>>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.

>> live from New York. It's the Q covering AWS Global Summit 2019 brought to you by Amazon Web service, is >> welcome back here to New York City. You're watching the Cube, the worldwide leader in Enterprise Tech cover jumps to minimum. My co host for today is Cory Quinn and happy to welcome to the program. A first time guest on the program, says Heart O. B. Who is a senior leader of global business development with Healthcare Life. Scientists know this group and AWS thanks so much for joining us. All right, so you know, we love digging into some of the verticals here in New York City. Of course, it's been a lot of time on the financial service is peas we actually had, Ah, another one of our teams out of the eight of us. Imagine show going on yesterday in Seattle with a lot of the education pieces. So healthcare, life sciences in genomics, little bit of tech involved in those groups, a lot of change going on in that world. So give us a thumbnail if you would as toe what what's happening in your >> world so well just from a scope one of you Health care includes life set paid on provider Life sciences is far more by attacking its most medical device and then genomics and what we're seeing in those spaces. Let's start with health care. It's such a broad thing, will just sort of back to back and forth in health care itself. What we're sort of seeing their customs ask us to focus on and to help them do falls into three categories. First, is a lot of customers ask us to help them personalized the consumer health journey. You and I, all of us, are so accustomed to that frictionless experiences we have elsewhere and in health care. There's a lot more friction. And so we're getting a lot of enquiries and request for us to help them transform that experience. Make it frictionless. So an example That would be if you're familiar with Doc. Doc started here in New York. Actually, when you want a book, an appointment, Doc, Doc, you can normally, if you go online, I have to put information for insurance. You type it all. Then it's full of friction. Have to put all the fields in. They use one of our A I service's image recognition, and you simply hold up your card to the camera and it able to pull your in transporation, determine eligibility and look the right appointment for you. So that's an example of removing friction for the consumer of the health consume over the patient as they're trying to go to that health care and excessive category one frictionless experiences using AWS to support it with a i service is category, too. We're getting a lot of interest for us to help health systems predict patient health events. So anything of value base care the way you actually are able to change the cost. Quality Curve is predicting events, not just dealing with math and so using a i Am L service is on top of data to predict and forecast events is a big part of one example would be with sooner where they moved, they're healthy and 10 platform, which is a launch to a patient record platform onto AWS. About 223,000,000 individuals that are on that platform Men we did a study with him where way consume about 210,000 individual patient data and created a machine learning model this is published where you can predict congestive heart failure 15 months in advance of it actually occurring. So when you look at that, that prediction are forecasting that sort of one of the powers of this princess. What category number two is predicting health events, and then the last one I'd be remiss in leaving out is that you probably have heard a lot of discussion on physician and a clinician. Burnout to the frustrations of the nurses or doctors and Muslims have the heart of that is not having the right information the right time to take care of the right patient. Data liquidity and in Trop ability is a huge challenge, and a lot of our customers are asking us to help solve those problems with them. You know it hims. This year we announced, together with change Healthcare Change Healthcare said they want to provide free and troubling to the country on AWS, with the platform supporting that. So those are sort of three categories. Personalize the consumer health journey. Predicting patient health events and promoting intra ability is sort of the signals that we're seeing in areas that were actively supporting our customers and sort of elevating the human condition. >> It's very easy to look at the regulation around things like health care and say, Oh, that gets in the way and its onerous and we're not gonna deal with it or it should be faster. I don't think anyone actively wants that. We like the fact that our hospitals were safe, that health care is regulated and in some of the ways that it is at least. But I saw an artifact of that means that more than many other areas of what AWS does is your subject to regulatory speed of Sloane. A speed of feature announcement, as opposed to being able to do it as fast technology allows relatively easy example of this was a few years back. In order to run, get eight of us to sign a B A. For hip, a certification, you have to run dedicated tendency instances and will not changed about a year and 1/2 2 years ago or even longer. Depending it's it all starts to run together after a time, but once people learn something, they don't tend to go back and validate whether it's still true. How do you just find that communicating to your customers about things that were not possible yesterday now are, >> yeah, when you look at hip eligibility. So as you know, a devious is about over 100 him eligible service's, which means that these are so this is that so compliance that you start their compliance, Remember, is an outcome, not a future. So compliance is a combination of people process platform, and we bring the platform that's hip eligible, and our customers bring the people in process, if you will, to use that platform, which then becomes complying with regulatory requirements. And so you're absolutely right. There's a diffusion of sort of understanding of eligibility, a platform, and then they worked with customers have to do in order as a shared responsibility to do it. That diffusion is sometimes slower. In fact, there's sometimes misinformation. So we always see it work with our customers and that shared, responsive model so that they can meet their requirements as they come to the cloud. And we can bring platforms that are eligible for hip. They can actually carry out the work clothes they need to. So it's it's that money, you know, the way I think of it is. This when you think of compliance, is that if if I were to build for you a deadbolt for your door and I can tell you that this complies boasted of things, but you put the key under the mat way might not be complying with security and regular requirements for our house. So it's a share responsible. I'll make the platform be eligible and compliant, and so that collective does daytime and dusting. People are saying that there is a flat from this eligible, and then they have to also, in their response to work to the people in process potion to make the totality of it comply with the requirements for regulatory for healthcare regulatory requirements. >> Some of the interesting conversations I've had in the last few years in health care in the industry is collaborations that are going on, you know, how do we share data while still maintaining all of the regulations that