(upbeat music) >> Welcome back friends. It's theCUBE LIVE in Las Vegas at the Venetian Expo, covering the first full day of AWS re:Invent 2022. I'm Lisa Martin, and I have the privilege of working much of this week with Dave Vellante. >> Hey. Yeah, it's good to be with you Lisa. >> It's always good to be with you. Dave, this show is, I can't say enough about the energy. It just keeps multiplying as I've been out on the show floor for a few minutes here and there. We've been having great conversations about cloud migration, digital transformation, business transformation. You name it, we're talking about it. >> Yeah, and I got to say the soccer Christians are really happy. (Lisa laughing) >> Right? Because the USA made it through. So that's a lot of additional excitement. >> That's true. >> People were crowded around the TVs at lunchtime. >> They were, they were. >> So yeah, but back to data. >> Back to data. We have a couple of guests here. We're going to be talking a lot with customer challenges, how they're helping to overcome them. Please welcome Kevin Zawodzinski, VP of Sales Engineering at COMMVAULT. >> Thank you. >> And Paul Meighan, Director of Product Management at AWS. Guys, it's great to have you on the program. Thank you for joining us. >> Thanks for having us. >> Thanks for having us. >> Isn't it great to be back in person? >> Paul: It really is. >> Kevin: Hell, yeah. >> You cannot replicate this on virtual, you just can't. It's nice to see how excited people are to be back. There's been a ton of buzz on our program today about Adam's keynote this morning. Amazing. A lot of synergies with the direction, Paul, that AWS is going in and where we're seeing its ecosystem as well. Paul, first question for you. Talk about, you know, in the customer environment, we know AWS is very customer obsessed. Some of the main challenges customers are facing today is they really continue this business transformation, this digital transformation, and they move to cloud native apps. What are some of those challenges and how do you help them eradicate those? >> Well, I can tell you that the biggest contribution that we make is really by focusing on the fundamentals when it comes to running storage at scale, right? So Amazon S3 is unique, distributed architecture, you know, it really does deliver on those fundamentals of durability, availability, performance, security and it does it at virtually unlimited scale, right? I mean, you guys have talked to a lot of storage folks in the industry and anyone who's run an estate at scale knows that doing that and executing on those fundamentals day after day is just super hard, right? And so we come to work every day, we focus on the fundamentals, and that focus allows customers to spend their time thinking about innovation instead of on how to keep their data durably stored. >> Well, and you guys both came out of the storage world. >> Right. >> Yeah, yeah. >> It was a box world, (Kevin laughs) and it ain't no more. >> Kevin: That's right, absolutely. >> It's a service and a service of scale. >> Kevin: Yeah. So architecture matters, right? >> Yeah. >> Yeah. >> Paul, talk a little bit about, speaking of innovation, talk about the evolution of S3. It's been around for a while now. Everyone knows it, loves it, but how has AWS architected it to really help meet customers where they are? >> Paul: Right. >> Because we know, again, there's that customer first focus. You write the press release down the road, you then follow that. How is it evolving? >> Well, I can tell you that architecture matters a lot and the architecture of Amazon S3 is pretty unique, right? I think, you know, the most important thing to understand about the architecture of S3 is that it is truly a regional service. So we're laid out across a minimum of 3 Availability Zones, or AZs, which are physically separated and isolated and have a distance of miles between them to protect against local events like floods and fires and power interruption, stuff like that. And so when you give us an object, we distribute that data across that minimum of 3 Availability Zones and then within multiple devices within each AZ, right? And so what that means is that when you store data with us, your data is on storage that's able to tolerate the failure of multiple devices with no impact to the integrity of your data, which is super powerful. And then again, super hard to do when you're trying to roll your own. So that's sort of a, like an overview of the architecture. In terms of how we think about our roadmap, you know, 90% of our roadmap comes directly from what customers tell us matters, and that's a tenant of how we think about customer obsession at AWS and it really is how we drive a roadmap. >> Right, so speaking of customers Kevin, what are customers asking you guys- >> Yeah. >> for, how does it relate to what you're doing with S3? >> Yeah, it's a wonderful question and one that is actually really appropriate for us being at re:Invent, right? So we got, last three years we've had customers here with us on stage talking about it. First of all, 3 years ago we did a virtual session, unfortunately, but glad to be back as you mentioned, with Coca-Cola and theirs was about scale and scope and really about how can we protect hundreds of thousands of objects, petabyte to data, in a simple and secure way, right. Then last year we actually met with a ACT, Inc. as well and co-presented with them and really talked about how we could protect modern workloads and their modern workloads around whether it was Aurora or as well as EKS and how they continue to evolve as well. And, last but not least it's going to be, this year we're talking with Illinois State University as well about how they're going to continue to grow, adapt and really leverage AWS and ourselves to further their support of their teachers and their staff. So that is really helping us quite a bit to continue to move forward. And the things we're doing, again, with our customer base it's really around, focused on what's important to them, right? Customer obsession, how are we working with that? How are we making sure that we're listening to them? Again, working with AWS to understand how can we evolve together and really ultimately their journeys. As you heard, even with those 3 examples they're all very different, right? And that's the point, is that everybody's at a different point in the journey. They're at a different place from a modernization perspective. So we're helping them evolve, as they're helping us evolve as well, and transform with AWS. >> So very mature COMMVAULT stack, the S3 bucket and all the other capabilities. Paul, you just talked about coming together- >> Right. >> Dave: for your customers. >> Yeah, yeah, absolutely. And just, you know, we were talking the other day, Paul and I were talking the other day, it's been, you know, we've worked with AWS, with integration since 2009, right? So a long time, right? I mean, for some that may not seem like a long time ago, but it is, right? It's, you know, over a decade of time and we've really advanced that integration considerably as well. >> What are some of the things that, I don't know if you had a chance to see the keynote this morning? >> Yeah, a little bit. >> What are some of the things that there was, and in fact this is funny, funny data point for you on data. One of my previous guests told me that Adam Selipsky spent exactly 52 minutes talking about data this morning. 52 minutes. >> Okay. >> That there's a data point. But talk about some of the things that he talked about, the direction AWS is going in, obviously new era in the last year. Talk about what you heard and how you think that will evolve the COMMVAULT-AWS relationship. >> Yeah, I think part of that is about flexibility, as Paul mentioned too, architecture matters, right? So as we evolve and some of the things that we pride ourselves on is that we developed our systems and our software and everything else to not worry about what do I have to build to today but how do I continue to evolve with my customer base? And that's what AWS does, right? And continues to do. So that's really how we would see the data environment. It's really about that integration. As they grow, as they add more features we're going to add more features as well. And we're right there with them, right? So there's a lot of things that we also talk about, Paul and I talk about, around, you know, how do we, like Graviton3 was brought up today around some of the innovations around that. We're supporting that with Auto Scale right now, right? So we're right there releasing, right when AWS releasing, co-developing things when necessary as well. >> So let's talk about security a little bit. First of all, what is COMMVAULT, right? You're not a security company but you're an adjacency to security. It's sort of, we're rethinking security. >> Kevin: Yep. >> including data protection, not a bolt-on anymore. You guys both have a background in that world and I'm sure that resonates. >> Yeah. >> So what is the security play here? What role does COMMVAULT play? I think we know pretty well what role AWS plays, but love to hear, Paul, your thoughts as well on security. >> Yeah, I'll start I guess. >> Go on Paul. >> Okay. Yeah, so on the security side of things, there's a quite a few things. So again, on the development side of things, we do things like file anomaly detection, so seeing patterns in data. We talked a lot about analytics as well in the keynote this morning. We look at what is happening in the customer environment, if there's something odd or out of place that's happening, we can detect that and we'll notify people. And we've seen that, we have case studies about that. Other things we do are simple, simple but elegant. Is with our security dashboard. So we'll use our security dashboard to show best practices. Are they using Multi-Factor Authentication? Are you viewing password complexity? You know, things like that. And allows people to understand from a security landscape perspective, how do we layer in protection with their other systems around security. We don't profess to be the security company, or a security company, but we help, you know, obviously add in those additional layers. >> And obviously you're securing, you know, the S3 piece of it. >> Mmmhmm. >> You know, from your standpoint because building it in. >> That's right. And we can tell you that for us, security is job zero. And anyone at AWS will tell you that, and not only that but it will always be our top priority. Right from the infrastructure on down. We're very focused on our shared responsibility model where we handle security from the hypervisor, or host operating system level, down to the physical security of the facilities in which our services run and then it's our customer's responsibility to build secure applications, right. >> Yeah. And you talk about Graviton earlier, Nitro comes into play and how you're, sort of, fencing off, you know, the various components of the system from the operating system, the VMs, and then that is designed in and that's a new evolution that it comes as part of the package. >> Yeah, absolutely. >> Absolutely. >> Paul, talk a little bit about, you know, security, talking about that we had so many conversations this year alone about the threat landscape and how it's dramatically changing, it's top of mind for everybody. Huge rise in ransomware attacks. Ransomware is now, when are we going to get hit? How often? What's the damage going to be? Rather than, are we going to get hit? It's, unfortunately it's progressed in that direction. How does ensuring data security impact how you're planning the roadmap at AWS and how are partners involved in shaping that? >> Right, so like I said, you know, 90% of our roadmap comes from what customers tell us matters, right? And clearly this is an issue that matters very much to customers right now, right? And so, you know, we're certainly hearing that from customers, and COMMVAULT, and partners like COMMVAULT have a big role to play in helping customers to secure and protect their applications, right? And that's why it's so critical that we come together here at re:Invent and we have a bunch of time here at the show with the COMMVAULT technical folks to talk through what they're hearing from customers and what we're hearing. And we have a number of regular touch points throughout the year as well, right? And so what COMMVAULT gets from the relationship is, sort of, early access and feedback into our features and roadmap. And what we get out of it really is that feedback from that large number of customers who interface with Amazon S3 through COMMVAULT. Who are using S3 as a backup target behind COMMVAULT, right? And so, you know, that partnership really allows us to get close to those customers and understand what really matters to them. >> Are you doing joint engineering, or is it more just, hey here you go COMMVAULT, here's the tools available, go, go build. Can you address that? >> Yeah, no, absolutely. There's definitely joint engineering like even things around, you know, data migration and movement of data, we integrate really well and we talk a lot about, hey, what are you, like as Paul mentioned, what are you seeing out there? We actually, I just left a conversation about an hour ago where we're talking about, you know, where are we seeing placement of data and how does that matter to, do you put it on, you know, instant access, or do you put it on Glacier, you know, what should be the best practices? And we tell them, again, some of the telemetry data that we have around what do we see customers doing, what's the patterns of data? And then we feed that back in and we use that to create joint solutions as well. >> You know, I wonder if we could talk about cloud, you know, optimization of cloud costs for a minute. That's obviously a big discussion point in the hallways with customers. And on your earnings call you guys talked about specifically some customers and they specifically mentioned, for example, pushing storage to lower cost tiers. So you brought up Glacier just then. What are you seeing in the field in that regard? How are customers taking advantage of that? And where does COMMVAULT play in, sort of, helping make that decision? >> You want to take part one or you want me to take it? >> I can take part one. I can tell you that, you know, we're very focused on helping customers optimize costs, however necessary, right? And, you know, we introduced intelligent hearing here at the show in 2019 and since launch it's helped customers to reduce costs by over $750 million, right? So that's a real commitment to optimizing costs on behalf of customers. We also launched, you know, later in 2020, Glacier Deep Archive, which is the lowest cost storage in the cloud. So it's an important piece of the puzzle, is to provide those storage options that can allow customers to match the workloads that are, that need to be on folder storage to the appropriate store. >> Yeah, and so, you know, S3 is not this, you know, backup and recovery system, not an archiving system and, you know, in terms of, but you have that intelligence in your platform. 'Cause when I heard that from the earnings call I was like, okay, how do customers then go about deciding what they can, you know, when it's all good times, like yeah, who cares? You know, just go, go, go. But when you got to tighten the belt, how do you guys? >> Yeah, and that goes back to understanding the data pattern. So some of that is we have intelligence and artificial intelligence and everything else and machine learning within our, so we can detect those patterns, right? We understand the patterns, we learn from that and we help customers right size, right. So ultimately we do see a blend, right? As Paul mentioned, we see, you know, hey I'm not going to put everything on Glacier necessarily upfront. Maybe they are, it all depends on their workloads and patterns. So we use the data that we collect from the different customers that we have to share those best practices out and create, you know, the right templates, so to speak, in ways for people to apply it. >> Guys, great joint, you talked about the joint engineering, joint go to market, obviously a very strong synergistic partnership between the two. A lot of excitement. This is only day one, I can only imagine what's going to be coming the next couple of days. But I have one final question for you, but I have same question for both of you. You had the chance to create your own bumper sticker, so you get a shiny new car and for some reason you want to put a bumper sticker on it. About COMMVAULT, what would it say? >> Yeah, so for me I would say comprehensive, yet simple, right? So ultimately about giving you all the bells and whistles but if you want to be very simple we can help you in every shape and form. >> Paul, what's your bumper sticker say about AWS? >> I would say that AWS starts with the customer and works backwards from there. >> Great one. >> Excellent. Guys- >> Kevin: Well done. >> it's been a pleasure to have you on the program. Thank you- >> Kevin: Thank you. >> for sharing what's going on, the updates on the AWS-COMMVAULT partnership and what's in it for customers. We appreciate it. >> Dave: Thanks you guys. >> Thanks a lot. >> Thank you. >> All right. For our guests and Dave Vellante, I'm Lisa Martin. You're watching theCUBE, the leader in live enterprise and emerging tech coverage. (upbeat music)

>>Good afternoon and welcome back to the Classiest Show in Technology. This is the Cube we are at AWS Reinvent 2022 in Fabulous Sin City. That's why I've got my sequence on. We love a little Vegas, don't we? I'm joined by John Farer, another, another Vegas >>Fan. I don't have my sequence, I left it in my room. We're >>Gonna have to figure out how to get us 20 as soon as possible. What's been your biggest shock for you at the show so far? >>Well, I think the data story and security is so awesome. I love how that's front and center. If you look at the minutes of the keynote of Adamski, the CEO on day one, it's all bulked into data and security. All worked hand in hand. That's on top of already the innovation of their infrastructure. So I think you're gonna see a lot of interplay going on in this next segment. It's gonna tell a lot of that innovation story that's coming next. It's pretty awesome. >>It is pretty awesome, and I'm super excited. It's not only what we do here on the Cube, it's also in my show notes. We are gonna be geeking out for the next segment. Please welcome Paul and Puja. Wonderful to have you both here. Paul from Amazon, s3, glacier, and Pujan, CEO of kuo. I wanna turn to you Pujan, to start us off, just in case the audience isn't familiar, give us the Kuo pitch. >>Yeah, so basically Kuo is a, a backup as a service offering, right? Built in AWS four aws, right? And effectively going after, you know, any service that a customer uses on top of aws, right? And so a lot of the data sitting on s3, right? So that's been like our, our big use case going and basically building backup and air gap protection for, for s3. But we basically go to every other service, e c two, ebs, dynamo, you know, you name it, right? So basically do the whole thing >>And the relationship with aws. Can you guys share, I mean, you got you here together. You guys are a great partnership. Born in the cloud, operation in the cloud. Absolutely. I think talk about the partnership with aws. >>Absolutely. I think the last five years of building on AWS has been phenomenal, right? And I love the platform. It's, it's a very pure platform for us. You know, the APIs and, and the access you get and access you get to the service teams like Paul sitting here and the other teams you have gotten access to, I think has been phenomenal. But we also have, I would say, pushed the envelope in terms of how innovative we have been and how aggressive we have been in utilizing all the innovation that AWS has built in over the last few years. But it would not have happened without the fantastic partnership with the service teams. >>Paul, talk about the, AM the S3 part of this. What's the story there? >>Well, it's been great working with the CUO team over the course of the last few years. We were just upstairs diving deep into the, to the features that they're taking advantage of. They really push us hard on behalf of customers, and it's been a, it's just been a great relationship over the last years. >>That's awesome. And the ecosystem at such a, we're gonna hear tomorrow, the keynote on the, from Aruba who's gonna tend over the ecosystem. You guys are working together. There's a lot of strategic partnerships, so much collaboration between you guys that makes it very, this is the next gen cloud of cloud environment we're seeing. And you heard the, the economies around the corner. It's still gonna be challenging, but still there's more growth in the cloud. This is not stopping. This is impacts the customers. What are the customers saying to you guys when you work backwards from their needs? They want it faster, easier, cheaper. They want it more integrated. What are some of the things, all those you guys hearing from customers? >>So for us, you know, if you think about it, like, you know, as people are moving to the cloud, especially like take a use case like s3, right? So much of critical data sitting on top of S3 today. And so what folks have realized that as they're, you know, putting all of those, you know, what, over two 50 trillion objects, you know, sitting on s3, a lot of them need backup and data protection because there could be accidental deletions, there could be software bugs, there could be a ransomware type event due to which you need a second copy of the data that is outside of your security domain, right? But again, that needs to get be done at the, at the right price point, right? And that's where like a technology like Columbia comes in because since we've been built on the cloud, we've optimized it correctly. So especially for folks who are very cost conscious, given the macroeconomic conditions, we are heading into a technology that's built correctly so that, you know, you get the right architecture and the right solution at the right price point and the scale, right? Talking about trillions of objects, billions of objects within a single customer, within a single bucket sometimes. And that's where Columbia comes in. Cause we basically do that at scale without, again, impacting the, the customer's wallet more than it needs to. >>The porridge has to be the right temperature and the right size bowl. With the right spoon. You've got a lot of complexity when it comes to solving those customer challenges. You have a couple customer story examples you're allowed to share with us. Correct? Paul, do you want to kick one off? Go ahead. Oh, puja. All right. >>No, absolutely. I think there's a ton of them. I, I'll talk about, you know, want to begin with like Cox Automotive, right? A phenomenal customer that we, all of us have worked together with them. And again, looking for a solution to backup S3 to essentially go air gap protection outside of their account, right? They looked at doing it themselves, right? They thought they'll go and basically do it themselves. And then they fortunately bumped into Columbia, they looked at our architecture, looked at what it would really go and take to build it. And guess what, sitting in 2022, getting 23 right now, nobody wants to go and build this themselves. They actually want a turnkey solution that just does it, right? And so, again, we are a phenomenal joint customer of ours doing this at a pretty massive scale, right? And there are many more like that. There's Warner Brothers that are essentially going into the cloud from on premises, right? And they're going really fast accelerating the usage on aws again, looking at, you know, backup and data protection and using clum because of our extreme simplicity that we provide. >>Yeah, I think it's, you've got a, a lot of different people solving different problems that you're working with all the time. Millions of customers. Well, how do you prioritize? >>Well, for us, it really all comes down to fundamentals, right? So Amazon, s3 s unique distributed architecture delivers industry leading durability, availability, performance and security at virtually unlimited scale, right? And it's really been delivering on the fundamentals that has earned the trust of so many customers of all sizes and industries over the course of over 16 years. Now, in terms of how we prioritize on behalf of those customers, we always say that 90% of our roadmap comes directly from what customers are telling us is important. And a large number of our customers now are using S3 through lumino, which is why the relationship is so important. We're here talking about customer use cases here at the show, and we do that regularly throughout the year as well. And that's, that's how we land on a road. >>And what are the, what are the top stories from customers? What, what are they telling you? What's the number one top three things you're hearing? >>I tell you, like, again, it just comes down to the fundamentals, right? Of security, availability, durability and performance at virtually unlimited scale. Like that is the first customer first discussions that we have with customers talking about durable storage, for >>Sure. What I find interesting in, you mentioned scale, right? That comes up a lot scale with data. Yeah. That we heard data. The big theme here, security, what's in my S3 bucket? Can you find out what's in there? Is it backed up properly? How do I get it back? Where's the ransomware? Why not just target the ransomware? So how do you navigate the, the security challenges, the, the need to store all that scale data? What's the secret sauce? >>Yeah, so I think the, the big thing is we'll start with the, you know, how we have architected the product, right? If you think about it, this, you're dealing with a lot of scale, right? You get to a hundred million, a billion and billions very fast on S3 few, especially on a cloud native application. So it starts with the visibility, right? It's basically about, like we have things where you do, where you create a subset of your buckets called protection groups that you can essentially, you know, do it based on prefixes. So now you can essentially figure out what prefix you want to back up and what you don't want to back up. Maybe there's log data that you don't care about, so you don't back that up, right? And it all starts with that visibility that you give. And the prefix level data protection then comes the scale, which is where I was telling you, right? We have basically built an orchestration engine, right? It's like we call the ES for Lambdas, right? So we have a internal orchestration engine and essentially what what we have done is we have our own language internally that spawns off these lambdas, right? And they go after these S3 partitions do the right things and then you basically reel them back. So things like that that we do that are not possible if you're not built on the >>Clock. Well also, I mean, just mind blowing and go back 10 years. Yeah. I mean you got Lambda. What you're talking about here is the gift of the cloud innovation. Yeah. So the benefit of S3 is now accelerated. This is the story this year. Yeah. I mean they're highlighting it at scale, not just in the data, but like what we knew when Lambda came out and what S3 could do. But now mainstream solutions are coming in. Does that change your backup plans? Because we're gonna see a lot more end to end, lot more solutions. We heard that on the keynote. Some are saying it's more complexity. Of course it might, but you can abstract another way with the cloud that's the best part of the cloud. So these abstraction leads. So what's your view on that? But I wanna get your thoughts because you guys are perfectly positioned for this scale, but there's more coming. Yes. Yes. Exactly. What, how are you looking at that? >>So again, I think the, you know, obviously the, the S3 teams and every team in AWS is basically pushing the envelope in terms of innovation. But the key for a partner like us is to go and take that innovation. A lot of complex architectures behind the scene. But what you deliver to the customer is simple. I'll give you one more example. One of the things we launched that, you know, Paul and others are very excited about, is this ability to do instant access on the backup, right? So you could have billions of objects that you backed up. Maybe you need just 10,000 of them for a DR test. And we can basically create like an instant virtual bucket on top of that backup that you can instantly restore >>Spinning up a sandbox of temporary data to go check it >>Out. Exactly. Offer an inte application. >>Think we're geeking out right now. >>Yeah, I know. Brought that part of the segment, John. Don't worry, we're safely there. But, >>But that's the thing, right? That all that is possible because of all the, the scale and innovation and all the APIs and everything that, you know, Paul and the team gives us that we go and build on top of >>Paul, geek out on with us on this. We >>Are super excited for instant restore >>For store. I mean, automation programmability. >>It is, I mean it's the logical next step for backup in the cloud. Exactly. Yeah. But it's a super hard engineering problem to go solve for customers. I mean, the RTO benefits alone are super compelling, but then there's a cost element as well of not having to bring back all that stuff for a test restore, for example. And so it's, it's been really great to, to work with the team on that. We have some ideas on how we may help solve it from our side, and we're looking forward to collaborating on it. >>This is a great illustration of what I was writing about this week around the classic cloud, which is great. And as Adam said, and used like to use the word and, and you got this new functionality we're seeing emerge from the growth. Yes. From the companies that are built on Amazon web services that are growing. You're a partner, they have a lot of other partners and people are taking over restaurant here off action. I mean, there's real growth and new functionality on top of aws. You guys are no different. What's, are you prepared for that? Are you ready to go? >>Yeah, no, absolutely. And I think if you think about, if you think about it, right, I think it's also about doing this without impacting the primary application. Like if the customer is running a primary application at scale on s3, a backup application like ours can't come in and really mess with that. So I think being able to do things where, and this is where you solve really hard computer science problems, right? Where you're bottling yourself. If you are essentially seeing any kind of, you know, interfering with the primary, you're going to cut yourself down. You're gonna go after a different partition. So there are a lot of things you need to do behind the scenes, which is again, all the complexity, all of that, but deliver the, to the customer a very, very simple thing. >>You know, Paul, I wanna get your thoughts and I want you to chime in. Yeah. In 2014, I interviewed Steven Schmidt, my first interview with the, he was the CISO then, and now he's a CSO and, and former ciso, he's back at that time, the word was the cloud's not secure. Now we're talking about security. Just in the complexity of how you're partitioning and managing your sub portions, how you explained it, it's harder for the attackers. The cloud in its in its architecture has become a more secure environment. Yeah. Well, and getting more secure as you have laying out this, this is a new dynamic. This is good. Can you explain the, >>I mean, I, I can just tell you that at AWS security is job zero and that it will always be our number one priority, right? We have a, an infrastructure with under AWS that is vetted and approved to run even top secret workloads, which benefits all customers in all regions. >>And your, your security posture is embedded on top of that. And you got your own stuff. >>Yeah. And if you think of it as a shared responsibility model, so security of the cloud is the responsibility of the cloud provider, but then security of the data on top of it. Like you, you go and delete stuff, your software goes and does something that resiliency, the integrity of the data is your responsibility as a customer. And that's where, you know, we come in. Who >>Shared responsibility has been such a hot topic all week. Yeah. >>I gotta ask him one more question. Cause this is fascinating. And we are talking about on the cube all day today after we saw the announcement and Adam's comment on the cube, Adams LE's comment on the keynote. I mean, he said, if you're gonna tighten your belt, meaning economic cost recovery, re right sizing. If you want to tighten your belt, come to the cloud. So I have to ask you guys, Puja, if you can comment, that'd be great. There's a lot of other competitors out there that aren't born on aws. What is the customer gonna do when they tighten the build? What does that mean? They're gonna go to, to the individual contracts. They're gonna work in the marketplace. I mean this, there's a new dynamic in town. It's called AWS 2022. They weren't really around much in the recession of 2008. They were just starting to grow. Now they're an economic force. People like yourselves have embedded in there. There's a lot of competition. What's gonna happen? >>I think people are gonna just go to a place like, you know, AWS marketplace. You're going to essentially look for solutions and essentially like, and, and the right solutions built in are going to be self-service like aws. It's a very self-service thing. A hundred percent. So you go and do self-service, you figure out what's working, what's not working. Also, the model has to be consumption oriented. No longer can you expect the customer to go and pay a bunch of money for shelfware, right? It's like, like how we charge how AWS charges, which is you pay for what you consume. That and all has to be front and center, >>Right? I think that's a really, I think that's a really important >>Point. It's time >>And I think it's time. So we have a new challenge on the cube. We give you 30 seconds roughly to give us your extraordinarily hot take your shining thought leadership moment and, and highlight what you think is the most important takeaway from the show. The biggest soundbite, the juiciest announcement. Paul, I'll >>Start with an Instagram. Real basically. Yeah. Okay. >>Yeah. Hi. Go. I would just say from an S3 perspective, over the course of the last several years, we've really seen workloads shift from just backup and recovery and static images on websites to data lake analytics applications. And you continue to see that here. And I can tell you that some of these scaled applications are running at enormous mind blowing scale, right? And so, so every year we come here, we talk to customers, and it's just every year it sort of blows me away. And I've been in the storage industry for a long time and it's just is, it blows me away. Just the scale at customers are running in >>And >>Blowing scale. And when it comes to backup, let me just say that it's easy to back up and recover a single object, but doing an easy thing, a billion or 10 billion times over, that's actually quite hard. >>And just to, just to bold that a little bit, just pull out my highlighter. S3 now has over 280 trillion objects. That's a lot. >>That's a lot of objects. >>Yeah. You are not, you are not kidding. When you talk about scale, I mean, this is the most scalable. >>That's not solution's not there. Yeah. That, that's right. And we wake up every, we have a culture of durability and we wake up every single day to raise the bar on the fundamentals and make sure that every single one of those objects is protected and safe. >>Okay. You, I, >>I can't imagine worrying about two, two 80 trillion different things. >>Let's go. You're Instagram real >>For me again, you know, between S3 and us, we are two players out there that are really, you know, processing the data at the end of the day, right? And so I'm very excited about, you know, what we are going to do more and more with the instant restore capability where we can integrate third party services on top of it that can do more things with the data that is not, not passively sitting, but now becomes active data that you can analyze and do things with. So that's something where we take this to the next level is something that I'm super excited about. >>There's a lot to be excited about and, and we're excited to have you. We're excited to hear what happens next. Excited to see more collaboration like this. Paul Pon, thank you so much for joining us here on the show. Thank all of you from for tuning into our continuous wall to wall super thrilling live coverage of AWS reinvent here in fabulous Las Vegas, Nevada, with John Furrier. I'm Savannah Peterson. We're the cube, the leading source for high tech coverage.

