretty significant to what the model actually does. Now we're getting a lot of this new area ofthe eyes coming in, which is the adversarial neural networks. And these areas are really just springing up because it can be creators to stop and block bad that's being done in the world next. So, for example, if you have malicious attacks on your website or hear militias, data collection on that data is being used against you. These adversarial networks and had built the trust in the data and in the so that is a whole new effort that has started in the latest world, which is >> critical because you mentioned everybody. I think, regardless of what generation you're in that's on. The planet today is aware of cybersecurity issues, whether it's H vac systems with DDOS attacks or it's ah baby boomer, who was part of the fifty million Facebook users whose data was used without their knowledge. It's becoming, I won't say accepted, but very much commonplace, Yes, so training the A I to be used for good is one thing. But I'm curious in terms of the potential that individuals have. What are your thoughts on some of these practices or concepts that we're hearing about data scientists taking something like a Hippocratic oath to start owning accountability for the data that they're working with. I'm just curious. What's >> more, I have a strong opinion on this because I think that data scientists are hugely responsible for what they are creating. We need a diversity of data scientists to have multiple models that are completely divorce, and we have to be very responsible when we start to create. Creators are by default, have to be responsible for their creation. Now where we get into tricky areas off, then you are the human auto or the creator ofthe Anay I model. And now the marshal has self created because it a self learned who owns the patent, who owns the copyright to those when I becomes the creator and whether it's malicious or non malicious right. And that's also ownership for the data scientist. So the group of people that are responsible for creating the environment, creating the morals the question comes into how do we protect the authors, the uses, the producers and the new creators off the original piece of art? Because at the end of the day, when you think about algorithms and I, it's just art its creation and you can use the creation for good or bad. And as the creation recreates itself like a learning on its own with massive amounts of data after an original data scientist has created the model well, how we how to be a confident. So that's a very interesting area that we haven't even touched upon because now the laws have to change. Policies have to change, but we can't stop innovation. Innovation has to go, and at the same time we have to be responsible about what we innovate >> and where do you think we are? Is a society in terms of catching As you mentioned, we can't. We have to continue innovation. Where are we A society and society and starting to understand the different principles of practices that have to be implemented in order for proper management of data, too. Enable innovation to continue at the pace that it needs. >> June. I would say that UK and other countries that kind of better than us, US is still catching up. But we're having great conversations. This is very important, right? We're debating the issues. We're coming together as a community. We're having so many discussions with experts. I'm sitting in so many panels contributing as an Aye aye expert in what we're creating. What? We see its scale when we deploy an aye aye, modern in production. What have we seen as the longevity of that? A marker in a business setting in a non business setting. How does the I perform and were now able to see sustained performance of the model? So let's say you deploy and am are in production. You're able inform yourself watching the sustained performance of that a model and how it is behaving, how it is learning how it's growing, what is its track record. And this knowledge is to come back and be part of discussions and part of being informed so we can change the regulations and be prepared for where this is going. Otherwise will be surprised. And I think that we have started a lot of discussions. The community's air coming together. The experts are coming together. So this is very good news. >> Theologian is's there? The moment of Edward is building. These conversations are happening. >> Yes, and policy makers are actively participating. This is very good for us because we don't want innovators to innovate without the participation of policymakers. We want the policymakers hand in hand with the innovators to lead the charter. So we have the checks and balances in place, and we feel safe because safety is so important. We need psychological safety for anything we do even to have a conversation. We need psychological safety. So imagine having a >> I >> systems run our lives without having that psychological safety. That's bad news for all of us, right? And so we really need to focus on the trust. And we need to focus on our ability to trust the data or a right to help us trust the data or surface the issues that are causing the trust. >> Janet, what a pleasure to have you back on the Cube. I wish we had more time to keep talking, but it's I can't wait till we talk to you next year because what you guys are doing and also your pact, true passion for data science for trust and a I for good is palpable. So thank you so much for carving out some time to stop by the program. Thank you. It's my pleasure. We want to thank you for watching the Cuba and Lisa Martin live at Stanford for the fourth annual Women in Data Science conference. We back after a short break.