>>Everyone welcome back from the cubes coverage here in Las Vegas for Aus re Mars. It's one of the re shows, as we know, reinvent is the big show. Now they have focus, shows reinforces coming up that security Remar is here. Machine learning, automation, robotics, and space. I'm John for your host, Michael Mike Miller here, director of machine learning thought leadership with AWS. Great to see you again. Yeah. Give alumni welcome back here. Back every time we got deep racer, always to talk >>About, Hey John, thanks for having me once again. It's great to be here. I appreciate it. >>So I want to get into the deep racer in context here, but first re Mars is a show. That's getting a lot of buzz, a lot of press. Um, not a lot of news, cuz it's not a newsy show. It's more of a builder kind of a convergence show, but a lot is happening here. It's almost a, a moment in time that I think's gonna be one of those timeless moments where we're gonna look back and saying that year at re Mars was an inflection point. It just seems like everything's pumping machine learning, scaling robotics is hot. It's now transforming fast. Just like the back office data center did years ago. Yeah. And so like a surge is coming. >>Yeah. >>What, what's your take of this show? >>Yeah. And all of these three or four components are all coming together. Right. And they're intersecting rather than just being in silos. Right. So we're seeing machine learning, enabled perception sort of on robots, um, applied to space and sort of these, uh, extra sort of application initiatives. Um, and that's, what's really exciting about this show is seeing all these things come together and all the industry-wide examples, um, of amazing perception and robotics kind of landing together. So, >>So the people out there that aren't yet inside the ropes of the show, what does it mean to them? This show? What, what, what they're gonna be what's in it for me, what's all this show. What does it mean? >>Yeah. It's just a glimpse into where things are headed. Right. And it's sort of the tip of the iceberg. It's sort of the beginning of the wave of, um, you know, these sort of advanced capabilities that we're gonna see imbued in applications, um, across all different industries. >>Awesome. Well, great to have you in the cube. Every time we have an event we wanna bring you on because deep racers become a, the hottest, I won't say cult following because it's no longer cult following. It's become massive following. Um, and which started out as an IOT, I think raspberry pie first time was like a, like >>A, we did a little camera initially camera >>And it was just a kind of a fun, little clever, I won't say hack, but just having a project that just took on a life OFS own, where are we? What's the update with racer you're here with the track. Yeah, >>Possibly >>You got the track and competing with the big dogs, literally dog, you got spot over there. Boston dynamics. >>Well we'll, we'll invite them over to the track later. Yeah. So deep razor, you know, is the fastest way to get hands on with machine learning. You know, we designed it as, uh, a way for developers to have fun while learning about this particular machine learning technique called reinforcement learning, which is all about using, uh, a simulation, uh, to teach the robot how to learn via trial and error. So deep racer includes a 3d racing simulator where you can train your model via trial and error. It includes the physical car. So you can take, uh, the model that you trained in the cloud, download it to this one 18th scale, um, kind of RC car. That's been imbued with an extra sensor. So we have a camera on the front. We've got an extra, uh, Intel, X, 86 processor inside here. Um, and this thing will drive itself, autonomously around the track. And of course what's a track and uh, some cars driving around it without a little competition. So we've got the deep racer league that sort of sits on top of this and adds a little spice to the whole thing. It's >>It's, it's like formula one for nerds. It really is. It's so good because a lot of people will have to readjust their models cuz they go off the track and I see people and it's oh my, then they gotta reset. This has turned into quite the phenomenon and it's fun to watch and every year it gets more competitive. I know you guys have a cut list that reinvent, it's almost like a, a super score gets you up. Yeah. Take, take us through the reinvents coming up. Sure. What's going on with the track there and then we'll get into some of the new adoption in terms of the people. >>Yeah, absolutely. So, uh, you know, we have monthly online races where we have a new track every month that challenges our, our developers to retrain their model or sort of tweak the existing model that they've trained to adapt for those new courses. Then at physical events like here at re Mars and at our AWS summits around the world, we have physical, uh, races. Um, and we crown a champion at each one of those races. You may have heard some cheering a minute ago. Yeah. That was our finals over there. We've got some really fast cars, fast models racing today. Um, so we take the winners from each of those two circuits, the virtual and the physical and they, the top ones of them come together at reinvent every year in November, December. Um, and we have a set of knockout rounds, championship rounds where these guys get the field gets narrowed to 10 racers and then those 10 racers, uh, race to hold up the championship cup and, um, earn, earn, uh, you know, a whole set of prizes, either cash or, or, you know, scholarships or, you know, tuition funds, whatever the, uh, the developer is most interested >>In. You know, I ask you this question every time you come on the cube because I I'm smiling. That's, it's so much fun. I mean, if I had not been with the cube anyway, I'd love to do this. Um, would you ever imagine when you first started this, that it would be such so popular and at the rise of eSports? So, you know, discord is booming. Yeah. The QB has a discord channel now. Sure, sure. Not that good on it yet, but we'll get there, but just the gaming culture, the nerd culture, the robotics clubs, the young people, just nerds who wanna compete. You never thought that would be this big. We, >>We were so surprised by a couple key things after we launched deep racer, you know, we envisioned this as a way for, you know, developers who had already graduated from school. They were in a company they wanted to grow their machine learning skills. Individuals could adopt this. What we saw was individuals were taking these devices and these concepts back to their companies. And they're saying, this is really fun. Like we should do something around this. And we saw companies like JPMC and Accenture and Morningstar into it and national Australia bank all adopting deep racer as a way to engage, excite their employees, but then also create some fun collaboration opportunities. Um, the second thing that was surprising was the interest from students. And