(upbeat music) >> And welcome back. I'm John Furrier, host of theCUBE. We're excited to welcome Teradata back to theCUBE and today with us at the ARIA is re:MARS conference coverage. It's great to hear with Hillary Ashton, Chief Product Officer of Teradata. Great to have you on. Thanks for coming on. >> John, thanks so much for having me. I'm super excited to be joining you today. >> So re:MARS, what a great event. It brings together the confluence of machine learning, which is data, automation, robotics, and space. Which is to me, is a whole new genre of conversations, around technology and business value. It is going to be a big kind of area. And it's just, again just getting started any one, as they say, and super excited. Tell us about what you guys are doing there and yourself. >> About two and a half years ago I head up the products organization. That means I have responsibility for our roadmap and our and our strategy overall on the product side. Prior to coming Teradata, gosh, I have spent the last 20 years, if I can say that, in the data and analytics space. I grew up in marketing application space, spent 11 years at SaaS, really cut my teeth on hardcore AI, ML and analytics at SaaS, and most recently was at PTC, where I was in charge of, I was a general manager of augmented reality, the business unit at PTC, focused on IOT data and how IOT data and augmented reality can really bring machines to life. >> It's interesting. You talked about SaaS and kind of your background, you know everything SaaSified with the cloud now. So you think about platform as a service, SaaS models emerging, software is an open source game now. So it's an integration cloud-scale data conversation we're seeing. What's your reaction to that? What's your reaction to that kind of idea that, okay, everything's open to source, software value integrating in with data. What's your reaction to that? >> Yeah, I mean, I think open source absolutely has some awesome things going on there. I think there's great opportunities for commercial, reliable, governed software and open source capabilities to come together in an open ecosystem that allow our customers to choose the best way to deliver the analytic outcomes that they're focused on. >> So you guys have been in the news lately around connecting multicloud data analytics platforms and transforming businesses around there, obviously, the background with Teradata is well documented. What's this news about? What's really going on there? You got Vantage platform. What's happening? Take us through that story. What's the key point? >> Yeah, we've worked super hard to deliver a true, multicloud, hybrid, data platform. So, if you think customers, many of our enterprise customers started with on-premises data systems and are moving violently to the cloud, right? So they're super excited about moving to the cloud but being able to deploy on multiple clouds, I think is important and then importantly, sort of this hybrid notion of being able to leverage data that's on-premises and combine it with data in the cloud on AWS, for example. And so being able to do those hybrid use cases you may have data that's like older and kind of archaic, needs to stay on-premises. There's not a lot of value in moving it to the cloud but you want to combine it with some of the innovative, analytic capabilities that perhaps you're doing on AWS. And so Teradata allows you to live in that hybrid multicloud environment and deliver analytic outcomes wherever your data is. >> Hillary, one of the top conversations is data cloud. You got to have a data cloud. I want to deal with this, move this around, but there's a lot of now integration opportunities to bring data from different sources together whether you're in healthcare, all the verticals have the same use case, multiple access to different databases, bringing them all together, ETL, all that old-school stuff is coming back in and being kind of refactored with machine learning, with cloud scale, with platforms like AWS, there's now this new commitment to bringing this to the next level for enterprises. And you mentioned some of those partnerships. What specifically is going on in the cloud that's notable, that's realistically that customers are executing on now? Not the hype, the reality. >> The reality. Yeah, absolutely. So I mean, I think today with Teradata our customers are leveraging something that we call a query fabric. And so this is the idea, as you said, John, that data might be in a lot of different places and you want to be able to get value out of that data without the difficulty of moving it around unnecessarily. Sometimes you want to move it around but unnecessary data movement is both expensive and an inefficient use of precious time. And so I think that there's an opportunity for this query fabric to be able to do remote push-down queries, wherever that data is and return back the results that you are looking for, analytic results, AI and ML results, combining different data that's in different locations to deliver that analytic outcome quickly without having to move the data around. So I would say query fabric is one of the areas that we are super invested in and, today, is delivering real value for our customers. >> It's really interesting. Data being addressable and available, low latency. I mean, we're talking about space, automation, robotics, real-time, so you have different data types stored in different data vehicles or mechanisms that need to be real-time and available. Because machine learning only works as good as the data they has available to it. So again, this is a key, kind of new way that folks are re-architecting. And again, we're here at, at re:MARS, right? I mean to machine learning automation, robotics and space, kind of the real world, physical, digital, trust, scale, huge concepts here. What's the partnership? How's it working with AWS? Take us through that strong partnership that you guys are developing. >> Yeah. I mean, we have a fantastic relationship with AWS. We're really excited that we signed a strategic collaboration agreement at the end of last year that really puts us in an elite category of AWS partners. We're really committed to co-investing and co-engineering with Amazon and our product development organization and also in go-to market and marketing and other parts of our business. As the Chief Product Officer, I'm really excited about three key areas. First is we've optimized Teradata Vantage to run in the AWS cloud at great scale, with unparalleled scale at the highest level for our customers. And so we've partnered with them to be able to handle some of the complex analytic workloads. And we think of analytic models are one part of a workload. There may be other ELT that you talked about, right? Workloads that you may need to run, all of that running at tremendous scale with AWS in the cloud. The second area is deep integration. So Teradata used to think that we were the ecosystem. We built everything soup connects end-to-end. Today, we live in a really exciting data and analytics space and we partner closely with CSPs like AWS, where we are deeply integrated. We have dozens of AWS native integrations in our AWS offer today. And that lets customers take advantage of AWS X3 for Cloud Lake, for example Amazon Kinesis for data ingestion and streaming and on and on. So we're really focused on the integration area there. And then finally, we've developed, co-developed with AWS, a fast and low risk migration approach to move from on-premises to the cloud for our enterprise customers. >> You know, what's interesting is as we kind of weave together, I hear you talking about those three areas. I mentioned earlier at the top of the interview, how integration is now the competitive advantage. Software is almost going commodity with open source because you mentioned that. All good, right? All good stuff. But when you think about kind of the big trends in this new computing world, it's hybrid cloud, it's edge, and IOT, okay? Again, cloud-scale and these new connected points, trust, access, all these things have to be integrated. So integration, you guys have been in the middle, Teradata has been around for a long time, leader in data warehousing, but now with cloud and in the data types, this is a game changer. I mean, this is notable. Can you share more about how you see this evolving with customers because at the end of the day the integration becomes super critical. >> Yeah, absolutely. And I'm super passionate about the opportunities of IOT streaming data. And that's one of the key areas of partnership with Amazon is taking that streaming data, leveraging the analytic opportunities with Amazon. We'll talk about that in just a second, but I think some of the examples that I could share with you, everyone loves to hear, I love to hear, about what actual customers are doing. So Brinker International, they're one of the world's largest casual dining restaurants. If you've ever been to a Chili's Grill or Maggiano's Little Italy those are the guys, Brinker International owns those brands. So we leveraged Amazon SageMaker and Teradata Vantage together to apply advanced analytic and predictive modeling to be able to understand things like demand. And you're in the middle of COVID and trying to understand how many people should you have on staff today? What is the demand going to look like? What should sales look like? What's foot traffic look like? So that demand forecasting capability across their 1,600 different store fronts or restaurant fronts is one of the examples that I could share with you. The other one is Hertz. So one of the world's largest vehicle rental companies. They are using Vantage and AWS together to track and analyze transaction data across all of its global locations and manage again that complex inventory. And some of that is streaming data, some of that is data that we're getting from the cars themselves, and then create a new value-added program to their loyalty members which is sort of the name of the game. Is customer acquisition and extension of brand across those customers. So those are two examples I can share with you. There's many, many others but I know you probably had some other questions. >> Yeah. I want to come back to the SageMaker thing. I think that's important partnership there because it's been one of the fastest growing services. It's always at the top or in the top two or three whenever I talk to Andy Jassy and the team over there. But I want to talk about scalability and I want to ask you, if you can scope for me the scalability of what's going on with this data challenging, 'cause where are we on that scale? Can you share how you would scope the scale? >> Absolutely. And I love talking about scale because it is a home run for Teradata. I think many customers start looking at the cloud and they start with kind of a little tiny baby footprints but we are an enterprise solution, an enterprise platform. And so I think that we're looking at tens of thousands of users and thousands of business critical applications. That's what our customers are doing and have done for decades with Teradata and bringing all of that scale to the cloud. And with AWS in particular, we recently did 1,000 node testing. I'm going to walk through this a little bit slowly, which is hard for me, as you can tell, but it was a single system of more than 1,000 nodes which is just to give you a sense, that's double our largest on-premises system. So it's huge. It was the single largest system. >> John: Double is your largest customer deployment? >> Double our largest customer deployment on-premises. Yeah, that's right. So it was 1,000 nodes with more than 1,000 different users submitting thousands of concurrent queries. So huge enterprise scale. And this was a real-world use case. We took not a traditional benchmark but a real world customer set of mixed workloads. So lots of long running strategic queries and lots of fast running queries that needed really tight SLAs. All of that running simultaneously. We saw no system down times, we were able to roll out and roll back new capabilities seamlessly in a true software as a service fashion. So that was an awesome test all run on AWS. And I think that their team was just as excited as we were about it. >> Well, I love the scale. I love that test you guys ran. I see you're sponsoring re:MARS which is great, congratulations. We love covering since the beginning, we believe of kind of a whole new genre of programming brings together the confluence of exciting technologies that just a decade ago weren't always working together. They were bespoke. >> That's right. Yeah. >> So now it's all integrated in at cloud scale, you got the test, got thousands of concurrents queries. What else are you showcasing? You mentioned the SageMaker because that's really where Amazon's connecting all these tools. How are you integrating in? It sounds like you're bringing all that Amazon goodness in with Teradata and vice versa. >> Absolutely. We're delivering sort of the best in class to our customers jointly. So here re:MARS today, we're really excited to be talking about SageMaker and our relationship with AWS to be able to deliver that seamless integration between our solutions for machine learning services and Teradata Vantage. So I'm sure it won't come as any surprise to you as we just talked about, but we're finding that massive investments in AI and ML and other advanced analytic capabilities are out there, and many organizations are really only experimenting. They're just starting to explore some of these opportunities. We think that there's tremendous value in this scale that we just talked about, that we can offer, combined with best in class AI and ML capabilities like SageMaker. And so we are excited to talk about it. If you want to see it, we've got a booth set up, you can come and take a look at what we're doing there but I think there's huge opportunities for customers to get to the analytic value with Teradata Vantage and AWS SageMaker. >> Yeah, it's great to see Teradata seeing that headroom opportunity to extend the value proposition to kind of new territory with your customers. I can definitely see it. Love the connection here. Where can they learn more about the Teradata partnership with AWS and Amazon? Is there a site? Is there a program coming? Is there any more content that they can be expecting to see? Take a little plug time to plug the company. >> If you insist, I will, John. Thank you. I think, if you're at the event right now, you can swing by Teradata's booth. We're at booth 111. You can get a demo of our SageMaker integration and learn more about both our enterprise scale and the advanced outcomes that we're able to provide to our customers. If you're not at re:MARS and we really think you should be, we would encourage you to sign up for one of our upcoming SageMaker webinars that we're doing with AWS this year. And if you'd like to, you can also just email us at aws@teradata.com. Again, that's aws@teradata.com and we'll set up a private demo for you. >> Well, Hillary Ashton, great to have you on. Chief Product Officer, Teradata, you must be feeling good. You got a lot to work with. You've got an install base. You have new territory to take down. As the Chief Product Officer, you got the keys to the kingdom. Give us a quick bumper sticker of where you guys are going with the product. >> We are fast and furious. My team will tell you, we are so excited to be here with AWS and Teradata is on an epic trajectory forward in our cloud first approach, so we are so excited about our roadmap. If you'd like to learn more, please swing by teradata.com. >> Lot of innovation happening. Thanks for coming on theCUBE. Okay, this is theCUBE coverage of Amazon re:MARS machine learning, automation, robotics, and space. It cuts the confluence of digital, virtual data and real-world and space. You can't get any more than this. That's a big edge out there in space. Talk about edge computing and space. Of course, theCUBE's here covering it. I'm John Furrier, your host. Stay with us for more coverage here at Amazon re:MARS. (upbeat music)

(upbeat music) >> Welcome back, everyone, to theCUBE's coverage here live on the floor at AWS re:MARS 2022. I'm John Furrier, host for theCUBE. Great event, machine learning, automation, robotics, space, that's MARS. It's part of the re-series of events, re:Invent's the big event at the end of the year, re:Inforce, security, re:MARS, really intersection of the future of space, industrial, automation, which is very heavily DevOps machine learning, of course, machine learning, which is AI. We have Luis Ceze here, who's the CEO co-founder of OctoML. Welcome to theCUBE. >> Thank you very much for having me in the show, John. >> So we've been following you guys. You guys are a growing startup funded by Madrona Venture Capital, one of your backers. You guys are here at the show. This is a, I would say small show relative what it's going to be, but a lot of robotics, a lot of space, a lot of industrial kind of edge, but machine learning is the centerpiece of this trend. You guys are in the middle of it. Tell us your story. >> Absolutely, yeah. So our mission is to make machine learning sustainable and accessible to everyone. So I say sustainable because it means we're going to make it faster and more efficient. You know, use less human effort, and accessible to everyone, accessible to as many developers as possible, and also accessible in any device. So, we started from an open source project that began at University of Washington, where I'm a professor there. And several of the co-founders were PhD students there. We started with this open source project called Apache TVM that had actually contributions and collaborations from Amazon and a bunch of other big tech companies. And that allows you to get a machine learning model and run on any hardware, like run on CPUs, GPUs, various GPUs, accelerators, and so on. It was the kernel of our company and the project's been around for about six years or so. Company is about three years old. And we grew from Apache TVM into a whole platform that essentially supports any model on any hardware cloud and edge. >> So is the thesis that, when it first started, that you want to be agnostic on platform? >> Agnostic on hardware, that's right. >> Hardware, hardware. >> Yeah. >> What was it like back then? What kind of hardware were you talking about back then? Cause a lot's changed, certainly on the silicon side. >> Luis: Absolutely, yeah. >> So take me through the journey, 'cause I could see the progression. I'm connecting the dots here. >> So once upon a time, yeah, no... (both chuckling) >> I walked in the snow with my bare feet. >> You have to be careful because if you wake up the professor in me, then you're going to be here for two hours, you know. >> Fast forward. >> The average version here is that, clearly machine learning has shown to actually solve real interesting, high value problems. And where machine learning runs in the end, it becomes code that runs on different hardware, right? And when we started Apache TVM, which stands for tensor virtual machine, at that time it was just beginning to start using GPUs for machine learning, we already saw that, with a bunch of machine learning models popping up and CPUs and GPU's starting to be used for machine learning, it was clear that it come opportunity to run on everywhere. >> And GPU's were coming fast. >> GPUs were coming and huge diversity of CPUs, of GPU's and accelerators now, and the ecosystem and the system software that maps models to hardware is still very fragmented today. So hardware vendors have their own specific stacks. So Nvidia has its own software stack, and so does Intel, AMD. And honestly, I mean, I hope I'm not being, you know, too controversial here to say that it kind of of looks like the mainframe era. We had tight coupling between hardware and software. You know, if you bought IBM hardware, you had to buy IBM OS and IBM database, IBM applications, it all tightly coupled. And if you want to use IBM software, you had to buy IBM hardware. So that's kind of like what machine learning systems look like today. If you buy a certain big name GPU, you've got to use their software. Even if you use their software, which is pretty good, you have to buy their GPUs, right? So, but you know, we wanted to help peel away the model and the software infrastructure from the hardware to give people choice, ability to run the models where it best suit them. Right? So that includes picking the best instance in the cloud, that's going to give you the right, you know, cost properties, performance properties, or might want to run it on the edge. You might run it on an accelerator. >> What year was that roughly, when you were going this? >> We started that project in 2015, 2016 >> Yeah. So that was pre-conventional wisdom. I think TensorFlow wasn't even around yet. >> Luis: No, it wasn't. >> It was, I'm thinking like 2017 or so. >> Luis: Right. So that was the beginning of, okay, this is opportunity. AWS, I don't think they had released some of the nitro stuff that the Hamilton was working on. So, they were already kind of going that way. It's kind of like converging. >> Luis: Yeah. >> The space was happening, exploding. >> Right. And the way that was dealt with, and to this day, you know, to a large extent as well is by backing machine learning models with a bunch of hardware specific libraries. And we were some of the first ones to say, like, know what, let's take a compilation approach, take a model and compile it to very efficient code for that specific hardware. And what underpins all of that is using machine learning for machine learning code optimization. Right? But it was way back when. We can talk about where we are today. >> No, let's fast forward. >> That's the beginning of the open source project. >> But that was a fundamental belief, worldview there. I mean, you have a world real view that was logical when you compare to the mainframe, but not obvious to the machine learning community. Okay, good call, check. Now let's fast forward, okay. Evolution, we'll go through the speed of the years. More chips are coming, you got GPUs, and seeing what's going on in AWS. Wow! Now it's booming. Now I got unlimited processors, I got silicon on chips, I got, everywhere >> Yeah. And what's interesting is that the ecosystem got even more complex, in fact. Because now you have, there's a cross product between machine learning models, frameworks like TensorFlow, PyTorch, Keras, and like that and so on, and then hardware targets. So how do you navigate that? What we want here, our vision is to say, folks should focus, people should focus on making the machine learning models do what they want to do that solves a value, like solves a problem of high value to them. Right? So another deployment should be completely automatic. Today, it's very, very manual to a large extent. So once you're serious about deploying machine learning model, you got a good understanding where you're going to deploy it, how you're going to deploy it, and then, you know, pick out the right libraries and compilers, and we automated the whole thing in our platform. This is why you see the tagline, the booth is right there, like bringing DevOps agility for machine learning, because our mission is to make that fully transparent. >> Well, I think that, first of all, I use that line here, cause I'm looking at it here on live on camera. People can't see, but it's like, I use it on a couple couple of my interviews because the word agility is very interesting because that's kind of the test on any kind of approach these days. Agility could be, and I talked to the robotics guys, just having their product be more agile. I talked to Pepsi here just before you came on, they had this large scale data environment because they built an architecture, but that fostered agility. So again, this is an architectural concept, it's a systems' view of agility being the output, and removing dependencies, which I think what you guys were trying to do. >> Only part of what we do. Right? So agility means a bunch of things. First, you know-- >> Yeah explain. >> Today it takes a couple months to get a model from, when the model's ready, to production, why not turn that in two hours. Agile, literally, physically agile, in terms of walk off time. Right? And then the other thing is give you flexibility to choose where your model should run. So, in our deployment, between the demo and the platform expansion that we announced yesterday, you know, we give the ability of getting your model and, you know, get it compiled, get it optimized for any instance in the cloud and automatically move it around. Today, that's not the case. You have to pick one instance and that's what you do. And then you might auto scale with that one instance. So we give the agility of actually running and scaling the model the way you want, and the way it gives you the right SLAs. >> Yeah, I think Swami was mentioning that, not specifically that use case for you, but that use case generally, that scale being moving things around, making them faster, not having to do that integration work. >> Scale, and run the models where they need to run. Like some day you want to have a large scale deployment in the cloud. You're going to have models in the edge for various reasons because speed of light is limited. We cannot make lights faster. So, you know, got to have some, that's a physics there you cannot change. There's privacy reasons. You want to keep data locally, not send it around to run the model locally. So anyways, and giving the flexibility. >> Let me jump in real quick. I want to ask this specific question because you made me think of something. So we're just having a data mesh conversation. And one of the comments that's come out of a few of these data as code conversations is data's the product now. So if you can move data to the edge, which everyone's talking about, you know, why move data if you don't have to, but I can move a machine learning algorithm to the edge. Cause it's costly to move data. I can move computer, everyone knows that. But now I can move machine learning to anywhere else and not worry about integrating on the fly. So the model is the code. >> It is the product. >> Yeah. And since you said, the model is the code, okay, now we're talking even more here. So machine learning models today are not treated as code, by the way. So do not have any of the typical properties of code that you can, whenever you write a piece of code, you run a code, you don't know, you don't even think what is a CPU, we don't think where it runs, what kind of CPU it runs, what kind of instance it runs. But with machine learning model, you do. So what we are doing and created this fully transparent automated way of allowing you to treat your machine learning models if you were a regular function that you call and then a function could run anywhere. >> Yeah. >> Right. >> That's why-- >> That's better. >> Bringing DevOps agility-- >> That's better. >> Yeah. And you can use existing-- >> That's better, because I can run it on the Artemis too, in space. >> You could, yeah. >> If they have the hardware. (both laugh) >> And that allows you to run your existing, continue to use your existing DevOps infrastructure and your existing people. >> So I have to ask you, cause since you're a professor, this is like a masterclass on theCube. Thank you for coming on. Professor. (Luis laughing) I'm a hardware guy. I'm building hardware for Boston Dynamics, Spot, the dog, that's the diversity in hardware, it's tends to be purpose driven. I got a spaceship, I'm going to have hardware on there. >> Luis: Right. >> It's generally viewed in the community here, that everyone I talk to and other communities, open source is going to drive all software. That's a check. But the scale and integration is super important. And they're also recognizing that hardware is really about the software. And they even said on stage, here. Hardware is not about the hardware, it's about the software. So if you believe that to be true, then your model checks all the boxes. Are people getting this? >> I think they're starting to. Here is why, right. A lot of companies that were hardware first, that thought about software too late, aren't making it. Right? There's a large number of hardware companies, AI chip companies that aren't making it. Probably some of them that won't make it, unfortunately just because they started thinking about software too late. I'm so glad to see a lot of the early, I hope I'm not just doing our own horn here, but Apache TVM, the infrastructure that we built to map models to different hardware, it's very flexible. So we see a lot of emerging chip companies like SiMa.ai's been doing fantastic work, and they use Apache TVM to map algorithms to their hardware. And there's a bunch of others that are also using Apache TVM. That's because you have, you know, an opening infrastructure that keeps it up to date with all the machine learning frameworks and models and allows you to extend to the chips that you want. So these companies pay attention that early, gives them a much higher fighting chance, I'd say. >> Well, first of all, not only are you backable by the VCs cause you have pedigree, you're a professor, you're smart, and you get good recruiting-- >> Luis: I don't know about the smart part. >> And you get good recruiting for PhDs out of University of Washington, which is not too shabby computer science department. But they want to make money. The VCs want to make money. >> Right. >> So you have to make money. So what's the pitch? What's the business model? >> Yeah. Absolutely. >> Share us what you're thinking there. >> Yeah. The value of using our solution is shorter time to value for your model from months to hours. Second, you shrink operator, op-packs, because you don't need a specialized expensive team. Talk about expensive, expensive engineers who can understand machine learning hardware and software engineering to deploy models. You don't need those teams if you use this automated solution, right? Then you reduce that. And also, in the process of actually getting a model and getting specialized to the hardware, making hardware aware, we're talking about a very significant performance improvement that leads to lower cost of deployment in the cloud. We're talking about very significant reduction in costs in cloud deployment. And also enabling new applications on the edge that weren't possible before. It creates, you know, latent value opportunities. Right? So, that's the high level value pitch. But how do we make money? Well, we charge for access to the platform. Right? >> Usage. Consumption. >> Yeah, and value based. Yeah, so it's consumption and value based. So depends on the scale of the deployment. If you're going to deploy machine learning model at a larger scale, chances are that it produces a lot of value. So then we'll capture some of that value in our pricing scale. >> So, you have direct sales force then to work those deals. >> Exactly. >> Got it. How many customers do you have? Just curious. >> So we started, the SaaS platform just launched now. So we started onboarding customers. We've been building this for a while. We have a bunch of, you know, partners that we can talk about openly, like, you know, revenue generating partners, that's fair to say. We work closely with Qualcomm to enable Snapdragon on TVM and hence our platform. We're close with AMD as well, enabling AMD hardware on the platform. We've been working closely with two hyperscaler cloud providers that-- >> I wonder who they are. >> I don't know who they are, right. >> Both start with the letter A. >> And they're both here, right. What is that? >> They both start with the letter A. >> Oh, that's right. >> I won't give it away. (laughing) >> Don't give it away. >> One has three, one has four. (both laugh) >> I'm guessing, by the way. >> Then we have customers in the, actually, early customers have been using the platform from the beginning in the consumer electronics space, in Japan, you know, self driving car technology, as well. As well as some AI first companies that actually, whose core value, the core business come from AI models. >> So, serious, serious customers. They got deep tech chops. They're integrating, they see this as a strategic part of their architecture. >> That's what I call AI native, exactly. But now there's, we have several enterprise customers in line now, we've been talking to. Of course, because now we launched the platform, now we started onboarding and exploring how we're going to serve it to these customers. But it's pretty clear that our technology can solve a lot of other pain points right now. And we're going to work with them as early customers to go and refine them. >> So, do you sell to the little guys, like us? Will we be customers if we wanted to be? >> You could, absolutely, yeah. >> What we have to do, have machine learning folks on staff? >> So, here's what you're going to have to do. Since you can see the booth, others can't. No, but they can certainly, you can try our demo. >> OctoML. >> And you should look at the transparent AI app that's compiled and optimized with our flow, and deployed and built with our flow. That allows you to get your image and do style transfer. You know, you can get you and a pineapple and see how you look like with a pineapple texture. >> We got a lot of transcript and video data. >> Right. Yeah. Right, exactly. So, you can use that. Then there's a very clear-- >> But I could use it. You're not blocking me from using it. Everyone's, it's pretty much democratized. >> You can try the demo, and then you can request access to the platform. >> But you get a lot of more serious deeper customers. But you can serve anybody, what you're saying. >> Luis: We can serve anybody, yeah. >> All right, so what's the vision going forward? Let me ask this. When did people start getting the epiphany of removing the machine learning from the hardware? Was it recently, a couple years ago? >> Well, on the research side, we helped start that trend a while ago. I don't need to repeat that. But I think the vision that's important here, I want the audience here to take away is that, there's a lot of progress being made in creating machine learning models. So, there's fantastic tools to deal with training data, and creating the models, and so on. And now there's a bunch of models that can solve real problems there. The question is, how do you very easily integrate that into your intelligent applications? Madrona Venture Group has been very vocal and investing heavily in intelligent applications both and user applications as well as enablers. So we say an enable of that because it's so easy to use our flow to get a model integrated into your application. Now, any regular software developer can integrate that. And that's just the beginning, right? Because, you know, now we have CI/CD integration to keep your models up to date, to continue to integrate, and then there's more downstream support for other features that you normally have in regular software development. >> I've been thinking about this for a long, long, time. And I think this whole code, no one thinks about code. Like, I write code, I'm deploying it. I think this idea of machine learning as code independent of other dependencies is really amazing. It's so obvious now that you say it. What's the choices now? Let's just say that, I buy it, I love it, I'm using it. Now what do I got to do if I want to deploy it? Do I have to pick processors? Are there verified platforms that you support? Is there a short list? Is there every piece of hardware? >> We actually can help you. I hope we're not saying we can do everything in the world here, but we can help you with that. So, here's how. When you have them all in the platform you can actually see how this model runs on any instance of any cloud, by the way. So we support all the three major cloud providers. And then you can make decisions. For example, if you care about latency, your model has to run on, at most 50 milliseconds, because you're going to have interactivity. And then, after that, you don't care if it's faster. All you care is that, is it going to run cheap enough. So we can help you navigate. And also going to make it automatic. >> It's like tire kicking in the dealer showroom. >> Right. >> You can test everything out, you can see the simulation. Are they simulations, or are they real tests? >> Oh, no, we run all in real hardware. So, we have, as I said, we support any instances of any of the major clouds. We actually run on the cloud. But we also support a select number of edge devices today, like ARMs and Nvidia Jetsons. And we have the OctoML cloud, which is a bunch of racks with a bunch Raspberry Pis and Nvidia Jetsons, and very soon, a bunch of mobile phones there too that can actually run the real hardware, and validate it, and test it out, so you can see that your model runs performant and economically enough in the cloud. And it can run on the edge devices-- >> You're a machine learning as a service. Would that be an accurate? >> That's part of it, because we're not doing the machine learning model itself. You come with a model and we make it deployable and make it ready to deploy. So, here's why it's important. Let me try. There's a large number of really interesting companies that do API models, as in API as a service. You have an NLP model, you have computer vision models, where you call an API and then point in the cloud. You send an image and you got a description, for example. But it is using a third party. Now, if you want to have your model on your infrastructure but having the same convenience as an API you can use our service. So, today, chances are that, if you have a model that you know that you want to do, there might not be an API for it, we actually automatically create the API for you. >> Okay, so that's why I get the DevOps agility for machine learning is a better description. Cause it's not, you're not providing the service. You're providing the service of deploying it like DevOps infrastructure as code. You're now ML as code. >> It's your model, your API, your infrastructure, but all of the convenience of having it ready to go, fully automatic, hands off. >> Cause I think what's interesting about this is that it brings the craftsmanship back to machine learning. Cause it's a craft. I mean, let's face it. >> Yeah. I want human brains, which are very precious resources, to focus on building those models, that is going to solve business problems. I don't want these very smart human brains figuring out how to scrub this into actually getting run the right way. This should be automatic. That's why we use machine learning, for machine learning to solve that. >> Here's an idea for you. We should write a book called, The Lean Machine Learning. Cause the lean startup was all about DevOps. >> Luis: We call machine leaning. No, that's not it going to work. (laughs) >> Remember when iteration was the big mantra. Oh, yeah, iterate. You know, that was from DevOps. >> Yeah, that's right. >> This code allowed for standing up stuff fast, double down, we all know the history, what it turned out. That was a good value for developers. >> I could really agree. If you don't mind me building on that point. You know, something we see as OctoML, but we also see at Madrona as well. Seeing that there's a trend towards best in breed for each one of the stages of getting a model deployed. From the data aspect of creating the data, and then to the model creation aspect, to the model deployment, and even model monitoring. Right? We develop integrations with all the major pieces of the ecosystem, such that you can integrate, say with model monitoring to go and monitor how a model is doing. Just like you monitor how code is doing in deployment in the cloud. >> It's evolution. I think it's a great step. And again, I love the analogy to the mainstream. I lived during those days. I remember the monolithic propriety, and then, you know, OSI model kind of blew it. But that OSI stack never went full stack, and it only stopped at TCP/IP. So, I think the same thing's going on here. You see some scalability around it to try to uncouple it, free it. >> Absolutely. And sustainability and accessibility to make it run faster and make it run on any deice that you want by any developer. So, that's the tagline. >> Luis Ceze, thanks for coming on. Professor. >> Thank you. >> I didn't know you were a professor. That's great to have you on. It was a masterclass in DevOps agility for machine learning. Thanks for coming on. Appreciate it. >> Thank you very much. Thank you. >> Congratulations, again. All right. OctoML here on theCube. Really important. Uncoupling the machine learning from the hardware specifically. That's only going to make space faster and safer, and more reliable. And that's where the whole theme of re:MARS is. Let's see how they fit in. I'm John for theCube. Thanks for watching. More coverage after this short break. >> Luis: Thank you. (gentle music)

