>> Announcer: 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. We're live here in Las Vegas. It's 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-host Stu Miniman. Exciting science, one of the biggest themes here is AI, IoT, data, Deep Learning, DeepLens, 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. >> Stu: Good to see you. >> Excited to be here. >> Thanks for coming on. You're the star of the show. Your team put out some great announcements, congratulations. We're seeing new obstruction layers of complexity going away. You guys have made it easy to do voice, Machine Learning, all those great stuff. >> Swami: Yeah. >> What are you most excited about, so many good things? Can you pick a child? I don't want to pick my favorite child among all my children. 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 developers who want to build their own Machine Learning models. That's where SageMakers and n21 platform that lets people build, train and deploy these models in a one-click fashion. It supports all popular Deep Learning frameworks. It can be TensorFlow, MXNet or PyCharm. We also not only help train but automatically tune where we use Machine Learning for Machine Learning to build these things. It's very powerful. The other thing we're excited about is the API services that you talked about, the new obstruction layer where app developers who do not want to know anything about Machine Learning but they want to transcribe their audio to convert from speech to text, or translate it or understand the text, or analyze videos. 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 Machine Learning in a hands-on fashion that's called DeepLens. We have developers right on re:Invent where from the time they take to un-box to actually build a computer with an application to build Hotdog or Not Hotdog, they can do it in less than 10 minutes. It's an amazing time to be a developer. >> John: Yeah. >> Stu: Oh my God, 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. >> Swami: That's awesome. >> Stu: They're super excited. Last year it was the Ecodot and everybody with new skills. This year, DeepLens definitely seems to be the one that all the geeks are playing with, really programing stuff. There's a bunch of other things here, but definitely some huge buzz and excitement. >> That's awesome, glad to hear. >> Talk about the culture at Amazon. Because I know in covering you guys for so many years and now being intimate with a lot of the developers in your teams. You guys just don't launch products, you actually listen to customers. You brought up Machine Learning for developers. What specifically jumped out at you from talking to customers around making it easier? It was too hard, was it, or it was confined to hardcore math driven data scientists? Was it just the thirst and desire for Machine Learning? Or you're just doing this for side benefits, it's like a philanthropy project? >> No, 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 roadmap is influenced by listening to customers. The other 5 to 10% is us reading between the lines. One of the things I actually ... When I started playing with Machine Learning, having built a bunch of database storage and analytics products. When I started getting into Deep Learning and various things I realized there's a transformative capability of these technologies. 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. Our data now, the right level of obstruction. That's why we actually think of it as in a multi-layered strategy where we cater to export practitioners and data scientists. For them we have SageMaker. Then for app developers who do not want to know anything about Machine Learning they say, "I'll give you an audio file, transcribe it for me," or "I'll give you text, get me insights or translate it." For them we actually we actually provide simple to use API services, so that they can actually get going without having to know anything about what is TensorFlow or PyCharm. >> TensorFlow got a lot of attention, because that really engaged the developer community in the current Machine Learning, because we're like, "Oh wow, this is cool." >> Swami: Yeah. >> Then it got, I won't say hard to use, but it was high end. Are you guys responding to TensorFlow in particular or you're responding to other forces? What was the driver? >> In amazon we have been using Machine Learning for like 20 years. 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 Elixir of course and Amazon Go. One of the things we actually hear is while frameworks like TensorFlow or PyCharm, MXNet or PyCharm 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 the, to be successful where they take from idea to product shell. And when we talk to developers, this process took anywhere from 6 to 18 months and it should not be this hard. 