>>Welcome back to the cubes coverage of AWS public sector summit live in Washington D. C. A face to face event were on the ground here is to keep coverage. I'm john Kerry, your hosts got two great guests. Both cuba alumni Shannon Kellogg VP of public policy for the Americas and john would ceo tell us congratulations on some announcement on stage and congressional john being a public company. Last time I saw you in person, you are private. Now your I. P. O. Congratulations >>totally virtually didn't meet one investor, lawyer, accountant or banker in person. It's all done over zoom. What's amazing. >>We'll go back to that and a great great to see you had great props here earlier. You guys got some good stuff going on in the policy side, a core max on stage talking about this Virginia deal. Give us the update. >>Yeah. Hey thanks john, it's great to be back. I always like to be on the cube. Uh, so we made an announcement today regarding our economic impact study, uh, for the commonwealth of Virginia. And this is around the amazon web services business and our presence in Virginia or a WS as we all, uh, call, uh, amazon web services. And um, basically the data that we released today shows over the last decade the magnitude of investment that we're making and I think reflects just the overall investments that are going into Virginia in the data center industry of which john and I have been very involved with over the years. But the numbers are quite um, uh, >>just clever. This is not part of the whole H. 20. H. Q. Or whatever they call HQ >>To HQ two. It's so Virginia Amazon is investing uh in Virginia as part of our HQ two initiative. And so Arlington Virginia will be the second headquarters in the U. S. In addition to that, AWS has been in Virginia for now many years, investing in both data center infrastructure and also other corporate facilities where we house AWS employees uh in other parts of Virginia, particularly out in what's known as the dullest technology corridor. But our data centers are actually spread throughout three counties in Fairfax County, Loudoun County in Prince William County. >>So this is the maxim now. So it wasn't anything any kind of course this is Virginia impact. What was, what did he what did he announce? What did he say? >>Yeah. So there were a few things that we highlighted in this economic impact study. One is that over the last decade, if you can believe it, we've invested $35 billion 2020 alone. The AWS investment in construction and these data centers. uh it was actually $1.3 billion 2020. And this has created over 13,500 jobs in the Commonwealth of Virginia. So it's a really great story of investment and job creation and many people don't know John in this Sort of came through in your question too about HQ two, But aws itself has over 8000 employees in Virginia today. Uh, and so we've had this very significant presence for a number of years now in Virginia over the last, you know, 15 years has become really the cloud capital of the country, if not the world. Uh, and you see all this data center infrastructure that's going in there, >>John What's your take on this? You've been very active in the county there. Um, you've been a legend in the area and tech, you've seen this many years, you've been doing so I think the longest running company doing cyber my 31st year, 31st year. So you've been on the ground. What does this all mean to you? >>Well, you know, it goes way back to, it was roughly 2005 when I served on the Economic Development Commission, Loudon County as the chairman. And at the time we were the fastest-growing county in America in Loudon County. But our residential real property taxes were going up stratospherically because when you look at it, every dollar real property tax that came into residential, we lose $2 because we had to fund schools and police and fire departments and so forth. And we realized for every dollar of commercial real property tax that came in, We made $97 in profit, but only 13% of the money that was coming into the county was coming in commercially. So a small group got together from within the county to try and figure out what were the assets that we had to offer to companies like Amazon and we realized we had a lot of land, we had water and then we had, you know this enormous amount of dark fiber, unused fibre optic. And so basically the county made it appealing to companies like amazon to come out to Loudon County and other places in northern Virginia and the rest is history. If you look today, we're Loudon County is Loudon County generates a couple $100 million surplus every year. It's real property taxes have come down in in real dollars and the percentage of revenue that comes from commercials like 33 34%. That's really largely driven by the data center ecosystem that my friend over here Shannon was talking. So >>the formula basically is look at the assets resources available that may align with the kind of commercial entities that good. How's their domicile there >>that could benefit. >>So what about power? Because the data centers need power, fiber fiber is great. The main, the main >>power you can build power but the main point is is water for cooling. So I think I think we had an abundance of water which allowed us to build power sources and allowed companies like amazon to build their own power sources. So I think it was really a sort of a uh uh better what do they say? Better lucky than good. So we had a bunch of assets come together that helps. Made us, made us pretty lucky as a, as a region. >>Thanks area too. >>It is nice and >>john, it's really interesting because the vision that john Wood and several of his colleagues had on that economic development board has truly come through and it was reaffirmed in the numbers that we released this week. Um, aws paid $220 million 2020 alone for our data centers in those three counties, including loud >>so amazon's contribution to >>The county. $220 million 2020 alone. And that actually makes up 20% of overall property tax revenues in these counties in 2020. So, you