it was actually really difficult for students to use deep racer because you needed an AWS account. You had to have a credit card. You might, you might get billed. There was a free tier involved. Um, so what we did this past year was we launched the deep racer student league, um, which caters to students 16 or over in high school or in college, uh, deep Razer student includes 10 hours a month of free training, um, so that they can train their models in the cloud. And of course the same series of virtual monthly events for them to race against each other and win, win prizes. >>So they don't have to go onto the dark web hack someone's credit card, get a proton email account just to get a deep Razer that's right. They can now come in on their own. >>That's right. That's right. They can log into that virtual the virtual environment, um, and get access. And, and one of the other things that we realized, um, and, and that's a common kind of, uh, realization across the industry is both the need for the democratization of machine learning. But also how can we address the skills gap for future ML learners? Um, and this applies to the, the, the world of students kind of engaging. And we said, Hey, you know, um, the world's gonna see the most successful and innovative ideas come from the widest possible range of participants. And so we knew that there were some issues with, um, you know, underserved and underrepresented minorities accessing this technology and getting the ML education to be successful. So we partnered with Intel and Udacity and launched the AI and ML scholarship program this past year. And it's also built on top of deep Bracer student. So now students, um, can register and opt into the scholarship program and we're gonna give out, uh, Udacity scholarships to 2000 students, um, at the end of this year who compete in AWS deep racer student racers, and also go through all of the learning modules online. >>Okay. Hold on, lets back up. Cuz it sounds, this sounds pretty cool. All right. So we kind went fast on that a little bit slow today at the end of the day. So if they sign up for the student account, which is lowered the batteries for, and they Intel and a desk, this is a courseware for the machine learning that's right. So in order to participate, you gotta take some courseware, check the boxes and, and, and Intel is paying for this or you get rewarded with the scholarship after the fact. >>So Intel's a partner of ours in, in putting this on. So it's both, um, helping kind of fund the scholarships for students, but also participating. So for the students who, um, get qualified for the scholarship and, and win one of those 2000 Udacity Nanodegree scholarships, uh, they also will get mentoring opportunities. So AWS and Intel, um, professionals will help mentor these students, uh, give them career advice, give them technical advice. C >>They'll they're getting smarter. Absolutely. So I'm just gonna get to data here. So is it money or credits for the, for the training? >>That's the scholarship or both? Yes. So, so the, the student training is free for students. Yep. They get 10 hours a month, no credits they need to redeem or anything. It's just, you log in and you get your account. Um, then the 2000, uh, Udacity scholarships, those are just scholarships that are awarded to, to the winners of the student, um, scholarship program. It's a four month long, uh, class on Python programming for >>AI so's real education. Yeah. It's like real, real, so ones here's 10 hours. Here's check the box. Here's here's the manual. Yep. >>Everybody gets access to that. That's >>Free. >>Yep. >>To the student over 16. Yes. Free. So that probably gonna increase the numbers. What kind of numbers are you looking at now? Yeah. In terms of scope to scale here for me. Yeah. Scope it >>Out. What's the numbers we've, we've been, uh, pleasantly surprised. We've got over 55,000 students from over 180 countries around the world that have signed up for the deep racer student program and of those over 30,000 have opted into that scholarship program. So we're seeing huge interest, um, from across the globe in, in this virtual students, um, opportunity, you know, and students are taking advantage of those 20 hours of learning. They're taking advantage of the fun, deep racer kind of hands on racing. Um, and obviously a large number of them are also interested in this scholarship opportunity >>Or how many people are in the AWS deep racer, um, group. Now, because now someone's gotta work on this stuff. It's went from a side hustle to like a full initiative. Well, >>You know, we're pretty efficient with what we, you know, we're pretty efficient. You've probably read about the two pizza teams at Amazon. So we keep ourselves pretty streamlined, but we're really proud of, um, what we've been able to bring to the table. And, you know, over those pandemic years, we really focused on that virtual experience in viewing it with those gaming kind of gamification sort of elements. You know, one of the things we did for the students is just like you guys, we have a discord channel, so not only can the students get hands on, but they also have this built in community of other students now to help support them bounce ideas off of and, you know, improve their learning. >>Awesome. So what's next, take us through after this event and what's going on for you more competitions. >>Yeah. So we're gonna be at the remainder of the AWS summits around the world. So places like Mexico city, you know, uh, this week we were in Milan, um, you know, we've got some AWS public sector, um, activities that are happening. Some of those are focused on students. So we've had student events in, um, Ottawa in Canada. We've had a student event in Japan. We've had a student event in, um, Australia, New Zealand. And so we've got events, both for students as well as for the professionals who wanna compete in the league happening around the world. And again, culminating at reinvent. So we'll be back here in Vegas, um, at the beginning of December where our champions will, uh, compete to ho to come. >>So you guys are going to all the summits, absolutely. Most of the summits or >>All of them, anytime there's a physical summit, we'll be there with a track and cars and give developers the opportunity to >>The track is always open. >>Absolutely. All >>Right. Well, thanks for coming on the cube with the update. Appreciate it, >>Mike. Thanks, John. It was great to be >>Here. Pleasure to know you appreciate it. Love that program. All right. Cube coverage here. Deep race are always the hit. It's a fixture at all the events, more exciting than the cube. Some say, but uh, almost great to have you on Mike. Uh, great success. Check it out free to students. The barrier's been lower to get in every robotics club. Every math club, every science club should be signing up for this. Uh, it's a lot of fun and it's cool. And of course you learn machine learning. I mean, come on. There's one to learn that. All right. Cube coverage. Coming back after this short break.

