 Live from Las Vegas, it's theCUBE, covering AWS re-invent 2017, presented by AWS, Intel, and our ecosystem of partners. Hey, welcome back everyone. Live here in Las Vegas is theCUBE's exclusive coverage of AWS Amazon web services re-invent 2017, Amazon web services annual conference, 45,000 people here, five years in a row for theCUBE and we're going to be continuing to cover years and decades after, it's on a tear. I'm John Furrier, my co-student and man. Exciting times, one of the biggest themes here is AI, IoT, data, deep learning, deep lens, all the stuff that's been really trending has been really popular at the show and the person behind that, Amazon is Swami, he's the vice president of machine learning at AWS, among other things, deep learning and data. Welcome to theCUBE, good to see you. Good to see you. Thanks for coming on. You guys, you're the star of the show. Your team put out some great announcements. Congratulations, we're seeing new abstraction layers of complexity going away. You guys have made it easy to do voice, machine learning, all this great stuff. Yeah. What are you most excited about? I mean, so many good things. Take a child and what, I mean. I don't want to pick my favorite child among all my children, but our goal is to actually put machine learning capabilities in the hands of all developers and data scientists. That's why, I mean, we want to actually provide different kinds of capabilities right from like machine learning developers who want to build their own machine learning models. That's where SageMaker is an end to end platform that is lets people build, train and deploy these models in a one click fashion. That supports all popular deep learning frameworks. It can be TensorFlow, MXNet or PyTorch and we also not only help train but automatically tune where we use machine learning for machine learning to build these things. I mean, that's very powerful. And the other thing we are excited about is the API services that you talked about, the new abstraction layer where app developers who do not want to know anything about machine learning but they want to transcribe their audio to converts from speech to text or translate it or understand the text and or analyze videos. And the other thing coming from academia where I'm excited about is I want to teach developers and students machine learning in a fun fashion where they should be excited about machine learning. It's such a transformative capability. That's why actually we built a device meant for learning machine learning in a hands-on fashion. That's called deep lens. And we have like our developers right now in our readment where from the time they take to unbox to actually build a computer vision application to build hot dog or not hot dog they can do it in less than 10 minutes. I mean it's an amazing time to be a developer. Oh my God, swan swami. I've had so many friends that have sat through that session. First of all, the people that sit through it they get like a kit. So they're super excited. Last year it was the Echo Dot and everybody doing skills. This year, deep lens definitely seems to be the one that all the geeks are playing with, really programming stuff. There's a bunch of other things here but definitely some huge buzz and excitement. Oh that's awesome, glad to hear. Talk about the culture at Amazon because I know in covering you guys for so many years and knowing being intimate with a lot of the developers and your teams you guys just don't launch products. You actually listen to customers. So you brought up a machine learning for developers. What specifically jumped out at you from talking to customers around making it easier? Was it, it was too hard? Was it or it was confined to hardcore math driven data scientists? Was it just a thirst and desire from machine learning? Are you just doing this for societal benefits as like a philanthropy project? In Amazon we don't build technology because it's cool. We build technology because that's what our customers want. Like 90 to 95% of our road map is influenced by what listening to customers. The other five to 10% is us reading between the lines and one of the things I actually, when I started playing with machine learning, having built a bunch of database storage and analytics products, what, when I started getting into deep learning and various things I realized it's a transformative capability of these technologies but it was too hard for developers to use it on a day to day fashion because these models are too hard to build and train or they didn't have the right level of abstraction. That's why we actually think of it as a multi-layered strategy where we cater to expert practitioners and data scientists. For them we have SageMaker and then for app developers who do not want to know anything about machine learning, they're saying I'll give you an audio file, I'll transcribe it for me or I'll give you text, get me insights or translated. For them we actually provide simple to use API services and so that they can actually get going without having to know anything about what is TensorFlow or PyTorch. So TensorFlow got a lot of attention because that really engaged the developer community in the machine learning, current machine learning because it was like, oh wow, this is cool. And then it got, I won't say hard to use but it was high end. Are you guys responding to TensorFlow in