(bright music) >> Hey, welcome back everyone, theCUBE coverage here at AWS re:Mars 2022. I'm John, your host of the theCUBE. re:Mars, part of the three re big events, re:Invent is the big one, re:Inforce the security, re:MARS is the confluence of industrial space, of automation, robotics and machine learning. Got a great guest here, Muhammad Faisal senior consultant solutions architect at Capgemini. Welcome to theCUBE. Thanks for coming on. >> Thank you. >> So we, you just we're hearing the classes we had with the professor from Okta ML from Washington. So he's in the weeds on machine learning. He's down getting dirty with all the hardcore, uncoupling it from hardware. Machine learning has gone really super nova in the past couple years. And this show points to the tipping point where machine learning's driving space, it's driving robotics industrial edge at unprecedented rates. So it's kind of moving from the old I don't want to say old, couple years ago and the legacy AI, I mean, old school AI is kind of the same new school with a twist it's just modernized and has faster, cheaper, smaller chips. >> Yeah. I mean, but there is a change also in the way it's working. So you had the classical AI, where you are detecting something and then you're making an action. You are perceiving something, making an action, you're detecting something, and you're assuming something that has been perceived. But now we are moving towards more deeper learning, deep. So AI, where you have to train your model to do things or to detect things and hope that it will work. And there's like, of course, a lot of research going on into explainable AI to help facilitate that. But that's where the challenges come into play. >> Well, Muhammad , first let's take, what do you do over there? Talk about your role specifically. You're doing a lot of student architecting around AI machine learning. What's your role? What's your focus. >> Yeah. So we basically are working in automotive to help OEMs and tier-one suppliers validate ADAS functions that they're working on. So advanced driving assistance systems, there are many levels that are, are when we talk about it. So it can be something simple, like, you know, a blind spot detection, just a warning function. And it goes all the way. So SAE so- >> So there's like the easy stuff and then the hard stuff. >> Muhammad : Exactly. >> Yeah. >> That's what you're getting at. >> Yeah. Yeah. And, and the easy stuff you can test validate quite easily because if you get it wrong. >> Yeah. >> The impact is not that high. The complicated stuff, if you have it wrong, then that can be very dangerous. (John laughs) >> Well, I got to say the automotive one was one was that are so fascinating because it's been so archaic and just in the past recent years, and Tesla's the poster child for this. You see that you go, oh my God, I love that car. I want to have a software driven car. And it's amazing. And I don't get a Tesla on now because that's, it's more like I should have gotten it earlier. Now I'm going to just hold my ground. >> Everyone has- >> Everyone's got it in Palo Alto. I'm not going to get another car, no way. So, but you're starting to see a lot of the other manufacturers, just in the past five years, they're leveling up. It may not be as cool and sexy as the Tesla, but it's, they're there. And so what are they dealing with when they talk about data and AI? What's the, what's some of the challenges that you're seeing that they're grappling with in terms of getting things integrated, developing pipelines, R and D, they wrangling data. Take us through some of the things. >> Muhammad: I mean, like when I think about the challenges that autonomous or the automakers are facing, I can think of three big ones. So first, is the amount of data they need to do their training. And more importantly, the validation. So we are talking about petabytes or hundred of petabytes of data that has to be analyzed, validated, annotated. So labeling to create gen, ground truth processed, reprocessed many times with every creation of a new software. So that is a lot of data, a lot of computational power. And you need to ensure that all of the processing, all of handling of the data allows you complete transparency of what is happening to the data, as well as complete traceability. So your, for home allocations, so approval process for these functions so that they can be released in cars that can be used on public roads. You need to have traceability. Like you can, you are supposed to be able to reproduce the data to validate your work that was done. So you can, >> John: Yeah >> Like, prove that your function is successful or working as expected. So this, the big data is the first challenge. I see that all the automotive makers are tackling. The second big one I see is understanding how much testing is enough. So with AI or with classical approach, you have certain requirements, how a function is supposed to work. You can test that with some test cases based on your architecture, and you have a successful or failed result. With deep learning, it gets more complicated. >> John: What are they doing with deep learning? Give an example of some of things. >> I mean, so you are, you need to then start thinking about statistics that I will test enough data with like a failure rate of potentially like 0.0, 0.1%. How much data do I need to test to make sure that I am achieving that rate. So then we are talking about, in terms of statistics, which requires a lot of data, because the failure rate that we want to have is so low. And it's not only like, failure in terms of that something is always detected, and if it's there, but it's also having like, a low false positive rate. So you are only detecting objects which are there and not like, phantom objects. >> What's some of the trends you're seeing across the client base, in terms of the patterns that they're all kind of, what, where's the state of their mindset and position with AI and some of the work they're doing, are they feeling, you feel like they're all crossed over across the chasm so to speak, in terms of executing, are they still in experimental mode in driving with the full capabilities is conservative or is it progressive? >> Muhammad: I mean, it's a mixture of both. So I'm in German automotive where I'm from, there is for functions, which are more complicated ones. There's definitely hesitancy to release them too early in the car, unless we are sure that they are safe. But of course, for functions which are assisting the drivers everyday usage they are widely available. Like one of the things like, so when we talk about this complex function. >> John: Highly available or available? >> Muhammad: I would say highly available. >> Higher? Is that higher availability and highly available. >> Okay. Yeah. (both laughing) >> Yeah, so. >> I know there's a distinction. >> Yeah. I mean >> I bring up as a joke cuz of the Jedi contract. (Muhammad laughs) >> I mean, in like, our architecture. So when we are developing our solution, high availability is one of our requirements. It is highly available, but the ADAS functions are now available in more and more cars. >> John: Well, latency, man. I mean, it's kind of a joke of storage, but it's a storage joke, but you know, it's latency, you got it, okay. (Muhammad laughs) But these are decisions that have to be made. >> Muhammad: They... >> I mean. >> Muhammad: I mean, they are still being made. >> So I mean, we are... >> John: Good. >> We haven't reached like, level five, which is the highest level of autonomous driving yet on public roads. >> John: That's hard. That's hard to do. >> Yeah. And I mean, the biggest difference, like, as you go above these levels is in terms of availability. So are they these functions? >> John: Yeah. >> Can they handle all possible scenarios or are they only available in certain scenarios? And of course the responsibility. So, it's, in the end, so with Tesla, you would be like, if you had a one you would be the person who is in control or responsible to monitor it. >> John: Yeah. But as we go >> John: Actually the reason I don't have a Tesla all my family would want one. I don't want to get anyone a Tesla. >> But I mean, but that's the sort the liabilities is currently on you, if like, you're not monitoring. >> Allright, so, talk about AWS, the relationship that Capgemini has with AWS, obviously, the partnerships there, you're here and this show is really a commitment to, this is a future to me, this is the future. >> Muhammad: Yeah. >> This is it. All right here, industrial, innovation's going to come massive. Back-office cloud, done deal. Data centers, hybrid somewhat multi-cloud, I guess. But hybrid is a steady state in the back-office cloud, game over. >> Muhammad: Yeah. >> Amazon, Azure, Google, Alibaba done. So super clouds underneath. Great. This is a digital transformation in the industrial area. >> Muhammad: Yeah. >> This is the big thing. What's your relationship with AWS >> Muhammad: So, as I mentioned, the first challenge, data, like, we have so much data, so much computational power and it's not something that is always needed. You need it like on demand. And this is where like a hyperscale or cloud provider, like AWS, can be the key to achieve, like, the higher, the acceleration that we are providing to our customers using our technology built on top of AWS services. We did a breakout session, this during re:MARS, where we demonstrated a couple of small tools that we have developed out of our offering. One of them was ability to stream data from the vehicle that is collecting data worldwide. So during the day when we did it from Vegas, driving on the strip, as well as from Germany, and while we are while this data is uploaded, it's at the same time real time anonymized to make sure it you're privacy aligned with the, the data privacy >> Of course. Yeah. That's hard to do right there. >> Yeah. And so the faces are blurred. The licenses are blurred. We also, then at the same time can run object detection. So we have real time monitoring of what our feed is doing worldwide. And... >> John: Do you, just curious, do you do that blurring? Is that part of a managed service, you call an API or is that built into the go? >> Muhammad: So from like part of our DSV, we have many different service offerings, so data production, data test strategy orchestration. So part of data production is worldwide data collection. And we can then also offer data management services, which include then anonymization data, quality check. >> John: And that's service you provide. >> Yeah. >> To the customer. Okay. Got it. Okay. >> So of course, like, in collaboration with the customer, so our like, platform is very modular. Microservices based the idea being if the customer already has a good ML model for anonymization, we can plug it into our platform, running on AWS. If they want to use it, we can develop one or we can use one of our existing ones or something off the shelf or like any other supplier can provide one as well. And we all integrate. >> So you are, you're tight with Amazon web services in terms of your cloud, your service. It's a cloud. >> Yeah. >> It's so Capgemini Super Cloud, basically. >> Exactly. >> Okay. So this we call we call it Super Cloud, we made that a thing and re:Invent Charles Fitzgerald would disagree but we will debate him. It's a Super Cloud, but okay. You got your Super Cloud. What's the coolest thing that you think you're doing right now that people should pay attention to. >> I mean, the cool thing that we are currently working on, so from the keynote today, we talked about also synthetic data for validation. >> John: Now That was phenomenal. So that was phenomenal. >> We are working on digital twin creation. So we are capturing data in real world creating a virtual identity of it. And that allows you the freedom to create multiple scenarios out of it. So that's also something where we are using machine learning to determine what are the parameters you need to change between, or so, you have one scenario, such as like, the cut-in scenario and you can change. >> John: So what scenario? >> A cut-in scenario. So someone is cutting in front of you or overtake scenario. And so, I mean, in real world, someone will do it in probably a nicer way, but of course, in, it is possible, at some point. >> Cognition to the cars. >> Yeah. >> It comes up as a vehicle. >> I mean, at some point some might, someone would be very aggressive with it. We might not record it. >> You might be able to predict too. I mean, the predictions, you could say this guy's weaving, he's a potential candidate. >> It it is possible. Yes. But I mean, but to, >> That's a future scenario. >> Ensure that we are testing these scenarios, we can translate a real world scenario into a digital world, change the parameters. So the distance between those two is different and use ML. So machine learning to change these parameters. So this is exciting. And the other thing we are... >> That is pretty cool. I will admit that's very cool. >> Yeah. Yeah. The other thing we like are trying to do is reduce the cost for the customer in the end. So we are collecting petabytes of data. Every time they make updates to the software, they have to re-simulate it or replay this data, so that they can- >> Petabytes? >> Petabytes of data. And, and physically sometimes on a physical hardware in loop device. And then this >> That's called a really heavy edge. You got to move, you don't want to be moving that around the Amazon cloud. >> Yeah. That that's, that's the challenge. And once we have replayed this or re-simulated it. we still have to calculate the KPIs out of it. And what we are trying to do is optimize this test orchestration, so that we are minimizing the REAP simulation. So you don't want the data to be going to the edge, >> Yeah. >> Unnecessarily. And once we get this data back to optimize the way we are doing the calculation, so you're not calculating- >> There's a huge data, integrity management. >> Muhammad: Yeah. >> New kind of thing going on here, it's kind of is it new or is it? >> Muhammad: I mean, it's- >> Sounds new to me. >> The scale is new, so- >> Okay, got it. >> The management of the data, having the whole traceability, that has been in automotive. So also Capgemini involved in aerospace. So in aerospace. >> Yeah. >> Having this kind of high, this validation be very strictly monitored is norm, but now we have to think about how to do it on this large scale. And that's why, like, I think that's the biggest challenge and hopefully what we are trying to, yeah, solve with our DSV offering. >> All right, Muhammad, thanks for coming on theCUBE. I really appreciate it. Great way to close out re:MARS, our last interview our the show. Thanks for coming on. Appreciate your time. >> I mean like just one last comment, like, so I think in automotive, like, so part of the automation the future is quite exciting, and I think that's where like- >> John: Yeah. >> It's, we have to be hopeful that like- >> John: Well, the show is all about hope. I mean, you had, you had space, moon habitat, you had climate change, potential solutions. You have new functionality that we've been waiting for. And, you know, I've watch every episode of Star Trek and SkyNet and kind of SkyNet going on air. >> The robots. >> Robots running cubes, robot cubes host someday. >> Yeah. >> You never know. Yeah. Thanks for coming on. Appreciate it. >> Thank you. Okay. That's theCUBE here. Wrapping up re:MARS. I'm John Furrier You're watching theCUBE, stay with us for the next event. Next time. Thanks for watching. (upbeat music)