(upbeat music) >> Okay, welcome back everyone, Cube coverage live on the floor in the Moscone center in San Francisco, California. I'm John Furrier host of the Cube. AWS summit 2022 is here in San Francisco, we're back in live events. Of course, Amazon summit in New York city is coming, Amazon summit this summer we'll be there as well. We've got a great guest Mike Miller, GN of AI devices at AWS always one of my favorite interviews. We've got a little prop here, we got the car, DeepRacer, very popular at the events. Mike, welcome to the Cube. Good to see you. >> Hey John, thank you for having me. It's really exciting to be back and chat with you a little bit about DeepRacer. >> Well I want to get into the prop in a second, not the prop, the product. >> Yeah. >> So DeepRacer program, you got the race track here. Just explain what it is real quick, we'll get that out of the way. >> Absolutely so, well, you know that AI, AWS is passionate about making AI and ML more accessible to developers of all skill levels. So DeepRacer is one of our tools to do that. So DeepRacer is a 3D cloud-based racing simulator, a 1/18th scale autonomously driven car and a league to add a little spicy competition into it. So developers can start with the cloud-based simulator where they're introduced to reinforcement learning which basically teaches the, our car to drive around a track through trial and error and of course you're in a virtual simulator so it's easy for it to make mistakes and restart. Then once that model is trained, it's downloaded to the car which then can drive around a track autonomously, kind of making its own way and of course we track lap time and your successful lap completions and all of that data feeds into our league to try to top the leaderboard and win prizes. >> This is the ultimate gamification tool. (chuckles) >> Absolutely >> Making it fun to learn about machine learning. All right, let's get into the car, let's get into the showcase of the car. show everyone what's going on. >> Absolutely. So this is our 1/18th scale autonomously driven car. It's built off of a monster truck chassis so you can see it's got four wheel drive, it's got steering in the front, we've got a camera on the front. So the camera is the, does the sensing to the compute board that's driven by an Intel atom a processor on the, on the vehicle, that allows it to make sense of the in front of it and then decide where it wants to drive. So you take the car, you download your trained model to it and then it races around the track. >> So the front is the camera. >> The front is the camera, that's correct. >> Okay, So... >> So it's a little bit awkward but we needed to give it plenty of room here so that I can actually see the track in front of it. >> John: It needs eyes. >> Yep. That's exactly right. >> Awesome. >> Yes. >> And so I got to buy that if I'm a developer. >> So, developers can start in two ways, they can use our virtual racing experience and so there's no hardware cost for that, but once you want the experience, the hands on racing, then the car is needed but if you come to one of our AWS summits, like here in San Francisco or anywhere else around the world we have one or more tracks set up and you can get hands on, you can bring the model that you trained at home download it to a car and see it race around the track. >> So use a car here. You guys are not renting cars, but you're letting people use the cars. >> Absolutely. >> Can I build my own car or does it have to be assembled by AWS? >> Yeah, we, we sell it as a, as a kit that's already assembled because we've got the specific compute board in there, that Intel processor and all of the software that's already built on there that knows how to drive around the track. >> That's awesome, so talk about the results. What's going on? What's the feedback from developers? Obviously it's a nerd dream, people like race cars, people love formula one now, all the racing there. IOT is always an IOT opportunity as well. >> Absolutely, and as you said, gamification, right? And so what we found and what we thought we would find was that adding in those sort of ease of learning so we make it the on-ramp to machine learning very easy. So developers of all skill levels can take advantage of this, but we also make it fun by kind of gamifying it. We have different challenges every month, we have a leader board so you can see how you rank against your peers and actually we have split our league into two, there's an open division which is more designed for novices so you'll get rewarded for just participating and then we have a pro league. So if you're one of the top performers in the open league each month, you graduate and you get to race against the big boys in the pro leagues. >> What's the purse? >> Oh, the, (John laughing) we definitely have cash and prizes that happen, both every month. We have prizes cause we do races every month and those winners of those races all get qualified to race at the championship, which of course happens in Las Vegas at re:Invent. So we bring all the winners to re:Invent and they all race against each other for the grand prize the big trophy and the, and the, and the cash prize. >> Well, you know, I'm a big fan of what you guys are doing so I'm kind of obviously biased on this whole program but you got to look at trend of what's going on in eSports and the online engagement is off the charts, are there plans to kind of make this more official and bigger? Is there traction there or is this just all part of the Amazon goodness, love that you guys give back? I mean, obviously it's got traction. >> Yeah. I mean, the thing that's interesting about eSports is the number of young people who are getting into it and what we saw over the last couple years is that, there were a lot of students who were adopting DeepRacer but there were some hurdles, you know, it wasn't really designed for them. So what we did was we made some changes and at the beginning of this year we launched a student focused DeepRacer program. So they get both free training every month, they get free educational materials and their own private league so they know students can race against other students, as part of that league. >> John: Yeah. >> So that was really our first step in kind of thinking about those users and what do we need to do to cater to their kind of unique needs? >> Tell about some of the power dynamics or the, or not power dynamics, the group dynamics around teams and individuals, can I play as an individual? Do I, do I have to be on a team? Can I do teams? How does that look? How do you think about those things? >> Yeah, absolutely. Great, great question. The primary way to compete is individually. Now we do have an offering that allows companies to use DeepRacer to excite and engage their own employees and this is where operating as a team and collaborating with your coworkers comes into play so, if, if I may there's, you know, Accenture and JPMC are a couple big customers of