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 consumer does in utility. That was our intel. >> Swami, I wonder if you can tell us. We've been talking for years about the flywheel of customers for Amazon. What are the economies of scale that you get for the data that you have there. I think of all the training of all the Machine Learning, the developers. How can you leverage the economies of scale that Amazon has in all those kind of environments? >> When you look at Machine Learning, Machine Learning tends to be mostly the icing on the cake. Even when we talk to 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. This is where data scientists keep saying the breath of storage and database and analytics offerings that exist really matter for them to build highly accurate models. When you talk about not just the data, but actually the underlying database technology and storage technology really is important. S3 is the world's most powerful data leg that exists that is highly secure, reliable, scalable and cost effective. We really wanted to make sure customers like Glacier Cloud who store high resolution satellite imagery on S3 and glacier. We wanted them to leverage ML capabilities in a really easy one-click fashion. That's important. >> I got to ask you about the roadmap, because you say customers are having input on that. I would agree with you that 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 ... And telegraphing here certainly heard well, Furner 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 on the data. 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 got 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. If you look at x-rays and service that we launched last year. That is one of the coolest capabilities, even I am a developer during the weekends when I cool out. Being able to dive into specific capabilities so one of the performance insights where is the borderline. It's so important that actually we are able to do things like x-raying into an application. We are just getting started. The Cloud 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 borderline on what kind of datasets. It's just going to be such an amazing time. >> You can 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 will 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 Lex. Lex, 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 chat bots or voice applications, you can actually add your own intents and several things and we customize it underlying Deep Learning model specific to your data. You're leveraging the amount of data that we have trained in addition to specifically tuning for yours. It's only going to get better and better, to your point. >> It's going to happen, it's already happening. >> It's already happening, yeah. >> Swami, great slate of announcements on the Machine Learning side. We're seeing the products get all updated. I'm wondering if you can talk to us a little bit about the human side of things. Because we've seen 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 going to help the data scientists, help retrain, help them get ready to be able to leverage and work even better with all these tools? >> Machine Learning, we have seen some amazing usage of how developers are using Machine Learning. For example, Mariness Analytics is a non-profit organization that its goal is to fight human trafficking. They use recognition which is our image processing. They do actually identify persons of interest and victims so that they can notify law enforcement officer. Like Royal National Institute of Blind. They actually are using audio text to speech to generate audio books for visually impaired. 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 in early days. >> 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 here and saying, "I can do so much more now. "I've got my EMR, it's so awesome. "I can do this solving problem." Obviously making it easy to use is super cool, that's one. I want to get your thoughts on where that goes next. And two, startups. We're seeing a lot of startups retooling on Cloud economics. I call it post-2013 >> Swami: Yeah. >> They don't need a lot of money, they can hit critical mass. They can get market 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 we know Amazon is going to grow and all Clouds grow and just for you guys. How do I play with Amazon? Where is the white space? How do I engage, do I just ...? Once I'm on the platform, how do I become the New Relic or slunk? How can I grow my marketplace and differentiate? Because Amazon might come out with something similar. How do I stay in that cadence of growth, even a startup? >> If you see in AWS we have tens of thousands of partners of course, right from ISV, SIs and whatnot. Software industry is an amazing industry where it's not like winner take all market. For example, in the document management space, even though we have S3 and WorkDocs, it doesn't mean Dropbox and Box are not successful either, and so forth. What we provide in AWS is the same infrastructure for any startup or for my team, even though I build 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 easy to S3. >> John: It's a level playing field. >> It's a level playing field. >> By the way, everyone should know, he wrote DynamoDB. As an intern or was that ...? (Swami laughs) And then SQS, rockstar techy 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. >> Innovation is the key. The key thing, what we have seen amazing startups who have 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. >> The biggest buzz of the show from your group. What's your biggest buzz from the show here? DeepLens? >> DeepLens has been ... Our idea was to actually come up with a fun way to learn Machine Learning. Machine Learning, it used to be, even until recently actually as well as last week, it was actually an intimate thing for developers to learn while there is, it's all the buzz. It's not really straight forward for developers to use it. We thought, "Hey, what is a fun way for developers "to get engaged and build Machine Learning?" That's why we actually can see DeepLens so that you can actually build fun applications. I talked about Hotdog, Not Hotdog. 