know, the vision that they had 15 years ago, 15, 16 years ago has really come true today. And that's just reaffirmed in these numbers. >>I mean, he's for the amazon. So I'll ask you the question. I mean, there's a lot of like for misinformation going around around corporate reputation. This is clearly an example of the corporation contributing to the, to the society. >>No, no doubt. And you think >>About it like that's some good numbers, 20 million, 30 >>$5 million dollar capital investment. You know, 10, it's, what is it? 8000 9000 >>Jobs. jobs, a W. S. jobs in the Commonwealth alone. >>And then you look at the economic impact on each of those counties financially. It really benefits everybody at the end of the day. >>It's good infrastructure across the board. How do you replicate that? Not everyone's an amazon though. So how do you take the formula? What's your take on best practice? How does this rollout? And that's the amazon will continue to grow, but that, you know, this one company, is there a lesson here for the rest of us? >>I think I think all the data center companies in the cloud companies out there see value in this region. That's why so much of the internet traffic comes through northern Virginia. I mean it's I've heard 70%, I've heard much higher than that too. So I think everybody realizes this is a strategic asset at a national level. But I think the main point to bring out is that every state across America should be thinking about investments from companies like amazon. There are, there are really significant benefits that helps the entire community. So it helps build schools, police departments, fire departments, etcetera, >>jobs opportunities. What's the what's the vision though? Beyond data center gets solar sustainability. >>We do. We have actually a number of renewable energy projects, which I want to talk about. But just one other quick on the data center industry. So I also serve on the data center coalition which is a national organization of data center and cloud providers. And we look at uh states all over this country were very active in multiple states and we work with governors and state governments as they put together different frameworks and policies to incent investment in their states and Virginia is doing it right. Virginia has historically been very forward looking, very forward thinking and how they're trying to attract these data center investments. They have the right uh tax incentives in place. Um and then you know, back to your point about renewable energy over the last several years, Virginia is also really made some statutory changes and other policy changes to drive forward renewable energy in Virginia. Six years ago this week, john I was in a coma at county in Virginia, which is the eastern shore. It's a very rural area where we helped build our first solar farm amazon solar farm in Virginia in 2015 is when we made this announcement with the governor six years ago this week, it was 88 megawatts, which basically at the time quadruple the virginias solar output in one project. So since that first project we at Amazon have gone from building that one facility, quadrupling at the time, the solar output in Virginia to now we're by the end of 2023 going to be 1430 MW of solar power in Virginia with 15 projects which is the equivalent of enough power to actually Enough electricity to power 225,000 households, which is the equivalent of Prince William county Virginia. So just to give you the scale of what we're doing here in Virginia on renewable energy. >>So to me, I mean this comes down to not to put my opinion out there because I never hold back on the cube. It's a posture, we >>count on that. It's a >>posture issue of how people approach business. I mean it's the two schools of thought on the extreme true business. The government pays for everything or business friendly. So this is called, this is a modern story about friendly business kind of collaborative posture. >>Yeah, it's putting money to very specific use which has a very specific return in this case. It's for everybody that lives in the northern Virginia region benefits everybody. >>And these policies have not just attracted companies like amazon and data center building builders and renewable energy investments. These policies are also leading to rapid growth in the cybersecurity industry in Virginia as well. You know john founded his company decades ago and you have all of these cybersecurity companies now located in Virginia. Many of them are partners like >>that. I know john and I both have contributed heavily to a lot of the systems in place in America here. So congratulations on that. But I got to ask you guys, well I got you for the last minute or two cybersecurity has become the big issue. I mean there's a lot of these policies all over the place. But cyber is super critical right now. I mean, where's the red line Shannon? Where's you know, things are happening? You guys bring security to the table, businesses are out there fending for themselves. There's no militia. Where's the, where's the, where's the support for the commercial businesses. People are nervous >>so you want to try it? >>Well, I'm happy to take the first shot because this is and then we'll leave john with the last word because he is the true cyber expert. But I had the privilege of hosting a panel this morning with the director of the cybersecurity and Infrastructure Security agency at the department, Homeland Security, Jenness easterly and the agency is relatively new and she laid out a number of initiatives that the DHS organization that she runs is working on with industry and so they're leaning in their partnering with industry and a number of areas including, you know, making sure that we have the right information sharing framework and tools in place, so the government and, and we in industry can act on information that we get in real time, making sure that we're investing for the future and the workforce development and cyber skills, but also as we enter national cybersecurity month, making sure that we're all