ours, really strong partners. >> John: Yeah. >> Who've been able to take advantage of DeepRacer to educate their workforce. So Accenture ran a 24 hour round the, round the globe race a couple years ago, encouraging their employees to collaborate and form teams to race and then this past year JPMC, had over 3000 of their builders participate over a three month period where they ran a private league and they went on to win the top two spots, first place and second place. >> John: Yeah. >> At reinvent last year. >> It reminds me the NASCAR and all these like competitions, the owners have multiple cars on the race. Do you guys at re:Invent have to start cutting people like, only two submissions or is it free for all? >> Well, you have to qualify to get to the races at re:invent so it's very, it's very cutthroat leading up to that point. We've got winners of our monthly virtual contests, the winners like of the summit races will also get invited. So it's interesting, this dynamic, you'll have some people who won virtual races, some people who won physical races, all competing together. >> And do you guys have a name for the final cup or is it like what's the, what's the final, how do you guys talk about the prizes and the... >> It's, it's the DeepRacer Championship Cup of course. >> John: Of course. (laughter) >> Big silver cup, you get to hoist it and... >> Are the names inscribed in it, is it like the Stanley cup or is it just one. >> It's a unique one, so you get to hold onto it each year. The champion gets their own version of the cup. >> It's a lot of fun. I think it's really kind of cool. What's the benefits for a student? Talk about the student ones. >> Yeah. Yeah. >> So I'm a student I'm learning machine learning, what's in it for me is a career path and the fund's obvious, I see that. >> Yeah absolutely. You know, the, for students, it's a hands on way that's a very easy on-ramp to machine learning and you know, one of the things, as I mentioned we're passionate about making it accessible to all. Well, when we mean all we were really do mean all. So, we've got a couple partners who are passionate about the same thing, right? Which is how do we, if, if AI and ML is going to transform our world and solve our most challenging problems, how can we get the right minds from all walks of life and all backgrounds to learn machine learning and get engaged? So with two of our partners, so with Udacity and with Intel we launched a $10 million AWS, AI and ML scholarship program and we built it around DeepRacer. So not only can students who are college and high school students, age 16 and over can use DeepRacer, can learn about machine learning and then get qualified to win one of several thousand scholarships. >> Any other promotions going on that people should know about? >> Yeah, one, one final one is, so we talked about enterprises like JPMC and Accenture, so we've got a promotion that we just started yesterday. So if you are an enterprise and you want to host a DeepRacer event at your company to excite your employees and get 'em collaborating more, if you have over 50 employees participating, we're going to give you up to a hundred thousand dollars in AWS credits, to offset the costs of running your DeepRacer event at your, at your company so >> That's real money. >> Yeah. Real, real, real exciting I think for companies now to pick up DeepRacer. >> So, I mean, honestly, I know Andy Jassy, I have many sports car conversations with him. He's a sports guy, he's now the CEO of Amazon, gets to go all the sporting events, NFL. I wish I could bring the Cube there but, we'll stick with with cloud for now. You got to look at the purse kind of thing. I'm interested in like the whole economic point of cause I mean, forget the learning for side for a second which is by the way awesome. This is great competition. You got leader boards, you got regional activities, you got a funneling system laddering up to the final output. >> And we've really done a decent job and, and of adding capabilities into that user experience to make it more engaging. You can see the countries that the different competitors are from, you can see how the lap times change over time, you know, we give awards as I mentioned, the two divisions now. So if you're not super competitive, we'll reward you for just participating in that open league but if you want to get competitive, we'll even better rewards monthly in the Pro League. >> Do you guys have any conversations internally like, this is getting too big, we might have to outsource it or you keep it in inside the fold? (laughter) >> We, we love DeepRacer and it's so much fun running this, >> You see where I'm going with this. You see where I'm going with this right? The Cube might want to take this over. >> Hey. >> And you know >> We're always looking for partners and sponsors who can help us make it bigger so, absolutely. >> It's a good business opportunity. I just love it. Congratulations, great stuff. What's the big learning in this, you know, as a as an executive, you look back you got GM, AI super important and, and I think it is great community, communal activity as well. What's the learning, what have you learned from this over the years besides that it's working but like what's the big takeaway? >> Yeah, I mean. We've got such a wide range of developers and builders who are customers that we need to provide a variety of opportunities for people to get hands on and there's no better way to learn a complex technology like AI and ML than getting hands on and seeing, you know, physically the result of the AI and I think that's been the biggest learning, is that just having the hands on and the sort of element of watching what it does, just light bulbs go off. When, when developers look at this and they start piecing the, the puzzle pieces together, how they can benefit. >> So I have to ask the question that might be on other peoples minds, maybe it's not, maybe I'm just thinking really dark here but gamers love to hack and they love cheat codes, they love to get, you know, get into the system, any attempts to do a little hacking to win the, the the game, have you guys, is there, you know? >> Well, well, you know, last year we, we we released an open source version of the vehicle so that people could start using it as a platform to explore and do that kind of hacking and give them an opportunity build on top of it. >> So using mods, mods modules, we can mod out on this thing. >> Yeah, absolutely. If you go to deepracer.com, we have sort of extensions page there, and you can see, somebody mounted a Nerf cannon onto the top of this, somebody built a computer vision model that could recognize you know, rodents and this thing would kind of drive to scare 'em, all kinds of fun topics. >> So it's a feature, not a bug. >> Absolutely. >> Open it up. >> Yeah. >> And also on transparency, if you have the source code out there you guys can have some review. >> Yeah. The whole idea is like, let's see what developers, >> It's really not hackable. It's