(upbeat music) >> Welcome back, everyone. It's theCUBE's coverage here in Las Vegas. I'm John Furrier, your host of theCUBE with re:MARS. Amazon re:MARS stands for machine learning, automation, robotics, and space. Lot of great content, accomplishment. AI meets meets robotics and space, industrial IoT, all things data. And we've got two great guests here to unpack the AI side of it. Manoj Suvarna, Managing Director at AI Ecosystem at Deloitte and Arte Merritt, Conversational AI Lead at AWS. Manoj, it's great to see you CUBE alumni. Art, welcome to theCUBE. >> Thanks for having me. I appreciate it. >> So AI's the big theme. Actually, the big disconnect in the industry has been the industrial OT versus IT, and that's happening. Now you've got space and robotics meets what we know is machine learning and AI which we've been covering. This is the confluence of the new IoT market. >> It absolutely is. >> What's your opinion on that? >> Yeah, so actually it's taking IoT beyond the art of possible. One area that we have been working very closely with AWS. We're strategic alliance with them. And for the past six years, we have been investing a lot in transformations. Transformation as it relate to the cloud, transformation as it relate to data modernization. The new edge is essentially on AI and machine learning. And just this week, we announced a new solution which is more focused around enhancing contact center intelligence. So think about the edge of the contact center, where we all have experiences around dealing with customer service and how to really take that to the next level, challenges that clients are facing in every part of that business. So clearly. >> Well, Conversational AI is a good topic. Talk about the relationship with Deloitte and Amazon for a second around AI because you guys have some great projects going on right now. That's well ahead of the curve on solving the scale problem 'cause there's a scale and problem, practical problem and then scale. What's the relationship with Amazon and Deloitte? >> We have a great alliance and relationship. Deloitte brings that expertise to help folks build high quality, highly effective conversational AI and enterprises are implementing these solutions to really try to improve the overall customer experience. So they want to help agents improve productivity, gain insights into the reasons why folks are calling but it's really to provide that better user experience being available 24/7 on channels users prefer to interact. And the solutions that Deloitte is building are highly advanced, super exciting. Like when we show demos of them to potential customers, the eyes light up and they want those solutions. >> John: Give an example when their eyes light up. What are you showing there? >> One solution, it's called multimodal interfaces. So what this is, is when you're call into like a voice IVR, Deloitte's solution will send the folks say a mobile app or a website. So the person can interact with both the phone touching on the screen and the voice and it's all kept in sync. So imagine you call the doctor's office or say I was calling a airline and I want to change my flight or sorry, change the seat. If they were to say, seat 20D is available. Well, I don't know what that means, but if you see the map while you're talking, you can say, oh, 20D is the aisle. I'm going to select that. So Deloitte's doing those kind of experiences. It's incredible. >> Manoj, this is where the magic comes into play when you bring data together and you have integration like this. Asynchronously or synchronously, it's all coming together. You have different platforms, phone, voice, silo databases potentially, the old way. Now, the new ways integrating. What makes it all work? What's the key to success? >> Yeah, it's certainly not a trivial feat. Bringing together all of these ecosystems of relationships, technologies all put together. We cannot do it alone. This is where we partner with AWS with some of our other partners like Salesforce and OneReach and really trying to bring a symphony of some of these solutions to bear. When you think about, going back to the example of contact center, the challenges that the pandemic posed in the last couple of years was the fact that who's a humongous rise in volume of number of calls. You can imagine people calling in asking for all kinds of different things, whether it's airlines whether it is doctor's office and retail. And then couple with that is the fact that there's the labor shortage. And how do you train agents to get them to be productive enough to be able to address hundreds or thousands of these calls? And so that's where we have been starting to, we have invested in those solutions bringing those technologies together to address real client problems, not just slideware but actual production environments. And that's where we launched this solution called TrueServe as of this week, which is really a multimodal solution that is built with preconceived notions of technologies and libraries where we can then be industry agnostic and be able to deliver those experiences to our clients based on whatever vertical or industry they're in. >> Take me through the client's engagement here because I can imagine they want to get a practical solution. They're going to want to have it up and running, not like a just a chatbot, but like they completely integrated system. What's the challenge and what's the outcome first set of milestones that you see that they do first? Do they just get the data together? Are they deploying a software solution? What's the use cases? >> There's a couple different use cases. We see there's the self-service component that we're talking about with the chatbots or voice IVR solutions. There's also use cases for helping the agents, so real-time agent assist. So you call into a contact center, it's transcribed in real time, run through some sort of knowledge base to give the agents possible answers to help the user out, tying in, say the Salesforce data, CRM data, to know more about the user. Like if I was to call the airline, it's going to say, "Are you calling about your flight to San Francisco tomorrow?" It knows who I am. It leverages that stuff. And then the key piece is the analytics knowing why folks are calling, not just your metrics around, length of calls or deflections, but what were the reasons people were calling in because you can use that data to improve your underlying products or services. These are the things that enterprise are looking for and this is where someone like Deloitte comes in, brings that expertise, speeds up the time to market and really helps the customers. >> Manoj, what was the solution you mentioned that you guys announced? >> Yeah, so this is called Deloitte TrueServe. And essentially, it's a combination of multiple different solutions combinations from AWS, from Salesforce, from OneReach. All put together with our joint engineering and really delivering that capability. Enhancing on that is the analytics component, which is really critical, especially because when you think about the average contact center, less than 10% of the data gets analyzed today, and how do you then extract value out of that data and be able to deliver business outcomes. >> I was just talking to some of the other day about Zoom. Everyone records their zoom meetings, and no one watches them. I mean, who's going to wade through that. Call center is even more high volume. We're talking about massive data. And so will you guys automate that? Do you go through every single piece of data, every call and bring it down? Is that how it works? >> Go ahead. >> There's just some of the things you can do. Analyze the calls for common themes, like figuring out like topic modeling, what are the reasons people are calling in. Summarizing that stuff so you can see what those underlying issues are. And so that could be, like I was mentioning, improving the product or service. It could also be for helping train the agents. So here's how to answer that question. And it could even be reinforcing positive experiences maybe an agent had a particular great call and that could be a reference for other folks. >> Yeah, and also during the conversation, when you think about within 60 to 90 seconds, how do you identify the intonation, the sentiments of the client customer calling in and be able to respond in real time for the challenges that they might be facing and the ability to authenticate the customer at the same time be able to respond to them. I think that is the advancements that we are seeing in the market. >> I think also your point about the data having residual values also excellent because this is a long tail of value in this data, like for predictions and stuff. So NASA was just on before you guys came on, talking about the Artemis project and all the missions and they have to run massive amounts of simulations. And this is where I've kind of seen the dots connect here. You can run with AI, run all the heavy lifting without human touching it to get that first ingestion or analysis, and then iterating on the data based upon what else happens. >> Manoj: Absolutely. >> This is now the new normal, right? Is this? >> It is. And it's transverse towards across multiple domains. So the example we gave you was around Conversational AI. We're now looking at that for doing predictive analytics. Those are some examples that we are doing jointly with AWS SageMaker. We are working on things like computer vision with some of the capabilities and what computer vision has to offer. And so when you think about the continuum of possibilities of what we can bring together from a tools, technology, services perspective, really the sky is the limit in terms of delivering these real experiences to our clients. >> So take me through a customer. Pretending I'm a customer, I get it. I got to do this. It's a competitive advantage. What are the outcomes that they are envisioning? What are some of the patterns you're seeing with customers? What outcomes are they expecting and what kind of high level upside you see them envisioning coming out of the data? >> So when you think about the CxOs today and the board, a lot of them are thinking about, okay, how do you build more efficiency in those system? How do you enable a technology or solution for them to not only increase their top line but as well as their bottom line? How do you enhance the customer experience, which in this case is spot on because when you think about, when customers go repeat to a vendor, it's based on quality, it's based on price. Customer experience is now topping that where your first experience, whether it's through a chat or a virtual assistant or a phone call is going to determine the longevity of that customer with you as a vendor. And so clearly, when you think about how clients are becoming AI fuel, this is where we are bringing in new technologies, new solutions to really push the art to the limit and the art of possible. >> You got a playbook too to do this? >> Yeah, yeah, absolutely. We have done that. And in fact, we are now taking that to the next level up. So something that I've mentioned about this before, which is how do you trust an AI system as it's building up. >> Hold on, I need to plug in. >> Yeah, absolutely. >> I put this here for a reason to remind me. No, but also trust is a big thing. Just put that trustworthy. This is an AI ethics question. >> Arte: It's a big. >> Let's get into it. This is huge. Data's data. Data can be biased from coming in >> Part of it, there are concerns you have to look at the bias in the data. It's also how you communicate through these automated channels, being empathetic, building trust with the customer, being concise in the answers and being accessible to all sorts of different folks and how they might communicate. So it's definitely a big area. >> I mean, you think about just normal life. We all lived situations where we got a text message from a friend or someone close to us where, what the hell, what are you saying? And they had no contextual bad feelings about it or, well, there's misunderstandings 'cause the context isn't there 'cause you're rapid fire them on the subway. I'm riding my bike. I stop and text, okay, I'm okay. Church response could mean I'm busy or I'm angry. Like this is now what you said about empathy. This is now a new dynamic in here. >> Oh, the empathy is huge, especially if you're say a financial institution or building that trust with folks and being empathetic. If someone's reaching out to a contact center, there's a good chance they're upset about something. So you have to take that. >> John: Calm them down first. >> Yeah, and not being like false like platitude kind of things, like really being empathetic, being inclusive in the language. Those are things that you have conversation designers and linguistics folks that really look into that. That's why having domain expertise from folks like Deloitte come in to help with that. 'Cause maybe if you're just building the chat on your own, you might not think of those things. But the folks with the domain expertise will say like, Hey, this is how you script it. It's the power of words, getting that message across clearly. >> The linguistics matter? >> Yeah, yeah. >> It does. >> By vertical too, I mean, you could pick any the tribe, whatever orientation and age, demographics, genders. >> All of those things that we take for granted as a human. When you think about trust, when you think about bias, when you think about ethics, it just gets amplified. Because now you're dealing with millions and millions of data points that may or may not be the right direction in terms of somebody's calling in depending on what age group they're in. Some questions might not be relevant for that age group. Now a human can determine that, but a bot cannot. And so how do you make sure that when you look at this data coming in, how do you build models that are ethically aware of the contextual algorithms and the alignment with it and also enabling that experience to be much enhanced than taking it backwards, and that's really. >> I can imagine it getting better with as people get scaled up a bit 'cause then you're going to have to start having AI to watch the AI at some point, as they say. Where are we in the progress in the industry right now? Because I know there's been a lot of news stories around, ethics and AI and bias and it's a moving train actually, but still problems are going to be solved. Are we at the tipping point yet? Are we still walking in before we crawl or crawling before we walk? I should say, I mean, where are we? >> I think we are in between a crawling or walk phase. And the reason for that is because it varies depending on whether you're regulated industry or unregulated. In the regulated industry, there are compliance regulations requirements, whether it's government whether it's banking, financial institutions where they have to meet Sarbanes-Oxley and all kinds of compliance requirements, whereas an unregulated industry like retail and consumer, it is anybody's gain. And so the reality of it is that there is more of an awareness now. And that's one of the reasons why we've been promoting this jointly with AWS. We have a framework that we have established where there are multiple pillars of trust, bias, privacy, and security that companies and organizations need to think about. Our data scientists, ML engineers need to be familiar with it, but because while they're super great in terms of model building and development, when it comes to the business, when it comes to the client or a customer, it is super important for them to trust this platform, this algorithm. And that is where we are trying to build that momentum, bring that awareness. One of my colleagues has written this book "Trustworthy AI". We're trying to take the message out to the market to say, there is a framework. We can help you get there. And certainly that's what we are doing. >> Just call Deloitte up and you're going to take care of them. >> Manoj: Yeah. >> On the Amazon side, Amazon Web Services. I always interview Swami every year at re:Invent and he always get the updates. He's been bullish on this for a long time on this Conversational AI. What's the update on the AWS side? Where are you guys at? What's the current trends that you're riding? What wave are you riding right now? >> So some of the trends we see in customer interest, there's a couple of things. One is the multimodal interfaces we we're just chatting about where the voice IVA is synced with like a web or mobile experience, so you take that full advantage of the device. The other is adding additional AI into the Conversational AI. So one example is a customer that included intelligent document processing as part of the chatbot. So instead of typing your name and address, take a photo of your driver's license. It was an insurance onboarding chatbot, so you could take a photo of your existing insurance policy. It'll extract that information to build the new insurance policy. So folks get excited about that. And the third area we see interest is what's called multi-bot orchestration. And this is where you can have one main chatbot. Marshall user across different sub-chatbots based on the use case persona or even language. So those things get people really excited and then AWS is launching all sorts of new features. I don't know which one is coming out. >> I know something's coming out tomorrow. He's right at corner. He's big smile on his face. He wouldn't tell me. It's good. >> We have for folks like the closer alliance relationships, we we're able to get previews. So there a preview of all the new stuff. And I don't know what I could, it's pretty exciting stuff. >> You get in trouble if you spill the beans here. Don't, be careful. I'll watch you. We'll talk off camera. All exciting stuff. >> Yeah, yeah. I think the orchestrator bot is interesting. Having the ability to orchestrate across different contextual datasets is interesting. >> One of the areas where it's particularly interesting is in financial services. Imagine a bank could have consumer accounts, merchant accounts, investment banking accounts. So if you were to chat with the chatbot and say I want to open account, well, which account do you mean? And so it's able to figure out that context to navigate folks to those sub-chatbots behind the scenes. And so it's pretty interesting style. >> Awesome. Manoj while we're here, take a minute to quickly give a plug for Deloitte. What your program's about? What customers should expect if they work with you guys on this project? Give a quick commercial for Deloitte. >> Yeah, no, absolutely. I mean, Deloitte has been continuing to lead the AI field organization effort across our client base. If you think about all the Fortune 100, Fortune 500, Fortune 2000 clients, we certainly have them where they are in advanced stages of multiple deployments for AI. And we look at it all the way from strategy to implementation to operational models. So clients don't have to do it alone. And we are continuing to build our ecosystem of relationships, partnerships like the alliances that we have with AWS, building the ecosystem of relationships with other emerging startups, to your point about how do you continue to innovate and bring those technologies to your clients in a trustworthy environment so that we can deliver it in production scale. That is essentially what we're driving. >> Well, Arte, there's a great conversation and the AI will take over from here as we end the segment. I see a a bot coming on theCUBE later and there might be CUBE be replaced with robots. >> Right, right, right, exactly. >> I'm John Furrier, calling from Palo Alto. >> Someday, CUBE bot. >> You can just say, Alexa do my demo for me or whatever it is. >> Or digital twin for John. >> We're going to have a robot on earlier do a CUBE interview and that's Dave Vellante. He'd just pipe his voice in and be fun. Well, thanks for coming on, great conversation. >> Thank you. Thanks for having us. >> CUBE coverage here at re:MARS in Las Vegas. Back to the event circle. We're back in the line. Got re:Inforce and don't forget re:Invent at the end of the year. CUBE coverage of this exciting show here. Machine learning, automation, robotics, space. That's MARS, it's re:MARS. I'm John Furrier. Thanks for watching. (gentle music)

(upbeat music) >> Okay, welcome back everyone to theCUBE's coverage here at re:MARS. I'm John Furrier, host of theCUBE. It's the event where it's part of the "re:" series: re:MARS, re:Inforce, re:Invent. MARS stands for machine learning, automation, robotics, and space. And a lot of conversation is all about AI machine learning. This one's about AI and business transformation. We've got Stepan Pushkarev CTO, CEO, Co-Founder of Provectus. Welcome to theCUBE. And Russ Lamb, eCommerce Retail Data Engineering Lead at PepsiCo, customer story. Gentlemen, thanks for coming on theCUBE. >> Great to be here, John. >> Yeah, thanks for having us. >> I love the practical customer stories because it brings everything to life. This show is about the future, but it's got all the things we want, we love: machine learning, robotics, automation. If you're in DevOps, or you're in data engineering, this is the world of automation. So what's the relationship? You guys, you're a customer. Talk about the relationship between you guys. >> Sure, sure. Provectus as a whole is a professional services firm, premier, a AWS partner, specializing in machine learning, data, DevOps. PepsiCo is our customer, our marquee customer, lovely customer. So happy to jointly present at this re:Invent, sorry, re:MARS. Anyway, Russ... >> I made that mistake earlier, by the way, 'cause re:Invent's always on the tip of my tongue and re:MARS is just, I'm not used to it yet, but I'm getting there. Talk about what are you guys working together on? >> Well, I mean, we work with Provectus in a lot of ways. They really helped us get started within our e-commerce division with AWS, provided a lot of expertise in that regard and, you know, just hands-on experience. >> We were talking before we came on camera, you guys just had another talk and how it's all future and kind of get back to reality, Earth. >> Russ: Get back to Earth. >> If we're on earth still. We're not on Mars yet, or the moon. You know, AI's kind of got a future, but it does give a tell sign to what's coming, industrial change, full transformation, 'cause cloud does the back office. You got data centers. Now you've got cloud going to the edge with industrial spaces, the ultimate poster child of edge and automation safety. But at the end of the day, we're still in the real world. Now people got to run businesses. And I think, you know, having you here is interesting. So I have to ask you, you know, as you look at the technology, you got to see AI everywhere. And the theme here, to me, that I see is the inflection point driving all this future robotics change, that everyone's been waiting for by the way, but it's like been in movies and in novels, is the machine learning and AI as the tipping point. This is key. And now you're here integrating AI into your company. Tell us your story. >> Well, I think that every enterprise is going to need more machine learning, more, you know, AI or data science. And that's the journey that we're on right now. And we've come a long way in the past six years, particularly with our e-commerce division, it's a really data rich environment. So, you know, going from brick and mortar, you know, delivering to restaurants, vending machines and stuff, it's a whole different world when you're, people are ordering on Amazon every couple minutes, or seconds even, our products. But they, being able to track all that... >> Can you scope the problem statement and the opportunity? Because if I just kind of just, again, I'm not, you're in, it's your company, you're in the weeds, you're at the data, you're everything, But it just seems me, the world's now more integration, more different data sources. You've got suppliers, they have their different IT back ends. Some are in the cloud, some aren't in the cloud. This is, like, a hard problem when you want to bring data together. I mean, API certainly help, but can you scope the problem, and, like, what we're talking about here? >> Well, we've got so many different sources of data now, right? So we used to be relying on a couple of aggregators who would pull all this data for us and hand us an aggregated view of things. But now we're able to partner with different retailers and get detail, granular information about transactions, orders. And it's just changed the game, changed the landscape from just, like, getting a rough view, to seeing the nuts and bolts and, like, all the moving parts. >> Yeah, and you see in data engineering much more tied into like cloud scale. Then you got the data scientists, more the democratization application and enablement. So I got to ask, how did you guys connect? What was the problem statement? How did you guys, did you have smoke and fire? You came in solved the problem? Was it a growth thing? How did this, how did you guys connect as a customer with Provectus? >> Yeah, I can elaborate on that. So we were in the very beginning of that journey when there was, like, just a few people in this new startup, let's call it startup within PepsiCo. >> John: Yeah. >> Calling like a, it's not only e-commerce, it was a huge belief from the top management that it's going to bring tremendous value to the enterprise. So there was no single use case, "Hey, do this and you're going to get that." So it's a huge belief that e-commerce is the future. Some industry trends like from brand-centric to consumer-centric. So brand, product-centric. Amazon has the mission to build the most customer-centric customer company. And I believe that success, it gets a lot of enterprises are being influenced by that success. So I remember that time, PepsiCo had a huge belief. We started building just from scratch, figuring out what does the business need? What are the business use cases? We have not started with the IT. We have not started with this very complicated migrations, modernizations. >> John: So clean sheet of paper. >> Yeah. >> From scratch. >> From scratch. >> And so you got the green light. >> Yeah. >> And the leadership threw the holy water on that and said, "Hey, we'll do this."? >> That's exactly what happened. It was from the top down. The CEO kind of set aside the e-commerce vision as kind of being able to, in a rapidly evolving business place like e-commerce, it's a growing field. Not everybody's figured it out yet, but to be able to change quickly, right? The business needs to change quickly. The technology needs to change quickly. And that's what we're doing here. >> So this is interesting. A lot of companies don't have that, actually, luxury. I mean, it's still more fun because the tools are available now that all the hyper scales built on their own. I mean, back in the day, 10 years ago, they had to build it all, Facebook. You didn't know, I had people on here from Pinterest and other companies. They had to build all of that from scratch. Now cloud's here. So how did you guys do this? What was the playbook? Take us through the AI because it sounds like the AI is core, you know, belief principle of the whole entire system. What did you guys do? Take me through the journey there. >> Yeah. Beyond management decisions, strategic decisions that has been made as a separate startup, whatever- >> John: That's great. >> So some practical, tactical. So it may sound like a cliche, but it's a huge thing because I work with many enterprises and this, like, "center of excellence" that does a nice technology stuff and then looks for the budget on the different business units. It just doesn't go anywhere. It could take you forever to modernize. >> We call that the Game of Thrones environment. >> Yes. >> Yeah. Nothing ever gets done 'till it blows up at the end. >> Here, these guys, and I have to admit, I don't want to steal their thunder. I just want to emphasize it as an external person. These guys just made it so differently. >> John: Yeah. >> They even physically sat in a different office in a WeWork co-working and built that business from scratch. >> That's what Andy Jackson talked about two years ago. And if you look at some of the big successes on AWS, Capital One, all the big, Goldman Sachs. The leadership, real commitment, not like BS, like total commitment says, "Go." But enough rope to give you some room, right? >> Yeah. I think that's the thing is, there was always an IT presence, right, overseeing what we were doing within e-commerce, but we had a lot of freedoms to make design choices, technology choices, and really accelerate the business, focus on those use cases where we could make a big impact with a technology choice. >> Take me through the stages of the AI transformation. What are some of the use cases and specific tactics you guys executed on? >> Well, I think that the supply chain, which I think is a hot topic right now, but that was one use case where we're using, like, data real time, real time data to inform our sales projections and delivery logistics. But also our marketing return on investment, I feel like that was a really interesting, complex problem to solve using machine learning, Because there's so much data that we needed to process in terms of countries, territories, products, like where do you spend your limited marketing budget when you have so many choices, and, using machine learning, boil that all down to, you know, this is the optimal choice, right now. >> What were some of the challenges and how did you overcome them in the early days to get things set up, 'cause it takes a lot of energy to get it going, to get the models. What were some of the challenges and how did you overcome them? >> Well, I think some of it was expertise, right? Like having a partner like Provectus and Stepan really helped because they could guide us, Stepan could guide us, give his expertise and what he knows in terms of what he's seen to our budding and growing business. >> And what were the things that you guys saw that you contributed on? And was there anything new that you had to do together? >> Yeah, so yeah. First of all, just a very practical tip. Yes, start with the use cases. Clearly talk to the business and say, "Hey, these are the list of the use cases" and prioritize them. So not with IT, not with technology, not with the migration thing. Don't touch anything on legacy systems. Second, get data in. So you may have your legacy systems or some other third party systems that you work with. There's no AI without data. Get all the pipelines, get data. Quickly boat strap the data lake house. Put all the pipelines, all the governance in place. And yeah, literally took us three months to get up and running. And we started delivering first analytical reports. It's just to have something back to business and keep going. >> By the way, that's huge, speed. I mean, this is speed. You go back and had that baggage of IT and the old antiquated systems, you'd be dragging probably months. Right? >> It's years, years. Imagine you should migrate SAP to the cloud first. No, you don't do don't need to do that. >> Pipeline. >> Just get data. I need data. >> Stream that data. All right, where are we now? When did you guys start? I want to get just going to timeline my head 'cause I heard three months. Where are we now? You guys threw it. Now you have impact. You have, you have results. >> Yeah. I mean that for our marketing ROI engine, we've built it and it's developed within e-commerce, but we've started to spread it throughout the organization now. So it's not just about the digital and the e-commerce space. We're deploying it to, you know, regionally to other, to Europe, to Latin America, other divisions within PepsiCo. And it's just grown exponentially. >> So you have scale to it right now? >> Yeah. Well- >> How far are you in now? What, how many years, months, days? >> E-commerce, the division was created six years ago, which is, so we've had some time to develop this, our machine learning capabilities and this use case particular, but it's increasingly relevant and expansion is happening as we speak. >> What are you most proud of? You look back at the impact. What are you most proud of? >> I think the relationship we built with the people, you know, who use our technology, right. Just seeing the impact is what makes me proud. >> Can you give an example without revealing any confidential information? >> Yeah. Yeah. I mean, there was an example from my talk about, I was approached recently by our sales team. They were having difficulty with supply chain, monitoring our fill rate of our top brands with these retailers. And they come up to me, they have this problem. They're like, "How do we solve it?" So we work together to find a data source, just start getting that data in the hands of people who can use it within days. You know, not talking like a long time. Bring that data into our data warehouse, and then surface the data in a tool they can use, you know, within a matter of a week or two. >> I mean, the transformation is just incredible. In fact, we were talking on theCUBE earlier today around, you know, data warehouses in the cloud, data meshes of different pros and cons. And the theme that came out of that conversation was data's a product now. >> Yes. >> Yes. >> And what you're kind of describing is, just gimme the product or find it. >> Russ: Right. >> And bring it in with everything else. And there's some, you know, cleaning and stuff people do if they have issues with that. But, if not, it's just bring it in, right? It's a product. >> Well, especially with the data exchanges now. AWS has a data exchange and this, I think, is the future of data and what's possible with data because you don't have to start from, okay, I've got this Excel file somebody's been working with on their desktop. This is a, someone's taken that file, put it into a warehouse or a data model, and then they can share it with you. >> John: So are you happy with these guys? >> Absolutely, yeah. >> You're actually telling the story. What was the biggest impact that they did? Was it partnering? Was it writing code, bringing development in, counseling, all the above, managed services? What? >> I think the biggest impact was the idea, you know, like being able to bring ideas to the table and not just, you know, ask us what we want, right? Like I think Provectus is a true partner and was able to share that sort of expertise with us. >> You know, Andy Jackson, whenever I interview on theCUBE, he's now in charge of all Amazon. But when he was at (inaudible). He always had to use their learnings, get the learnings out. What was the learnings you look back now and say, Hey, those were tough times. We overcome them. We stopped, we started, we iterated, we kept moving forward. What was the big learning as you look back, some of the key success points, maybe some failures that you overcome. What was the big learnings that you could share with folks out there now that are in the same situation where they're saying, "Hey, I'd rather start from scratch and do a reset." >> Yeah. So with that in particular, yes, we started this like sort of startup within the enterprise, but now we've got to integrate, right? It's been six years and e-commerce is now sharing our data with the rest of the organization. How do we do that, right? There's an enterprise solution, and we've got this scrappy or, I mean, not scrappy anymore, but we've got our own, you know, way of doing. >> Kind of boot strap. I mean, you were kind of given charter. It's a start up within a big company, I mean- >> But our data platform now is robust, and it's one of the best I've seen. But how do we now get those systems to talk? And I think Provectus has came to us with, "Here, there's this idea called data mesh, where you can, you know, have these two independent platforms, but share the data in a centralized way. >> So you guys are obviously have a data mesh in place, big part of the architecture? >> So it is in progress, but we know the next step. So we know the next step. We know the next two steps, what we're going to do, what we need to do to make it really, to have that common method, data layer. between different data products within organization, different locations, different business units. So they can start talking to each other through the data and have specific escalates on the data. And yeah. >> It's smart because I think one of the things that people, I think, I'd love to get your reaction to this is that we've been telling the story for many, many years, you have horizontally scalable cloud and vertically specialized domain solutions, you need machine learning that's smart, but you need a lot of data to help it. And that's not, a new architecture, that's a data plane, it's control plane, but now everyone goes, "Okay, let's do silos." And they forget the scale side. And then they go, "Wait a minute." You know, "I'm not going to share it." And so you have this new debate of, and I want to own my own data. So the data layer becomes an interesting conversation. >> Yeah, yes. Meta data. >> Yeah. So what, how do you guys see that? Because this becomes a super important kind of decision point architecturally. >> I mean, my take is that there has to be some, there will always be domains, right? Everyone, like there's only so much that you can find commonality across, like in industry, for example. But there will always be a data owner. And, you know, kind of like what happened with rush to APIs, how that enabled microservices within applications and being sharing in a standardized way, I think something like that has to happen in the data space. So it's not a monolithic data warehouse, it's- >> You know, the other thing I want to ask you guys both, if you don't mind commenting while I got you here, 'cause you're both experts. >> We just did a showcase on data programmability. Kind of a radical idea, but like data as code, we called it. >> Oh yeah. >> And so if data's a product and you're acting on, you've got an architecture and system set up, you got to might code it's programmable. You need you're coding with data. Data becomes like a part of the development process. What do you guys think of when you hear data as code and data being programmable? >> Yeah, it's a interesting, so yeah, first of all, I think Russ can elaborate on that, Data engineering is also software engineering. Machine learning engineering is a software. At the end of the day, it's all product. So we can use different terms and buzz words for that but this is what we have at the end of the day. So having the data, well I will use another buzz word, but in terms of the headless architecture- >> Yes. >> When you have a nice SDK, nice API, but you can manipulate with the data as your programming object to build reach applications for your users, and give it, and share not as just a table in Redshift or a bunch of CSV files in S3 bucket, but share it as a programmable thing that you can work with. >> Data as code. >> Yeah. This is- >> Infrastructure code was a revolution for DevOps, but it's not AI Ops so it's something different. It's really it's data engineering. It's programming. >> Yeah. This is the way to deliver data to your consumers. So there are different ways you can show it on a dashboard. You can show it, you can expose it as an API, or you can give it as an object, programmable interface. >> So now you're set up with a data architecture that's extensible 'cause that's the goal. You don't want to foreclose. You must think about that must keep you up at night. What's going to foreclose that benefit? 'Cause there's more coming. Right? >> Absolutely. There's always more coming. And I think that's why it's important to have that robust data platform to work from. And yeah, as Stepan mentioned, I'm a big believer in data engineering as software engineering. It's not some like it's not completely separate. You have to follow the best practices software engineers practice. And, you know, really think about maintainability and scalability. >> You know, we were riffing about how cloud had the SRE managing all those servers. One person, data engineering has a many, a one to many relationships too. You got a lot going on. It's not managing a database. It's millions of data points and data opportunity. So gentlemen, thanks for coming on theCUBE. I really appreciate it. And thanks for telling the story of Pepsi. >> Of course, >> And great conversation. Congratulations on this great customer. And thanks for >> coming on theCUBE. >> Thanks, thank you. Thanks, Russ, would you like to wrap it up with the pantry shops story? >> Oh, yeah! I think it will just be a super relevant evidence of the agility and speed and some real world applicable >> Let's go. Close us out. >> So when, when the pandemic happened and there were lockdowns everywhere, people started buying things online. And we noticed this and got a challenge from our direct to consumer team saying, "Look, we need a storefront to be able to sell to our consumers, and we've got 30 days to do it." We need to be able to work fast. And so we built not just a website, but like everything that behind it, the logistics of supply chain aspects, the data platform. And we didn't just build one. We built two. We got pantry shop.com and snacks.com, within 30 days. >> Good domains! >> The domain broker was happy on that one. Well continue the story. >> Yeah, yeah. So I feel like that the agility that's required for that kind of thing and the like the planning to be able to scale from just, you know, an idea to something that people can use every day. And, and that's, I think.- >> And you know, that's a great point too, that shows if you're in the cloud, you're doing the work you're prepared for anything. The pandemic was the true test for who was ready because it was unforeseen force majeure. It was just like here it comes and the people who were in the cloud had that set up, could move quickly. The ones that couldn't. >> Exactly. >> We know what happened. >> And I would like to echo this. So they have built not just a website, they have built the whole business line within, and launched that successfully to production. That includes sales, marketing, supply chain, e-commerce, aside within 30 days. And that's just a role model that could be used by other enterprises. >> Yeah. And it was not possible without, first of all, right culture. And second, without cloud Amazon elasticity and all the tools that we have in place. >> Well, the right architecture allows for scale. That's the whole, I mean, you did everything right at the architecture that's scale. I mean, you're scaling. >> And we empower our engineers to make those choices, right. We're not, like, super bureaucratic where every decision has to be approved by the manager or the managers manager. The engineers have the power to just make good decisions, and that's how we move fast. >> That's exactly the future right there. And this is what it's all about. Reliability, scale agility, the ability to react and have applications roll out on top of it without long timeframes. Congratulations. Thanks for being on theCUBE. Appreciate it. All right. >> Thank you. >> Okay, you're watching theCUBE here at re:MARS 2020, I'm John Furrier. Stay tuned. We've got more coverage coming after this short break. (upbeat music)