particular or are you responding to other forces? What was the driver? Our driver is, I mean in Amazon we have been using machine learning for like 20 years. I mean since the year of like 1995 we have been leveraging machine learning for recommendation engine, fulfillment center where we use robots to pick packages and then Alexa of course and Amazon Go. But one of the things we actually hear is while frameworks like TensorFlow or Apache MaxNet or PyTorch is cool it is just too hard for developers to make use of it. We actually don't mind our users use Cafe or TensorFlow. We want them to be successful where they take from idea to production. And when we talk to developers this process took anywhere from six to 18 months and it should not be this hard. So we wanted to do what AWS did to IT industry for compute storage and databases. We want to do the same for machine learning by making it really easy to get started and consume it as a neutrality. So that was our intent. Yeah, so many, I wonder if you can tell us, we've been talking for years about kind of the flywheel of customers for Amazon. What are the economies of scale that you get for the data that you have there? I think about all the training of all the machine learning, the developers. How do you, can you leverage the economies of scale that Amazon has in all those kind of environments? Oh, when you look at machine learning, machine learning tends to be mostly the icing on the cake. So even when you talk to like the expert professors who are the top 10 scientists in the world, the data that goes into the machine learning is going to be the determining factor for how good it is, in terms of how well you train it and so forth. So this is where data scientists keep saying the breadth of storage and database and analytics, offerings that exist really matter for them to build highly accurate models. And when you talk about not just the data, but actually the underlying database technology and storage technology really is important. And S3 is the world's most powerful data lake that exists that is highly secure, reliable, scalable and cost effective. And we really wanted to make sure customers like Digital Globe who stole high resolution satellite imagery on S3 and Glacier, we wanted them to leverage ML capabilities in a really easy one click fashion. That's- So I got to ask you about the roadmap because you say customers are having input on that. I would agree with you that would be true because you guys have a track record there. But I got to put the dots that I'm connecting in my mind right now forward by saying, you guys are telegraphing here. Certainly we heard more burners say it, and Andy, data is key and opening up that data. And we're seeing new relic here, sumo logic. They're sharing anonymous data from usage, workloads, really instructive. Data is instructive for the marketplace, but you got to feed the models from the data. So the question for you is, you guys get so much data. It's really a systems management dream. It's an application performance dream. You get more use case data. Are you going to open that up? And what's the vision behind it? Because it seems like you could offer more and more services. Actually, we already have. You look at X-rays and service that we launched last year, and that is one of the coolest capabilities. Even I'm a developer during the weekends when I write cool apps, being able to dive into specific capabilities of what are the performance insights, where is the bottleneck. It's so important that actually we are able to do things like X-raying into an application. So we are just getting started. I mean, the cloud kind of transformed how we are building applications. Now with machine learning, what is going to happen is we can even do various things like which is going to be the bottleneck or what kind of data sets. I mean, it's just going to be such an amazing time. You could literally reimagine applications that are once dominant with all the data you have if you opened it up and then let me bring my data in. Then that would open up a bigger aperture of data. Wouldn't that make the machine learning and then AI more effective? Actually, you already can do similar things with legs. Legs, for think of it as it's an automatic speech recognition, natural language understanding, where we are pre-trained on our data, but then to customize it for your own chatbots or voice applications, you can actually add your own intents and several things. And we customize the underlying deep learning model specific to your data. So you are leveraging the amount of data that we have trained in addition to specifically tuning for us, so it's only going to get better and better to your part. It's going to happen. It's already happening. It's happening, yeah. So, Swami, great slate of announcements on the machine learning side. We're seeing the products get all updated. I wonder if you could talk to us a little bit about the human side of things because seeing a lot of focus, right. It's not just these tools, but it's the tools and the people putting those together. How does Amazon help the data scientists? Help retrain, help them get ready to be able to leverage and work even better with all of these tools. No, I mean, machine learning, we have seen some amazing usage of how developers are using machine learning. For example, Marinus Analytics is a nonprofit