not hackable. >> Yeah, I mean, for the, if you think about it when we do the races, we bring the cars ourselves, the only way a developer interacts is by giving us their trained models so... >> And you, do you guys review the models? Nothing to review, right? >> Yeah. There's nothing really to review. It's all about, you know, there, there was a model that we saw one time where the car went backwards and then went forwards across the finish line but we, we, we gently told them, well that's really not a valid way to race. >> That was kind of a hack, not really a hack. That was a hack hack. (laughter) That was just a growth hack. >> Exactly, but everybody just has a lot of fun with it across the board. >> Mike, great, thanks for coming on. Love the prop. Thanks for bringing the car on, looks great. Success every year. I want to see the purse, you know, big up to $1,000,000 you know, the masters, you know, tournament. >> Someday. (John chuckles) >> You guys.. >> Thank you for having me John. >> DeepRacer again, Fun Start has a great way to train people on machine learning, IOT device, turns into a league of its own. Great stuff for people to learn, especially students and people in companies, but the competitive juices flowing. That's what it's all about, having fun, learning. It's the Cube here in San Francisco. Stay with us for more coverage after this short break. (gentle music)

>>from around the >>globe. It's the Cube with digital coverage of AWS reinvent 2020 sponsored by Intel and AWS. Yeah, >>Hi. We are the Cube live covering AWS reinvent 2020. I'm Lisa Martin, and I've got one of our cube alumni back with me. Mike Miller is here. General manager of A W s AI Devices at AWS. Mike, welcome back to the Cube. >>Hi, Lisa. Thank you so much for having me. It's really great to join you all again at this virtual reinvent. >>Yes, I think last year you were on set. We have always had to. That's at reinvent. And you you had the deep race, your car, and so we're obviously socially distance here. But talk to me about deepracer. What's going on? Some of the things that have gone on the last year that you're excited >>about. Yeah, I'd love to tell. Tell you a little bit about what's been happening. We've had a tremendous year. Obviously, Cove. It has restricted our ability to have our in person races. Eso we've really gone gone gangbusters with our virtual league. So we have monthly races for competitors that culminate in the championship. Um, at reinvent. So this year we've got over 100 competitors who have qualified and who are racing virtually with us this year at reinvent. They're participating in a series of knockout rounds that are being broadcast live on twitch over the next week. That will whittle the group down to AH Group of 32 which will have a Siris of single elimination brackets leading to eight finalists who will race Grand Prix style five laps, eight cars on the track at the same time and will crown the champion at the closing keynote on December 15th this year. >>Exciting? So you're bringing a reinforcement, learning together with with sports that so many of us have been missing during the pandemic. We talked to me a little bit about some of the things that air that you've improved with Deep Racer and some of the things that are coming next year. Yeah, >>absolutely so, First of all, Deep Racer not only has been interesting for individuals to participate in the league, but we continue to see great traction and adoption amongst big customers on dare, using Deep Racer for hands on learning for machine learning, and many of them are turning to Deep Racer to train their workforce in machine learning. So over 150 customers from the likes of Capital One Moody's, Accenture, DBS Bank, JPMorgan Chase, BMW and Toyota have held Deep Racer events for their workforces. And in fact, three of those customers Accenture, DBS Bank and J. P. Morgan Chase have each trained over 1000 employees in their organization because they're just super excited. And they find that deep racers away to drive that excitement and engagement across their customers. We even have Capital one expanded this to their families, so Capital One ran a deep raise. Their Kids Cup, a family friendly virtual competition this past year were over. 250 Children and 200 families got to get hands on with machine learning. >>So I envisioned some. You know, this being a big facilitator during the pandemic when there's been this massive shift to remote work has have you seen an uptick in it for companies that talking about training need to be ableto higher? Many, many more people remotely but also train them? Is deep Racer facilitator of that? Yeah, >>absolutely. Deep Racer has ah core component of the experience, which is all virtualized. So we have, ah, console and integration with other AWS services so that racers can participate using a three d racing simulator. They can actually see their car driving around a track in a three D world simulation. Um, we're also selling the physical devices. So you know, if participants want to get the one of those devices and translate what they've done in the virtual world to the real world, they can start doing that. And in fact, just this past year, we made our deep race or car available for purchase internationally through the Amazon Com website to help facilitate that. >>So how maney deep racers air out there? I'm just curious. >>Oh, thousands. Um, you know, And there what? What we've seen is some companies will purchase you, know them in bulk and use them for their internal leagues. Just like you know, JP Morgan Chase on DBS Bank. These folks have their own kind of tracks and racers that they'll use to facilitate both in person as well as the virtual racing. >>I'm curious with this shift to remote that we mentioned a minute ago. How are you seeing deepracer as a facilitator of engagement. You mentioned engagement. And that's one of the biggest challenges that so Maney teams develops. Processes have without being co located with each other deep Brister help with that. I mean, from an engagement perspective, I think >>so. What we've seen is that Deep Racer is just fun to get your hands on. And we really lower the learning curve for machine learning. And in particular, this branch called reinforcement Learning, which is where you train this agent through trial and error toe, learn how to do a new, complex task. Um, and what we've seen is that customers who have introduced Deep Racer, um, as an event for their employees have seen ah, very wide variety of employees. Skill sets, um, kind of get engaged. So you've got not just the hardcore deep data scientists or the M L engineers. You've got Web front end programmers. You even have some non technical folks who want to get their hands dirty. Onda learn about machine learning and Deep Racer really is a nice, gradual introduction to doing that. You can get engaged with it with very little kind of coding knowledge at all. >>So talk to me about some of the new services. And let's look at some specific use