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 DeepLens with a pre-train 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. >> Here's an idea we want to do, maybe your team can build it for us. CUBE Cam, we put the DeepLens here and then as anyone goes by, if they're a Twitter follower of theCUBE they can send me a message. (John and Swami laughing) Swami, great stuff. Deep Learning again, more goodness coming. >> Swami: That's awesome. >> What are you most excited about? >> 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." We have just scratched the surface with Machine Learning, there is so much stuff to do. I'm super excited about this space. >> 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. >> Well, congratulations Swami here on theCUBE. Vice president of Machine Learning and a lot more, all those applications that were announced Wednesday along with the Deep Leaning and the AI and the DeepLens all part of his innovative team here at Amazon. Changing the game is theCUBE 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 theCUBE.net. I'm John Furrier and Stu Miniman. Thanks for watching, we'll be right back.

>> Narrator: Live from Las Vegas. It's the CUBE. Covering NAB 2017, brought to you by HGST. >> Welcome back to the CUBE live in Las Vegas at the NAB show. We're having a great day so far. Very excited to introduce you to my next guest, Kevin Baillie, cofounder and VFX supervisor at Atomic Fiction and the CEO of Conductor Technologies. Never a boring day for you with those two titles, I can imagine. >> No, I like to joke that I like to make sure that I always have the most exciting job in the world so I had to pick three to make sure that I never have a down moment spoil that, that day >> Wow, I am impressed. So you just spoke at the virtual NAB conference last month on the visual effects in the cloud, power, and control. Something that I found very interesting was that six years ago, you were kind of on an island going "I have this hunch about cloud." Tell us about, what was that hunch, why did you have it, and what has it generated so far? >> Yeah, yeah, that's a great question. The hunch was less of like, "Hey cloud looks like a great opportunity." It was more of like knowing what wasn't working in the industry as it was at that time. There were all kinds of companies that were kind of like having financial troubles or having a hard time delivering projects, tons of bankruptcies and just really sad stories everywhere. And we looked at the market and said, "There's a ton of work here, this doesn't make sense." Some of the best entertainment is being made right now and it all relies on visual effects, what's wrong? And the further we broke down the problem, the more we realized that like fixed infrastructure within a market that naturally ebbs and flows, it just didn't, there wasn't a match there. So, through that problem, we looked for solutions and cloud was a very obvious one at that point. So we just made the jump. >> And tell us about Atomic Fiction versus Conductor Technologies. Chicken, egg, which one came first? And how are they collaborating together? >> Atomic Fiction came first. It was almost seven years ago at this point that we started Atomic. And we looked for any kind of a way to use cloud. We started using an AWS directly, we then used a tool called Zync. And as we grew, we found that the needs of the company were changing so radically that nothing that was out there could actually keep up with our pace of growth. We had all this customized pipeline that we couldn't find a way to like get it into the cloud. So we built our own and that was called Conductor. And after, I think we were working on like Game of Thrones and The Walk and had just started on Deadpool that we realized it was working so well that we decided to spin it off as it's own company and make a go for actually turning it into a product that could help everybody in the same way that the cloud had helped Atomic Fiction. >> Fantastic, one of my favorite movies is The Walk. I was looking at your website and you think as the viewer, "How did they film this?" You know, this day and age, so much is CGI. Talk to us about what realtime cloud rendering is. How does it enable a movie like The Walk or Deadpool to have that awe inspiring, jaw dropping reaction from the audience? >> Well I think a large portion of bringing that jaw dropping reaction to the audience and that level of realism is being able to run productions in the way that they want to be run. And what I mean by that is, let's