are involved? Where does that leave us get involved? There >> should. That's a fact. There is a data sharing part of that did a liquidity story that we talked about earlier in terms of instability. I'll give an example of where AWS actually actively working in that space. You may be familiar with a service we launched last November at Reinvent called Amazon Campion Medical and Campion Medical. What it does is it looks at a medical note and can extract key information. So if you think back to in high school, when you used to read a book in highlighting yellow key concepts that you wanted to remember for an exam Amazon Carmen Medical Same thing exactly, can lift key elements and goes from a text blob, too discrete data that has relationship ontology and that allows data sharing where you where you need to. But then there's one of the piece, so that's when you're allowed to disclose there's one of me. Sometimes you and I want to work on something, but we want to actually read act the patient information that allows data sharing as well. So Amazon coming medical also allows you to read, act. Think of when a new challenge shows that federally protected doctor that's blacked out Amazon com for American also remove patient identifying information. So if you and I want to collaborate on research project, you have a set of data that you wanna anonima de identify. I have data information of I D identified. To put it together, I can use Amazon com Medical Read Act All the patient information Make it d identified. You can do the same. And now we can combine the three of us that information to build models, to look a research and to do data sharing. So whether you have full authority to to share patient information and use the ontological portion of it, or whether you want to do the identifying matter, Amazon competent medical helps you do that. >> What's impressive and incredible is that whether we like it or not, there's something a little special about health care where I can decide I'm not going to be on the Internet. Social media things all stop tweeting. Most people would thank me for that, or I can opt out of ride sharing and only take taxis, for example. But we're all sooner or later going to be customers of the health care industry, and as a result, this is some of that effects, all of us, whether we want to acknowledge that or not. I mean, where some of us are still young enough to believe that we have this immortality streak going on. So far, so good. But it becomes clear that this is the sort of thing where the ultimate customer is all of us. As you take a look at that, does that inform how AWS is approaching this entire sector? >> Absolutely. In fact, I'd like to think that a W brought a physician toe lead sector because they understood that in addition to our customer obsession that we see through the customer to the individual and that we want to elevate the human condition we wanted obsess over our customers success so that we can affect positive action on the lives of individuals everywhere. To me, that is a turn. The reason I joined it of U. S s. So that's it. Certainly practice of healthcare Life's I said on genomic Seti ws has been around for about six years. A doubIe s double that. And so actually it's a mature practice and our understanding of our customers definitely includes that core flame that it's about people and each of us come with a special story. In fact, you know the people that work in the U. S. Healthcare life, science team people that have been to the bedside there, people that have been adventure that I worked in the farm industry, healthcare, population, health. They all are there because of that thing you just said. Certainly I'm there because that on the entire practice of self life sciences is keenly aware of looking through the customers to the >> individual pieces. All right, how much? You know, mix, you know, definitely an area where compute storage are critically important than we've seen. Dramatic change. You know, in the last 5 to 10 years, anything specific you could share on that >> Genomics genomex is an area where you need incredible computer storage on. In our case, for example, alumina, which is one of our customers, runs about 85% of all gene sequencing on the planet is in aws customer stores. All that data on AWS. So when you look at genomex, real power of genomics is the fact that enables precision diagnostics. And so when you look at one of our customers, Grail Grail, that uses genomic fragments in the blood that may be coming from cancer and actually sequences that fragment and then on AWS will use the power of the computer to do machine learning on that Gino Mexicans from to determine if you might have one of those 1 10 to 12 cancers that they're currently screening for. And so when you talk to a position health, it really can't be done without position diagnostics, which depends on genomex, which really is an example of that. It runs on AWS because we bring compute and storage essentially infinite power. To do that you want, For example, you know the first whole genome sequence took 14 years. And how many billions of dollars Children's Hospital Philadelphia now does 1000 whole genome sequences in two hours and 20 minutes on AWS, they spike up 20,000 see few cores, do that desi and then moved back down. Genomics. The field that literally can't be. My humble opinion can't be done outside the cloud. It just the mechanics of needed. The storage and compute power is one that is born in the cloud on AWS has those examples that I shared with you. >> It's absolutely fantastic and emerging space, and it's it's interesting to watch that despite the fact there is a regulatory burden that everything was gonna dispute that and the gravity of what it does. I'm not left with sense that feature enhancement and development and velocity of releases is slower somehow in health care than it is across the entire rest of the stack. Is that an accurate assessment, or is there a bit of a drag effect on that? >> Do you mean in the health care customers are on AWS speaking >> on AWS aside, citizen customers are going to be customers. Love them. We >> do aws. You know, we obviously innovation is a rowdy and we release gosh everything. About 2011 we released 80 front service than features and jumped 1015 where it was like 702 jumped 2018. Where was 1957 features? That's like a 25 fold. Our pace of innovation is not going to slow down. It's going to continue. It's in our blood in our d. N. A. We in fact, hire people that are just not satisfied. The status quo on want to innovate and change things. Just, you know, innovation is the beginning of the end of the story, so, no, I don't have to spend any slowdown. In fact, when you add machine learning models on machine learning service that we're putting in? I only see it. An even faster hockey stick of the service is that we're gonna bring out. And I want you to come to reinvent where we're going to announce the mall and you you will be there and see that. All >> right, well, on that note thank you so much for giving us the update on healthcare Life Sciences in genomics. Absolutely. Want to see the continued growth and innovation in that? >> My pleasure. Thank you for having a show. All >> right. For Cory, Queen of Stupid Men. The Cube's coverage never stops either. We, of course, will be at eight of us reinvent this fall as well as many other shows. So, as always, thanks for watching the cue.