case customer use cases with each service. Yeah, >>absolutely. So just to set the context. You know, Amazon's got hundreds. A ws has hundreds of thousands of customers doing machine learning on AWS. No customers of all sizes are embedding machine learning into their no core business processes. And one of the things that we always do it Amazon is We're listening to customers. You know, 90 to 95% of our road maps are driven by customer feedback. And so, as we've been talking to these industrial manufacturing customers, they've been telling us, Hey, we've got data. We've got these processes that are happening in our industrial sites. Um, and we just need some help connecting the dots like, how do we really most effectively use machine learning to improve our processes in these industrial and manufacturing sites? And so we've come up with these five services. They're focused on industrial manufacturing customers, uh, two of the services air focused around, um, predictive maintenance and, uh, the other three services air focused on computer vision. Um, and so let's start with the predictive maintenance side. So we announced Amazon Monitor On and Amazon look out for equipment. So these services both enable predictive maintenance powered by machine learning in a way that doesn't require the customer to have any machine learning expertise. So Mono Tron is an end to end machine learning system with sensors, gateway and an ML service that can detect anomalies and predict when industrial equipment will require maintenance. I've actually got a couple examples here of the sensors in the gateway, so this is Amazon monitor on these little sensors. This little guy is a vibration and temperature sensor that's battery operated, and wireless connects to the gateway, which then transfers the data up to the M L Service in the cloud. And what happens is, um, the sensors can be connected to any rotating machinery like pump. Pour a fan or a compressor, and they will send data up to the machine learning cloud service, which will detect anomalies or sort of irregular kind of sensor readings and then alert via a mobile app. Just a tech or a maintenance technician at an industrial site to go have a look at their equipment and do some preventative maintenance. So um, it's super extreme line to end to end and easy for, you know, a company that has no machine learning expertise to take advantage of >>really helping them get on board quite quickly. Yeah, >>absolutely. It's simple tea set up. There's really very little configuration. It's just a matter of placing the sensors, pairing them up with the mobile app and you're off and running. >>Excellent. I like easy. So some of the other use cases? Yeah, absolutely. >>So So we've seen. So Amazon fulfillment centers actually have, um, enormous amounts of equipment you can imagine, you know, the size of an Amazon fulfillment center. 28 football fields, long miles of conveyor belts and Amazon fulfillment centers have started to use Amazon monitor on, uh, to monitor some of their conveyor belts. And we've got a filament center in Germany that has started using these 1000 sensors, and they've already been able to, you know, do predictive maintenance and prevent downtime, which is super costly, you know, for businesses, we've also got customers like Fender, you know, who makes guitars and amplifiers and musical equipment. Here in the US, they're adopting Amazon monitor on for their industrial machinery, um, to help prevent downtime, which again can cost them a great deal as they kind of hand manufacture these high end guitars. Then there's Amazon. Look out for equipment, which is one step further from Amazon monitor on Amazon. Look out for equipment. Um provides a way for customers to send their own sensor data to AWS in order to build and train a model that returns predictions for detecting abnormal equipment behavior. So here we have a customer, for example, like GP uh, E P s in South Korea, or I'm sorry, g S E P s in South Korea there in industrial conglomerate, and they've been collecting their own data. So they have their own sensors from industrial equipment for a decade. And they've been using just kind of rule basic rules based systems to try to gain insight into that data. Well, now they're using Amazon, look out for equipment to take all of their existing sensor data, have Amazon for equipment, automatically generate machine learning models on, then process the sensor data to know when they're abnormalities or when some predictive maintenance needs to occur. >>So you've got the capabilities of working with with customers and industry that that don't have any ML training to those that do have been using sensors. So really, everybody has an opportunity here to leverage this new Amazon technology, not only for predicted, but one of the things I'm hearing is contact list, being able to understand what's going on without having to have someone physically there unless there is an issue in contact. This is not one of the words of 2020 but I think it probably should be. >>Yeah, absolutely. And in fact, that that was some of the genesis of some of the next industrial services that we announced that are based on computer vision. What we saw on what we heard when talking to these customers is they have what we call human inspection processes or manual inspection processes that are required today for everything from, you know, monitoring you like workplace safety, too, you know, quality of goods coming off of a machinery line or monitoring their yard and sort of their, you know, truck entry and exit on their looking for computer vision toe automate a lot of these tasks. And so we just announced a couple new services that use computer vision to do that to automate these once previously manual inspection tasks. So let's start with a W A. W s Panorama uses computer vision toe improve those operations and workplace safety. AWS Panorama is, uh, comes in two flavors. There's an appliance, which is, ah, box like this. Um, it basically can go get installed on your network, and it will automatically discover and start processing the video feeds from existing cameras. So there's no additional capital expense to take a W s panorama and have it apply computer vision to the cameras that you've already got deployed, you know, So customers are are seeing that, um, you know, computer vision is valuable, but the reason they want to do this at the edge and put this computer vision on site is because sometimes they need to make very low Leighton see decisions where if you have, like a fast moving industrial process, you can use computer vision. But I don't really want to incur the cost of sending data to the cloud and back. I need to make a split second decision, so we need machine learning that happens on premise. Sometimes they don't want to stream high bandwidth video. Or they just don't have the bandwidth to get this video back to the cloud and sometimes their data governance or privacy restrictions that restrict the company's ability to send images