(up-tempo music) >> Welcome to today's session of the cube's presentation of the AWS startup showcase. The next big thing in AI security and life sciences. Today, we'll be speaking with Aizon, as part of our life sciences track and I'm pleased to welcome the co-founder as well as the chief science officer of Aizon: Toni Monzano, will be discussing how artificial intelligence is driving key processes in pharma manufacturing. Welcome to the show. Thanks so much for being with us today. >> Thank you Natalie to you and to your introduction. >> Yeah. Well, as you know industry 4.0 is revolutionizing manufacturing across many industries. Let's talk about how it's impacting biotech and pharma and as well as Aizon's contributions to this revolution. >> Well, actually pharmacogenetics is totally introducing a new concept of how to manage processes. So, nowadays the industry is considering that everything is particularly static, nothing changes and this is because they don't have the ability to manage the complexity and the variability around the biotech and the driving factor in processes. Nowadays, with pharma - technologies cloud, our computing, IOT, AI, we can get all those data. We can understand the data and we can interact in real time, with processes. This is how things are going on nowadays. >> Fascinating. Well, as you know COVID-19 really threw a wrench in a lot of activity in the world, our economies, and also people's way of life. How did it impact manufacturing in terms of scale up and scale out? And what are your observations from this year? >> You know, the main problem when you want to do a scale-up process is not only the equipment, it is also the knowledge that you have around your process. When you're doing a vaccine on a smaller scale in your lab, the only parameters you're controlling in your lab, they have to be escalated when you work from five liters to 2,500 liters. How to manage this different of a scale? Well, AI is helping nowadays in order to detect and to identify the most relevant factors involved in the process. The critical relationship between the variables and the final control of all the full process following a continued process verification. This is how we can help nowadays in using AI and cloud technologies in order to accelerate and to scale up vaccines like the COVID-19. >> And how do you anticipate pharma manufacturing to change in a post COVID world? >> This is a very good question. Nowadays, we have some assumptions that we are trying to overpass yet with human efforts. Nowadays, with the new situation, with the pandemic that we are living in, the next evolution that we are doing humans will take care about the good practices of the new knowledge that we have to generate. So AI will manage the repetitive tasks, all the human condition activity that we are doing, So that will be done by AI, and humans will never again do repetitive tasks in this way. They will manage complex problems and supervise AI output. >> So you're driving more efficiencies in the manufacturing process with AI. You recently presented at the United nations industrial development organization about the challenges brought by COVID-19 and how AI is helping with the equitable distribution of vaccines and therapies. What are some of the ways that companies like Aizon can now help with that kind of response? >> Very good point. Could you imagine you're a big company, a top pharma company, that you have an intellectual property of COVID-19 vaccine based on emergency and principle, and you are going to, or you would like to, expand this vaccination in order not to get vaccination, also to manufacture the vaccine. What if you try to manufacture these vaccines in South Africa or in Asia in India? So the secret is to transport, not only the raw material not only the equipment, also the knowledge. How to appreciate how to control the full process from the initial phase 'till their packaging and the vials filling. So, this is how we are contributing. AI is packaging all this knowledge in just AI models. This is the secret. >> Interesting. Well, what are the benefits for pharma manufacturers when considering the implementation of AI and cloud technologies. And how can they progress in their digital transformation by utilizing them? >> One of the benefits is that you are able to manage the variability the real complexity in the world. So, you can not create processes, in order to manufacture drugs, just considering that the raw material that you're using is never changing. You cannot consider that all the equipment works in the same way. You cannot consider that your recipe will work in the same way in Brazil than in Singapore. So the complexity and the variability is must be understood as part of the process. This is one of the benefits. The second benefit is that when you use cloud technologies, you have not a big care about computing's licenses, software updates, antivirals, scale up of cloud ware computing. Everything is done in the cloud. So well, this is two main benefits. There are more, but this is maybe the two main ones. >> Yeah. Well, that's really interesting how you highlight how this is really. There's a big shift how you handle this in different parts of the world. So, what role does compliance and regulation play here? And of course we see differences the way that's handled around the world as well. >> Well, I think that is the first time the human race in the pharma - let me say experience - that we have a very strong commitment from the 30 bodies, you know, to push forward using this kind of technologies actually, for example, the FDA, they are using cloud, to manage their own system. So why not use them in pharma? >> Yeah. Well, how does AWS and Aizon help manufacturers address these kinds of