>>Okay, welcome back everyone to the cubes coverage here in Las Vegas for AWS re Mars, Remar stands for machine learning, automation, robotics, and space. Part of thehow is reinforces security. And the big show reinvent at the end of the year is the marquee event. Of course, the queues at all three and more coverage here. We've got a great guest here. Ryan re practice lead data analytics, machine learning at mission cloud. Ryan. Thanks for joining me. Absolutely >>Glad. >>So we were talking before he came on camera about mission cloud. It's not a mission as in a space mission. That's just the name of the company to help people with their mission to move to the cloud. And we're a space show to make that it's almost like plausible. I can see a mission cloud coming someday. >>Yeah, absolutely. >>You got >>The name. We got it. We're ready. >>You guys help customers get to the cloud. So you're working with all the technologies on AWS stack and people who are either lifting and shifting or cloud native born in the cloud, right? Absolutely. >>Yeah. I mean, we often see some companies talk about lift and shift, but you know, we try to get them past that because often a lift and shift means like, say you're on Oracle, you're bringing your Oracle licensing, but a lot of companies want to, you know, innovate and migrate more than they want to lift and shift. So that's really what we're seeing in market. >>You see more migration. Yeah. Less lift and shift. >>Yeah, exactly. Because they, they're trying to get out of an Oracle license. Right. They're seeing if that's super expensive and you know, you can get a much cheaper product on AWS. >>Yeah. What's the cutting up areas right now that you're seeing with cloud Amazon. Cause you know, Amazon, you know, is at their, their birthday, you know, dynamo you to sell with their 10th birthday. Where are they in your mind relative to the enterprise in terms of the services and where this goes next in terms of the on-prem you got the hybrid model. Everyone sees that, but like you got outpost. Mm. Not doing so as good as say EKS or other cool serverless stuff. >>Yeah. I mean, that's a great question. One of the things that's you see from AWS is really innovation, right? They're out there, they have over 400 microservices. So they're looking at all the different areas you have on the cloud and that people are trying to use. And they're creating these microservices that you string together, you architect them all up so that you can create what you're looking for. One of the big things we're seeing, right, is with SageMaker. A lot of people are coming in, looking for ML projects, trying to use all the hype that you see around that doing prediction, NLP and computer vision are super hot right now we've helped a lot of companies, you know, start to build out these NLP models where they're doing, you know, all kinds of stuff you use. 'em in gene research, you know, they're trying to do improvements in drugs and therapeutics. It's really awesome. And then we do some eCommerce stuff where people are just looking at, you know, how do I figure out what are similar things on similar websites, right. For, for search companies. So >>Awesome. Take me through the profile of your customer. You have the mix of business. Can you break down the, the target of the small, medium size enterprise, large all the above. >>Yeah. So mission started working with a lot of startups and SMBs and then as we've grown and become, you know, a much larger company that has all the different focus areas, we started to get into enterprise as well and help a lot of pretty well known enterprises out there that are, you know, not able to find the staff that they need and really want to get into >>The cloud. I wanted to dig into the staffing issues and also to the digital transformation journey. Okay. It okay. We all kind of know what's turning into the more dashboards, more automation, DevOps, cloud, native applications. All good. Yeah. And I can see that journey path. Now the reality is how do you get people who are gonna be capable of doing the ML, doing the DevOps dev sec ops. But what about cyber security? I mean is a ton of range of issues that you gotta be competent on to kind of survive in this multi-disciplined world, just to the old days of I'm the top of rack switch guy is over. >>Absolutely. Yeah. You know, it's a really good question. It's really hard. And that's why, you know, AWS has built out that partner ecosystem because they know companies can't hire enough people to do that. You know, if you look at just a migration into a data lake, you know, on-prem often you had one guy doing it, but if you want to go to the cloud, it's like you said, right, you need a security guy. You need to have a data architect. You need to have a cloud architect. You need to have a data engineer. So, you know, in the old days maybe you needed one guy. Now you have to have five. And so that's really why partners are valuable to customers is we're able to come in, bring those resources, get everything done quickly, and then, you know, turn >>It over. Yeah. We were talking again before we came on camera here live, you, you guys have a service led business, but the rise of MSPs managed service providers is huge. We're seeing it everywhere mainly because the cloud actually enables that you're seeing it for things like Kubernetes, serverless, certain microservices have certain domain expertise and people are making a living, providing great managed services. You guys have managed services. What's that phenomenon. Do you agree with it? And how do you, why did that come about and what, how does it keep going? Is it a trend or is it a one trick pony? >>I think it's a trend. I mean, what you have, it's the same skills gap, right? Is companies no longer want that single point of failure? You know, we have a pool model with our managed services where your team's working with a group of people. And so, you know, we have that knowledge and it's spread out. And so if you're coming in and you need help with Kubernetes, we got a Kubernetes guy in that pool to help you, right. If you need, you know, data, we got a data guy. And so it just makes it a lot easier where, Hey, I can pay the same as one guy and get a whole team of like 12 people that can be interchangeable onto my project. So, you know, I think you're gonna see managed services continue to rise and companies, you know, just working in that space. >>Do you see a new skill set coming? That's kind of got visibility right now, but not full visibility. That's going to be needed. I asked this because the environment's changing for the better obviously, but you're seeing companies that are highly valued, like data bricks, snowflake, they're getting killed on valuation. So they gotta have a hard time retaining talent. In my opinion, my opinion probably be true, but you know, you can't, you know, if you're data breach, you can't raise that 45 billion valuation try to hire senior people. They're gonna be underwater from day one. So there's gonna be a real slow down in these unicorns, these mega unicorns, deck, unicorns, whatever they're called because they gotta refactor the company, stock equity package. They attract people. So they gotta put them on a flat foot. And the next question is, do they actually have the juice, the goods to go to the new market? That's another question. So what I mean, what's your take on you're in the trenches. You're in the front lines. >>Yeah, that's a great question. I mean, and it's hard for me to think about whether they have the juice. I think snowflake and data bricks have been great for the market. They've come in. They've innovated, you know, snowflake was cloud native first. So they were built for the cloud. And what that's done is push all the hyperscalers to improve their products, right. AWS has gone through and you know, drastically over the last three years, improved Redshift. Like, I mean it's night and day from three years ago. Did, >>And you think snowflake put that pressure on them? >>Snowflake. Absolutely. Put that pressure on them. You know, I don't know whether they would've gotten to that same level if snowflake wasn't out there stealing market share. But now when you look at it, Redshift is much cheaper than snowflake. So how long are people gonna pay that tax to have snowflake versus switching over snowflakes? >>Got a nice data. Clean room, had some nice lock in features. Only on snowflake. The question is, will that last clean room? I see you smiling. Go ahead. >>Clean. Room's a concept that was actually made by Google. I know Snowflake's trying to capture it as their own, but, but Google's the one that actually launched the clean room concept because of marketing and, and all of that. >>Google also launches semantic layer, which Snowflake's trying to copy that. Does that, what does that mean to you when you hear the word semantic layer? What does that mean? >>And semantic layer just is really all about meta tags, right? How am I going through to figure out what data do I actually have in my data lake so that I can pull it for whatever I'm trying to do, whether it's dashboarding or whether it's machine learning. You're just trying to organize your data better. >>Ryan, you should be a cue post. You're like a masterclass here in, in it and cloud native. I gotta ask you since you're here, since we're having the masterclass being put in a clinic here, lot of clients are confused between how to handle the control plane and the data plane cause machine learning right now is at an all time high. You're seeing deep racer. You're seeing robotic space, all driving by machine learning. SW. He said it today, the, the companion coder, right? The, the code whisperer, that's only gonna get stronger. So machine learning needs data. It feeds on data. So everyone right now is trying to put data in silos. Okay? Cause they think, oh, compliance, you gotta create a data plane and a control plane that makes it highly available. So that can be shared >>Right >>Now. A lot of people are trying to own the data plane and some are trying to own the control plane or both. Right? What's your view on that? Because I see customers say, look, I want to own my own data cause I can control it. Control plane. I can maybe do other things. And some are saying, I don't know what to do. And they're getting forced to take both to control plane and a data plane from a vendor, right? What's your, what's your reaction to that? >>So it's pretty interesting. I actually was presenting at a tech target conference this week on exactly this concept, right, where we're seeing more and more words out there, right? It was data warehouse and it was data lake and it's lake house. And it's a data mesh and it's a data fabric. And some of the concepts you're talking about really come into that data, match data fabric space. And you know, what you're seeing is data's gonna become a product right, where you're gonna be buying a product and the silos yes. Silos exist. But what, what companies have to start doing is, and this is the whole data mesh concept is, Hey yes, you finance department. You can own your silo, but now you have to have an output product. That's a data product that every other part of your company can subscribe to that data product and use it in their algorithms or their dashboard so that they can get that 360 degree view of the customer. So it's really, you know, key that, you know, you work within your business. Some business are gonna have that silo where the data mesh works. Great. Others are gonna go. >>And what do you think about that? Because I mean, my thesis would be, Hey, more data, better machine learning. Right. Is that the concept? >>So, or that's a misconception or, >>Okay. So what's the, what's the rationale to share the data like that and data mission. >>So having more of the right data here, it is improves. Just having more data in general, doesn't improve, right? And often the problem is in the silos you're getting to is you don't have all the data you want. Right. I was doing a big project about shipping and there's PII data. When you talk about shipping, right? Person's addresses, that's owned by one department and you can't get there. Right. But how am I supposed to estimate the cost of shipping if I can't get, you know, data from where a person lives. Right. It's just >>Not. So none of the wrinkle in the equation is latency. Okay. The right data at the right time is another factor is that factored into data mesh versus these other approaches. Because I mean, you can, people are streaming data. I get that. We're seeing a lot of that. But talking about getting data fast enough before the decisions are made, is that an issue or is this just BS? >>I'm going with BS. Okay. So people talk about real time real. Time's great if you need it, but it's really expensive to do. Most people don't need real time. Right. They're really looking for, I need an hourly dashboard or I need a daily dashboard. And so pushing into real time, just gonna be an added expense that you don't >>Really need. Like cyber maybe is that not maybe need real time. >>Well, cyber security add. I mean, there's definitely certain applications that you need real time, >>But don't over invest in fantasy if you don't need an an hour's fine. Right, >>Right. Yeah. If you're, if you're a business and you're looking at your financials, do you need your financials every second? Is that gonna do anything for you? Got >>It. Yeah. Yeah. And so this comes back down to data architecture. So the next question I asked, cause I had a great country with the Fiddler AI CEO, CEO earlier, and he was at Facebook and then Pinterest, he was a data, you know, an architect and built everything. He said themselves. We were talking about all the stuff that's available now are all the platforms and tools available to essentially build the next Facebook if someone wanted to from scratch. I mean, hypothetically thought exercise. So the ability to actually ramp up and code a complete throwaway and rebuild from the ground up is possible. >>Absolutely. >>And so the question is, okay, how do you do it? How long would it take? I mean, in an ideal scenario, not, you know, make some assumptions here, you got the budget, you got the people, how long to completely roll out a brand new platform. >>Now it's funny, you asked that because about a year ago I was asked that exact same question by a customer that was in the religious space that basically wanted to build a combination of Facebook, Netflix, and Amazon altogether for the religious space, for religious goods and you know, church sermons, we estimated for him about a year and about $9 million to do it. >>I mean, that's a, that's a, a round these days. Yeah. Series a. So it's possible. Absolutely. So enterprises, what's holding them back, just dogma process, old school legacy, or are people taking the bold move to take more aggressive, swiping out old stuff and just completely rebuilding? Or is it a talent issue? What's the, what's the enterprise current mode of reset, >>You know, I think it really depends on the enterprise and their aversion to risk. Right. You know, some enterprises and companies are really out there wanting to innovate, you know, I mean there's companies, you know, an air conditioning company that we worked with, that's totally, you know, nest was eaten all their business. So they came in and created a whole T division, you know, to, to chase that business, that nest stole from them. So I think it, I think often a company's not necessarily gonna innovate until somebody comes in and starts stealing their >>Lunch. You know, Ryan, Andy, Jess, we talked about this two reinvents ago. And then Adam Eski said the same thing this year on a different vector, but kind of building on what Andy Jessey said. And it's like, you could actually take new territory down faster. You don't have to kill the old, no I'm paraphrasing. You don't have to kill the old to bring in the new, you can actually move on new ideas with a clean sheet of paper if you have that builder mindset. And I think that to me is where I'm seeing. And I'd love to get your reaction because if you see an opportunity to take advantage and take territory and you have the right budget time and people, you can get it. Oh absolutely. It's gettable. So a lot of people have this fear of, oh, we're, that's not our core competency. And, and they they're the frog and boiling water. >>You know, my answer to that is I think part of it's VCs, right? Yeah. VCs have come in and they see the value of a company often by how many people you hire, right. Hire more people. And the value is gonna go up. But often as a startup, you can't hire good people. So I'm like, well, why are you gonna go hire a bunch of random people? You should go to a firm like ours that knows AWS and can build it quickly for you, cuz then you're gonna get to the market faster versus just trying to hire a bunch of people in >>Someone. Right. I really appreciate you coming on. I'd love to have you back on the cube again, sometime your expertise and your insights are awesome. Give a commercial for the company, what you guys are doing, who you're looking for, what you want to do, hiring or whatever your goals are. Take a minute to explain what you guys are doing and give a quick plug. >>Awesome. Yeah. So mission cloud, you know, we're a premier AWS consulting firm. You know, if you're looking to go to AWS or you're in AWS and you need help and support, we have a full team, we do everything. Resell, MSP professional services. We can get you into the cloud optimize. You make everything run as fast as possible. I also have a full machine learning team. Since we're here at re Mars, we can build you models. We can get 'em into production, can make sure everything's smooth. The company's hiring. We're looking to double in size this year. So, you know, look me up on LinkedIn, wherever happy to, to take, >>You mentioned the cube, you get a 20% discount. He's like, no, I don't approve that. Thanks for coming on the key. Really appreciate it. Again. Machine learning swaping said on stage this, you can be a full time job just tracking just the open source projects. Never mind all the different tools and like platform. So I think you're gonna have a good, good tailwind for your business. Thanks for coming on the queue. Appreciate it. Ryan Reese here on the queue. I'm John furry more live coverage here at re Mars 2022. After this short break, stay with us.

(upbeat music) >> Okay, welcome back everyone. The cube coverage of AWS re:Mars, 2022. I'm John Furrier, host of theCUBE. We got Eric Foellmer, vice president of marketing at Boston Dynamics. Famous for Spot. We all know, we've seen the videos, zillion views. Mega views all over the internet. The dog robotics, it's famous. Rolls over, bounces up and down. I mean, how many TikTok videos are out there? Probably a ton. >> Oh, Spot is- Spot is world famous (John laughs) at this point, right? So it's the dance videos, and all the application videos that we have out there. Spot is become has become world famous. >> Eric, thanks for joining us on theCUBE here at re:Mars. This show really is back. There was still a pandemic hiatus there. But it's not a part of the re's. It's re Mars, reinforcement of security, and then reinvent the flagship show for AWS. But this show is different. It brings together a lot of disciplines. But it's converging in on what we see as the next general- Industrial space is a big poster child for that. Obviously in space, it's highly industrial, highly secure. Machine learning's powering all the devices. You guys have been in this, I mean a leader, in a robotics area. What's this show about? I mean, what's really happening here. What if you had to boil the essence of the top story of what's happening here? What would it be? >> So the way that I look at this show is it really is a convergence of innovation. Like this is really just the cutting edge of the innovation that's really happening throughout robotics, but throughout technology in general. And you know, part of this cultural shift will be to adopt these types of technologies in our everyday life. And I think if you ask any technology specialist here or any innovator here or entrepreneur. They'll tell you that they want their technologies to become ubiquitous in society, right? I mean, that's really what everyone is sort of driving towards from the perspective of- >> And we, and we got some company behind it. Look at this. >> Oh, there we go. >> All right. >> There's a (Eric laughs) There's one of our Spots. >> It's got one of those back there. All right so sorry to interrupt, got a little distracted by the beautiful thing there. >> So they're literally walking around and literally engulfing the show. So when I look at the show, that's what I see. >> Let's see the picture of- >> I see the future of technology. >> Get a camera on our photo bomb here going on. Get a photo bomb action. (Eric chuckles) It's just super exciting because it really, it humanizes, it makes you- Everyone loves dogs. And, you know, I mean, people have more empathy if you kicked Spot than, you know, a human. Because there's so much empathy for just the innovation. But let's get into the innovation because let's- The IOT tech scene has been slow. Cloud computing Amazon web services, the leader hyper scaler. They dominated the back office you know, data centers, all the servers, digital transformation. Now that's coming to the edge. Where robotics is now in play. Space, material handling, devices for helping people who are sick or in healthcare. >> Eric: Mhm. >> So a whole surge of revolutionary or transitionary technologies coming. What's your take on that? >> So I think, you know, data has become the driving force behind technology innovation. And so robotics are an enabler for the tech, for the data collection that is going to drive IOT and manufacturing 4.0 and other important edge related and, you know, futuristic technology innovations, right? So the driver of all of that is data. And so robots like Spot are collectors of data. And so instead of trying to retrofit a manufacturing plant, you know, with 30, 40, 50 year old equipment in some cases. With IOT sensors and, you know, fixed sensors throughout the network. We're bringing the sensors to the equipment in the form of an agile mobile robot that brings that technology forward and is able to assess. >> So explain that a little slower for me. So the one method would be retrofitting all the devices. Or the hardware currently installed. >> Eric: Sure. >> Versus almost like having a mobile unit next to it, kind of thing. Or- >> Right. So, I mean, if you're looking at antiquated equipment which is what most, you know, manufacturing plants are running off of. It's not really practical or feasible to update them with fixed sensors. So sensors that specifically take measurements from that machine. So, we enable Spot with a variety of sensors from audio sensors to listen for audio anomalies. Thermal detectors, to look for thermal hotspots in equipment. Or visual detectors, where it's reading analog gauges, that sort of thing. So by doing that, we are bringing the sensors to the machines. >> Yeah. >> And to be able to walk anywhere where a human can walk throughout a manufacturing plant. To inspect the equipment, take that reading. And then most importantly upload that to the cloud, to the users >> It's a service dog. >> you can apply some- >> It's a service dog. >> It really is. And it serves data for the understanding of how that equipment is operated. >> This is big agility for the customer. Get that data, agile. Talk about the cost impact of that, just alone. What the alternative would be versus say, deploying that scenario. Because I'd imagine the time and cost would be huge. >> Well, if you think, you know, about how much manufacturing facilities put into the predictive maintenance and being able to forecast when their equipment needs maintenance. But also when pieces of equipment are going to fail. Unexpected downtime is one of the biggest money drains of any manufacturing facility. So the ability to be able to forecast and get some insight into when that equipment is starting to perform less than optimally and start to degrade. The ability to forecast that in advance is massive. >> Well I think you just win on just in retrofit cost alone, nevermind the downside scenarios of manufacturing problems. All right, let's zoom out. You guys have been pioneers for a long time. What's changed in your mind now versus just a few years ago. I mean, look at even 5, 10 years ago. The evolution, cost and capability. What's changed the most? >> Yeah, I think the accessibility of robots has really changed. And we're just on the beginning stages of that evolution. We really are. We're at the precipice right now of robots becoming much more ubiquitous in people's lives. And that's really our foundation as a company. Is we really want to bring robots to mankind for the good of humanity, right? So if you think about, you know, taking humans out of harm's way. Or, you know, putting robots in situations where, you know, where it's assessing damage for a building, for example, right. You're taking people out of the, out of that harm's way and really standardizing what you're able to do with technology. So we see it as really being on the very entry point of having not only robotics, but technology in general to become much more prevalent in people's lives. >> Yeah. >> I mean, what, you know. 30 years ago, did you ever think that you would have the power of a supercomputer in your pocket to, you know. Which also happens to allow you to talk to people but it is so much more, right? So the power of a cell phone has changed our lives forever. >> A computer that happens to be a phone. You know, it's like, come on. >> Right. >> What's going on with that. >> That's almost secondary at this point. (John laughing) It really is. So, I mean, when you think about that transition from you know, I think we're at the cusp of that right now. We're at the beginning stages of it. And it's really, it's an exciting time to be part of this. An entire industry. >> Before I get your views on integration and scale. Because that's the next level. We're seeing a lot of action and growth. Talk about the use case. You've mentioned a few of them, take people out of harms way. What have you guys seen as use cases within Boston Dynamics customer base and or your partner network around use cases. That either you knew would happen, or ones that might have surprised you? >> Yeah. One of the biggest use cases for us right now is what we're demonstrating here at re:MARS. Which is the ability to walk through a manufacturing plant and collect data off various pieces of equipment. Whether that's pump or a gauge or seeing whether a valve is open or closed. These are all simple mundane tasks that people are, that manufacturers are having difficulty finding people to be able to perform. So the ability for a robot to go over and do that and standardize that process is really valuable. As companies are trying to collect that data in a consistent way. So that's one of the most prevalent use cases that we're seeing right now. And certainly also in cases where, you know, Spot is going into buildings that have been structurally damaged. Or, you know, assessing situations where we don't want people to be in harm's way. >> John: Yeah. >> You know- >> Bomb scares, or any kind of situation with police or, you know, threatening or danger situations. >> Sure. And fire departments as well. I mean, fire departments are becoming a huge, you know, a huge user of the robots themselves. Fire department in New York recently just adopted some of our robots as well. For that purpose, for search and rescue applications. >> Yeah. Go in, go see what's in there. See what's around the corner. It gives a very tactical edge capability for say the firefighter or law enforcement. I see that- I see the military applications must be really insane. >> Sure. From a search and rescue perspective. Absolutely. I mean, Spot helps you put eyes on situations that will allow a human to be operating at a safe distance. So it's really a great value for protecting human life and making sure that people stay out of harm's way. >> Well Eric, I really appreciate you coming on theCUBE and sharing your insight. One other question I'd like to ask if you don't mind is, you know. The one of the things I see next to your booth is the university piece. And then you see the Amazon, you know, material management. I don't know what to call it, but it's pretty impressive. And then I saw some of the demos on the keynotes. Looking at the scale of synthetic data. Just it's mind blowing what's going on in manufacturing. Amazon is pretty state of the art. I'm sure there are a customer of yours already. But they look complex these manufacturing sites. I mean, it looks like a maze. So how do you... I mean, I could see the consequences of something breaking, to be catastrophic. Because it's almost like, it's so integrated. Is this where you guys see success and how do these manufacturers deal with this? What's the... Is it like one big OS? >> Yeah, so the robots, because the robots are able to act independently. They can traverse difficult terrain and collect data on their own. And then, you know, what happens to that data afterwards is really up to the manufacturing. It can be delivered from the cloud and you can, it can be delivered via the edge. You know, edge devices and really that's where some of the exciting work is being done right now. Because that's where data can scale. And that's where robot deployments can scale as well, right? So you've got instead of a single robot. Now you have an operator deploying multiple robots. Monitoring, controlling, and assessing the data from multiple robots throughout a facility. And it really helps to scale that investment. >> All right, final question for you. This is personal question. Okay, I know- Saw your booth over there. And you have a lot of fan base. Spot's got a huge fan base. What are some of the crazy things that these nerd fans do? I mean, everyone get selfies with the Spot. They want to- I jump over the fence. I see, "Don't touch the dog." signs everywhere. The fan base is off the charts. What are the crazy things that people do to get either access to it. There's probably, been probably some theft, probably. Attempts, or selfies. Share some funny stories. >> I'll say this. My team is responsible for fielding a lot of the inbound inquiries that we get. Much of which comes from the entertainment industry. And as you've seen Spot has been featured in some really prominent, you know, entertainment pieces. You know, we were in that Super Bowl ad with Sam Adams. We were on Jimmy Kimmel, you know, during the Super Bowl time period. So the amount of entertainment... >> Value >> Pitches. Or the amount of entertainment value is immeasurable. But the number of pitches that we turn down is staggering. And when you can think about how most companies would probably pull out all the stops to take, you know. To be able to execute half the things that we're just, from a time perspective, from a resource perspective >> Okay, so Spots an A- not always able to do. >> So Spots an A-lister, I get that. Is there a B-lister now? I mean, that sounds like there's a market developing for Spot two. Is there a Spot two? The B player coming in? Understudy? >> So, I mean, Spot is always evolving. I think, you know, the physical- the physical statue that you see of Spot right now, Is where we're going to be in terms of the hardware, but we continue to move the robot forward. It becomes more and more advanced and more and more capable to do more and more things for people. So. >> All right. Well, we'll roll some B roll on this, on theCUBE. Thanks for coming on theCUBE. Really appreciate it. Boston Dynamics here in theCUBE, famous for Spot. And then here, the show packed here in re:MARS featuring, you know, robotics. It's a big feature hall. It's a set piece here in the show floor. And of course theCUBE's covering it. Thanks for watching. More coverage. I'm John Furrier, your host. After the short break. (upbeat music)