or video from their site, um, off site to the cloud. And so this is why Panorama takes this machine learning and makes it happen right here on the edge for customers. So we've got customers like Cargill who uses or who is going to use Panorama to improve their yard management. They wanna use computer vision to detect the size of trucks that drive into their granaries and then automatically assign them to an appropriately sized loading dock. You've got a customer like Siemens Mobility who you know, works with municipalities on, you know, traffic on by other transport solutions. They're going to use AWS Panorama to take advantage of those existing kind of traffic cameras and build machine learning models that can, you know, improve congestion, allocate curbside space, optimize parking. We've also got retail customers. For instance, Parkland is a Canadian fuel station, um, and retailer, you know, like a little quick stop, and they want to use Panorama to do things like count the people coming in and out of their stores and do heat maps like, Where are people visiting my store so I can optimize retail promotions and product placement? >>That's fantastic. The number of use cases is just, I imagine if we had more time like you could keep going and going. But thank you so much for not only sharing what's going on with Deep Racer and the innovations, but also for show until even though we weren't in person at reinvent this year, Great to have you back on the Cube. Mike. We appreciate your time. Yeah, thanks, Lisa, for having me. I appreciate it for Mike Miller. I'm Lisa Martin. You're watching the cubes Live coverage of aws reinvent 2020.

>> Announcer: Live from Las Vegas, it's theCUBE! Covering AWS re:Invent 2019. Brought to you by Amazon Web Services and Intel, along with its ecosystem partners. >> Hey welcome back, everyone, it's theCUBE's coverage here live in Las Vegas for re:Invent 2019, this is theCUBE's seventh year covering re:Invent, the event's only been going for eight years, it feels like a decade, so much growth, so much action, I'm John Furrier with my co-host Dave Vellante, here extracting the signal from the noise in the Intel AWS studio of theCUBE, thank you for that sponsorship. Mike Miller is our next guest, he's director of AI devices at AWS, super excited for this segment, because DeepRacer's here, and we got some music, AI is the front and center, great to see you again, thanks for coming on. >> Absolutely, thank you for having me on again, I appreciate it. >> All right, let's just jump right in, the toys. Developers are geeking out over DeepRacer and the toys you guys are putting out there as a fun way to play and learn. >> Absolutely, getting hands-on with these new broadly applicable machine learning technologies. >> Let's jump into DeepRacer, so first of all, give us a quick update on what's happened between last year and this year in the DeepRacer community, there's been a lot of froth, competitiveness, street battles, and then we'll get an update, give us a quick update on the community. >> So we launched DeepRacer last year as a 1/18 scale race car designed to teach reinforcement learning, so this thing drives by itself around the tracks. We've got an online experience where customers can train models, so we launched a DeepRacer league where we plan to visit 22 sites around the world at AWS summits, where developers can come visit us and race a car physically around a track, and we had online contests, so every month we had a new track for developers to be challenged by and race their cars around the track. We've seen tremendous engagement and excitement, a little bit of competition really gets developers' juices going. >> It's been a lot of fun, congratulations, by the way. >> Absolutely, thank you. >> All right, let's get into the new toy, so DeepRacer 2.0, whatever you're calling it, just DeepRacer-- >> DeepRacer Evo. >> Evo, okay. >> New generation, so we've basically provided more opportunities to race for developers, more challenges for them to learn, and more ways for them to win. So we integrated some new sensors on this car, so on top there's a LIDAR, which is a laser range finding device that can detect other cars or obstacles in the rear of the car and to the sides, and in the front of the car we have stereo cameras that we added so that the car can sense depth in front of it, so with those new sensors, developers can now be challenged by integrating depth sensing and object avoidance and head to head racing into their machine learning models. >> So currently it's not an obstacle course, correct, it's a race track, right? >> So we call it a time trial, so it's a single car on the track at a time, how fast can you make a lap, our world record actually is 7.44 seconds, set by a young lady from Tokyo this past year, really exciting. >> And she was holding up the trophy and said this is basically a dream come true. And so, what are they trying to optimize, is it just the speed at the turn, what are they sort of focused on? >> Yeah, it's a little bit of art and a little bit of science, so there's the reinforcement learning model that learns through what's called a reward function, so you give the car rewards for achieving specific objectives, or certain behaviors, and so it's really up to the developer to decide what kind of behaviors do they want to reward the car with, whether it's stay close to the center line, reduce the amount of turns, they can also determine its position on the track and so they can reward it for cutting corners close, speeding up or slowing down, so it's really a little bit of art and science through some experimentation and deciding. >> So we had Intel on yesterday, talking about some of their AI, Naveen Rao, great guy, but they were introducing this concept called GANs, Generative Adversarial Networks, which is kind of like neural network technology, lot of computer science in some of the tech here, this is not kiddie scripting kind of thing, this is like real deal. >> Yeah, so GANs actually formed the basis of the product that we just announced this year called DeepComposer, so DeepComposer is a keyboard and a cloud service designed to work together to teach developers about generative AI, and GANs are the technique that we teach developers. So what's interesting about generative AI is that machine learning moves from a predictions-based technology to something that can actually create new content, so create new music, new stories, new art, but also companies are using generative AI to do more practical things like take a sketch and turn it into a 3D model, or autocorrect colorize black and white photos, Autodesk even has a generative design product, where you can give, an industrial designer can give a product some constraints and it'll generate hundreds of ideas for the design. >> Now this is interesting to me, because I think this takes it to, I call basic machine learning, to really some more advanced practical examples, which is super exciting for people learning AI and machine