considerations? >> Well, we have a very great partner. AWS, for us, is simplifying a lot our life. So, we are a very, let me say different startup company, Aizon, because we have a lot of PhDs in the company. So we are not in the classical geeky company with guys all day parameter developing. So we have a lot of science inside the company. So this is our value. So everything that is provided by Amazon, why we have to aim to recreate again so we can rely on Sage Maker. we can rely on Cogito, we can rely on Landon we can rely on Esri to have encryption data with automatic backup. So, AWS is simplifying a lot of our life. And we can dedicate all our knowledge and all our efforts to the things that we know: pharma compliance. >> And how do you anticipate that pharma manufacturing will change further in the 2021 year? Well, we are participating not only with business cases. We also participate with the community because we are leading an international project in order to anticipate this kind of new breakthroughs. So, we are working with, let me say, initiatives in the - association we are collaborating in two different projects in order to apply AI in computer certification in order to create more robust process for the MRA vaccine. We are collaborating with the - university creating the standards for AI application in GXP. We collaborating with different initiatives with the pharma community in order to create the foundation to move forward during this year. >> And how do you see the competitive landscape? What do you think Aizon provides compared to its competitors? >> Well, good question. Probably, you can find a lot of AI services, platforms, programs softwares that can run in the industrial environment. But I think that it will be very difficult to find a GXP - a full GXP-compliant platform working on cloud with AI when AI is already qualified. I think that no one is doing that nowadays. And one of the demonstration for that is that we are also writing some scientific papers describing how to do that. So you will see that Aizon is the only company that is doing that nowadays. >> Yeah. And how do you anticipate that pharma manufacturing will change or excuse me how do you see that it is providing a defining contribution to the future of cloud-scale? >> Well, there is no limits in cloud. So as far as you accept that everything is varied and complex, you will need power computing. So the only way to manage this complexity is running a lot of power computation. So cloud is the only system, let me say, that allows that. Well, the thing is that, you know pharma will also have to be compliant with the cloud providers. And for that, we created a new layer around the platform that we say qualification as a service. We are creating this layer in order to qualify continuously any kind of cloud platform that wants to work on environment. This is how we are doing that. >> And in what areas are you looking to improve? How are you constantly trying to develop the product and bring it to the next level? >> Always we have, you know, in mind the patient. So Aizon is a patient-centric company. Everything that we do is to improve processes in order to improve at the end, to deliver the right medicine at the right time to the right patient. So this is how we are focusing all our efforts in order to bring this opportunity to everyone around the world. For this reason, for example, we want to work with this project where we are delivering value to create vaccines for COVID-19, for example, everywhere. Just packaging the knowledge using AI. This is how we envision and how we are acting. >> Yeah. Well, you mentioned the importance of science and compliance. What do you think are the key themes that are the foundation of your company? >> The first thing is that we enjoy the task that we are doing. This is the first thing. The other thing is that we are learning every day with our customers and for real topics. So we are serving to the patients. And everything that we do is enjoying science enjoying how to achieve new breakthroughs in order to improve life in the factory. We know that at the end will be delivered to the final patient. So enjoying making science and creating breakthroughs; being innovative. >> Right, and do you think that in the sense that we were lucky, in light of COVID, that we've already had these kinds of technologies moving in this direction for some time that we were somehow able to mitigate the tragedy and the disaster of this situation because of these technologies? >> Sure. So we are lucky because of this technology because we are breaking the distance, the physical distance, and we are putting together people that was so difficult to do that in all the different aspects. So, nowadays we are able to be closer to the patients to the people, to the customer, thanks to these technologies. Yes. >> So now that also we're moving out of, I mean, hopefully out of this kind of COVID reality, what's next for Aizon? Do you see more collaboration? You know, what's next for the company? >> The next for the company is to deliver AI models that are able to be encapsulated in the drug manufacturing for vaccines, for example. And that will be delivered with the full process not only materials, equipment, personnel, recipes also the AI models will go together as part of the recipe. >> Right, well, we'd love to hear more about your partnership with AWS. How did you get involved with them? And why them, and not another partner? >> Well, let me explain to you a secret. Seven years ago, we started with another top cloud provider, but we saw very soon, that this other cloud provider were not well aligned with the GXP requirements. For this reason, we met with AWS. We went together to some seminars, conferences with top pharma