(upbeat music) >> Okay, welcome back everyone to theCube's coverage of AWS re:Mars here in Las Vegas. I'm John Furrier, host of theCube. Got Alexey Surkov, Partner at Deloitte joining me today. We're going to talk about AI biased AI trust, trust in the AI for the, to save the planet to save us from the technology. Alexey thanks for coming on. >> Thank you for having me. >> So you had a line before you came on camera that describe the show, and I want you to say it if you don't mind because it was the best line that for me, at least from my generation. >> Alexey: Sure. >> That describes the show and then your role at Deloitte in it. >> Alexey: Sure. Listen, I mean, I, you know, it may sound a little corny, but to me, like I look at this entire show, at this whole building really, and like everybody here is trying to build a better Skynet, you know, better, faster, stronger, more potent, you know, and it's like, we are the only ones, like we're in this corner of like Deloitte trustworthy AI. We're trying to make sure that it doesn't take over the world. So that's, you know, that's the gist of it. How do you make sure that AI serves the good and not evil? How do you make sure that it doesn't have the risk? It doesn't, you know, it's well controlled that it does what we're, what we're asking it to do. >> And of course for all the young folks out there the Terminator is the movie and it's highly referenced in the nerd circles Skynet's evil and helps humanity goes away and lives underground and fights for justice and I think wins at the end. The Terminate three, I don't, I can't remember what happened there, but anyway. >> Alexey: I thought the good guys win, but, you know, that's. >> I think they do win at the end. >> Maybe. >> So that brings up the whole point because what we're seeing here is a lot of futuristic positive messages. I mean, three areas solve a lot of problems in the daily lives. You know, machine learning day to day hard problems. Then you have this new kind of economy emerging, you know, machine learning, driving new economic models, new industrial capabilities. And then you have this whole space save the world vibe, you know, like we discover the moon, new water sources maybe save climate change. So very positive future vibe here at re:Mars. >> Alexey: Absolutely. Yeah, and it was really exciting just watching, you know, watching the speakers talk about the future, and conquering space, and mining on the moon like it's happening already. It's really exciting and amazing. Yeah. >> Let's talk about what you guys are working at Deloitte because I think it's fascinating. You starting to see the digital transformation get to the edge. And when I say edge, I mean back office is done with cloud and you still have the old, you know, stuff that the old models that peoples will use, but now new innovative things are happening. Pushing software out there that's driving you with the FinTech, these verticals, and the trust is a huge factor. Not only do the consumers have a trust issues, who owns my data, there's also trust in the actual algorithms. >> Exactly. >> You guys are in the middle of this. What's your advice to clients, 'cause they want to push the envelope hard be cutting edge, >> Alexey: Right. >> But they don't want to pull back and get caught with their, you know, data out there that might been a misfire or hack. >> Absolutely. Well, I mean the simple truth is that, you know, with great power comes great responsibility, right? So AI brings a lot of promise, but there are a lot of risks, you know. You want to make sure that it's fair, that it's not biased. You want to make sure that it's explainable, that you can figure out and tell others what it's doing. You might want to make sure that it's well controlled, that it's responsible, that it's robust, that, you know, if somebody feeds it bad data, it doesn't produce results that don't make sense. If somebody's trying to provoke it, to do something wrong, that it's robust to those types of interactions. You want to make sure that it preserves privacy. You know, you want to make sure that it's secure, that nobody can hack into it. And so all of those risks are somewhat new. Not all of them are entirely new. As you said, the concept of model risk management has existed for many years. We want to make sure that each black box does what it's supposed to do. Just AI machine learning just raises it to the next level. And we're just trying to keep up with that and make sure that we develop processes, you know, controls that we look at technology that can orchestrate all this de-risking of transition to AI. >> Deloitte's a big firm. You guys saw you in the US open sponsorship was all over the TV. So that you're here at re:Mars show that's all about building up this next infrastructure in space and machine learning, what's the role you have with AWS and this re:Mars. And what's that in context of your overall relationship to the cloud players? >> Alexey: Well, we are, we're one of the largest strategic alliances for AWS, and AWS is one of the largest ones for Deloitte. We do a ton of work with AWS related to cloud, related to AI machine learning, a lot of these new areas. We did a presentation here just the other day on conversational AI, really cutting edge stuff. So we do all of that. So in some ways we participate in that part of the, the part of the room that I mentioned that is trying to kind of push the envelope and get the new technologies out there, but at the same time, Deloitte is a brand that carries a lot of, you know, history of trust, and responsibility, and controls, and compliance, and all of that comes, >> John: You get a lot of clients. I mean, you have big names. Get a lot of big name enterprises >> Right. >> That relied on you. >> Right, and so >> They rely on you now. >> Exactly, yeah. And so, it is natural for us to be in the marketplace, not only with the message of, you know, let's get to the better mouse trap in AI and machine learning, but also let's make sure that it's safe, and secure, and robust, and reliable, and trustworthy at the end of the day. And so, so this trustworthy message is intertwined with everything that we do in AI. We encourage companies to consider trustworthiness from the start. >> Yeah. >> It shouldn't be an afterthought, you know. Like I always say, you know, if you have deployed a bot and it's been deciding whether to issue loans to people, you don't want to find out that it was like, you know, biased against a certain type of (indistinct) >> I can just see in the boardroom, the bot went rogue. >> Right, yeah. >> Through all those loans you know. >> And you don't want to find out about it like six months later, right? That's too late, right? So you want to build in these controls from the beginning, right? You want to make sure that, you know, you are encouraging innovation, you're not stifling any development, and allowing your- >> There's a lot of security challenges too. I mean, it's like, this is the digital transformation sweet spot you're in right now. So I have to ask you, what's the use case, obviously call center's obvious, and bots, and having, you know, self-service capabilities. Where is the customers at right now on psychology and their appetite to push the envelope? And what do you guys see as areas that are most important for your customers to pay attention to? And then where do you guys ultimately deliver the value? >> Sure. Well, our clients are, I think, are aware of the risks of AI. They are not, that's not the first thing that they're thinking about for the most part. So when we come to them with this message they listen, they're very interested. And a lot of them have begun this journey of putting in kind of governance, compliance, controls, to make sure that as they are proceeding down this path of building out AI, that they're doing it responsibly. So it is in a nascent stage. >> John: What defines responsibility? >> Well, you want to, okay, so responsibility is really having governance. Like you have a, you build a robot dog, right? So, but you want to make sure that it has a leash, right? That it doesn't hurt anybody, right? That you have processes in place that at the end of the day, humans are in control, right? I don't want to go back to the Skynet analogy, right? >> John: Yeah. >> But humans should always be in control. There should always be somebody responsible for the functioning of the algorithm that can throw the switch at the right time, that can tweak it at the right time, that can make sure that you nudge it in the right direction that at no point should somebody be able to say, oh, well, it's not my fault. The algorithm did it, and that's why we're in the papers today, right? So that's the piece that's really complex, and what we try to do for our clients as Deloitte always does is kind of demystify that, right? >> John: Yeah. >> So what does it actually mean from a procedures, policies, >> John: Yeah, I mean, I think, >> Tools, technology, people. >> John: Yeah, I mean, this is like the classic operationalizing a new technology, managing it, making sure it doesn't get out of control if you will. >> Alexey: Exactly. >> Stay on the leash if you will. >> Alexey: Exactly. Yeah. And I guess one piece that I always like to mention is that, it's not to put breaks on these new technologies, right? It's not to try to kind of slow people down in developing new things. I actually think that making AI trustworthy is enabling the development of these technologies, right? The way to think about it is that, we have, you know, seat belts, and abs brakes, and, you know, airbags today. And those are all things that didn't exist like 100 years ago, but our cars go a lot faster, and we're a lot safer driving them. So, you know, when people say, oh, I hate seatbelts, you know, you're like, okay, yes, but first of all, there are some safety technologies that you don't even notice, which is how a lot of AI controls work. They blend into the background. And more importantly, the idea is for you to go faster, not slower. And that's what we're trying to enable our clients to do. >> Well, Alexey, great to have you on theCube. We love Deloitte come on to share their expertise. Final question for you is, where do you see this show going? Where do you guys, obviously you here, you're participating, you got a big booth here, where's this going? And what's next, where's the next dots that connect? Share your vision for this show, and kind of how it, or the ecosystem, and this ecosystem, and where you're going to intersect that? >> Wow. I mean, this show is already kind of pushing the boundaries. You know, we're talking about machine learning, artificial intelligence, you know, robotics, space. You know, I guess next thing I think, you know, we'll be probably spending a lot of time in the metaverse, right? So I can see like next time we come here, you know, half of us are wearing VR headsets and walking around and in meta worlds, but, you know, it's been an exciting adventure and, you know I'm really excited to partner and spend, you know spend time with AWS folks, and everybody here because they're really pushing the envelope on the future, and I look forward to next year >> The show is small, so it feels very intimate, which is actually a good feeling. And I think the other thing in metaverse I heard that too. I heard quantum. I said next, I heard, I've heard both those next year quantum and metaverse. >> Okay. >> Well, why not? >> Why not? Exactly, yeah. >> Thanks for coming on theCube. Appreciate it. >> Thank you. >> All right. It's theCube coverage here on the ground. Very casual Cube. Two days of live coverage. It's not as hot and and heavy as re:Invent, but it's a great show bringing all the best smart people together, really figure out the future, you know, solving problems day to day problems, and setting the new economy, the new industrial economy. And of course, a lot of the world problems are going to be helped and solved, very positive message space among other things here at re:Mars. I'm John furrier. Stay with us for more coverage after this short break. (upbeat music)

>>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.

>>Okay, welcome back everyone to the Cube's live coverage here in Las Vegas for Amazon re Mars two days of coverage, we're getting down to wrapping up day one. I'm John furrier host of the cube space is a big topic here. You got machine learning, you got automation, robotics, all spells Mars. The two great guests here to really get into the whole geo scene. What's going on with the data. We've got Marcus Norren business development and geo data. Sogeti part of cap Gemini group, and Yoki well kissed portfolio lead data and AI with Sogeti part of cap, Gemini gentlemen, thanks for coming on the queue. Appreciate it. Thanks >>For having us. >>Let me so coming all the way from Sweden to check out the scene here and get into the weeds and the show. A lot of great technology being space is the top line here, but software drives it. Um, you got robotics. Lot of satellite, you got the aerospace industry colliding with hardcore industrial. I say IOT, robotics, one, whatever you want, but space kind of highlights the IOT opportunity. There is no edge in space, right? So the edge, the intelligent edge, a lot going on in space. And satellite's one of 'em you guys are in the middle of that. What are you guys working on? What's the, the focus here for cap gem and I Sogeti part of cap >>Gemini. I would say we focus a lot of creating business value, real business value for our clients, with the satellites available, actually a free available satellite images, working five years now with this, uh, solutioning and, uh, mostly invitation management and forestry. That's our main focus. >>So what's the product value you guys are offering. >>We basically, for now the, the most value we created is working with a forest client to find park Beal infests, uh, in spruce forest. It's a big problem in European union and, uh, Northern region Sweden, where we live now with the climate change, it's getting warmer, the bark beetle bases warm more times during the summer, which makes it spread exponentially. Uh, so we help with the satellite images to get with data science and AI to find these infestations in time when they are small, before it's spread. >>So satellite imagery combined with data, this is the intersection of the data piece, the geo data, right? >>Yeah. You can say that you have, uh, a lot of open satellite data, uh, and uh, you want to analyze that, that you also need to know what you're looking for and you need data to understand in our case, a certain type of damage. So we have large data sets that we have to sort of clean and train ML models from to try to run that on that open data, to detect these models. And, and when we're saying satellite data and open data, it's basically one pixel is 10 by 10 meters. So it's not that you will see the trees, but we're looking at the spectral information in the image and finding patterns. So we can actually detect attacks that are like four or five trees, big, uh, using that type. And we can do that throughout the season so we can see how you start seeing one, two attacks and it's just growing. And then you have this big area of just damage. So >>How, how long does that take? Give me some scope to scale because it sounds easy. Oh, the satellites are looking down on us. It's not, it's a lot of data there. What's the complexity. What are the challenges that you guys are overcoming scope to scale? >>It's so much complexity in this first, you have clouds, so it's, uh, open data set, you download it and you figure out here, we have a satellite scene, which is cloudy. We need to have some analytics doing that, taking that image away basically, or the section of the image with it cloudy. Then we have a cloud free image. We can't see anything because it's blurry. It's too low resolution. So we need to stack them on top of each other. And then we have the next problem to correlate them. So they are pixel perfect overlapping. Yeah. So we can compare them in time. And then they have the histogram adjustment to make them like, uh, the sensitivity is the same on all the images, because you have solar storms, you have shady clouds, which, uh, could be used still that image. So we need to compare that. Then we have the ground proof data coming from, uh, a harvester. For instance, we got 200,000 data points from the harvester real data points where they had found bark Beal trees, and they pulled them down. The GPS is drifting 50 meters. So you have an uncertainty where the actually harvest it was. And then we had the crane on 20 meters. So, you know, the GPS is on the home actually of the home actual machine and the crane were somewhere. So you don't really know you have this uncertainty, >>It's a data integration problem. Yeah. Massive, >>A lot of, of, uh, interesting, uh, things to adjust for. And then you could combine this into one deep learning model and build. >>But on top of that, I don't know if you said that, but you also get the data in the winter and you have the problem during the summer. So we actually have to move back in time to find the problem, label the data, and then we can start identifying. >>So once you get all that heavy lifting done or, or write the code, or I don't know if something's going on there, you get the layering, the pixel X see all the, how complex that is when the deep learning takes over. What happens next? Is it scale? Is it is all the heavy lifting up front? Is the work done front or yeah. Is its scale on the back end? >>So first the coding is heavy work, right? To gets hands on and try different things. Figure out in math, how to work with this uncertainty and get everything sold. Then you put it into a deep learning model to train that it actually run for 10 days before it was accurate, or first, first ation, it wasn't accurate enough. So we scrap that, did some changes. Then we run it again for 10 days. Then we have a model which we could use and interfere new images. Like every day, pretty quickly, every day it comes a new image. We run it. We have a new outcome and we could deliver that to clients. >>Yeah. I can almost imagine. I mean, the, the cloud computing comes in handy here. Oh yeah. So take me through the benefits because it sounds like the old, the old expression, the juice is not worth the squeeze here. It is. It's worth the squeeze. If you can get it right. Because the alternative is what more expensive gear, different windows, just more expensive monolithic solutions. Right? >>Think about the data here. So it's satellite scene. Every satellite scene is hundred by a hundred kilometers. That pretty much right. And then you need a lot of these satellite scene over multiple years to combine it. So if you should do this over the whole Northern Europe, over the whole globe, it's a lot of data just to store that it's a problem. You, you cannot do it on prem and then you should compute it with deep learning models. It's a hard problem >>If you don't have, so you guys got a lot going on. So, so talk about spaghetti, part of cap, Gemini, explain that relationship, cuz you're here at a show that, you know, you got, I can see the CAPI angle. This is like a little division. Is it a group? Are you guys like lone wolves? Like, what's it like, is this dedicated purpose built focus around aerospace? >>No, it's actually SOI was the, the name of the CAPI company from the beginning. And they relaunched the brand, uh, 2001, I think roughly 10, 20 years ago. So we actually celebrate some anniversary now. Uh, and it's a brand which is more local close to clients out in different cities. And we also tech companies, we are very close to the new technology, trying things out. And this is a perfect example of this. It was a crazy ID five years ago, 2017. And we started to bring in some clients explore, really? Open-minded see, can we do something on these satellite data? And then we took it step by step together of our clients. Yeah. And it's a small team where like 12 >>People. Yeah. And you guys are doing business development. So you have to go out there and identify the kinds of problems that match the scope of the scale. >>So what we're doing is we interact with our clients, do some simple workshops or something and try to identify like the really valuable problems like this Bruce Park people that that's one of those. Yep. And then we have to sort of look at, do we think we can do something? Is it realistic? And we will not be able to answer that to 100% because then there's no innovation in this at all. But we say, well, we think we can do it. This will be a hard problem, but we do think we can do it. And then we basically just go for it. And this one we did in 11 to 12 weeks, a tightly focused team, uh, and just went at it, uh, super slim process and got the job done and uh, the >>Results. Well, it's interesting. You have a lot of use cases. We gotta go down, do that face to face belly to belly, you know, body to body sales, BI dev scoping out, have workshops. Now this market here, Remar, they're all basically saying a call to arms more money's coming in. The problems are putting on the table. The workshop could be a lunch meeting, right. I mean, because Artis and there's a big set of problems to tackle. Yes. So I mean, I'm just oversimplifying, but that being said, there's a lot going on opportunity wise here. Yeah. That's not as slow maybe as the, the biz dev at, you know, coming in, this is a huge demand. It will be >>Explode. >>What's your take on the demand here, the problems that need to be solved and what you guys are gonna bring to bear for the problem. >>So now we have been focus mainly in vegetation management and forestry, but vegetation management can be applicable in utility as well. And we actually went there first had some struggle because it's quite detailed information that's needed. So we backed out a bit into vegetation in forestry again, but still it's a lot of application in, in, uh, utility and vegetation management in utility. Then we have a whole sustainability angle think about auditing of, uh, rogue harvesting or carbon offsetting in the future, even biodiversity, offsetting that could be used. >>And, and just to point out and give it a little extra context, all the keynotes, talk about space as a global climate solution, potentially the discoveries and or also the imagery they're gonna get. So you kind of got, you know, top down, bottoms up. If you wanna look at the world's bottom and space, kind of coming together, this is gonna open up new kinds of opportunities for you guys. What's the conversation like when you, when this is going on, you're like, oh yeah, let's go in. Like, what are you guys gonna do? What's the plan, uh, gonna hang around and ride that wave. >>I think it's all boils down to finding that use case that need to be sold because now we understand the satellite scene, they are there. We could, there is so many new satellites coming up already available. They can come up the cloud platform, AWS, it's great. We have all the capabilities needed. We have AI and ML models needed data science skills. Now it's finding the use cases together with clients and actually deliver on them one by >>One. It's interesting. I'd like to get your reaction to this Marcus two as well. What you guys are kind of, you have a lot bigger and, and, and bigger than some of the startups out there, but a startup world, they find their niches and they, the workflows become the intellectual property. So this, your techniques of layering almost see is an advantage out there. What's your guys view of that on intellectual property of the future, uh, open source is gonna run all the software. We know that. So software's no going open source scale and integration. And then new kinds of ways are new methods. I won't say for just patents, but like just for intellectual property, defen differentiation. How do you guys see this? As you look at this new frontier of intellectual property? >>That's, it's a difficult question. I think it's, uh, there's a lot of potential. If you look at open innovation and how you can build some IP, which you can out license, and some you utilize yourself, then you can build like a layer business model on top. So you can find different channels. Some markets we will not go for. Maybe some of our models actually could be used by others where we won't go. Uh, so we want to build some IP, but I think we also want to be able to release some of the things we do >>Open >>Works. Yeah. Because it's also builds presence. It it's >>Community. >>Yeah, exactly. Because this, this problem is really hard because it's a global thing. And, and it's imagine if, if you have a couple of million acres of forest and you just don't go out walking and trying to check what's going on because it's, you know, >>That's manuals hard. Yeah. It's impossible. >>So you need this to scale. Uh, and, and it's a hard problem. So I think you need to build a community. Yeah. Because this is, it's a living organism that we're trying to monitor. If you talk about visitation of forest, it's, it's changing throughout the year. So if you look at spring and then you look at summer and you look at winter, it's completely different. What you see. Yeah. Yeah. So >>It's, it's interesting. And so, you know, I wonder if, you know, you see some of these crowdsourcing models around participation, you know, small little help, but that doesn't solve the big puzzle. Um, but you have open source concepts. Uh, we had Anna on earlier, she's from the Amazon sustainability data project. Yeah, exactly. And then just like open up the data. So the data party for her. So in a way there's more innovation coming, potentially. If you can get that thing going, right. Get the projects going. Exactly. >>And all this, actually our work is started because of that. Yes, exactly. So European space agency, they decided to hand out this compar program and the, the Sentinel satellites central one and two, which we have been working with, they are freely available. It started back in 2016, I think. Yeah. Uh, and because of that, that's why we have this work done during several years, without that data freely available, it wouldn't have happened. Yeah. I'm, I'm >>Pretty sure. Well, what's next for you guys? Tell, tell me what's happening. Here's the update put a plug in for the, for the group. What are you working on now? What's uh, what are you guys looking to accomplish? Take a minute to put a plug in for the opportunity. >>I would say scaling this scaling, moving outside. Sweden. Of course we see our model that they work in in us. We have tried them in Canada. We see that we work, we need to scale and do field validation in different regions. And then I would say go to the sustainability area. This goes there, there is a lot of great >>Potential international too is huge. >>Yeah. One area. I think that is really interesting is the combination of understanding the, like the carbon sink and the sequestration and trying to measure that. Uh, but also on top of that, trying to classify certain Keystone species habitats to understand if they have any space to live and how can we help that to sort of grow back again, uh, understanding the history of the, sort of the force. You have some date online, but trying to map out how much of, of this has been turned into agricultural fields, for example, how much, how much of the real old forest we have left that is really biodiverse? How much is just eight years young to understand that picture? How can we sort of move back towards that blueprint? We probably need to, yeah. And how can we digitize and change forestry and the more business models around that because you, you can do it in a different way, or you can do both some harvesting, but also, yeah, not sort of ruining the >>Whole process. They can be more efficient. You make it more productive, save some capital, reinvest it in better ways >>And you have robotics and that's not maybe something that we are not so active in, but I mean, starting to look at how can autonomy help forestry, uh, inventory damages flying over using drones and satellites. Uh, you have people looking into autonomous harvesting of trees, which is kind of insane as well, because they're pretty big <laugh> but this is also happening. Yeah. So I mean, what we're seeing here is basically, >>I mean, we, I made a story multiple times called on sale drone. One of my favorite stories, the drones that are just like getting Bob around in the ocean and they're getting great telemetry data, cuz they're indestructible, you know, they can just bounce around and then they just transmit data. Exactly. You guys are creating a opportunity. Some will say problem, but by opening up data, you're actually exposing opportunities that never have been seen before because you're like, it's that scene where that movie, Jody frost, a contact where open up one little piece of information. And now you're seeing a bunch of new information. You know, you look at this large scale data, that's gonna open up new opportunities to solve problems that were never seen before. Exactly. You don't, you can't automate what you can't see. No. Right. That's the thing. So no, we >>Haven't even thought that these problems can be solved. It's basically, this is how the world works now. Because before, when you did remote sensing, you need to be out there. You need to fly with a helicopter or you put your boots on out and go out. Now you don't need that anymore. Yeah. Which opened up that you could be, >>You can move your creativity in another problem. Now you open up another problem space. So again, I like the problem solving vibe of the, it's not like, oh, catastrophic. Well, well, well the earth is on a catastrophic trajectory. It's like, oh, we'll agree to that. But it's not done deal yet. <laugh> I got plenty of time. Right. So like the let's get these problems on the table. Yeah. Yeah. And I think this is, this is the new method. Well, thanks so much for coming on the queue. Really appreciate the conversation. Thanks a lot. Love it. Opening up new world opportunities, challenges. There's always opportunities. When you have challenges, you guys are in the middle of it. Thanks for coming on. I appreciate it. Thank you. Thanks guys. Okay. Cap Gemini in the cube part of cap Gemini. Um, so Getty part of cap Gemini here in the cube. I'm John furrier, the host we're right back with more after this short break.

