>> Hi I'm Peter Burris. Welcome to Action Item. (upbeat electronic music) Every week I bring the Wikibon research team together to talk about some of the issues that are most important in the computing industry and this week is no different. This week I'm joined by four esteemed Wikibon analysts, David Floyer, Neil Radon, Jim Kobielus, Ralph Finos, and what we're going to do is we're going to talk a few minutes about some of the predictions that we did not get into, our recent predictions webinar. So, I'd like to start off with Jim Kobielus. Jim, one of the things that we didn't get a chance to talk about yesterday in the overall predictions webinar was some of the new AI frameworks that are on the horizon for developers. So, let's take a look at it. What's the prediction? >> Prediction for 2018, Peter, is that the AI community will converge on an open framework. An open framework for developing, training and deploying deep learning and machine learning applications. In fact, in 2017, we've seen the momentum in this direction, strong momentum. If you were at AWS re:Invent just a few weeks ago, you'll notice that on the main stage, they discuss what they're doing in terms of catalyzing an open API, per building AI, an open model interchange format, and an open model compilation framework, and they're not the only vendor who's behind this. Microsoft has been working with AWS, as well as independently and with other partners to catalyze various aspects of this open framework. We also see Intel and Google and IBM and others marching behind a variety of specifications such as Gluon (mumbles) NNVM and so forth, so we expect continued progress along these lines in 2018, and that we expect that other AI solution provider, as well as users and developers will increasingly converge on this, basically, the abstraction framework that will make it irrelevant whether you build your model in TensorFlow or MXNet or whatever, you'd be able to compile it and run it in anybody else's back end. >> So Jim, one question then we'll move on to Neil really quickly but one question that i have is the relationship between tool choice and role in the organization has always been pretty tight. Roles have changed as a consequence of the availability of tools. Now we talked about some of the other predictions. How the data scientist role is going to change. As we think about some of these open AI development frameworks, how are they going to accommodate the different people that are going to be responsible for building and creating business value out of AI and data? >> Pete, hit it on another level that i didn't raise in my recent predictions document, but i'll just quickly touch on it. We're also seeing the development of open devops environments within which teams of collaborators, data scientists, subject matter experts, data engineers and so forth will be able to build and model and train and deploy deep learning and so forth within a standard workflow where each one of them has task-oriented tools to enable their piece but they all share a common governance around the models, the data and so forth. In fact, we published a report several months ago, Wikibon, talking about devops for data science, and this is a huge research focus for us going forward, and really, for the industry as a whole. It's productionizing of AI in terms of building and deploying the most critical applications, the most innovative applications now in business. >> Great, Jim, thanks very much for that. So Neil, I want to turn to you now. One of the challenges that the big data and the computing industry faces overall is that how much longer are we going to be able to utilize the technologies that have taken us through the first 50 years at the hardware level, and there is some promise in some new approaches to thinking about computing. What's your prediction? >> Well in 2018, you're going to see a demonstration of an actual quantum computer chip that's built on top of existing silicone technology and fabrication. This is a real big deal because what this group in the University of New South Wales came up with was a way to layer traditional transistors and silicon on top of those wacky quantum bits to control them, and to deal with, I don't want to get too technical about that, but the point is that quantum computing has the promise of moving computing light years ahead of where we are now. We've managed to build lots of great software on things that go on or off, and quantum computing is much more than that. I think what you're going to see in 2018 is a demonstration of actual quantum computing chips built on this, and the big deal in that is that we can take these existing machines and factories and capital equipment designed for silicone, and start to produce quantum chips without basically developing a whole new industry. Now why is this important? It's only the first step because these things are not going to be based on the existing Intel i86 instruction set, so all new software will have to be developed, software engineers are going to have to learn a whole new way of doing things, but the possibilities are endless. If you can think about a drug discovery, or curing disease, or dealing with the climate, or new forms of energy to propel us into space, that's where quantum computing is likely to take this. >> Yeah, quantum computing, just to bring a, kind of a fine point on it, allows, at any given time, the machine to be in multiple different states, and it's that fact that allows, in many respects, a problem to be attacked from a large number of directions at the same time, and then test each of them out, so it has a natural affinity with some of the things that we think about in AI, so it's going to have an enormous impact over the course of the next few years and it's going