doing our part in cyber security awareness and training, for example, one of the things that are amazon ceo Andy Jassy recently announced as he was participating in a White house summit, the president biden hosted in late august was that we were going to at amazon make a tool that we've developed for information and security awareness for our employees free, available to the public. And in addition to that we announced that we were going to provide free uh strong authentication tokens for AWS customers as part of that announcement going into national cybersecurity months. So what I like about what this administration is doing is they're reaching out there looking for ways to work with industry bringing us together in these summits but also looking for actionable things that we can do together to make a difference. >>So my, my perspective echoing on some of Shannon's points are really the following. Uh the key in general is automation and there are three components to automation that are important in today's environment. One is cyber hygiene and education is a piece of that. The second is around mis attribution meaning if the bad guy can't see you, you can't be hacked. And the third one is really more or less around what's called attribution, meaning I can figure out actually who the bad guy is and then report that bad guys actions to the appropriate law enforcement and military types and then they take it from there >>unless he's not attributed either. So >>well over the basic point is we can't as industry hat back, it's illegal, but what we can do is provide the tools and methods necessary to our government counterparts at that point about information sharing, where they can take the actions necessary and try and find those bad guys. >>I just feel like we're not moving fast enough. Businesses should be able to hack back. In my opinion. I'm a hawk on this one item. So like I believe that because if people dropped on our shores with troops, the government will protect us. >>So your your point is directly taken when cyber command was formed uh before that as airlines seeing space physical domains, each of those physical domains have about 100 and $50 billion they spend per year when cyber command was formed, it was spending less than Jpmorgan chase to defend the nation. So, you know, we do have a ways to go. I do agree with you that there needs to be more uh flexibility given the industry to help help with the fight. You know, in this case. Andy Jassy has offered a couple of tools which are, I think really good strong tokens training those >>are all really good. >>We've been working with amazon for a long time, you know, ever since, uh, really, ever since the CIA embrace the cloud, which was sort of the shot heard around the world for cloud computing. We do the security compliance automation for that air gap region for amazon as well as other aspects >>were all needs more. Tell us faster, keep cranking up that software because tell you right now people are getting hit >>and people are getting scared. You know, the colonial pipeline hack that affected everybody started going wait a minute, I can't get gas. >>But again in this area of the line and jenny easterly said this this morning here at the summit is that this truly has to be about industry working with government, making sure that we're working together, you know, government has a role, but so does the private sector and I've been working cyber issues for a long time to and you know, kind of seeing where we are this year in this recent cyber summit that the president held, I really see just a tremendous commitment coming from the private sector to be an effective partner in securing the nation this >>full circle to our original conversation around the Virginia data that you guys are looking at the Loudon County amazon contribution. The success former is really commercial public sector. I mean, the government has to recognize that technology is now lingua franca for all things everything society >>well. And one quick thing here that segues into the fact that Virginia is the cloud center of the nation. Um uh the president issued a cybersecurity executive order earlier this year that really emphasizes the migration of federal systems into cloud in the modernization that jOHN has worked on, johN had a group called the Alliance for Digital Innovation and they're very active in the I. T. Modernization world and we remember as well. Um but you know, the federal government is really emphasizing this, this migration to cloud and that was reiterated in that cybersecurity executive order >>from the, well we'll definitely get you guys back on the show, we're gonna say something. >>Just all I'd say about about the executive order is that I think one of the main reasons why the president thought was important is that the legacy systems that are out there are mainly written on kobol. There aren't a lot of kids graduating with degrees in COBOL. So COBOL was designed in 1955. I think so I think it's very imperative that we move has made these workloads as we can, >>they teach it anymore. >>They don't. So from a security point of view, the amount of threats and vulnerabilities are through the >>roof awesome. Well john I want to get you on the show our next cyber security event. You have you come into a fireside chat and unpack all the awesome stuff that you're doing. But also the challenges. Yes. And there are many, you have to keep up the good work on the policy. I still say we got to remove that red line and identified new rules of engagement relative to what's on our sovereign virtual land. So a whole nother Ballgame, thanks so much for coming. I appreciate it. Thank you appreciate it. Okay, cute coverage here at eight of public sector seven Washington john ferrier. Thanks for watching. Mhm. Mhm.