>>Okay, welcome back everyone. This is the Cube's coverage of S reinve rein Mars. I said reinvent all my VES months away. Re Mars machine learning, automation, robotics, and space. I'm John feer, host of the cube, an exciting guest here, bringing on special guest more robot robots are welcome on the cube. We're gonna have that segment here. Mike Dooley co-founder and CEO of Labrador systems. Mike, welcome to the cube. Thanks. >>Coming on. Thank, thank you so much. Yeah. Labrador systems. We're a company is developing a new type of assistive robot for people in the home. And you know, our mission is really to help people live independently. And so we're about to show a robot that's it looks like my, what used to be in a warehouse or other places, but it's being designed to be both robust enough to operate in real world settings, help people that may be aging and using a Walker wheelchair. A cane could have early onset health conditions like Parkinson's and things like that. So >>Let me, let me set this up first, before you get into the, the demo, because I think here at re Mars, one of the things that's coming outta the show besides the cool vibe, right? Is that materials handling? Isn't the only thing you've seen with robotics. Yeah. You're seeing a lot more life industrial impact. And this is an example of one of that, isn't >>It? Yeah. We just actually got an award. It's a Joseph EGL Bergo was the first person to actually put robots in factories and automation. And in doing that, um, he set up grant for robots going beyond that, to help people live in it. So we're the first recipient of that. But yeah, I think that robots, they're not the, what you think about with Rosie yet. We're the wrong way from that, but they're, they can do really meaningful things. >>And before we get the demo, your mission hearing, what you're gonna show here is a lot of hard work and we know how hard it is. What's the mission. What's the vision. >>The mission is to help people live more independently on their own terms. Uh, we're, there's, it's an innate part of the human condition that at some point in our lives, it becomes more difficult to move ourselves or move things around it. And that is a huge impact on our independence. So when we're putting this robot in pilots, we're helping people try to regain degrees of independence, be more active deal with whatever situation they want, but under their terms and have, have control over their life. >>Okay, well, let's get into it. May I offer you a glass of water? Well, you >>Know, I have a robot that just happens to be really good at delivering things, including water. Um, we just actually pulled these out of our refrigerator on our last demo. So why don't we bring over the retriever? And so we're gonna command it to come on in. So this is a Labrador retriever. These robots have been in homes. This robot itself has been in homes, helping people do activities like this. It's able to sort of go from place to place it automatically navigates itself. Uh, just like we've been called a self-driving shelf, um, as an example, but it's meant to be very friendly, can come to a position like this could be by my armchair and it would automatically park. And then I could do something like I can pick up, okay, I want some water and maybe I want to drink it out of a cup and I can do this. And if I have a cough or something else, cough drops. My phone, all sorts of things can be in there. Um, so the purpose of the retriever is really to be this extra pair of hands, to keep things close by and move things. And it can automatically adjust to any hide or position. And if I, even if I block it like a safety, it, it >>Stops. And someone who say disabled or can't move is recovering or has some as aging or whatever the case is. This comes to them. It's autonomous in it sense. Is that that what works or yeah. Is it guided? How does >>It, it works on a series of bus stops. So the in robotics, we call those way points. But when we're talking to people, the bus stops are the places you want it to go. You have a bus stop by the front door, your kitchen sink, the refrigerator, your armchair, the laundry machine, you won't closet it. <laugh>. And with that simple metaphor, we, we train the robot in a couple hours. We create all these routes, just like a subway map. And then the robot is autonomous. So I can hit a button. I can hit my cell phone, or I can say Alexa ass lab, one to come to the kitchen. The robot will autonomously navigate through everything, go around the pets park itself. And it raises and lowers to bring things with and reach. So I'm sitting and it might lower itself down. So I can just comfortably get something at the kitchen. I, it could just go right to the level of the countertop. So it's very easy for someone that has an issue to move things with with limited, uh, challenges. >>And this really illustrates to this show again. Yeah. Talk about the impact here. Cause we're at a historic moment in robotics. >>We are. Yeah. >>What's your reaction to that? Tell your, share your vision >>On that. I've been in robotics for 25 years. Um, and I started, I actually started working actually at Lego and launching Lego Mindstorms, the end of the nineties. So I have like CEO just last night again, they gush over like you did that. Yeah. <laugh> and again, I'm pretty old school. And so we've my career. If I've been working through from toys onto like robotic floor cleaners, the algorithms that are on Roomba today came from the startup that we were all part of. We're, we're moving things to be bigger and bigger and have a bigger >>Impact. What's it feel like? I mean, cuz I mean I can see the experience and by the way, it's hardcore robotics communities out there, but now it's still mainstream. It's opening up the aperture of robotics. Yeah. It's the prime time is right now and it's an inflection point. >>Well, and it's also a point where we desperately need it. So we have incredible work for shortages <laugh> and it's not that we're, these robots are not to take people jobs away it's to do the work that people don't want to do and try to make, you know, free them up for things that are more important. Yeah. In senior care, that's the high touch we want caregivers to be helping people get outta their bed, help them safely move from place to place things that robots aren't at yet. Yeah. But for getting the garbage, for getting a drink or giving the person the freedom to say, do I wanna ask my caregiver or my spouse to do that? Or do I wanna do it myself? And so robots can be incredibly liberating experience if they're, if they're done in the right way and they're done well, >>It's a choice. It actually comes down to choice. I remember this argument way back when, oh, ATM's gonna kill the bank teller. In fact more bank tellers emerged. Right. And so there's choices come out there and, but there's still more advances to do. What is you, what do you see as milestones for the industry as you start to seeing better handling better voice activation cameras on board. I noticed some cameras in there. Yeah. So we're starting to see the, some of the smaller, faster, cheaper >>It's it's especially yeah. Faster. Cheaper is what we're after. So can we redo? So like the gyros that are on this type of robot used to be like in the tens of thousands of dollars 20 or 30 years ago. And, and then when you started seeing Roomba and the floor cleaners come out, those started what happened was basically the gyro on here that what's happening in consumer electronics, the ability for the iPhone to play, you know, the game in turn and, and do portrait and landscape. That actually is what enables all these robots that clean your floors to do very tight angles. What we're doing is this migration of consumer electronics then gets robbed and, and adopted over in that. So it's really about it's I, it's not that you're gonna see things radically change. It's just that you're gonna see more and more applications get more sophisticated and become more affordable. Our target is to bring this for a few hundred dollars a month into people's homes. Yeah. Yeah. Um, and make that economy work for as many people as >>Possible. Yeah. Mike, what a great, great illustration of great point there now on your history looking forward. Okay. Smaller Fest are cheaper. Yeah. You're gonna see a human aspect. So technology's kind of getting out of the way now you got a lot in the cloud, you got machine learning, big thing here. There's a human creative side now gonna be a big part of this. Yeah. Can you talk about like how you see that unfolding? Because again, younger people gonna come in, you got a lot more things pre-built I just saw a swaping on stage saying, oh, we, we write subroutines automatically the machine learning like, oh my God, that's so cool. Like, so more is coming for, to, for builders, right. To build what's the playbook gonna look like? How do you see the human aspect, creative crafting building? >>I, I it's, you know, it's a hard Fu future to predict. I think the issue is that humans are always gonna have to be more clever than the AI <laugh>, you know, I, I can't say that enough is that AI can solve some things and it can get smarter and smarter. You task that over and then let's work on the things that can't do. And I think that's intellectually challenging. Like, and I, and I think we have a long way to go, uh, to sort of keep on pushing that forward. So the whole mission is people get to do more interesting things with their life, more dynamic. Think about what the machines should be working on. Yeah. And then move on to the next things. >>Well, a lot of good healthcare implications. Yeah. Uh, senior living people who are themselves, >>All those are place. Yeah. >>Now that you have, um, this kind of almost a perfect storm of innovation coming, and I just think it's gonna be the beginning. You're gonna see a lot of young people come in. Yeah. And a lot of people in school now going down to the elementary school level yeah. Are really immersed in robotics. They're born with it. And certainly as they get older, what kind of disciplines do you see coming into robot? I used to be pretty clear. Yeah. Right. Nerdy, builder, builder. Now it's like what? I got Mac and rice. My code. >>Yeah. My, my co-founder and CEO has a good example. Anybody we interview, we say we really like it. If you think of yourself as an astronaut, going on to a space mission. And, and it's really appropriate being here at R Mars is that normally the astronaut has one specialty, but they have to know enough of the other skills to be able to help out. In case of an emergency robotics is so complex. There's so there's mechanical, there's electrical, there's software, they're perceptual, there's user interface, all of those Fs together. So when we're trying to do a demo and something goes wrong, I can't say why. I only do mechanical. Yeah. You got it. You really have to have a system. So I think if any system architects, people that if you're gonna, if, if you're gonna be, if mechanical is your thing, you better learn a little bit electrical and software. Yeah. If software is your thing, you better not just write code because you need to understand where you're >>Your back. Well, the old days you have to know for trend to run any instrumentation in the old days. So same kind of vibe. So what does that impact on the teamwork side? Because now I can imagine, okay, you got some general purpose knowledge, so math, science, all the disciplines, but the specialties there, I love that right now. Teamwork. Yeah. Because you, you know, I could be a generalism at some point. There's another component I'm gonna need to call my teammate for. >>Yeah. Yeah. And you have to have, yeah. So it, yeah, we're a small team, so it's a little bit easier right now, but even the technology. So like there's a, what, this is, this runs on Linux and that runs on Ross, which is a robotics operating system. The modules are, are the, are sorry, the modules, I mean redundant there, but the, the part that makes the robot go, okay, I'm gonna command it to go here. It's gonna go around it, see an obstacle. This module kicks in, even the elements become module. So that's part of how teams work is that, and, and Amazon has a rule around that is that everything has to have an API. Yeah. I have to be able to express my work and the way that somebody else can come in and talk to it in a very easy way. So you're also going away from like, sort of like the hidden code that only I touch you can't have ownership of that. You have to let your team understand how it works and let them control it and edit it. Well, >>Super exciting. Dan, first of all, great to bring robots on the cube set. Thanks to your team here. Doing that. Yeah. Um, talk about the company. Um, put a plug in, what are you guys doing? Sure. Raising money, getting more staff, more sales. We're give, give a commercial. >>Yeah. So we, we closed the seed round. So we've been around it's actually five years next month. Um, did pre-seed and then we closed the seed round that we announced back at CS. So we debuted the retriever for the first time we had it under wraps. We had it in people's homes for a year before we did that. Um, I, Amazon was one of our early investors and they actually co-led on this last round, along with our friends at iRobot. So yeah. Uh, so we've raised that we're right in the next phase of deploying this, especially going more into senior living now that that's opening up with COVID coming down and looking at helping these workforce issues where there's that crisis. So we'll be raising later this year. So we're starting to sort of do the preview for series a. We're starting to take those pre-orders for robots and for Lois. And then our goal is we're and we're actually already at the factory. So we've been converting this, these there's a version of this robot underway right now at the factory that will probably have engineering units at the end of this year. Yeah. Goal is for, uh, full production with all the supply chain issues for second half of, of next >>Year. Yeah. Well, congratulations. It's a great product. And I gotta ask you what's on the roadmap, how you see this product unfolding. What's the wishlist look like if you had all the dough in the world, what would you do next? What would you be putting on there? Sure. If you had the magic wand what's happening, >>It's a couple variables. I think it's scale. So it's driving the, this whole thing is designed to go down in cost, which improves basically accessibility. More people can afford it. The health system, Medicare, those sorts of folks. See it one. So basically get us into reduction and get us into volume is one part, I think the other ones is adding layers. I, what we, when we see our presentation and the speech we're doing tomorrow, we see this as a force multiplier for a lot of other things in healthcare. So if I bring the blood pressure cuff, like we have on the retrieval, I can be a physical reminder to take your medication, to take the, my, my readings, or we are just con having a conversation with some of our friends of Amazon is bringing an echo show to you when you want to have a conversation and take it away. >>When you don't think about that metaphor of how do I wanna live my life and what do I have control over? And then on top of it, the sensors on the robot, they're pretty sophisticated. So in my case, my mom is still around she's 91, but now in a hospital beded wheelchair could, we've seen her walking differently early, early on, and using things like Intel, real sense and, and computer vision and AI to detect things and just say to her, don't even tell anybody else, we're noticing this. Do you wanna share this with your doctor? Yeah. That's the world. I think that what we're trying to do is lay this out as version 1.0, so that when folks like us are around, it'll something like decades from now, life is so much more better for the options and choices we have. It's >>Really interesting. You know, I liked, um, kind of the theme here. There's a lot of day to day problems that people like to solve. And then there's like the new industrial problems that are emerging that are opportunities. And then there's the save the world kind of vibe. <laugh>, there's help people make things positive, right. You know, solve the climate problems, help people. And so we're kind of at this new era and it's beyond just like sustainability and, you know, bias. That's all gotta get done a new tipping point around the human aspect of >>Things. And you do it economically. I think sometimes you think that, okay, well, you're just doing this cuz you're, you're socially motivated and doesn't, you don't care how many you sell it to just so you can accomplish it. It's their link. The, the cheaper that we can make this, the more people you can impact. I think you're talking about the kids today is the work we did at Lego. In the end of the nineties, you made a, a robotics kit for 200 bucks and millions of kids. Yeah. Did that. And >>Grape pie. I mean, you had accessories to it. Make a developer friendly. >>Yeah, no, exactly. And we're getting all those requests. So I think that's the thing is like, get a new platform, learn what it's like to have this sort of capability and then let the market drive. It, let the people sort of the folks who are gonna be using it that are in a wheelchair, are dealing with Parkinson's or Ms, or other issues. What can we add to that ecosystem? So you it's, it's all about being very human centric in that. Yeah. And making the other parts of the economy make it work for them, make it so that the health system, they get an ROI on this so that, Hey, this is a good thing to put into people's homes. >>And well, I think you have the nice, attractive value proposition to investors. Obviously robotics is super cool and really relevant. Cool, cool. And relevant to me always is nice to have that. So check that, then you got the economics on price, pressure, prove the price down lower. Yeah. Open up the Tams of the market. Right. Make it more viable economically. >>Yeah, definitely. And then, and what we're having, what's driving us that wasn't around seven when we started this about four and a half years ago. Uh, my joke and I don't mean to offend them, but after doing pitching the vision of this in six months, don't be, >>Don't be afraid. We're do we, >>My, my joke. And I'm sort to see more bold about is that VCs don't think they're gonna get old. They're just gonna get rich. And so the idea is that they didn't see themselves in this position and we not Gloo and doom, you can work out, you can be active, but we're living older, longer. We are it's. My mom is born in the depression. She's been in a wheelchair for five years. She might be around for a good, another 10 or 15. And that's wonderful for her, but her need for care is really high. >>Yeah. And the pressure on the family too, there's always, there's always collateral damage on all these impacts. >>There's 53 million unpaid family caregivers in the us. Yeah. Just in the time that we've grown, been doing this, it's grown 4% a year and it's a complicated thing. And it's, it's not just the pressure on you to help your mom or dad or whoever. It's the frustration on their face when they have to always ask for that help. So it's, it's twofold. It's give them some freedom back so they can make a choice. Like my classic example is my mom wants tea. My dad's trying to watch the game. He, she asks for it. It's not hot enough. Sends it back. And that's a currency. Yeah, yeah. That she's losing and, and it's frustration as opposed to give her a choice to say, I'm gonna do this on my own. And I that's just, >>You wanna bring the computer out, do a FaceTime with the family, send it back. Or you mentioned the Alexa there's so many use cases. Oh >>No. We talked about, uh, we talked about putting like a, a device with a CA with a screen on it so she could chat and see pictures. And it says, I don't want to have this in my bedroom. That's my private space. Yeah. But if we could have the robot, bring it in when it's appropriate and take it on go the retriever that's that's >>The whole go fetch what I need right now. That's and then go lie down. Yeah. >>That's what I, I called >>Labrador. Doesn't lie down >>Actually. But well, it lowers down, it lowers down about 25 inches. That's about lying. >>Down's super exciting. And congratulations. I know, um, how passionate you are. It's obvious. Yeah. And being in the business so long, so many accomplishment you had. Yeah. But now is a whole new Dawn. A new era here. >>Yeah. Oh yeah. No, I, we just, it was real. It was on impromptu. It wasn't scheduled. There's a, a post circle on LinkedIn where all the robots got together. <laugh> you know, and they were seeing to hang out. No, and you're seeing stuff that wasn't possible. You look at this and you go, well, what's the big thing. It's a box on wheels. It's like, it wasn't possible to navigate something around the complexity of a home 10 years ago for the price we're doing. Yeah. It wasn't possible to wa have things that walk or spot that can go through construction sites. I, I think people don't realize it's it. It really is changing. And then we're, I think every five years you're gonna be seeing this more bold deployment of these things hitting our lives. It's >>It's super cool. And that's why this show's so popular. It's not obvious to mainstream, but you look at the confluence of all those forces coming together. Yeah. It's just a wonderful thing. Thanks for coming on. Appreciate >>It really, really appreciate you for this >>Time. Great success. Great demo. Mike, do cofounder, the CEO of Labrador systems. Check him out. They have the retriever, uh, future of robotics here. It's all impact all life on the planet. And more space. Two is to keep coverage here at re Mars, stay tuned for more live coverage. After this short break.

>>Okay, welcome back. Everyone. Live cube coverage here in Las Vegas for Amazon re Mars hot event, machine learning, automation, robotics, and space. Two days of live coverage. We're talking to all the hot technologists. We got all the action startups and segment on sustainability and F pan hero for vet global lead, Amazon sustainability data initiative. Thanks for coming on the cube. Can I get that right? Can >>You, you, you did. >>Absolutely. Okay, great. <laugh> thank >>You. >>Great to see you. We met at the analyst, um, mixer and, um, blown away by the story going on at Amazon around sustainability data initiative, because we were joking. Everything's a data problem now, cuz that's cliche. But in this case you're using data in your program and it's really kind of got a bigger picture. Take a minute to explain what your project is, scope of it on the sustainability. >>Yeah, absolutely. And thank you for the opportunity to be here. Yeah. Um, okay. So, um, I, I lead this program that we launched several years back in 2018 more specifically, and it's a tech for good program. And when I say the tech for good, what that means is that we're trying to bring our technology and our infrastructure and lend that to the world specifically to solve the problems related to sustainability. And as you said, sustainability, uh, inherently needs data. You need, we need data to understand the baseline of where we are and also to understand the progress that we are making towards our goals. Right? But one of the big challenges that the data that we need is spread everywhere. Some of it is too large for most people to be able to, um, access and analyze. And so, uh, what we're trying to tackle is really the data problem in the sustainability space. >>Um, what we do more specifically is focus on Democrat democratizing access to data. So we work with a broader community and we try to understand what are those foundational data sets that most people need to use in the space to solve problems like climate change or food security or think about sustainable development goals, right? Yeah. Yeah. Like all the broad space. Um, and, and we basically then work with the data providers, bring the data to the cloud, make it free and open to everybody in the world. Um, I don't know how deep you want me to go into it. There's many other layers into that. So >>The perspective is zooming out. You're, you're, you're looking at creating a system where the democratizing data means making it freely available so that practitioners or citizens, data, Wrangler, people interested in helping the world could get access to it and then maybe collaborate with people around the world. Is that right? >>Absolutely. So one of the advantages of using the cloud for this kind of, uh, effort is that, you know, cloud is virtually accessible from anywhere where you have, you know, internet or bandwidth, right? So, uh, when, when you put data in the cloud in a centralized place next to compute, it really, uh, removes the, the need for everybody to have their own copy. Right. And to bring it into that, the traditional way is that you bring the data next to your compute. And so we have this multiple copies of data. Some of them are on the petabyte scale. There's obviously the, the carbon footprint associated with the storage, but there's also the complexity that not everybody's able to actually analyze and have that kind of storage. So by putting it in the cloud, now anyone in the world independent of where of their computer capabilities can have access to the same type of data to solve >>The problems. You don't remember doing a report on this in 2018 or 2017. I forget what year it was, but it was around public sector where it was a movement with universities and academia, where they were doing some really deep compute where Amazon had big customers. And there was a movement towards a open commons of data, almost like a national data set like a national park kind of vibe that seems to be getting momentum. In fact, this kind of sounds like what you're doing some similar where it's open to everybody. It's kinda like open source meets data. >>Uh, exactly. And, and the truth is that these data, the majority of it's and we primarily work with what we call authoritative data providers. So think of like NASA Noah, you came me office organizations whose mission is to create the data. So they, their mandate is actually to make the data public. Right. But in practice, that's not really the case. Right. A lot of the data is stored like in servers or tapes or not accessible. Um, so yes, you bring the data to the cloud. And in this model that we use, Amazon never actually touches the data and that's very intentional so that we preserve the integrity of the data. The data provider owns the data in the cloud. We cover all the costs, but they commit to making it public in free to anybody. Um, and obviously the computer is next to it. So that's, uh, evaluated. >>Okay. Anna. So give me some examples of, um, some successes. You've had some of the challenges and opportunities you've overcome, take me through some of the activities because, um, this is really needed, right? And we gotta, sustainability is top line conversation, even here at the conference, re Mars, they're talking about saving climate change with space mm-hmm <affirmative>, which is legitimate. And they're talking about all these new things. So it's only gonna get bigger. Yeah. This data, what are some of the things you're working on right now that you can share? >>Yeah. So what, for me, honestly, the most exciting part of all of this is, is when I see the impact that's creating on customers and the community in general, uh, and those are the stories that really bring it home, the value of opening access to data. And, and I would just say, um, the program actually offers in addition to the data, um, access to free compute, which is very important as well. Right? You put the data in the cloud. It's great. But then if you wanna analyze that, there's the cost and we want to offset that. So we have a, basically an open call for proposals. Anybody can apply and we subsidize that. But so what we see by putting the data in the cloud, making it free and putting the compute accessible is that like we see a lot, for instance, startups, startups jump on it very easily because they're very nimble. They, we basically remove all the cost of investing in the acquisition and storage of the data. The data is connected directly to the source and they don't have to do anything. So they easily build their applications on top of it and workloads and turn it on and off if you know, >>So they don't have to pay for it. >>They have to pay, they basically just pay for the computes whenever they need it. Right. So all the data is covered. So that makes it very visible for, for a lot of startups. And then we see anything like from academia and nonprofits and governments working extensively on the data, what >>Are some of the coolest things you've seen come out of the woodwork in terms of, you know, things that built on top of the, the data, the builders out there are creative, all that heavy, lifting's gone, they're being creative. I'm sure there's been some surprises, um, or obvious verticals that jump healthcare jumps out at me. I'm not sure if FinTech has a lot of data in there, but it's healthcare. I can see, uh, a big air vertical, obviously, you know, um, oil and gas, probably concern. Um, >>So we see it all over the space, honestly. But for instance, one of the things that is very, uh, common for people to use this, uh, Noah data like weather data, because no, basically weather impacts almost anything we do, right? So you have this forecast of data coming into the cloud directly streamed from Noah. And, um, a lot of applications are built on top of that. Like, um, forecasting radiation, for instance, for the solar industry or helping with navigation. But I would say some of the stories I love to mention because are very impactful are when we take data to remote places that traditionally did not have access to any data. Yeah. And for instance, we collaborate with a, with a program, a nonprofit called digital earth Africa where they, this is a basically philanthropically supported program to bring earth observations to the African continents in making it available to communities and governments and things like illegal mining fighting, illegal mining are the forestation, you know, for mangroves to deep forest. Um, it's really amazing what they are doing. And, uh, they are managing >>The low cost nature of it makes it a great use case there >>Yes. Cloud. So it makes it feasible for them to actually do this work. >>Yeah. You mentioned the Noah data making me think of the sale drone. Mm-hmm <affirmative> my favorite, um, use case. Yes. Those sales drones go around many them twice on the queue at reinvent over the years. Yeah. Um, really good innovation. That vibe is here too at the show at Remar this week at the robotics showcases you have startups and growing companies in the ML AI areas. And you have that convergence of not obvious to many, but here, this culture is like, Hey, we have, it's all coming together. Mm-hmm <affirmative>, you know, physical, industrial space is a function of the new O T landscape. Mm-hmm <affirmative>. I mean, there's no edge in space as they say, right. So the it's unlimited edge. So this kind of points to the major trend. It's not stopping this innovation, but sustainability has limits on earth. We have issues. >>We do have issues. And, uh, and I, I think that's one of my hopes is that when we come to the table with the resources and the skills we have and others do as well, we try to remove some of these big barriers, um, that make it things harder for us to move forward as fast as we need to. Right. We don't have time to spend that. Uh, you know, I've been accounted that 80% of the effort to generate new knowledge is spent on finding the data you need and cleaning it. Uh, we, we don't have time for that. Right. So can we remove that UN differentiated, heavy lifting and allow people to start at a different place and generate knowledge and insights faster. >>So that's key, that's the key point having them innovate on top of it, right. What are some things that you wanna see happen over the next year or two, as you look out, um, hopes, dreams, KPIs, performance metrics, what are you, what are you driving to? What's your north star? What are some of those milestones? >>Yeah, so some, we are investing heavily in some areas. Uh, we support, um, you know, we support broadly sustainability, which as, you know, it's like, it's all over, <laugh> the space, but, uh, there's an area that is, uh, becoming more and more critical, which is climate risk. Um, climate risk, you know, for obvious reasons we are experienced, but also there's more regulatory pressures on, uh, business and companies in general to disclose their risks, not only the physical, but also to transition risks. And that's a very, uh, data heavy and compute heavy space. Right. And so we are very focusing in trying to bring the right data and the right services to support that kind of, of activity. >>What kind of break was you looking for? >>Um, so I think, again, it goes back to this concept that there's all that effort that needs to be done equally by so many people that we are all repeating the effort. So I'll put a plug here actually for a project we are supporting, which is called OS climates. Um, I don't know if you're familiar with it, but it's the Linux foundation effort to create an open source platform for climate risk. And so they, they bought the SMP global Airbus, you know, Alliance all these big companies together. And we are one of the funding partners to basically do that basic line work. What are the data that is needed? What are the basic tools let's put it there and do the pre-competitive work. So then you can do the build the, the, the competitive part on top of it. So >>It's kinda like a data clean room. >>It kind of is right. But we need to do those things, right. So >>Are they worried about comp competitive data or is it more anonymized out? How do you, >>It has both actually. So we are primarily contributing, contributing with the open data part, but there's a lot of proprietary data that needs to be behind the whole, the walls. So, yeah, >>You're on the cutting edge of data engineering because, you know, web and ad tech technologies used to be where all that data sharing was done. Mm-hmm <affirmative> for the commercial reasons, you know, the best minds in our industry quoted by a cube alumni are working on how to place ads better. Yeah. Jeff Acker, founder of Cloudera said that on the cube. Okay. And he was like embarrassed, but the best minds are working on how to make ads get more efficient. Right. But that tech is coming to problem solving and you're dealing with data exchange data analysis from different sources, third parties. This is a hard problem. >>Well, it is a hard problem. And I'll, I'll my perspective is that the hardest problem with sustainability is that it goes across all kinds of domains. Right. We traditionally been very comfortable working in our little, you know, swimming lanes yeah. Where we don't need to deal with interoperability and, uh, extracting knowledge. But sustainability, you, you know, you touch the economic side, it touches this social or the environmental, it's all connected. Right. And you cannot just work in the little space and then go sets the impact in the other one. So it's going to force us to work in a different way. Right. It's, uh, big data complex data yeah. From different domains. And we need to somehow make sense of all of it. And there's the potential of AI and ML and things like that that can really help us right. To go beyond the, the modeling approaches we've been done so >>Far. And trust is a huge factor in all this trust. >>Absolutely. And, and just going back to what I said before, that's one of the main reasons why, when we bring data to the cloud, we don't touch it. We wanna make sure that anybody can trust that the data is nowhere data or NASA data, but not Amazon data. >>Yes. Like we always say in the cube, you should own your data plane. Don't give it up. <laugh> well, that's cool. Great. Great. To hear the update. Is there any other projects that you're working on you think might be cool for people that are watching that you wanna plug or point out because this is an area people are, are leaning into yeah. And learning more young, younger talents coming in. Um, I, whether it's university students to people on side hustles want to play with data, >>So we have plenty of data. So we have, uh, we have over a hundred data sets, uh, petabytes and petabytes of data all free. You don't even need an AWS account to access the data and take it out if you want to. Uh, but I, I would say a few things that are exciting that are happening at Mars. One is that we are actually got integrated into ADX. So the AWS that exchange and what that means is that now you can find the open data, free data from a STI in the same searching capability and service as the paid data, right. License data. So hopefully we'll make it easier if I, if you wanna play with data, we have actually something great. We just announced a hackathon this week, uh, in partnership with UNESCO, uh, focus on sustainable development goals, uh, a hundred K in prices and, uh, so much data <laugh> you >>Too years, they get the world is your oyster to go check that out at URL at website, I'll see it's on Amazon. It use our website or a project that can join, or how do people get in touch with you? >>Yeah. So, uh, Amazon SDI, like for Amazon sustainability, that initiative, so Amazon sdi.com and you'll find, um, all the data, a lot of examples of customer stories that are using the data for impactful solutions, um, and much more >>So, and these are, there's a, there's a, a new kind of hustle going out there, seeing entrepreneurs do this. And very successfully, they pick a narrow domain and they, they own it. Something really obscure that could be off the big player's reservation. Mm-hmm <affirmative> and they just become fluent in the data. And it's a big white space for them, right. This market opportunities. And at the minimum you're playing with data. So this is becoming kind of like a long tail domain expertise, data opportunity. Yeah, absolutely. This really hot. So yes. Yeah. Go play around with the data, check it outs for good cause too. And it's free. >>It's all free. >>Almost free. It's not always free. Is it >>Always free? Well, if you, a friend of mine said is only free if your time is worth nothing. <laugh>. Yeah, >>Exactly. Well, Anna, great to have you on the cube. Thanks for sharing the stories. Sustainability is super important. Thanks for coming on. Thank you for the opportunity. Okay. Cube coverage here in Las Vegas. I'm Sean. Furier, we've be back with more day one. After this short break.