to be interesting to see how this plays out. So David Floyer, I now want to turn to you. We're not likely to see quantum computing at the edge anytime soon, by virtue of some of the technologies we face. More likely it'll be specialized processors up in the cloud service provider in the near term. But what are you going to talk about when we think about the role that the edge is going to play in the industry, and the impacts it's going to have on, quite frankly, the evolution of de facto standards? >> Well, I'd like to focus on the economics of edge devices. And my prediction is that the economics of consumer-led volume will dominate the design of IoT devices at the edge. If you take an IoT device, it's made up of sensors and advanced analytics and AI, and specifically designed compute elements, and together with the physical setup of fitting it into wherever you're going to put it, that is the overall device that will be put into the edge, and that's where all of the data is going to be generated, and obviously, if you generate data somewhere, the most efficient way of processing that data is actually at the edge itself, so you don't have to transport huge amounts of data. So the prediction is that new vendors with deep knowledge of the technology itself, using all the tools that Jim was talking about, and deep knowledge of the end user environments and the specific solutions that they're going to offer, they will come out with much lower cost solutions than traditional vendors. So to put a little bit of color around it, let's take a couple of real-world examples where this is already in place in the consumer world, and will be the basis of solutions in the enterprise. If we take the Apple iPhone X, it has facial recognition built-in, and it has facial recognition built-in on their A11 chips, but they're bionic chips. They've got GPUs, they've got neural networks all in the chip itself, and the total cost of that solution is around a hundred dollars in terms of these parts, and that includes the software. So if we take that hundred dollars and put it into what it would actually be priced at, that's around $300. So that's a much, much lower cost than a traditional IT vendor could ever do, and a much, at least an order of magnitude, and probably two orders of magnitude cheaper than an IT department could produce for its own use. So that leaves (mumbles) inclusions, going to be a lot of new vendors. People like Sony, for example, Hitachi, Fujitsu, Honeywell. Possibly people like Apple and Microsoft. Nvidia, Samsung, and many companies that we'll predict are going to come out of India, China and Russia who have strong mathematical educational programs. So the action item is for CIOs, is to really look carefully at the projects that you are looking at, and determine, do I really have the volume to be unique in this area? If that volume, if it's a problem which is going to be industry-wide, the advice we would give is wait for that device to come out from a specialized vendor rather than develop it yourself. And focus investment on areas where you have both the volume of devices and the volume of data that will allow you to be successful. >> All right, David, thank you very much. So let me wrap this week's Action Item, which has been kind of a bridge, but we've looked specifically at some of the predictions that didn't make it into our recent predictions webinar, and if I want to try to summarize or try to bring all these things together, here's what I think what we'd say. Number one, we'd say that the development community has to prepare itself for some pretty significant changes as a consequence of having an application development environment that's more probabilistic, driven by data and driven by AI and related technologies, and we think that there will be new frameworks that are deployed in 2018, and that's just where it's going to start, and will mature over the next few years as we heard from Jim Kobielus. We've also heard that there is going to be a new computing architecture that's going to drive change, perhaps for the next 50 years, and the whole concept of quantum computing is very, very real, and it's going to have significant implications. Now it will take some time to roll out, but again, software developers have to think about the implications of some these new architectures on their work because not only are they going to have to deal with technology approaches that are driven by data, but they're also going to have to look at entirely new ways of framing problems because it used to be about something different than it is today. The next thing that we need to think about is that there still is going to be the economics of computing that are going to ultimately shape how all of this plays out. David Floyer talked about, specifically at the edge, where Wikibon believes it's going to have an enormous implication on the true cost of computing and how well some of these complex problems actually find their way into commercial and other domains. So with a background of those threee things, we think, ultimately, that's an addendum to the predictions that we have and once again, i'm Peter Burris. Thank you very much for joining us for Action Item, and we look forward to working with you more closely over the course of the next year, 2018, as we envision the new changes and the practice of how to make those changes a reality. From our Palo Alto theCUBE studios, this has been Action Item. (bright electronic music)

