 From around the globe, it's theCUBE with digital coverage of AWS re-invent 2020. Sponsored by Intel and AWS. Hi, we are theCUBE live covering AWS re-invent 2020. I'm Lisa Martin and I've got one of our CUBE alumni back with me, Mike Miller is here, general manager of AWS AI devices at AWS. Mike, welcome back to theCUBE. Hi, Lisa, thank you so much for having me. It's really great to join you all again at this virtual re-invent. Yes, I think last year you were on set. We have always had two sets at re-invent and you had the deep racer car. And so we're obviously socially distanced here, but talk to me about deep racer, what's going on some of the things that have gone on in the last year that you're excited about? Yeah, I'd love to tell you a little bit about what's been happening. We've had a tremendous year. Obviously COVID has restricted our ability to have our in-person races. So we've really gone gangbusters with our virtual league. So we have monthly races for competitors that culminate in a championship at re-invent. So this year we've got over a hundred competitors who have qualified and who are racing virtually with us this year at re-invent. They're participating in a series of knockout rounds that are being broadcast live on Twitch over the next week. That'll whittle the group down to a group of 32, which will have a series 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 re-enforcement learning together with sports that so many of us have been missing during the pandemic. Talk to me a little bit about some of the things that you've improved with DeepRacer and some of the things that are coming next year. Yeah, absolutely. So first of all, DeepRacer not only has been interesting for individuals to participate in the league, but we continue to see great traction and adoption amongst big customers. And they're using DeepRacer for hands-on learning for machine learning. And many of them are turning to DeepRacer to train their workforce in machine learning. So over 150 customers from the likes of Capital One, Moody's, Accenture, DBS Bank, JP Morgan Chase, BMW, and Toyota have held DeepRacer events for their workforces. And in fact, three of those customers, Accenture, DBS Bank, and JP Morgan Chase have each trained over a thousand employees in their organizations because they are just super excited and they find that DeepRacer is a way to drive that excitement and engagement across their customers. We even have Capital One expanded this to their families. So Capital One ran a DeepRacer Kids Cup, a family-friendly virtual competition this past year where over 250 children and 200 families got to get hands-on with machine learning. So I envisioned this being a big facilitator during the pandemic when there's been this massive shift to remote work has you seen an uptick in it for companies that talking to me about training need to be able to hire many, many more people remotely but also train them as DeepRacer facilitator of that? Yeah, absolutely. DeepRacer has a core component of the experience which is all virtualized. So we have a console and integration with other AWS services so that racers can participate using a 3D racing simulator. They can actually see their car driving around a track in a 3D world simulation. We're also selling the physical devices. So if participants want to get 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 DeepRacer car available for purchase internationally through the amazon.com website to help facilitate that. So how many DeepRacers are out there? I'm just curious. Oh, thousands. And what we've seen is some companies will purchase them in bulk and use them for their internal leagues just like JP Morgan Chase and DBS Bank. These folks have their own tracks and racers that they'll use to facilitate both the 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 many teams and DevOps processes have without being co-located with each other. Does DeepRacer help with that? I mean, from an engagement perspective? I think so. What we've seen is that DeepRacer 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 to learn how to do a new complex task. And what we've seen is that customers who have introduced DeepRacer as an event for their employees have seen a very wide variety of employee skill sets kind of get engaged. So you've got not just the hardcore deep data scientists or the ML engineers, you've got web front-end programmers. You even have some non-technical folks who want to get their hands dirty and learn about machine learning. And DeepRacer 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, Amazon's got hundreds, AWS has hundreds of thousands of customers doing machine learning on AWS. Customers of all sizes are embedding machine learning into their core business processes. And one of the things that we always do at Amazon is we're listening to customers, 90 to 95% of our roadmaps are driven by customer feedback. And so as we've been talking to these industrial and manufacturing customers, they've been telling us, hey, we've got data, we've got these processes that are happening in our industrial sites. 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 that are focused on industrial manufacturing customers. Two of the services are focused around predictive maintenance and the other three services are focused on computer vision. And so let's start with the predictive maintenance side. So we announced Amazon Monotron and Amazon Lookout 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 Monotron is an end-to-end machine learning system with sensors, a gateway, and an ML service that can detect anomalies and predict when industrial equipment will require maintenance. And I've actually got a couple examples here of the sensors and the gateway. So this is Amazon Monotron. 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 ML service in the cloud. And what happens is the sensors can be connected to any rotating machinery like a pump or 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 alerts 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 it's super streamlined end-to-end and easy for 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 to 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 we've seen, so Amazon fulfillment centers actually have enormous amounts of equipment. You can imagine the size of an Amazon fulfillment center, 28 football fields long, miles of conveyor belts and Amazon fulfillment centers have started to use Amazon monotron to monitor some of their conveyor belts. And we've got a fulfillment center in Germany that has started using these with a thousand sensors and they've already been able to do predictive maintenance and prevent downtime, which is super costly for businesses. We've also got customers like Fender, who makes guitars and amplifiers and musical equipment here in the U.S. They're adopting Amazon monotron for their industrial machinery 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 Lookout for Equipment, which is one step further from Amazon monotron. Amazon Lookout for Equipment 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 GPS in South Korea, or I'm sorry, GS EPS in South Korea, they're an 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 basic rules-based systems to try to gain insight into that data. Well, now they're using Amazon Lookout for Equipment to take all of their existing sensor data have Amazon for Equipment automatically generate machine learning models and then process the sensor data to know when their abnormalities or when some predictive maintenance needs to occur. So you've got the capabilities of working with customers and industry 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 predictive, but one of the things I'm hearing is contactless, being able to understand what's going on without having to have someone physically there unless there is an issue in contactless is not one of the words of 2020, but I think it probably should be. Yeah, absolutely. And in fact, 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 and 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 monitoring, like workplace safety to quality of goods coming off of a machinery line or monitoring their yard and sort of their truck entry and exit and they're looking for computer vision to automate a lot of these tasks. And so we just announced a couple of new services that use computer vision to do that, to automate these once previously manual inspection tasks. So let's start with AWS Panorama, uses computer vision to improve those operations and workplace safety. And AWS Panorama comes in two flavors. There's an appliance, which is a box like this. 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 AWS Panorama and have it apply computer vision to the cameras that you've already got deployed. So customers are seeing that 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 latency 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 there are data governance or privacy restrictions that restrict a company's ability to send images or video from their site offsite 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's going to use Panorama to improve their yard management. They want to 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 works with municipalities on traffic and 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 improve congestion, allocate curbside space, optimize parking. We've also got retail customers. For instance, Parkland is a Canadian fuel station and retailer 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 DeepRacer and the innovations, but also for show and tell, even though we weren't in person at Reinvent this year. Great to have you back on theCUBE, Mike. We appreciate your time. Yep, thanks Lisa for having me. I appreciate it. For Mike Miller, I'm Lisa Martin. You're watching theCUBE's live coverage of AWS Reinvent 2020.