communities and pharma organizations. We went there to make speeches and talks. We felt that we fit very well together because AWS has a GXP white paper describing very well how to rely on AWS components. One by one. So this is for us, this is a very good credential, when we go to our customers. Do you know that when customers are acquiring and are establishing the Aizon platform in their systems, they are outbidding us. They are outbidding Aizon. Well we have to also outbid AWS because this is the normal chain in pharma supplier. Well, that means that we need this documentation. We need all this transparency between AWS and our partners. This is the main reason. >> Well, this has been a really fascinating conversation to hear how AI and cloud are revolutionizing pharma manufacturing at such a critical time for society all over the world. Really appreciate your insights, Toni Monzano: the chief science officer and co-founder of Aizon. I'm your host, Natalie Erlich, for the Cube's presentation of the AWS startup showcase. Thanks very much for watching. (soft upbeat music)

>>from around the globe. It's the Cube with digital coverage of AWS reinvent 2020 sponsored by Intel, AWS and our community partners. Welcome back to the Cubes Live coverage of AWS reinvent 2020. It's virtual this year. We're not in person because of the pandemic. We're doing the remote Cube Cube Virtual were the Cube virtual. I'm your host, John for here with Andy Jassy, the CEO of Amazon Web services, in for his annual at the end of the show comes on the Cube. This year, it's virtual Andy. Good to see you remotely in Seattle or in Palo Alto. Uh, Dave couldn't make it in a personal conflict, but he says, Hello, great to see you. >>Great to see you as well, John. It's an annual tradition. On the last day of reinvent. I wish we were doing it in person, but I'm glad at least were able to do it. Virtually >>the good news is, I know you could arrested last night normally at reinvent you just like we're all both losing our voice at the end of the show. At least me more than you, your and we're just at the end of like okay, Relief. It happens here. It's different. It's been three weeks has been virtual. Um, you guys had a unique format this year went much better than I expected. It would go on because I was pretty skeptical about these long, um, multiple days or weeks events. You guys did a good job of timing it out and creating these activations and with key news, starting with your keynote on December 1st. Now, at the end of the three weeks, um, tell me, are you surprised by the results? Can you give us, Ah, a feeling for how you think everything went? What's what's your take So far as we close out reinvented >>Well, I think it's going really well. I mean, we always gnome or a Z get past, reinvent and you start, you know, collecting all the feedback. But we've been watching all the metrics and you know, there's trade offs. Of course, now I think all of us giving our druthers would be together in Las Vegas, and I think it's hard to replace that feeling of being with people and the excitement of learning about things together and and making decisions together after you see different sessions that you're gonna make big changes in your company and for your customer experience. And yeah, and there's a community peace. And there's, you know, this from being there. There's a concert. The answer. I think people like being with one another. But, you know, I think this was the best that any of us could imagine doing doing a virtual event. And we had to really reinvent, reinvent and all the pieces to it. And now I think that some of the positive trade offs are they. You get a lot mawr engagement than you would normally get in person So normally. Last year, with about 65,000 people in Las Vegas this year, we had 530,000 people registered to reinvent and over 300,000 participate in some fashion. All the sessions had a lot more people who are participating just because you remove the constraints of of travel in costs, and so there are trade offs. I think we prefer being together, but I think it's been a really good community event, um, in learning event for for our customers, and we've been really pleased with it so >>far. No doubt I would totally agree with you. I think a lot of people like, Hey, I love to walk the floor and discover Harry and Sarah Davis moments of finding an exhibit her and the exhibit hall or or attending a session or going to a party, bumping into friends and seeing making new friends. But I think one of the things I want to get your reaction to it. So I think this is comes up. And, you know, we've been doing a lot of Q virtual for the past year, and and everyone pretty much agrees that when we go back, it's gonna be a hybrid world in the sense of events as well as cloud. You know that. But you know, I think one of the things that I noticed this year with reinvent is it almost was a democratization of reinvent. So you really had to reinvent the format. You had 300,000 plus people attend 500 pending email addresses, but now you've got a different kind of beehive community. So you're a bar raiser thinker. It's with the culture of Amazon. So I gotta ask you do the economics does this new kind of extra epiphany impact you and how you raise the bar to keep the best of the face to face when it comes back. And then if you keep the virtual any thoughts on how to leverage this and kind of get more open, it was free. You guys made it free this year and people did show up. >>Yeah, it's a really good question, and it's probably a question will be better equipped to answer in a month or two after we kind of debrief we always do after reading that we spend. Actually, I really enjoy the meeting because the team, the Collective A. W s team, works so hard