>> Announcer: Live from Austin, Texas, It's The Cube. Covering KubeCon 2017 and CloudNativeCon 2017. Brought to you by Red Hat, The Lennox Foundation, and The Cube's ecosystem partners. >> Okay, welcome back everyone. Live here in Austin, Texas, this is The Cube's exclusive coverage of the CNCF CloudNativeCon which was yesterday, and today is KubeCon, for Kubernetes conference, and a little bit tomorrow as well, some sessions. Our next guest is Adrian Cockcroft, VP of Cloud Architecture Strategy at AWS, Amazon Web Services, and my co-host Stu Miniman. Obviously, Adrian, an industry legend on Twitter and the industry, formerly with Netflix, knows a lot about AWS, now VP of Cloud Architecture, thanks for joining us. Appreciate it. >> Thanks very much. >> This is your first time as an AWS employee on The Cube. You've been verified. >> I've been on The Cube before. >> Many times. You've been verified. What's going on now with you guys, obviously coming off a hugely successful reinvent, there's a ton of video of me ranting and raving about how you guys are winning, and there's no second place, in the rear-view mirror, certainly Amazon's doing great. But CloudNative's got the formula, here. This is a cultural shift. What is going on here that's similar to what you guys are doing architecturally, why are you guys here, are you evangelizing, are you recruiting, are you proposing anything? What's the story? >> Yeah, it's really all of those things. We've been doing CloudNative for a long time, and the key thing with AWS, we always listen to our customers, and go wherever they take us. That's a big piece of the way we've always managed to keep on top of everything. And in this case, the whole container industry, there's a whole whole market there, there's a lot of different pieces, we've been working on that for a long time, and we found more and more people interested in CNCF and Kubernetes, and really started to engage. Part of my role is to host the open source team that does outbound engagement with all the different open source communities. So I've hired a few people, I hired Arun Gupta, who's very active in CNCF earlier this year, and internally we were looking at, we need to join CNCF at some point. We got to do that eventually and venture in, let's go make it happen. So last summer we just did all the internal paperwork, and running around talking to people and got everyone on the same page. And then in August we announced, hey, we're joining. So we got that done. I'm on the board of CNCF, Arun's my alternate for the board and technical, running around, and really deeply involved in as much of the technology and everything. And then that was largely so that we could kind of get our contributions from engineering on a clear footing. We were starting to contribute to Kupernetes, like as an outsider to the whole thing. So that's why we're, what's going on here? So getting that in place was like the basis for getting the contributions in place, we start hiring, we get the teams in place, and then getting our ducks in a row, if you like. And then last week at Reinvent, we announced EKS, the EC2 Kubernete's Service. And this week, we all had to be here. Like last week after Reinvent, everyone at AWS wants to go and sleep for a week. But no, we're going to go to Austin, we're going to do this. So we have about 20 people here, we came in, I did a little keynote yesterday. I could talk through the different topics, there, but fundamentally we wanted to be here where we've got the engineering teams here, we've got the engineering managers, they're in full-on hiring mode, because we've got the basic teams in place, but there's a lot more we want to do, and we're just going out and engaging, really getting to know the customers in detail. So that's really what drives it. Customer interactions, little bit of hiring, and just being present in this community. >> Adrian, you're very well known in the open source community, everything that you've done. Netflix, when you were on the VC side, you evangelized a bunch of it, if I can use the term. Amazon, many of us from the outside looked and, trying to understand. Obviously Amazon used lots of open source, Amazon's participated in a number of open source. MXNet got a lot of attention, joining the CNCF is something, I know this community, it's been very positively received, everybody's been waiting for it. What can you tell us about how Amazon, how do they think about open source? Is that something that fits into the strategy, or is it a tactic? Obviously, you're building out your teams, that sends certain signals to market, but can you help clarify for those of us that are watching what Amazon thinks about when it comes to this space? >> I think we've been, so, we didn't really have a team focused on outbound communication of what we were doing in open source until I started building this team a year ago. I think that was the missing link. We were actually doing a lot more than most people realized. I'd summarize it as saying, we were doing more than most people expected, but less than we probably could have been given the scale of what we are, the scale that AWS is at. So part of what we're doing is unlocking some internal demand where engineering teams were going. We'd like to open source something, we don't know how to engage with the communities. We're trying to build trust with these communities, and I've hired a team, I've got several people now, who are mostly from the open source community, we were also was kind of interviewing people like crazy. That was our sourcing for this team. So we get these people in and then we kind of say, all right, we have somebody that understands how to build these communities, how to respond, how to engage with the open source community. It's a little different to a standard customer, enterprise, start up, those are different entities that you'd want to relate to. But from a customer point of view, being customer-obsessed as AWS is, how do we get AWS to listen to an open source community and work with them, and meet all their concerns. So we've been, I think, doing a better