(inspiring music) >> Welcome back to The Cube's coverage here in Las Vegas. I'm John Furrier for re:Mars coverage. Two days of live action, a lot of things happening in space, robotics, automation, and machine learning. That's Mars spelled backwards, but that's machine learning, automation, robotics and space. Got a great guest, Jens Ortmann, associate director at Boston Consulting Group, also known as BCG. Jens, welcome to The Cube. >> Thank you very much. >> So tell me what you're working on. You've got a very cool project you're working on, 'Involved'. Take us through what it is, explain what the project is. >> Yeah, so I'm part of the data science unit within BCG Gamma and I'm focusing on solving business problems for the automotive industry. What I would like to talk about is actually a small internal site project that we were building. It's a conversion rate engine, where we built an advanced analytics tool that computes the conversion rate for car dealerships, at scale. So for every single car dealer in a market, we can compute the conversion rate. >> John: What is a conversion rate? Can you explain that? >> So a conversion rate is very simple. It's actually out of the people that come into your car dealership, how many do you, as a car dealer, manage to sell a car to? >> So, what's your sell, through monthly kind of- >> Per visitor that come into, so your walk-ins. >> So, physical? >> Physical, yeah. So this was for physical stores. It's actually a key metric for sales performance for car dealerships, or for the automotive manufacturers to be aware of. >> So I'm watching here in the show floor at re:Mars, you've got the 'Just Walk Through', which is Amazon's 'take whatever you want and go', are you seeing you're getting analytics on like people coming in, you can see them, there's a drop off rate? Take me through how it works, the challenges because I don't envision like, "Oh, so they walked in and they left but they didn't leave with a car." It's not take and walk out, it's not grab and go. But the concept of using computer vision, I can imagine it being a popular thing. So how do you measure this, people coming in? >> It's actually a big challenge that we learned when we were doing this project. Traditionally, people were measuring it with like these laser sensors but the signal is very, very messy. Now when we wanted to do it at scale, we partnered with an Israeli startup called Play Sense, who aggregate mobile phone data. So we used mobile footfall data to measure how many people visit a store. So it actually is a combination of three main data sources to get to the conversion rate. One, as I mentioned, the mobile footfall data, the second one is building footprints, actual outlines of buildings that we source from the cadastral agency that we need to use it to cut out the footfall data to get the visitors. And the third one, of course, is sales that we get from the official car registration data. Then we combine those to have the key numbers. >> Is there a facial recognition involved in this? >> There's no facial recognition involved. >> So the tire kickers that come in and kick the tires and leave, but might come back. Is that factored in too, or? >> So there is a lot of pre-processing going on to really only get the signals from visitors. So filtering out people that maybe come into the store after hours, cleaning crews, people that come into the store every day, people that work there, they would be in the footfall data. So we applied some logic to identify exactly those people that are most likely actually visitors interested in buying a car >> Well everyone can relate to buying a car, obviously. I wanted you to step back and you mentioned scale. Can you scope the scale of the problem for us? How big is this observation space? What systems are involved? 'Cause when you say scale, I'm thinking all the dealerships in the aggregate. Or, is it by franchise or is it anonymous data? Can you scale the scope of this thing, or scope the scale? >> So we built this as a prototype for the German market and we used the top 10 car brands in Germany. They have around 10,000 car dealerships, for which we all have data. The actual mobile phone footprint data, it's a lot more. I think it was 30 million data points. >> Are you triangulating that? How does that mobile data work? Signal? >> So the mobile data is coming through apps. So mobile apps where you allow the app to track your location. >> Got it, okay. >> That gets anonymized and then you have these mobile data aggregators, like Play Sense. >> Got it, okay. >> That sell the data on. >> So you have to plug into a lot of systems? >> Yes. >> To make all this work. >> Yes and a lot of different data sources. >> And how easy is that? What's the challenge there? Is it cloud integration? How are you guys pulling this together? >> So we build it as a prototype initially, based on our own internal infrastructure, using basic Python and regular cloud infrastructure to process the data. >> All right, so I'm looking at my notes here. Data sets, you have a lot of data sets. What kind of analytics are you running on that? Can you share some examples? >> So I have to be careful since we filed a patent on this but a lot of the thing is actually in data processing, making sure that the data points we get are accurate and usable for this, and then differentiating between the different types of businesses that people are running. So there is on the one hand, you have the problem of outliers, basically filtering out when numbers don't make sense. On the other hand, there is a lot going on in the business itself. Like what do you do when a car dealership sells cars of multiple brands? You see only one visitor seeing cars of different brands but you see sales for two different types of brands. So this is just two examples of some of the processing that we had to implement to make this happen. >> So where can people find out information on this project? Or is it pretty much not public? Are you sharing anything publicly? >> So currently, we have held off the publication on this because we filed a patent on it. We're now about to go to market, building out a solution for the US as well, to then bring this to clients. >> What do you think about this show here at re:Mars? What's your assessment of the vibe? What's it like? Share with the folks who aren't here, what's your takeaway? >> It's really fun. It's really impressive. And it has a great, really inspiring vibe of cool innovative solutions. >> Yeah, you get the creative geniuses, you got the industrial geniuses, you got the software geniuses, all kind of coming together, and they're real people and they're here as a community. To me, the positive future vibe of this show, really is resonating in the keynotes and the energy. It's a forward thinking, positive message. And it's not marketing, this is the vibe. >> Exactly, I think it's something we really need at the moment. >> Yeah, we can solve all of the global problems by going to the moon and Mars. First the moon, then Mars. Who knows, maybe the breakthrough is there. >> People solve a lot of fundamental issues along the way that'll help in a lot of different areas as well. >> I wonder if I'll be alive when there's tourism in the moon. I was just joking with the folks earlier, "Oh yeah, I left that part on Earth, I have to go get it." Cause there's going to be a whole infrastructure there. Construction, all in good time. Okay, what's next for you guys? Tell me what's next on the project. You got a patent pending, so you're a little bit tight lipped and quiet on the secret sauce, I get that. What's next for the vision of the project? >> So this is just one example of how we can use this. Especially this footfall data set in an innovative way in the automotive industry. What we would like to look into is getting more details. Currently, we only see a single data point for a visitor. What would be interesting to understand, also, like the journey of visitors. Did they visit other car dealerships? Or, where are they from? What demographics do they come from? If you can tie that to a geographic location. And then on the business side as well, linking this for example, for companies to marketing campaigns. Does advertisement catch on? Do discounts catch on? Do they drive more people into the stores? Do they drive more sales? How does it affect conversion rate? Also, benchmark within the network, how different car dealerships are performing, how different brands are performing. And then eventually, everything is going to online. This can also be a foundation to set a baseline for online sales, which is still at the very early stages in the automotive industry. >> Yeah, I think there's a lot of reference implementations here for other applications, not just dealerships, all footfall traffic. That's interesting. The question I have for you, and the final question really before we wrap up, is the convergence of online, offline, physical, virtual. It's pretty clear we're living in a hybrid steady state right now, with all the post pandemic and the innovations pulled forward. So, having a device on me, IOT device or phone, will be a big part of things. So I'm buying online and I'm walking in, I'm one presence, virtually and physical. How do you guys see that around the corner? What's next there? Because I can see that coming together in my mind. >> It is. I mean, we can see it happen at Tesla. Tesla barely has any physical dealerships anymore, they have showrooms and do all the sales online. And I think that has a large impact on the industry at the moment. Driving the more traditional manufacturers also to think about what can be and what can be in a digital and online first world. >> Yeah, well this is happening. Well, Jens, thanks for coming on. I appreciate the commentary on re:Mars. Thanks for sharing your perspective and sharing about your project at Boston Consulting Group, also known as BCG. >> Thank you very much. >> Very reputable firm. Okay, that's the Cube coverage here at re:Mars. I'm John Furrier, your host. Two days of wall to wall coverage here. It's a great show. Machine learning, automation, robotics, and space, Mars. Of course, you got Reinvent, the big show, and at Reinforce, the security show. You got the space-software-robotics show, security. And then of course Reinvent is the big show. The Cube covers it, all three will be here. So keep watching here for more coverage. We'll be right back. (gentle inspiring music)

>>Okay, welcome back. Everyone's Cube's coverage here in Las Vegas for Amazon re Mars machine learning, automation, robotics, and space out. John fir host of the queue. Got a great set of guests here talking about AI, Jason Montgomery CTO and co-founder man and Ryans CEO, founder guys. Thanks for coming on. We're just chatting, lost my train of thought. Cuz we were chatting about something else, your history with DataRobot and, and your backgrounds entrepreneurs. Welcome to the queue. Thanks >>Tur. Thanks for having >>Us. So first, before we get into the conversation, tell me about the company. You guys have a history together, multiple startups, multiple exits. What are you guys working on? Obviously AI is hot here as part of the show. M is Mars machine learning, which we all know is the basis for AI. What's the story. >>Yeah, really. We're we're here for two of the letters and Mars. We're here for the machine learning and the automation part. So at the high level, man is a no code AI application development platform. And basically anybody could log in and start making AI applications. It could be anything from just texting it with the Twilio integration to tell you that you're doing great or that you need to exercise more to integrating with zenes to get support tickets classified. >>So Jason, we were talking too about before he came on camera about the cloud and how you can spin up resources. The data world is coming together and I, and I like to see two flash points. The, I call it the 2010 big data era that began and then failed Hadoop crashed and burned. Yeah. Then out of the, out of the woodwork came data robots and the data stacks and the snowflakes >>Data break snowflake. >>And now you have that world coming back at scale. So we're now seeing a huge era of, I need to stand up infrastructure and platform to do all this heavy lifting. I don't have time to do. Right. That sounds like what you guys are doing. Is that kind of the case? >>That's absolutely correct. Yeah. Typically you would have to hire a whole team. It would take you months to sort of get the infrastructure automation in place, the dev ops DevOps pipelines together. And to do the automation to spin up, spin down, scale up scale down requires a lot of special expertise with, you know, Kubernetes. Yeah. And a lot of the other data pipelines and a lot of the AWS technologies. So we automate a lot of that. So >>If, if DevOps did what they did, infrastructure has code. Yeah. Data has code. This is kind of like that. It's not data ops per se. Is there a category? How do you see this? Cuz it's you could say data ops, but that's also it's DevOps dev. It's a lot going on. Oh yeah. It's not just seeing AI ops, right? There's a lot more, what, what would you call this? >>It's a good question. I don't know if we've quite come up with the name. I know >>It's not data ops. It's not >>Like we call it AI process automation >>SSPA instead of RPA, >>What RPA promised to be. Yes, >>Exactly. But what's the challenge. The number one problem is it's I would say not, not so much all on ever on undifferent heavy lifting. It's a lot of heavy lifting that for sure. Yes. What's involved. What's the consequences of not going this way. If I want to do it myself, can you take me through the, the pros and cons of what the scale scope, the scale of without you guys? >>Yeah. Historically you needed to curate all your data, bring it together and have some sort of data lake or something like that. And then you had to do really a lot of feature engineering and a lot of other sort of data science on the back end and automate the whole thing and deploy it and get it out there. It's a, it's a pretty rigorous and, and challenging problem that, you know, we there's a lot of automation platforms for, but they typically focus on data scientists with these large language models we're using they're pre-trained. So you've sort of taken out that whole first step of all that data collection to start out and you can basically start prototyping almost instantly because they've already got like 6 billion parameters, 10 billion parameters in them. They understand the human language really well. And a lot of other problems. I dunno if you have anything you wanna add to that, Ryan, but >>Yeah, I think the other part is we deal with a lot of organizations that don't have big it teams. Yeah. And it would be impossible quite frankly, for them to ever do something like deploy text, track as an example. Yeah. They're just not gonna do it, but now they can come to us. They know the problem they want solved. They know that they have all these invoices as an example and they wanna run it through a text track. And now with us they can just drag and drop and say, yeah, we want tech extract. Then we wanted to go through this. This is what we >>Want. Expertise is a huge problem. And the fact that it's changing too, right? Yeah. Put that out there. You guys say, you know, cybersecurity challenges. We guys do have a background on that. So you know, all the cutting edge. So this just seems to be this it, I hate to say transformation. Cause I not the word I'm looking for, I'd say stuck in the mud kind of scenario where they can't, they have to get bigger, faster. Yeah. And the scale is bigger and they don't have the people to do it. So you're seeing the rise of managed service. You mentioned Kubernetes, right? I know this young 21 year old kid, he's got a great business. He runs a managed service. Yep. Just for Kubernetes. Why? Because no, one's there to stand up the clusters. >>Yeah. >>It's a big gap. >>So this, you have these sets of services coming in now, where, where do you guys fit into that conversation? If I'm the customer? My problem is what, what is my, what is my problem that I need you guys for? What does it look like to describe my problem? >>Typically you actually, you, you kind of know that your employees are spending a lot of time, a lot of hours. So I'll just give you a real example. We have a customer that they were spending 60 hours a week just reviewing these accounts, payable, invoices, 60 hours a week on that. And they knew there had to be a better way. So manual review manual, like when we got their data, they were showing us these invoices and they had to have their people circle the total on the invoice, highlight the customer name, the >>Person who quit the next day. Right? >>No like they, they, Hey, you know, they had four people doing this, I think. And the point is, is they come to us and we say, well, you know, AI can, can just basically using something like text track can just do this. And then we can enrich those outputs from text track with the AI. So that's where the transformers come in. And when we showed them that and got them up and running in about 30 minutes, they were mind blown. Yeah. And now this is a company that doesn't have a big it department. So the >>Kind, and they had the ability to quantify the problem >>They knew. And, and in this case it was actually a business user. It was not a technical >>In is our she consequence technical it's hours. She consequences that's wasted. Manual, labor wasted. >>Exactly. Yeah. And, and to their point, it was look, we have way more high, valuable tasks that our people could be doing yeah. Than doing this AP thing. It takes 60 hours. And I think that's really important to remember about AI. What're I don't think it's gonna automate away people's jobs. Yeah. What it's going to do is it's going to free us up to focus on what really matters and focus on the high value stuff. And that's what people should >>Be doing. I know it's a cliche. I'm gonna say it again. Cause I keep saying, cause I keep saying for people to listen, the bank teller argument always was the big thing. Oh yeah. They're gonna get killed by the ATM machine. No, they're opening up more branches. That's right. That's right. So it's like, come on. People let's get, get over that. So I, I definitely agree with that. Then the question, next question is what's your secret sauce? I'm the customer I'm gonna like that value proposition. You make something go away. It's a pain relief. Then there's the growth side. Okay. You can solve from problems. Now I want this, the, the vitamin you got aspirin. And I want the vitamin. What's the growth angle for you guys with your customers. What's the big learnings. Once they get the beach head with problem solving. >>I think it, it, it it's the big one is let's say that we start with the account payable thing because it's so our platform's so approachable. They go in and then they start tinkering with the initial, we'll call it a template. So they might say, Hey, you know what, actually, in this edge case, I'm gonna play with this. And not only do I want it to go to our accounting system, but if it's this edge case, I want it to email me. So they'll just drag and drop an email block into our canvas. And now they're making it >>Their own. There is the no code, low code's situation. They're essentially building a notification engine under the covers. They have no idea what they're doing. That's >>Right. They get the, they just know that, Hey, you know what? When, when like the amount's over $10,000, I want an email. They know that's what they want. They don't, they don't know that's the notification engine. Of >>Course that's value email. Exactly. I get what I wanted. All right. So tell me about the secret sauce. What's under the covers. What's the big, big, big scale, valuable, valuable, secret sauce. >>I would say part of it. And, and honestly, the reason that we're able to do this now is transformer architecture. When the transformer papers came out and then of course the attention is all you need paper, those kind of unlocked it and made this all possible. Beyond that. I think the other secret sauce we've been doing this a long time. >>So we kind of, we know we're in the paid points. We went to those band points. Cause we weren't data scientists or ML people. >>Yeah. >>Yeah. You, you walked the snow and no shoes on in the winter. That's right. These kids now got boots on. They're all happy. You've installed machines. You've loaded OSS on, on top of rack switches. Yeah. I mean, it's unbelievable how awesome it's right now to be a developer and now a business user's doing the low code. Yep. If you have the system architecture set up, so back to the data engineering side, you guys had the experience got you here. This is a big discussion right now. We're having in, in, on the cube and many conversations like the server market, you had that go away through Amazon and Google was one of the first, obviously the board, but the idea that servers could be everywhere. So the SRE role came out the site reliability engineer, right. Which was one guy or gal and zillions of servers. Now you're seeing the same kind of role with data engineering. And then there's not a lot of people that fit the requirement of being a data engineer. It's like, yeah, it's very unique. Cause you're dealing with a system architecture, not data science. So start to see the role of this, this, this new persona, because they're taking on all the manual challenges of doing that. You guys are kind of replaced that I think. Well, do you agree with it about the data engineer? First of all? >>I think, yeah. Well and it's different cuz there's the older data engineer and then there's sort of the newer cloud aware one who knows how to use all the cloud technologies. And so when you're trying, we've tried to hire some of those and it's like, okay, you're really familiar with old database technology, but can you orchestrate that in a serverless environment with a lot of AWS technology for instance. And it's, and that's hard though. They don't, they don't, there's not a lot of people who know that space, >>So there's no real curriculum out there. That's gonna teach you how to handle, you know, ETL. And also like I got I'm on stream data from this source. Right. I'm using sequel I'm I got put all together. >>Yeah. So it's yeah, it's a lot of just not >>Data science. It's >>Figure that out. So its a large language models too. We don't have to worry about some of the data there too. It's it's already, you know, codified in the model. And then as we collect data, as people use our platform, they can then curate data. They want to annotate or enrich the model with so that it works better as it goes. So we're kind of curating, collecting the data as it's used. So as it evolves, it just gets better. >>Well, you guys obviously have a lot of experience together and congratulations on the venture. Thank you. What's going on here at re Mars. Why are you here? What's the pitch. What's the story. Where's your, you got two letters. You got the, you got the M for the machine learning and AI and you got the, a for automation. What's the ecosystem here for you? What are you doing? >>Well, I mean, I think you, you kind of said it right. We're here because the machine learning and the automation part, >>But >>More, more widely than that. I mean we work very, very closely with Amazon on a number of front things like text track, transcribe Alexa, basically all these AWS services are just integrations within our system. So you might want to hook up your AI to an Alexa so that you could say, Hey Alexa, tell me updates about my LinkedIn feed. I don't know, whatever, whatever your hearts content >>Is. Well what about this cube transcription? >>Yeah, exactly. A hundred percent. >>Yeah. We could do that. You know, feed all this in there and then we could do summarization of everything >>Here, >>Q and a extraction >>And say, Hey, these guys are >>Technicals. Yeah, >>There you go. No, they mentioned Kubernetes. We didn't say serverless chef puppet. Those are words straight, you know, and no linguistics matters right into that's a service that no one's ever gonna build. >>Well, and actually on that point, really interesting. We work with some healthcare companies and when you're basically, when people call in and they call into the insurance, they have a question about their, what like is this gonna be covered? And what they want to key in on are things like I just went to my doctor and got a cancer diagnosis. So the, the, the relevant thing here is they just got this diagnosis. And why is that important? Well, because if you just got a diagnosis, they want to start a certain triage to make you successful with your treatments. Because obviously there's an >>Incentive to do time. That time series matters and, and data exactly. And machine learning reacts to it. But also it could be fed back old data. It used to be time series to store it. Yeah. But now you could reuse it to see how to make the machine learning better. Are you guys doing anything, anything around that, how to make that machine learning smarter, look doing look backs or maybe not the right word, but because you have data, I might as well look back at it's happened. >>So part of, part of our platform and part of what we do is as people use these applications, to your point, there's lots of data that's getting generated, but we capture all that. And that becomes now a labeled data set within our platform. And you can take that label data set and do something called fine tuning, which just makes the underlying model more and more yours. It's proprietary. The more you do it. And it's more accurate. Usually the more you do it. >>So yeah, we keep all that. I wanna ask your reaction on this is a good point. The competitive advantage in the intellectual property is gonna be the workflows. And so the data is the IP. If this refinement happens, that becomes intellectual property. Yeah. That's kind of not software. It's the data modeling. It's the data itself is worth something. Are you guys seeing that? >>Yeah. And actually how we position the company is man team is a control plane and you retain ownership of the data plane. So it is your intellectual property. Yeah. It's in your system, it's in your AWS environment. >>That's not what everyone else is doing. Everyone wants to be the control plane and the data plan. We >>Don't wanna own your data. We don't, it's a compliance and security nightmare. Yeah. >>Let's be, Real's the question. What do you optimize for? Great. And I think that's a fair, a fair bet. Given the fact that clients want to be more agile with their data anyway, and the more restrictions you put on them, why would that this only gets you in trouble? Yeah. I could see that being a and plus lock. In's gonna be a huge factor. Yeah. I think this is coming fast and no one's talking about it in the press, but everyone's like run to silos, be a silo and that's not how data works. No. So the question is how do you create siloing of data for say domain specific applications while maintaining a horizontally scalable data plan or control plan that seems to be kind of disconnected everyone to lock in their data. What do you guys think about that? This industry transition we're in now because it seems people are reverting back to fourth grade, right. And to, you know, back to silos. >>Yeah. I think, well, I think the companies probably want their silo of data, their IP. And so as they refine their models and, and we give them the ability to deploy it in their own stage maker and their own VPC, they, they retain and own it. They can actually get rid of us and they still have that model. Now they may have to build, you know, a lot of pipelines and other technology to support it. But well, >>Your lock in is usability. Exactly. And value. Yeah. Value proposition is the lock in bingo. That's not counterintuitive. Exactly. Yeah. You say, Hey, more value. How do I wanna get rid of it? Valuable. I'll pay for it. Right. As long as you have multiple value, step up. And that's what cloud does. I mean, think that's the thing about cloud. That's gonna make all this work. In my opinion, the value enablement is much higher. Yeah. So good business model. Anything else here at the show that you observed that you like, that you think people would be interested in? What's the most important story coming out of the, the holistic, if you zoom up and look at re Mars, what's, what's coming out of the vibe. >>You know, one thing that I think about a lot is we're, you know, we have Artis here, humanity hopefully soon gonna be going to Mars. And I think that's really, really exciting. And I also think when we go to Mars, we're probably not gonna send a bunch of software engineers up there. >>Right. So like robots will do break fix now. So, you know, we're good. It's gone. So services are gonna be easy. >>Yeah. But I, oh, >>I left that device back at earth. I just think that's not gonna be good. Just >>Replicated it in one. I think there's like an eight >>Minute, the first monopoly on next day delivery in space. >>They'll just have a spaceship that sends out drones to Barss. Yeah. But I think that when we start going back to the moon and we go to Mars, people are gonna think, Hey, I need this application now to solve this problem that I didn't anticipate having. And in science fiction, we kind of saw this with like how, right? Like you had this AI on this computer or this, on this spaceship that could do all this stuff. We need that. And I haven't seen that here yet. >>No, it's not >>Here yet. And >>It's right now I think getting the hardware right first. Yep. But we did a lot of reporting on this with the D O D and the tactile edge, you know, military applications. It's a fundamental, I won't say it's a tech, religious argument. Like, do you believe in agile realtime data or do you believe in democratizing multi-vendor, you know, capability? I think, I think the interesting needs to sort itself out because sometimes multi vendor multi-cloud might not work for an application that needs this database or this application at the edge. >>Right. >>You know, so if you're in space, the back haul, it matters. >>It really does. Yeah. >>Yeah. Not a good time to go back and get that highly available data. You mean highly, is it highly available or there's two terms highly available, which means real time and available. Yeah. Available means it's on a dis, right? >>Yeah. >>So that's a big challenge. Well guys, thanks for coming on. Plug for the company. What are you guys up to? How much funding do you have? How old are you staff hiring? What's some of the details. >>We're about 45 people right now. We are a globally distributed team. So we hire every like from every country, pretty much we are fully remote. So if you're looking for that, hit us up, definitely always look for engineers, looking for more data scientists. We're very, very well funded as well. And yeah. So >>You guys headquarters out, you guys headquartered. >>So a lot of us live in Columbus, Ohio that's technically HQ, but like I said, we we're in pretty much every continent except in Antarctica. So >>You're for all virtual. >>Yeah. A hundred percent virtual, a hundred percent. >>Got it. Well, congratulations and love to hear that Datadog story at another time >>Or DataBot >>Yeah. I mean data, DataBot sorry. Let's get, get all confused >>Data dog data company. >>Well, thanks for coming on and congratulations for your success and thanks for sharing. Yeah. >>Thanks for having us for having >>Pleasure to be here. It's a cube here at rebars. I'm John furier host. Thanks for watching more coming back after this short break.