in this event. There's so many months across everything. All the product teams, um, you know, all the marketing folks, all the event folks, and I think they do a terrific job with it. And we we do about 2.5 3 hour debrief on everything we did, things that we thought was really well the things that we thought we could do better and all the feedback we get from our community and so I wouldn't be surprised if we didn't find things from what we tried this year that we incorporate into what we do when we're back to being a person again. You know, of course, none of us really know when we'll be back in person again. Re event happens to fall on the time of the year, which is early December. And so you with with a lot of people seemingly able to get vaccinated, probably by you know, they'd spring early summer. You could kind of imagine that we might be able to reinvent in person next year. We'll have to see e think we all hope we will. But I'm sure there are a number of pieces that we will take from this and incorporate into what we do in person. And you know, then it's just a matter of how far you go. >>Fingers crossed and you know it's a hybrid world for the Cube two and reinvent and clouds. Let's get into the announcement. I want to get your your take as you look back now. I mean, how many announcements is you guys have me and a lot of announcements this year. Which ones did you like? Which one did you think were jumping off the page, which ones resonated the most or had impact. Can you share kind of just some stats on e mean how many announcements launches you did this >>year? But we had about 100 50 different new services and features that we announced over the last three weeks and reinvent And there, you know the question you're asking. I could easily spend another three hours like my Kino. You know, answering you all the ones that I like thought were important. You know, I think that, you know, some of the ones I think that really stood out for people. I think first on the compute side, I just think the, um the excitement around what we're doing with chips, um, is very clear. I think what we've done with gravitas to our generalized compute to give people 40% better price performance and they could find in the latest generation X 86 processors is just It's a huge deal. If you could save 40% price performance on computer, you get a lot more done for less on. Then you know some of the chip work we're doing in machine learning with inferential on the inference chips that we built And then what? We announced the trainee, um, on the machine learning training ship. People are very excited about the chip announcements. I think also, people on the container side is people are moving to smaller and smaller units of compute. I think people were very taken with the notion of E. K s and D. C s anywhere so they can run whatever container orchestration framework they're running in A. W s also on premises. To make it easier, Thio manage their deployments and containers. I think data stores was another space where I think people realize how much more data they're dealing with today. And we gave a couple statistics and the keynote that I think are kind of astonishing that, you know, every every hour today, people are creating mawr content that there was in an entire year, 20 years ago or the people expect more data to be created. The next three years in the prior 30 years combined these air astonishing numbers and it requires a brand new reinvention of data stores. And so I think people are very excited about Block Express, which is the first sand in the cloud and there really excited about Aurora in general, but then Aurora surveillance V two that allow you to scale up to hundreds of thousands of transactions per second and saved about 90% of supervision or people very excited about that. I think machine learning. You know, uh, Sage Maker has just been a game changer and the ease with which everyday developers and data scientists can build, train, tune into play machine learning models. And so we just keep knocking out things that are hard for people. Last year we launched the first i D for Machine Learning, the stage maker studio. This year, if you look at things that we announced, like Data Wrangler, which changes you know the process of Data Prep, which is one of the most time consuming pieces in machine learning or our feature store or the first see, I see deeper machine learning with pipelines or clarify, which allow you to have explain ability in your models. Those are big deals to people who are trying to build machine learning models, and you know that I'd say probably the last thing that we hear over and over again is really just the excitement around Connect, which is our call center service, which is just growing unbelievably fast and just, you know, the the fact that it's so easy to get started and so easy to scale so much more cost effective with, you know, built from the ground up on the cloud and with machine learning and ai embedded. And then adding some of the capabilities to give agents the right information, the right time about customers and products and real time capabilities for supervisors. Throw when calls were kind of going off the rails and to be ableto thio, stop the the contact before it becomes something, it hurts. The brand is there. Those are all big deals that people have been excited about. >>I think the connecting as I want to just jump on that for a second because I think when we first met many, many years ago, star eighth reinvent. You know the trends are always the same. You guys do a great job. Slew of announcements. You keep raising the bar. But one of the things that you mentioned to me when we talked about the origination of a W S was you were doing some stuff for Amazon proper, and