job of that now we've pretty much got the team in place. >> That's your point, is customer focus is the ethos there. The communities are your customers in this case. So you're formalizing, you're formalizing that for Amazon, which has been so busy building out, and contributing here and there, so it sounds like there was a lot of activity going on within AWS, it was just kind of like contributing, but so much work on building out cloud ... >> Well there's a lot going on, but if no one was out there telling the story, you didn't know about it. Actually one of the best analogies we have for the EKS is actually our EMR, our Hadoop service, which launched 2010 or something, 2009, we've had it forever. But from the first few years when we did EMR, it was actually in a fork. We kept just sort of building our own version of it to do things, but about three or four years ago, we started upstreaming everything, and it's a completely clean, upstreamed version of all the Hadoop and all the related projects. But you make one API call, a cluster appears. Hey, give me a Hadoop cluster. Voom, and I want Spark and I want all these other things on it. And we're basically taking Kubernetes, it's very similar, we're going to reduce that to a single API call, a cluster appears, and it's a fully upstreamed experience. So that's, in terms of an engineering relationship to open source, we've already got a pretty good success story that nobody really knew about. And we're following a very similar path. >> Adrian, can you help us kind of unpack the Amazon Kubernetes stack a little bit? One of the announcements had a lot of attention, definitely got our attention, Fargate, kind of sits underneath what Kubernetes is doing, my understanding. Where are you sitting with the service measures, kind of bring us through the Amazon stack. What does Amazon do on its own versus the open source, and how those all fit together. >> Yeah, so everyone knows Amazon is a place where you can get virtual machines. It's easy to get me a virtual machine from ten years ago, everyone gets that, right? And then about three years ago, I think it was three years ago, we announced Lambda - was that two or three years ago? I lose track of how many reinvents ago it was. But with Lambda it's like, well, just give me a function. But as a first class entity, there's a, give me a function, here's the code I want you to run. We've now added two new ways that you can deploy to, two things you can deploy to. One of them's bare metal, which is already announced, one of the many, many, many announcements last week that might have slipped by without you noticing, but Bare Metal is a service. People go, 'those machines are really big'. Yes, of course they're really big! You get the whole machine and you can be able to bring your own virtualization or run whatever you want. But you could launch, you could run Kubernetes on that if you wanted, but we don't really care what you run it on. So we had Bare Metal, and then we have container. So Fargate is container as a first class entity that you deploy to. So here's my container registry, point you at it, and run one of these for me. And you don't have to think about deploying the underlying machines it's running on, you don't have to think about what version of Lennox it is, you have to build an AMI, all of the agents and fussing around, and you can get it in much smaller chunks. So you can say you get a CPU and half a gig of ram, and have that as just a small container. So it becomes much more granular, and you can get a broader range of mixes. A lot of our instances are sort of powers of two of a ratio of CPU to memory, and with Fargate you can ask for a much broader ratio. So you can have more CPU, less memory, and go back the other way, as well. 'Cause we can mix it up more easily at the container level. So it gives you a lot more flexibility, and if you buy into this, basically you'll get to do a lot of cost reduction for the sort of smaller scale things that you're running. Maybe test environments, you could shrink them down to just the containers and not have a lot of wasted space where you're trying to, you have too many instances running that you want to put it in. So it's partly the finer grain giving you more ability to say -- >> John: Or consumption choice. >> Yeah, and the other thing that we did recently was move to per-second billing, after the first minute, it's per-second. So the granularity of Cloud is now getting to be extremely fine-grained, and Lambda is per hundred millisecond, so it's just a little bit -- >> $4.03 for your bill, I mean this is the key thing. You guys have simplified the consumption experience. Bare Metal, VM's, containers, and functions. I mean pick one. >> Or pick all of them, it's fine. And when you look at the way Fargate's deployed in ECS it's a mixture. It's not all one or all the other, you deploy a number of instances with your containers on them, plus Fargate to deploy some additional containers that maybe didn't fit those instances. Maybe you've got a fleet of GPU enhanced machines, but you want to run a bit of Logic around it, some other containers in the same execution environment, but these don't need to be on the GPU. That kind of thing, you can mix it up. The other part of the question was, so how does this play into Kubernetes, and the discussions are just that we had to release the thing first, and then we can start talking, okay, how does this fit. Parts of the model fit into Kubernetes, parts don't. So we have to expose some more functionality in Fargate for this to make sense, 'cause we've got a really minimal initial release right now, we're going to expose it and add some more features. And then we possibly have to look at ways that we mutate Kubernetes a little bit for it to fit. So