organization that is goal is to fight human trafficking. They use recognition, which is our image processing thing to actually identify persons of interest and victims and so that they can notify law enforcement officer, like Royal National Institute of Blind. They actually are using poly or text-to-speech to generate audio books for visually impaired. So, I'm really excited about all the innovative applications that we can do to simply improve our everyday lives using machine learning and it's such an early day. I mean, Swami, the innovation is endless in my mind, but I want to get two thoughts from you. One startup and one practitioner because we've heard here in theCUBE, people come in saying, I can do so much more now. I got my EMR, it's so awesome, I can do this. They're solving problems, so obviously making it easy to use is super cool, so that's one. I want to get your thoughts on where that goes next. And two startups. You're seeing a lot of startups retooling on cloud economics. I call it post-2013. They don't need a lot of money. They can hit critical mass. They can get product market fit earlier. They can get economic value quicker. So, they're changing the dynamics. But the worry is how do I leverage the benefit of Amazon? Because I think we know Amazon's going to grow and all clouds grow, especially you guys. How do I play with Amazon? Where's the white space? How do I engage, Joe, I just, and how do I, once I'm on the platform, how do I become the new relic? Or how can I grow my marketplace and differentiate because Amazon might come out with something similar. So, how do I stay in that cadence of growth if I'm a startup? I mean, you see in AWS, we have tens of thousands of partners, of course, like right from ISV, SSIs, and whatnot. So, I mean, software industry is an amazing industry where it's not like a winner-take-all market. You actually, for example, in the document management space, even though we have S3 and work dogs, it doesn't mean Dropbox and Box are not successful either or in so forth. So, I think what we provide in AWS is the same infrastructure for any startup or for my team. Even though I built probably many of the underlying infrastructure. Nowadays, for my AI team, it's literally like a startup except I probably stay in an AWS building, but otherwise, I don't get any internal APIs. It's the same API, so EC2-S3. It's a level-paying field. The other way, everyone should know, he wrote DynamoDB as an intern, or what was that? And then, SQS, Rockstar techie here. So, it's great to have, you're what we call a tech athlete. Great to have you on. No white space, just go for it. It's the innovation is the key. And the key thing, I mean, what we have seen amazing startups you've done exceptionally well is they intently listen to customers and innovate and really look for what it matters for their customers and go for it. All right, the biggest buzz of the show that you're from your group, what's your biggest buzz from the show here? Deep Lens? Deep Lens. Deep Lens has been our idea was to actually come up with a fun way to learn machine learning. And machine learning, it used to be, even until recently, actually, as early as last week, it was actually an intimidating thing for developers to learn. While there is, it's all the buzz. It was not really straightforward for developers to use it. So we thought, hey, what is the fun way for developers to get engaged and build machine learning? And that's why we actually concede Deep Lens so that you can actually build fun applications. I talked about hot dog, not hot dog. I'm personally going to be building what I call as a bear cam because I live in the suburbs of Seattle where we actually have bears visiting our backyard, digging our trash. I want to actually have Deep Lens with a pre-trained model that I'm going to train to detect bears that it sends me a message through SQS and SNS, so I get a text. All right, so here's an idea we want to do. Maybe your team can build it for us. Cube cam, we put the deep lens here and then as anyone goes by up there, Twitter follower, the cube, they can send me a message. Swamming, great stuff, Deep Learning. Again, more goodness coming. That's awesome. What are you excited about? What are you most excited about? I think if you see, in Amazon, we have a phrase called, it's day one. Even though we are a 22 year old company, I jokingly tell my team that it's day one for us except we just woke up and we haven't even had a cup of coffee yet. So, I mean, it is, we had just crashed the surface of machine learning and there is so much stuff to do. I mean, super excited about this space. And your goals for this year is what? What's your goals? Our goals for this year was to put machine learning capabilities in the hands of all developers of all skill levels. I think we have done pretty well so far, I think. All right, well congratulations. Swamming here on the cube, Vice President of Machine Learning and a lot more. All those applications that were announced Wednesday live with the deep learning, the AI and the deep lens, all part of his innovative team here at Amazon. Changing the game, what's the cube doing our part bringing data to you, video and more coverage? Go to siliconangle.com for all the stories, wikibon.com for research and of course the cube.net. I'm John Furrier, Stu Miniman. Thanks for watching, we'll be right back.