>> from Seattle Washington It's the Q covering AWS Imagine brought to you by Amazon Web service is >> Hey, welcome back there, buddy Geoffrey here with the Cube. We're in downtown Seattle Day Ws Imagine Edie, you event. It's their education event and every education Everything from K through 12. The higher education community College Retraining after service is a really great show. It's a second year. We're happy to be here. We've got somebody has come all the way from Spain to talk about his very special company. It's Ignasi. Nuclear is he is >> the CEO of click dot edu. Yeah, nice. You see? Welcome. >> Thank you are way really pleased to be with you. >> Great. So tell us, kind of what is clicky? Do you What? What is kind of your core value? >> It's ah, platform that makes all the things that the school needs seeing atleast in Spain. So it's a miss system also on elements also the communication with the family that Petra is Ah Wei Tau financial the school and also a lot of things that they are related on >> right? And you've been around for a while. So when did the company started? How was kind of some basic numbers on how many customers do you have? Could you operate in a lot of countries? A lot of schools? >> The as we have schools working with us already in all of Spain, Also in Chile, Colombia, Arneson, UK. On also in a little country in Europe that is called Andorra. So we're really happy because you have more than 1,000,000 off users working with us. >> 1,000,000. Congratulations. And is it mainly do you specialize between, say, K through 12 or higher education? Or we're kind of all over the place? >> Yes, we're focusing K 12 schools. So the one off the important parts are the communication with parents on dhe to follow all the things that the student. That's >> right. So you guys have a very special thing that you're announcing here at the show is really focusing on Alexa for K through 12 which nobody else is doing. That's really something unique that you guys, How did you get in that? What did you see in voice communication and Alexa that you couldn't do in the platform before that? You really saw the opportunity? >> Yes. All the people say is that >> the future or the present Now is the voice on all we will communicate by boys in the future over Internet. You see a lot off young guys doing all the things my boys know, right? Texting, etcetera. So we thought that it could be a nice idea that the communication between parents and also for a students to the school and be on in the other way, could be could be by boys. So we imagine how to do >> it on. We did it. It's really knew. >> When did you start it? When did you start that project? >> This project we began three months ago, >> three months ago. So, >> yeah, it's really, really knew the boy's idea, right? It was in >> a show that I have seen. Ah ah, law. A lot of people were talking about that, but there were, at least in Spain, in the Spanish. Nothing about so with it, we can be the first. So >> we leave. That's >> great. So before we turn the >> cameras on, we're talking about some of the issues that you have in one of the ones is integration to all these systems because, you know, I have kids. I might have multiple kids in a couple different grades. You have kids and a fine looking for access on their homework or their test scores. You know he's got integrate with all those different back ends to keep things private. But you're kind of in a good spot because your system is the one that's on the back end, right? Yeah, so that worked pretty well. And then the other piece, he talked about his two way voice. I don't think a lot of people think in voice communication, yet it's still more of an ask and get a reply asking and get a reply. But you guys are actually pushing notice vacations from the school, out to the families using voice. How's that working out? You know what are some of the use cases? Yeah, >> it's like it's like the parent can ask Toe elixir, for example, What's a home or for tomorrow for one of your son or daughter on DA on The Echo tell you about that. So it's really impressive, because in that moment the system goes to the school system to get that information on our system. Yeah, on Alexa translating voice So it's It's It's funny >> I just think it's funny that I get e mails from all my digital assistants telling me, suggesting things that I should ask them because it's really not native yet as as an interface to work with these machines. But, well, he's mentioned that the young people voices much more natural. So I wonder if there's been some surprises or some things you didn't expect in terms of people comfort level with voice as a way to communicate with me. >> Say, I think it's, ah most natural way also for us that we are not not if but off course. So we communicate better by boys and writing or texting. So, so off course. It's the future because it's another away. So the use off that systems goes up because off that. So I think it's the most the most thing that for for causes more surprising, >> right? And so will you guys supply the Alexa? It's for people's homes. Or is it something they can tap into their existing Alexa Yeah, >> uh, usually, ah, the case for using that is in your home or else on your phone so you can install licks on your phone and you can ask them. I'll see if the UK fun ankle, >> but handle it. But how do I look? How do I hook my existing echo? Yeah, yes, I bought into the school system. >> Yes, because sometimes some universities are They pulled their A coin. I don't know in the university, or but you can use your echo that you are using it for other things. Listen, music me Listen, missing music or whatever >> and you >> can use the >> same. Yeah, you can. You >> only have to, like, download an >> app for >> your phone. There >> is more less is the same us Alexa to >> install, click in the Web or a skill that it's cow. It's called right, and then you >> have it. So what's next? What's on the road? Map on the voice specifically, Where do you see this kind of evolving over the next little while? >> Yes, our our next goal in the parties that they can use the teachers in the school. The boy systems also so for doing what they do every day in ah Maur writing or whatever, we can do it by voice. For example, interview with the parents, a transcript or, for example, to say that somebody hasn't come to the school or toe tell to the Transportacion that something is company. These kind of things is what we are. Imagine it's in our next things that we will do it with voice. >> It'll be Lexa in the classroom, hoping, thinking, Yeah, right. What about privacy? I would imagine knows funny. In the early days of Cloud, security was a was was not good of the show stopper. People were concerned about 10 years later. Now security is a strength of cloud, right? It's probably more secure than most people's data centers or disgruntled employees. I would imagine privacy and security. This is probably pretty top of mind in the school district as well as a lot of personal information. Are they comfortable? Do they kind of get the security of cloud and cloud infrastructure, or is that still sticking point? >> You know that in Europe there are really strict low of our protection off that right, so we are really concerned about that. So we are talking with the school's what kindof systems. They will be comfortable because you want to use it, so we'll have to find >> the clue to do that. But It's really >> important, I think, all over the world, but in the stage or in Europe who are really concerned about that. So we'll see how to find it. But we can create a private skill, right? Yes, because there are birds shown off, Alexa, that is for business. So you can create your provide things on. You don't have to be for that. Somebody's listening. You >> right? All right. So the last last question here at the conference and you come last year? >> No. So what do >> you know? Just your impressions of the conference Has it nice to be with a bunch of like minded, you know, kind of forward thinking educators because because education doesn't always get the best reputation being kind of forward looking. But here you're surrounded. So I just wonder you could share some of your thoughts of the of the event so far. Yeah, >> I think this guy no five ins give you more motivation on you. Increase your you're way t to see that there are a lot of people that is pushing to innovate and do the things different. So really, really interesting to goto some machine learning. Ah, suppose is shown about California. What? They are doing that right? So I'm really interested. >> Good. Get all right. Look Nazi. Thanks for taking a few minutes. And, uh, congratulations on that project. That's really crazy. Thank >> you for your interest in. >> All right, >> Jeff, you're watching the Cube. Where it aws Imagine in downtown Seattle. Thanks for watching. We'll see you next time.

>> 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.