the initial EKS release won't include Fargate, because we're just trying to get it out based on what everyone knows today, we'd rather get that out earlier. But we'll be doing development work in the meantime, so a subsequent release we'll have done the integration work, which will all happen in public, in discussion with the community, and we'll have a debate about, okay, this is the features Fargate needs to properly integrate into Kubernetes, and there are other similar services from other top providers that want to integrate to the same API. So it's all going to be done as a public development, how we architect this. >> I saw a tweet here, I want to hear your comments on, it's from your keynote, someone retweeted, "managing over 100,000 clusters on ACS, hashtag Fargate," integrated into ECS, your hashtag, open, ADM's open. What is that hundred thousand number. Is that the total number, is that an example? On elastic container service, what does that mean? >> So ECS is a very large scale, multi-tenant container operation service that we've had for several years. It's in production, if you compare it to Kubernetes it's running much larger clusters, and it's been running at production-grade for longer. So it's a little bit more robust and secure and all those kinds of things. So I think it's missing some Kubernetes features, and there's a few places where we want to bring in capabilities from Kubernetes and make ECS a better experience for people. Think of Kubernetes as some what optimized for the developer experience, and ECS for more the operations experience, and we're trying to bring all this together. It is operating over a hundred thousand clusters of containers, over a hundred thousand clusters. And I think the other number was hundreds of millions of new containers are launched every week, or something like that. I think it was hundreds of millions a week. So, it's a very large scale system that is already deployed, and we're running some extremely large customers on, like Expedia and Macbook. Macbook ... Mac Box. Some of these people are running tens of thousands of containers in production as a single, we have single clusters in the tens of thousands range. So it's a different beast, right? And it meets a certain need, and we're going to evolve it forwards, and Kubernetes is serving a very different purpose. If you look at our data science space, if you want exactly the same Hadoop thing, you can get that on prem, you can run EMR. But we have Athena and Red Shift and all these other ways that are more native to the way we think, where we can go iterate and build something very specific to AWS, so you blend these two together and it depends on what you're trying to achieve. >> Well Adrian, congratulations on a great opportunity, I think the world is excited to have you in your role, if you could clarify and just put the narrative around, what's actually happening in AWS, what's been happening, and what you guys are going to do forward. I'll give you the last minute to let folks know what your job is, what your objective is, what you're looking for to hire, and your philosophy in the open source for AWS. >> I think there's a couple of other projects, and we've talked, this is really all about containers. The other two key project areas that we've been looking at are deep learning frameworks, since all of the deep learning frameworks are open source. A lot of Kubernetes people are using it to run GPUs and do that kind of stuff. So Apache MXNet is another focus on my team. It went into the incubation phase last January, we're walking it through, helping it on its way. It's something where we're 30, 40% of that project is AWS contribution. So we're not dominating it, but we're one of its main sponsors, and we're working with other companies. There's joint work with, it's lots of open source projects around here. We're working with Microsoft on Gluon, we're working with Facebook and Microsoft on Onyx which is an open URL network exchange. There's a whole lot of things going on here. And I have somebody on my team who hasn't started yet, can't tell you who it is, but they're starting pretty soon, who's going to be focusing on that open source, deep learning AI space. And the final area I think is interesting is IOT, serverless, Edge, that whole space. One announcement recently is free AltOS. So again, we sort of acquired the founder of this thing, this free real-time operating system. Everything you have, you probably personally own hundreds of instances of this without knowing it, it's in everything. Just about every little thing that sits there, that runs itself, every light bulb, probably, in your house that has a processor in it, those are all free AltOS. So it's incredibly pervasive, and we did an open source announcement last week where we switched its license to be a pure MIT license, to be more friendly for the community, and announced an Amazon version of it with better Amazon integration, but also some upgrades to the open source version. So, again, we're pushing an open source platform, strategy, in the embedded and IOT space as well. >> And enabling people to build great software, take the software engineering hassles out for the application developers, while giving the software engineers more engineering opportunities to create some good stuff. Thanks for coming on The Cube and congratulations on your continued success, and looking forward to following up on the Amazon Web Services open source collaboration, contribution, and of course, innovation. The Cube doing it's part here with its open source content, three days of coverage of CloudNativeCon and KubeCon. It's our second day, I'm John Furrier, Stu Miniman, we'll be back with more live coverage in Austin, Texas, after this short break. >> Offscreen: Thank you.