you had a, you know, bootstrap team and you're solving your own problems, getting some scar tissue, the affiliate thing, all these examples. The trend is you guys tend to do stuff for yourself and then re factor it into potentially opportunities for your customers. And you're working backwards. All that good stuff. We'll get into that next section. But this year, more than ever, I think with the pandemic connect, you got chime, you got workspaces. This acceleration of you guys being pretty nimble on exposing these services. I mean, connect was a call center. It's an internal thing that you guys had been using. You re factored that for customer consumption. You see that kind of china? But you're not competing with Zoom. You're offering a service toe bundle in. Is this mawr relevant? Now, as you guys get bigger with more of these services because you're still big now you're still serving yourself. What? That seems to be a big trend now, coming out of the pandemic. Can you comment on um, >>yeah, It's a good question, John. And you know we do. We do a bunch of both. Frankly, you know, there there's some services where our customers. We're trying to solve certain problems and they tell us about those problems and then we build new services for him. So you know a good example that was red shift, which is our data warehouse and service, you know, two or three very large customers of ours. When we went to spend time with them and asked them what we could do to help them further, they just said, I wish I had a data warehousing service for the cloud that was built in the AWS style way. Um and they were really fed up with what they were using. Same thing was true with relation databases where people were just fed up with the old guard commercial, great commercial, great databases of Oracle and Sequel Server. And they hated the pricing and the proprietary nature of them and the punitive licensing. And they they wanted to move to these open engines like my sequel and post dress. But to get the same performance is the commercial great databases hard? So we solve that problem with them. With Aurora, which is our fastest growing service in our history, continues to be so there's sometimes when customers articulate a need, and we don't have a service that we've been running internally. But we way listen, and we have a very strong and innovative group of builders here where we build it for customers. And then there are other cases where customers say and connect with a great example of this. Connect with an example where some of our customers like into it. And Capital One said, You know, we need something for our contact center and customer service, and people weren't very happy with what they were using in that space. And they said, You, you've had to build something just to manage your retail business last 15, 20 years Can't you find a way to generalize that expose it? And when you have enough customers tell you that there's something that they want to use that you have experienced building. You start to think about it, and it's never a simple. It's just taking that technology and exposing it because it's often built, um, internally and you do a number of things to optimize it internally. But we have a way of building services and Amazon, where we do this working backwards process that you're referring to, where We build everything with the press release and frequently asked questions document, and we imagine that we're building it to be externalized even if it's an internal feature. But our feature for our retail business, it's only gonna be used as part of some other service that you never imagine Externalizing to third party developers. We always try and build it that way, and we always try to have well documented, hardened AP eyes so that other teams can use it without having to coordinate with those teams. And so it makes it easier for us to think about Externalizing it because we're a good part of the way there and we connect we. That's what we did way generalized it way built it from the ground up on top of the cloud. And then we embedded a bunch of AI and it so that people could do a number of things that would have taken him, you know, months to do with big development teams that they could really point, click and do so. We really try to do both. >>I think that's a great example of some of the scale benefits is worth calling out because that was a consistent theme this past year, The people we've reported on interviewed that Connect really was a lifeline for many during the pandemic and way >>have 5000 different customers who started using connect during the pandemic alone. Where they, you know, overnight they had to basically deal with having a a call center remotely. And so they picked up connect and they spun up call center remotely, and they didn't really quickly. And you know, it's that along with workspaces, which are virtual desktops in the cloud and things like Chime and some of our partners, Exume have really been lifelines for people. Thio have business continuity during a tandem. >>I think there's gonna be a whole set of new services that are gonna emerge You talked about in your keynote. We talked about it prior to the event where you know, if this pandemic hit with that five years ago, when there wasn't the advancements in, say, videoconferencing, it'd be a whole different world. And I think the whole world can see on full display that having integrated video communications and other cool things is gonna have a productivity benefit. And that's kind >>of could you imagine what the world would have been like the last nine months and we didn't have competent videoconferencing. I mean, just think about how different it would have been. And I think that all of these all of these capabilities today are