 Live from Las Vegas, it's theCUBE, covering AWS re-invent 2018. Brought to you by Amazon Web Services, Intel, and their ecosystem partners. Okay, welcome back everyone, live coverage here in Las Vegas for Amazon re-invent 2018. Day three, we're winding down over 150 videos. We'll have over 500 clips losing the voice. Dave Vellante, my co-host. This is the analyst segment. We're going to extract theCUBE insights. James Cabilius, David Floyer from Wikibon. Jim, you've been prolific on the blog, silkandangle.com, great stories. David, you got some research. What's your take, Jim? You're all over what's going on in the news. What's the impact? I think what this year's re-invent shows is that AWS is doubling down on AI. If you look at the sheer range of innovative AI capabilities they've introduced into their portfolio in terms of their announcements. It's really significant. A, they have optimized TensorFlow for their cloud. B, they now have an automated labeling, called ground truth labeling capability that leverages mechanical Turk, which has been an Amazon capability for a while. They've also got now the industry's first, what's called reinforcement learning plugin to their data science tool chain, in this case SageMaker. Reinforcement learning is becoming so important for robotics and gaming and lots of other applications of AI. And I'm just scratching the surface. So they've announced a lot of things and David can discuss other things. But I'm seeing the depth of AI, their investment in it, shows that they've really got their fingers on what enterprises are doing and will be doing to differentiate themselves with this technology over the next five to 10 years. What's an area that you see that people are getting clearly AI? What areas are people missing that are that's compelling that you've observed here? When you say people are missing, you mean the general? Journalists, audience, there's so much news. Yeah, yeah. We're the nuggets that are hidden in all the news. What are you seeing that people might not see that that's different? Getting back to the point I was raising, which is that robotics is becoming a predominant application realm for AI. Robotics, outside the laboratory or outside of the industrial IoT, I mean robots are coming into everything and there's a special type of AI you build into robots. Reinforcement learning is a big part of it. So I think the general, if you look at the journalists, they've missed the fact that I've seen in the past couple of years, robotics and reinforcement learning have become almost on the verge of being mainstream in this space. And AWS gets it. I mean, just the depth of their investments, like DeepRacer, that cute little autonomous vehicle, they've been, you know, they've rolled out here at this event. That just shows that they totally get it. That's a huge, that will be a huge growth sector. David Floyer, Outpost is their on-premises cloud. You've been calling this for I don't know how many years. Three years. Three years. What's the impact? And people said, no way, Floyer's wrong. So you get vindication. People in particular in AWS. So you're right. Okay, so you're right. But it's going to be out in a year. Yeah. Will this thing actually make it to the market? And if it does, what is the impact? Who wins and who loses? Well, let's start with, will it get to the market? Absolutely. It is Outpost, AWS Outpost is the name. It is taking AWS in the cloud and putting it on premise. The same APIs, the same services. It'll be eventually identical between the two. And that has enormous increase in the range and the reach that AWS and the time that AWS can go after. It is a major, major impact on the marketplace. Puts pressure on a whole number of people, the traditional vendors who are supplying that marketplace at the moment. And in my opinion, it's going to be wildly successful. This people have been waiting that, wanting that, particularly in the enterprise market. The reasons for a simple latency, low latency, you've got to have the data on the compute very close together. Moving data is very, very expensive over long distances. And the third one is many people want or need to have the data in certain places. So the combination is meeting the requirements. They've taken a long time to get there. I think it's going to be however, wildly successful. It's going to be coming out in 2019. They'll have their basis in the beginning of it. They'll have some announcements, probably about mid 2019. Who's threatened by this? Everybody, Cisco, HP, Dell. The integration of everything, storage, networking, compute, all in the same box is obviously a threat to all suppliers within that. And they're going to have to adapt to that pretty strongly. It's going to be a declining market. Declining markets are good if you adapt properly. A lot of people make a lot of money like IBM from mainframe. You're playing a safe, not naming names. Okay, I'll rephrase. What's your prediction? What's my prediction on? Of the landscape after this is wildly successful. The landscape is that the alternatives is going to be a much, much smaller pie. And only those that have volume and only those that can adapt