learning. Can you talk about the composer and how it works, because pretend I'm just a musician, I'm 16 years old, I'm composing music, I got a keyboard, how can I get involved, what would be a path, do I buy a composer device, do I link it to Ableton Live, and these tools that are out there, there's a variety of different techniques, can you take us through the use case? >> Yeah, so really our target customer for this is an aspiring machine learning developer, maybe not necessarily a musician. So any developer, whether they have musical experience or machine learning background, can use the DeepComposer system to learn about the generative AI techniques. So GANs are comprised of these two networks that have to be trained in coordination, and what we do with DeepComposer is we walk users through or walk developers through exactly how to set up that structure, how these two things train, and how is it different from traditional machine learning where you've got a large data set, and you're training a single model to make a prediction. How do these multiple networks actually work against each other, and how do you make sure that they're generating new content that's actually of the right type of quality that you want, and so that's really the essence of the Generative Adversarial Networks and these two networks that work against each other. >> So a young musician who happens to like machine learning. >> So if I give this to my kid, he'll get hooked on machine learning? That's good for the college apps. >> Plug in his Looper and set two systems working together or against each other. >> When we start getting to visualization, that's going to be very interesting when you start getting the data at the fundamental level, now this is early days. Some would say day zero, because this is really early. How do you explain that to developers, and people you're trying to get attention to, because this is certainly exciting stuff, it's fun, playful, but it's got some nerd action in it, it's got some tech, what are some of the conversations you're having with folks when they say "Hey, how do I get involved, why should I get involved," and what's really going to be the impact, what's the result of all this? >> Yeah, well it's fascinating because through Amazon's 20 years of artificial intelligence investments, we've learned a lot, and we've got thousands of engineers working on artificial intelligence and machine learning, and what we want to do is try to take a lot of that knowledge and the experiences that those folks have learned through these years, and figure out how we can bring them to developers of all skill levels, so developers who don't know machine learning, through developers who might be data scientists and have some experience, we want to build tools that are engaging and tactile and actually tangible for them to learn and see the results of what machine learning can do, so in the DeepComposer case it's how do these generative networks actually create net new content, in this case music. For DeepRacer, how does reinforcement learning actually translate from a simulated environment to the real world, and how might that be applicable for, let's say, robotics applications? So it's really about reducing the learning curve and making it easy for developers to get started. >> But there is a bridge to real world applications in all this, it's a machine learning linchpin. >> Absolutely, and you can just look at all of the innovations that are being done from Amazon and from our customers, whether they're based on improving product recommendations, forecasting, streamlining supply chains, generating training data, all of these things are really practical applications. >> So what's happening at the device, and what's happening in the cloud, can you help us understand that? >> Sure, so in DeepComposer, the device is really just a way to input a signal, and in this case it's a MIDI signal, so MIDI is a digital audio format that allows machines to kind of understand music. So the keyboard allows you to input MIDI into the generative network, and then in the cloud, we've got the generative network takes that input, processes it, and then generates four-part accompaniments for the input that you provide, so say you play a little melody on the keyboard, we're going to generate a drum track, a guitar track, a keyboard track, maybe a synthesizer track, and let you play those back to hear how your input inspired the generation of this music. >> So GANs is a big deal with this. >> Absolutely, it forms the basis of the first technique that we're teaching using DeepComposer. >> All right, so I got to ask you the question that's on everyone's mind, including mine, what are some of the wackiest and/or coolest things you've seen this year with DeepComposer and DeepRacer because I can imagine developers' creativity straying off the reservation a little bit, any cool and wacky things you've seen? >> Well we've got some great stories of competitors in the DeepRacer league, so we've got father-son teams that come in and race at the New York summit, a 10 year old learning how to code with his dad. We had one competitor in the US was at our Santa Clara summit, tried again at our Atlanta summit, and then at the Chicago summit finally won a position to come back to re:Invent and race. Last year, we did the race here at re:Invent, and the winning time, the lap time, a single lap was 51 seconds, the current world record is 7.44 seconds and it's been just insane how these developers have been able to really optimize and generate models that drive this thing at incredible speeds around the track. >> I'm sure you've seen the movie Ford v Ferrari yet. You got to see that movie, because this DeepRacer, you're going to have to need a stadium soon, with eSports booming, this has got its own legs for its own business. >> Well we've got six tracks set up down at the MGM Grand Arena, so we've already got the arena set up, and that's where we're doing all the knock-out rounds and competitors. >> And you mentioned father-son, you remember when we were kids, Cub Scouts, I think it was, or Boy Scouts, whatever it was, you had the pinewood derby, right, you'd make a car and file down the nails that you use for the axles and, taking it to a whole new level here. >> It's a modern-day version. >> All right, Mike, thanks for coming on, appreciate it, let's keep in touch. If you can get us some of that B-roll for any video, I'd love to get some B-roll of some DeepRacer photos, send 'em our way, super excited, love what you're doing, I think this is a great way to make it fun, instructive, and certainly very relevant. >> Absolutely, that's what we're after. Thank you for having me. >> All right, theCUBE's coverage here, here in Las Vegas for our seventh, Amazon's eighth re:Invent, we're documenting history as the ecosystem evolves, as the industry wave is coming, IoT edge, lot of cool things happening, we're bringing it to you, we're back with more coverage after this short break. (techno music)