kind of the occult 1.5 capabilities where, by the way, thank God for them. We've we've all been able to be productive because of them. But there's so early stage, they're all going to get evolved. I'm so significantly, I mean, even just today, you know, I was spending some time with with our team thinking about when we start to come back to the office and bigger numbers. And we do meetings with our remote partners, how we think about where the center of gravity should be and who should be on video conferencing and whether they should be allowed to kind of video conference in conference rooms, which are really hard to see them. We're only on their laptops, which are easier and what technology doesn't mean that you want in the conference rooms on both sides of the table, and how do you actually have it so that people who are remote could see which side of the table. I mean, all this stuff is yet to be invented. It will be very primitive for the next couple few years, even just interrupting one another in video conferencing people. When you do it, the sound counsel cancels each other out. So people don't really cut each other off and rip on one another. Same way, like all that, all that technology is going to get involved over time. It's a tremendous >>I could just see people fighting for the mute button. You know, that's power on these meetings. You know, Chuck on our team. All kidding aside, he was excited. We talked about Enron Kelly on your team, who runs product marketing on for your app side as well as computer networking storage. We're gonna do a green room app for the Q because you know, we're doing so many remote videos. We just did 112 here for reinvent one of things that people like is this idea of kind of being ready and kind of prepped. So again, this is a use case. We never would have thought off if there wasn't a pandemic. So and I think these are the kinds of innovation, thinking that seems small but works well when you start thinking about how easy it could be to say to integrate a chime through this sdk So this is the kind of things, that kind thing. So so with that, I want to get into your leadership principles because, you know, if you're a startup or a big company trying to reinvent, you're looking at the eight leadership principles you laid out, which were, um don't be afraid to reinvent. Acknowledge you can't fight gravity. Talent is hungry to reinvent solving real customer problems. Speed don't complex. If I use the platform with the broader set of tools, which is more a plug for you guys on cloud pull everything together with top down goals. Okay, great. How >>do you >>take those leadership principles and apply them broadly to companies and start ups? Because I think start ups in the garage are also gonna be there going. I'm going to jump on this wave. I'm inspired by the sea change. I'm gonna build something new or an enterprise. I'm gonna I'm gonna innovate. How do you How do you see these eight principles translating? >>Well, I think they're applicable to every company of every size and every industry and organization. Frankly, also, public sector organizations. I think in many ways startups have an advantage. And, you know, these were really keys to how to build a reinvention culture. And startups have an advantage because just by their very nature, they are inventive. You know, you can't you can't start a company that's a direct copy of somebody else that is an inventive where you have no chance. So startups already have, you know, a group of people that feel insurgent, and they wanted their passionate about certain customer experience. They want to invent it, and they know that they they only have so much time. Thio build something before money runs out and you know they have a number of those built in advantages. But I think larger companies are often where you see struggles and building a reinvention and invention culture and I've probably had in the last three weeks is part of reinvent probably about 40 different customer meetings with, you know, probably 75 different companies were accomplished in those or so and and I think that I met with a lot of leaders of companies where I think these reinvention principles really resonated, and I think they're they're battling with them and, you know, I think that it starts with the leaders if you, you know, when you have big companies that have been doing things a certain way for a long period of time, there's a fair bit of inertia that sets in and a lot of times not ill intended. It's just a big group of people in the middle who've been doing things a certain way for a long time and aren't that keen to change sometimes because it means ripping up something that they that they built and they remember how hard they worked on it. And sometimes it's because they don't know what it means for themselves. And you know, it takes the leadership team deciding that we are going to change. And usually that means they have to be able to have access to what's really happening in their business, what's really happening in their products in the market. But what customers really think of it and what they need to change and then having the courage and the energy, frankly, to pick the company up and push him to change because you're gonna have to fight a lot of inertia. So it always starts with the leaders. And in addition to having access that truth and deciding to make the change, you've gotta also set aggressive top down goal. The force of the organization moved faster than otherwise would and that also, sometimes leaders decide they're gonna want to change and they say they're going to change and they don't really set the goal. And they were kind of lessons and kind of doesn't listen. You know, we have a term the principal we have inside Amazon