take a movie like The Walk where you have to recreate 1974 New York and the Twin Towers, and all these different lighting scenarios. That means we have to build every building, every rain gutter, every hotdog stand in the street down to exacting detail, and that just takes a lot of time. So we spent a ton of time, probably the first three quarters of the schedule just building the city, building the city. And we couldn't render anything at that point And it wasn't only until the very end of the show that we were able to say, "alright, now we have New York is there, let's just put it on the screen." But that takes millions of hours of computing to get that done. The Walk for example, it used 9.1 million processor hours of rendering. That's over a thousand years on a single processor to get it done. So if we hadn't had the cloud, we would have had to been like, "Oh what can we render first "so we don't bottleneck at the end of the schedule?" And really kind of like trying to bend production into the box that we, of fixed infrastructure that we have. But with the cloud, we don't have to do that. We can say, we can go as big as we want to at the very end of the show and get it done if that's what makes sense for the show. Because that's what makes sense for the show, the creative just ends up being that much better. The same was true for Deadpool, the same is true for Star Trek. These movies, they just sort of, you want to craft love into the beginning part of it so the stuff you generate at the end is as beautiful as it can be. >> So is cloud really freeing production from being able to operate in the way that it needs to operate? >> Yeah, yeah, exactly. Because the traditional model is, a visual effects company builds a data center and stuffs it full of computers. In best case, with like three weeks lead time you can like rent a bunch of racks of computers and like shove them in a closet somewhere and get your project done. It ends up being expensive and painful. You need a big team to man all that stuff. Whereas with cloud, we can say, "Hey, I need a thousand computers three minutes from now." And boom, a thousand computers spin up out of nowhere. And the great thing that we've done with Conductor as well is we've gone and negotiated per minute software licensing with Autodesk and the Foundry and IsoTropic and Chaos Group. All these big software vendors in the industry. So not only can you get compute by the minute, you can also get all the software that you need by the minute, right. So you can have three thousand nodes running Autodesk to Arnold, and you, but you run it for 42 minutes and you only pay for 42 minutes of three thousand licenses of Arnold, right. So it's really transformative from a flexibility standpoint. >> And the cost model really flips it on it's head. >> And by the way, the artists get the result back faster. Because you can scale up so big and get the result back to them so quickly without any cost penalty, they see the fruits of their labor while the ideas are still fresh in their head, which is like a huge, like, intangible benefit which has real economic benefits. >> Absolutely, one of the things and themes that we've heard of today is that speed is key. Absolutely critical to whatever is going to happen or whether or not on a shoot, a vision changes direction. And without having the power of the cloud to facilitate something on a dime, there's delays, which all adds up to economic impact. >> Yeah, and you know, back on one of our earliest projects rendered in the cloud, Flight. The Robert Zemeckis movie with Denzel Washington. That exact thing happened, where it was like at the very end, he, Zemeckis realized that he needed this extra set of like a hundred visual effects shots. And if it hadn't have been for the cloud, we would have had to say, "No, sorry we can't do these." "We have to find somebody else to do them." But because the ability of the cloud to accommodate that last minute creative epiphany, we were able to actually do the work. So it really is truly transformative and allowed us to bring in, you know, hundreds of thousands of dollars of extra revenue that we wouldn't have been able to do otherwise. >> Absolutely. In terms of some of the public cloud providers, tell us who you're working with on that end. >> Yeah, so we're working with Google right now, using Google Compute Engine on the back end. And we're actually moving forward with Microsoft and Azure. Adding it as an option later in the year. So, hopefully at the end of the year, we'll be able to support all the large cloud providers. And be able to say, "Hey, Studio X. "We know you have an affinity for Google right now, "but on the next project maybe you need "a very specific GPU type." Or there's a company in China that needs to do some work and Google isn't there. Now Azure is your thing, right. So, I think that the world of cloud providers competing against one another is going to be really beneficial for everyone in our industry for sure. And we want to be there to facilitate a little bit of like, choose whoever's best, right. >> Right, giving you the ability to really be like agnostic on the back end. >> Yeah that's exactly right. >> So