>> Announcer: From the SiliconANGLE Media office in Boston, Massachusetts, it's theCUBE. Now here's your host, Dave Vellante. >> In the early days of big data and Hadoop, the focus was really on operational efficiency where ROI was largely centered on reduction of investment. Fast forward 10 years and you're seeing a plethora of activity around machine learning, and deep learning, and artificial intelligence, and deeper business integration as a function of machine intelligence. Welcome to this Cube conversation, The Skinny on Machine Intelligence. I'm Dave Vellante and I'm excited to have Jim Kobielus here up from the District area. Jim, great to see you. Thanks for coming into the office today. >> Thanks a lot, Dave, yes great to be here in beautiful Marlboro, Massachusetts. >> Yes, so you know Jim, when you think about all the buzz words in this big data business, I have to ask you, is this just sort of same wine, new bottle when we talk about all this AI and machine intelligence stuff? >> It's actually new wine. But of course there's various bottles and they have different vintages, and much of that wine is still quite tasty, and let me just break it out for you, the skinny on machine intelligence. AI as a buzzword and as a set of practices really goes back of course to the early post-World War II era, as we know Alan Turing and the Imitation Game and so forth. There are other developers, theorists, academics in the '40s and the '50s and '60s that pioneered in this field. So we don't want to give Alan Turing too much credit, but he was clearly a mathematician who laid down the theoretical framework for much of what we now call Artificial Intelligence. But when you look at Artificial Intelligence as a ever-evolving set of practices, where it began was in an area that focused on deterministic rules, rule-driven expert systems, and that was really the state of the art of AI for a long, long time. And so you had expert systems in a variety of areas that became useful or used in business, and science, and government and so forth. Cut ahead to the turn of the millennium, we are now in the 21st century, and what's different, the new wine, is big data, larger and larger data sets that can reveal great insights, patterns, correlations that might be highly useful if you have the right statistical modeling tools and approaches to be able to surface up these patterns in an automated or semi-automated fashion. So one of the core areas is what we now call machine learning, which really is using statistical models to infer correlations, anomalies, trends, and so forth in the data itself, and machine learning, the core approach for machine learning is something called Artificial Neural Networks, which is essentially modeling a statistical model along the lines of how, at a very high level, the nervous system is made up, with neurons connected by synapses, and so forth. It's an analog in statistical modeling called a perceptron. The whole theoretical framework of perceptrons actually got started in the 1950s with the first flush of AI, but didn't become a practical reality until after the turn of this millennium, really after the turn of this particular decade, 2010, when we started to see not only very large big data sets emerge and new approaches for managing it all, like Hadoop, come to the fore. But we've seen artificial neural nets get more sophisticated in terms of their capabilities, and a new approach for doing machine learning, artificial neural networks, with deeper layers of perceptrons, neurons, called deep learning has come to the fore. With deep learning, you have new algorithms like convolutional neural networks, recurrent neural networks, generative adversarial neural networks. These are different ways of surfacing up higher level abstractions in the data, for example for face recognition and object recognition, voice recognition and so forth. These all depend on this new state of the art for machine learning called deep learning. So what we have now in the year 2017 is we have quite a mania for all things AI, much of it is focused on deep learning, much of it is focused on tools that your average data scientist or your average developer increasingly can use and get very productive with and build these models and train and test them, and deploy them into working applications like going forward, things like autonomous vehicles would be impossible without this. >> Right, and we'll get some of that. But so you're saying that machine learning is essentially math that infers patterns from data. And math, it's new math, math that's been around for awhile or. >> Yeah, and inferring patterns from data has been done for a long time with software, and we have some established approaches that in many ways predate the current vogue for neural networks. We have support vector machines, and decision trees, and Bayesian logic. These are different ways of approaches statistical for inferring patterns, correlations in the data. They haven't gone away, they're a big part of the overall AI space, but it's a growing area that I've only skimmed the surface of. >> And they've been around for many many years, like SVM for example. Okay, now describe further, add some color to deep learning. You sort of painted a picture of this sort of deep layers of these machine learning algorithms and this network with some depth to it, but help us better understand the difference between machine learning and deep learning, and then ultimately AI. >> Yeah, well with machine learning generally, you know, inferring patterns from data that I said, artificial neural networks of which the deep learning networks are one subset. Artificial neural networks can be two or more layers of perceptrons or neurons, they have relationship to each other in terms of their activation according to various mathematical functions. So when you look at an artificial neural network, it basically does very complex math equations through a combination of what they call scalar functions, like multiplication and so forth, and then you have these non-linear functions, like cosine and so forth, tangent, all that kind of math playing together in these deep structures that are triggered by data, data input that's processed according to activation functions that set weights and reset the weights among all the