to that environment are going to survive. Well, and let's name names. So who's threatened by this? Clearly Dell, EMC is threatened by this. HPE, Nutanix, the VX Rack guys, Lenovo's in there. Are they wiped out? No, but they have to respond. How do they respond? They have to have self-service. They have to have utility pricing. They have to connect to the cloud. So either they go hard after AWS, or they belly up to Microsoft with Azure Stack. That's clearly going to be their fallback place. So in a way, Microsoft with Azure Stack is also threatened by this, but in a way it's goodness for them because the ecosystem's going to evolve to that. So listen, these guys don't just give up. They're hard competitors, they're fighters. It's also to me a confirmation of Oracle's same, same strategy. On paper, Oracle's got that down. They're executing on that even though it's in a narrow Oracle world. So I think it does sort of indicate that that iPhone for the enterprise strategy is actually quite viable. If I may, if I may jump in here, four things stood out to me. The satellite as a service was to me amazing. What's next? I mean, Amazon with scale, there's just so many opportunities for them. The Edge, if we have time to talk about the Edge, the hybrid evolution and open source. Amazon used to make it easy for the enterprise players to compete. They had limited sales and service capabilities. They had no open source give back. They were hybrid deniers. Everything's going to go into the public cloud. That's all changed. They're making it much, much more difficult for what they call the old guard to compete. So they're taking away the objection. Yeah, they're removing those barriers, those objections. Awesome. And to come in on one of the things you're talking about, which is the Edge, they have completely changed their approach to the Edge. They have put in Neo as part of SageMaker, which allows them to push out inference code. And they themselves are pointing out that inference code is 90% of all the compute into all sorts of training. Not the training, the inference code after that, that's 90% of the compute. They're pushing that into the devices at the Edge, all sorts of architectures. That's a major shift in mindset about that. And in fact, I was really impressed by Elastic Inference for the same reasons because it very much is a validation of a trend I've been seeing in the AI space for the last several years, which is you can increasingly build AI in your preferred visual declarative environment with Python code. And then the abstraction layers of the AI ecosystem have developed to the point where the ecosystem increasingly will auto compile to TensorFlow or MXNet or PyTorch. And then from there, further auto compile your deployed trained model to the most efficient format for the Edge device, for the GP or whatever, wherever it's going to be executed, that's already a well-established trend. The fact that AWS has productized that with this Elastic Inference in their cloud shows that not only do they get that trend, they're just going to push really hard on making sure that AWS, it becomes in many ways the hub of efficient inferencing for everybody. One more quick point on the Edge, if I may. It reminds me, what's going on in the Edge reminds me of the days when Microsoft was trying to take Windows and stick it on mobile, right, the Windows phone. Top-down, IT guys coming at it. And that's what a lot of people are doing today in IT. It's not going to work. What Amazon is doing is saying, we're going to build an environment that you can produce, build applications on that are secure, you can manage them from a bottoms up approach, identifying what the operations technology developers want, giving them the tools to do that. That's a winning strategy. And focusing on them producing the devices, not themselves, and not declaring where the boundaries are. Spot on. Very, very important. And obviously, inferencing is, you get most value out of the data if you put that inferencing as close as you possibly can to that data. Within a camera, it's in the camera itself. And I alluded to it earlier. Another key announcement from AWS here is, first of all, the investment in SageMaker itself is super impressive. And in the years since they introduced it, look at, they've already added, I mean, they had that slide with all the feature enhancements and new modules. They've had it. SageMaker Ground Truth, really important. The fully managed service for automating, labeling of training data sets using mechanical and circuit storage. I mean, the vast majority of the cost in a lot of AI initiatives involves human annotators of training data. And without human annotated training data, you can't do supervised learning, which is the magic underlying a lot of AI. AWS gets the fact that they have to auto, their customers want to automate that to the end of the grade. Now they got that. That's going to be wildly popular. As we say, clean data makes good ML and good ML makes great AI. Yeah. So you don't want any dirty data out there. Cube, more coverage here. Cube Insights panel here in theCUBE at ReInvent. Stay with us for more after this short break.