as we look at these massive resources that studios are generating, creating such interactive films, what are some of the precautions that you see and you can help them mitigate against leveraging the power of cloud. >> Well, one of the benefits of cloud is you only have to pay for what you use, just like electricity, right. One of the downsides of cloud is you have to pay for what you use, right. So, if you're not careful about the render you put in the cloud or the simulation you put in the cloud, or how long you keep data in the cloud, things can get really expensive really quickly. So, one of the things we did, and this is actually why we kind of spun Conductor off as it's own company. And we just raised our Series A round of funding back in December to build the team out, because a lot of this stuff is really complicated, is one of the big efforts, in kind of a post funding world for Conductor, is on analytics and being able to use data to help people drive production better. So you know, in the very beginning, we have cost limits where you can say, "On this shot, I don't want to spend "more than a thousand dollars." Or, "I never want this artist to be able to spend "more than fifteen hundred bucks a day." But in the future, I think that there is kind of like cloud buzz-wordy things that actually come into real play here where we can use machine learning to detect when things are taking too long and alert people. We can tell people how much a render is going to cost before they even submit it maybe. We can use computer vision to check for bad things happening in the middle of a render before a human ever has a chance to lay eyes on it. So there's all kinds of stuff we can do with data to help mitigate some of the downsides of cloud and hopefully only leave people with like great insights to help them run production better. >> That's fantastic. One of the things that really interests me is the machine learning and the artificial intelligence. To be able to look at whether it's a broadcast outlet or a film studio, to be able to take a look at and evaluate the value and additional revenue streams that can come. But also, in your case, maybe even leveraging AI and machine learning to make certain processes faster thereby lowering costs. >> Yeah, we can actually make proactive suggestions based on, like, you know, thousands or millions of data points and say like, "Hey if you tweak this value on your shading rate here, "you're going to end up with a great visual "and not spend any more time, or actually spend less." So things like that and then also working together with production management systems. Like the guys at Autodesk have a product called Shotgun that deals with schedules and artist assignments. And they can have all the schedule information. We have all the sort of infrastructure information. If we correlate those two data sets together, then we'll be able to actually proactively tell somebody when we think a shot is running behind schedule. Or a shot needs more optimization. And I mean, there's all kinds of things that we can use just purely using data and a trained machine learning model to actually help people run their entire business better, not just an individual shot. >> Right, well, six years ago, when you had this hunch, you said there were some skeptics around there. One, you must feel pretty validated by now, but are you kind of one of the go-to guys, go-to companies of this is how to do it properly? These are all of the advantages, economic advantages, etc, that we can provide? >> Yeah, I think that there were definitely people that told me I was absolutely crazy when I first got started. Some of them are actually using Conductor now, so that's kind of like good. >> That must feel good right? >> Yeah, it's a good validation point and they had a lot of reasons for thinking that we were insane, cause we kind of were. But we just sort of believed deep down that it was going to work. So, yeah, I mean now, I think we're in a great position to help people. And for me, and you know, this is always like a thing that I sometimes get a hard time for, but I'm so passionate about this industry moving into the cloud that I'm just as happy to talk to somebody about how to do it maybe on their own if they're trying to do it on a small scale. Or what our competitors might be doing. Really, through that, I've kind of, we've found a space where we don't really have any competitors yet and we're breaking new ground. Really servicing the sort of medium and enterprise scale customers, and that kind of flexibility and scale and security that they kind of need. So it's sort of interesting in this, in a way, this sort of like selfless, just being excited about cloud has helped us to find a market that we can really and truly add insane value to. >> Wow, that is fascinating. Well, your passion for it is evident. Thank you so much Kevin for joining us on the CUBE. >> Yeah, thank you so much. >> Have a great time at the rest of the show and we'll see you on the CUBE sometimes soon. >> I always do, thank you again. >> Excellent, we want to thank you for watching. Again, we are live at NAB Las Vegas. Stick around. We will be right back.