various neural processing elements, that ultimately output something, the insight or the intelligence that you're looking for, like a yes or no, is this a face or not a face, that these incoming bits are presenting. Or it might present output in terms of categories. What category of face is this, a man, a woman, a child, or whatever. What I'm getting at is that so deep learning is more layers of these neural processing elements that are specialized to various functions to be able to abstract higher level phenomena from the data, it's not just, "Is this a face," but if it's a scene recognition deep learning network, it might recognize that this is a face that corresponds to a person named Dave who also happens to be the father in the particular family scene, and by the way this is a family scene that this deep learning network is able to ascertain. What I'm getting at is those are the higher level abstractions that deep learning algorithms of various sorts are built to identify in an automated way. >> Okay, and these in your view all fit under the umbrella of artificial intelligence, or is that sort of an uber field that we should be thinking of. >> Yeah, artificial intelligence as the broad envelope essentially refers to any number of approaches that help machines to think like humans, essentially. When you say, "Think like humans," what does that mean actually? To do predictions like humans, to look for anomalies or outliers like a human might, you know separate figure from ground for example in a scene, to identify the correlations or trends in a given scene. Like I said, to do categorization or classification based on what they're seeing in a given frame or what they're hearing in a given speech sample. So all these cognitive processes just skim the surface, or what AI is all about, automating to a great degree. When I say cognitive, but I'm also referring to affective like emotion detection, that's another set of processes that goes on in our heads or our hearts, that AI based on deep learning and so forth is able to do depending on different types of artificial neural networks are specialized particular functions, and they can only perform these functions if A, they've been built and optimized for those functions, and B, they have been trained with actual data from the phenomenon of interest. Training the algorithms with the actual data to determine how effective the algorithms are is the key linchpin of the process, 'cause without training the algorithms you don't know if the algorithm is effective for its intended purpose, so in Wikibon what we're doing is in the whole development process, DevOps cycle, for all things AI, training the models through a process called supervised learning is absolutely an essential component of ascertaining the quality of the network that you've built. >> So that's the calibration and the iteration to increase the accuracy, and like I say, the quality of the outcome. Okay, what are some of the practical applications that you're seeing for AI, and ML, and DL. >> Well, chat bots, you know voice recognition in general, Siri and Alexa, and so forth. Without machine learning, without deep learning to do speech recognition, those can't work, right? Pretty much in every field, now for example, IT service management tools of all sorts. When you have a large network that's logging data at the server level, at the application level and so forth, those data logs are too large and too complex and changing too fast for humans to be able to identify the patterns related to issues and faults and incidents. So AI, machine learning, deep learning is being used to fathom those anomalies and so forth in an automated fashion to be able to alert a human to take action, like an IT administrator, or to be able to trigger a response work flow, either human or automated. So AI within IT service management, hot hot topic, and we're seeing a lot of vendors incorporate that capability into their tools. Like I said, in the broad world we live in in terms of face recognition and Facebook, the fact is when I load a new picture of myself or my family or even with some friends or brothers in it, Facebook knows lickity-split whether it's my brother Tom or it's my wife or whoever, because of face recognition which obviously depends, well it's not obvious to everybody, depends on deep learning algorithms running inside Facebook's big data network, big data infrastructure. They're able to immediately know this. We see this all around us now, speech recognition, face recognition, and we just take it for granted that it's done, but it's done through the magic of AI. >> I want to get to the development angle scenario that you specialize in. Part of the reason why you came to Wikibon is to really focus on that whole application development angle. But before we get there, I want to follow the data for a bit 'cause you mentioned that was really the catalyst for the resurgence in AI, and last week at the Wikibon research meeting we talked about this three-tiered model. Edge, as edge piece, and then something in the middle which is this aggregation point for all this edge data, and then cloud which is where I guess all the deep modeling occurs, so sort of a three-tier model for the data flow. >> John: Yes. >> So I wonder if you could comment on that in the context of AI, it means more data, more I guess opportunities for machine learning and digital twins, and all this other cool stuff that's going on. But I'm really interested in how that is going to affect the application development and the programming model. John Farrier has a phrase that he says that, "Data is the new development kit." Well, if you got all this data that's distributed all over the place, that changes the application development model, at least you think it does. So I wonder if you could comment on that edge explosion, the data explosion as a result, and what it means for application development. >> Right, so more and more deep learning algorithms are being pushed to edge devices, by that I mean smartphones, and smart appliances like the ones that incorporate Alexa and so forth. And so what we're talking about is the algorithms themselves are being put into CPUs and FPGAs and ASICs and GPUs. All that stuff's getting embedded in everything that we're using, everything's that got autonomous, more and more devices have the ability if not to be autonomous in terms of making decisions, independent of us, or simply to serve as augmentation vehicles for our own whatever we happen to be doing thanks to the power of deep learning at the client. Okay, so when deep learning algorithms are embedded in say an internet of things edge device, what the deep learning algorithms are doing is A, they're ingesting the data through the sensors of that device, B, they're making inferences, deep learning algorithmic-driven inferences, based on that data. It might be speech recognition, face recognition, environmental sensing and being able to sense geospatially where you are and whether you're in a hospitable climate for whatever. And then the inferences might drive what we call actuation. Now in the autonomous vehicle scenario, the autonomous vehicle is equipped with all manner of sensors in terms of LiDAR and sonar and GPS and so forth, and it's taking readings all the time. It's doing inferences that either autonomously or in conjunction with inferences that are being made through deep learning and machine learning algorithms that are executing in those intermediary hubs like you described, or back in the cloud, or in a combination of all of that. But ultimately, the results of all those analytics, all those deep learning models, feed the what we call actuation of the car itself. Should it stop, should it put on the brakes 'cause it's about to hit a wall, should it turn right, should it turn left, should it slow down because it happened to have entered a new speed zone or whatever. All of the decisions, the actions that the edge device, like a car would be an edge device in this scenario, are being driven by evermore complex algorithms that are trained by data. Now, let's stay with the autonomous vehicle because that's an extreme case of a very powerful edge device. To train an autonomous vehicle you need of course lots and lots of data that's acquired from possibly a prototype that you, a Google or a Tesla, or whoever you might be, have deployed into the field or your customers are using, B, proving grounds like there's one out by my stomping ground out in Ann Arbor, a proving ground for the auto industry for self-driving vehicles and gaining enough real training data based on the operation of these vehicles in various simulated scenarios, and so forth. This data is used to build and iterate and refine the algorithms, the deep learning models that are doing the various operations of not only the vehicles in isolation but the vehicles operating as a fleet within an entire end to end transportation system. So what I'm getting at, is if you look at that three-tier model, then the edge device is the car, it's running under its own algorithms, the middle tier the hub might be a hub that's controlling a particular zone within a traffic system, like in my neck of the woods it might be a hub that's controlling congestion management among self-driving vehicles in eastern Fairfax County, Virginia. And then the cloud itself might be managing an entire fleet of vehicles, let's say you might have an entire fleet of vehicles under the control of say an Uber, or whatever is managing its own cars from a cloud-based center. So when you look at the tiering model that analytics, deep learning analytics is being performed, increasingly it will be for various, not just self-driving vehicles, through this tiered model, because the edge device needs to make decisions based on local data. The hub needs to make decisions based on a wider view of data across a wider range of edge entities. And then the cloud itself has responsibility or visibility for making deep learning driven determinations for some larger swath. And the cloud might be managing both the deep learning driven edge devices, as well as monitoring other related systems that self-driving network needs to coordinate with, like the government or whatever, or police. >> So envisioning that three-tier model then, how does the programming paradigm change and evolve as a result of that. >> Yeah, the programming paradigm is the modeling itself, the building and the training and the iterating the models generally will stay centralized, meaning to do all these functions, I mean to do modeling and training and iteration of these models, you need teams of data scientists and other developers who are both adept as to statistical modeling, who are adept at acquiring the training data, at labeling it, labeling is an important function there, and who are adept at basically developing and deploying one model after another in an iterative fashion through DevOps, through a standard release pipeline with version controls, and so forth built in, the governance built in. And that's really it needs to be a centralized function, and it's also very compute and data intensive, so you need storage resources, you need large clouds full of high performance computing, and so forth. Be able to handle these functions over and over. Now the edge devices themselves will feed in the data in just the data that is fed into the centralized platform where the training and the modeling is done. So what we're going to see is more and more centralized modeling and training with decentralized execution of the actual inferences that are driven by those models is the way it works in this distributive environment. >> It's the Holy Grail. All right, Jim, we're out of time but thanks very much for helping us unpack and giving us the skinny on machine learning. >> John: It's a fat stack. >> Great to have you in the office and to be continued. Thanks again. >> John: Sure. >> All right, thanks for watching everybody. This is Dave Vellante with Jim Kobelius, and you're watching theCUBE at the Marlboro offices. See ya next time. (upbeat music)