when we talk about the difference between good intentions and mechanisms and good intentions is saying we need to change and we need to invent, reinvent who we are and everyone has the right intentions. But nothing happens. Ah, mechanism, as opposed to good intention, is saying like Capital One did. We're going to reinvent our consumer digital banking platform in the next 18 months, and we're gonna meet every couple of weeks to see where we are into problem solved, like that's a mechanism. It's much harder to escape getting that done. Then somebody just saying we're going to reinvent, not checking on it, you know? And so, you know, I think that starts with the leaders. And then I think that you gotta have the right talent. You gotta have people who are excited about inventing, as opposed to really, Justin, what they built over a number of years, and yet at the same time, you're gonna make sure you don't hire people who were just building things that they're interested in. They went where they think the tech is cool as opposed to what customers want. And then I think you've got to Really You gotta build speed into your culture. And I think in some ways this is the very biggest challenge for a lot of enterprises. And I just I speak to so many leaders who kind of resigned themselves to moving slowly because they say you don't understand my like, companies big and the culture just move slow with regulator. There are a lot of reasons people will give you on why they have to move slow. But, you know, moving with speed is a choice. It's not something that your preordained with or not it is absolutely a leadership choice. And it can't happen overnight. You can't flip a switch and make it happen, but you can build a bunch of things into your culture first, starting with people. Understand that you are gonna move fast and then building an opportunity for people. Experiment quickly and reward people who experiment and to figure out the difference between one way doors and two way doors and things that are too way doors, letting people move quick and try things. You have to build that muscle or when it really comes, time to reinvent you won't have. >>That's a great point in the muscle on that's that's critical. You know, one of things I want to bring up. You brought on your keynote and you talk to me privately about it is you gave attribute in a way to Clay Christensen, who you called out on your keynote. Who was a professor at Harvard. Um, and he was you impressed by him and and you quoted him and he was He was your professor there, Um, your competitive person and you know, companies have strategy departments, and competitive strategy is not necessarily departments of mindset, and you were kind of brought this out in a zone undertone in your talk, we're saying you've got to be competitive in the sense of you got to survive and you've got to thrive. And you're kind of talking about rebuilding and building and, you know, Clay Christians. Innovative dilemma. Famous book is a mother, mother teachings around metrics and strategy and prescriptions. If he were alive today and he was with us, what would he be talking about? Because, you know, you have kind of stuck in the middle. Strategy was not Clay Christensen thing, but, you know, companies have to decide who they are. Their first principles face the truth. Some of the things you mentioned, what would we be talking with him about if we were talking about the innovator's dilemma with respect to, say, cloud and and some of the key decisions that have to be made right now? >>Well, then, Clay Christensen on it. Sounds like you read some of these books on. Guy had the fortunate, um, you know, being able to sit in classes that he taught. And also I got a chance. Thio, meet with him a couple of times after I graduated. Um, school, you know, kind of as more of a professional sorts. You can call me that. And, uh, he he was so thoughtful. He wasn't just thoughtful about innovation. He was thoughtful about how to get product market fit. And he was thoughtful about what your priorities in life were and how to build families. And, I mean, he really was one of the most thoughtful, innovative, um, you know, forward thinking, uh, strategist, I had the opportunity Thio encounter and that I've read, and so I'm very appreciative of having the opportunity Thio learn from him. And a lot of I mean, I think that he would probably be continuing to talk about a lot of the principles which I happen to think are evergreen that he he taught and there's it relates to the cloud. I think that one of the things that quite talked all the time about in all kinds of industries is that disruption always happens at the low end. It always happens with products that seem like they're not sophisticated enough. Don't do enough. And people always pooh pooh them because they say they won't do these things. And we learned this. I mean, I watched in the beginning of it of us. When we lost just three, we had so many people try and compare it Thio things like e m. C. And of course, it was very different than EMC. Um, but it was much simpler, but And it and it did a certain set of activities incredibly well at 1 1/100 of the price that's disrupted, you know, like 1 1/100 of the price. You find that builders, um, find a lot of utility for products like that. And so, you know, I think that it always starts with simple needs and products that aren't fully developed. That overtime continue to move their way up. Thio addressing Maura, Maura the market. And that's what we did with is what we've done with all our services. That's three and easy to and party ass and roar and things like that. And I think that there are lots of lessons is still apply. I think if you look at, um, containers and how that's changing what compute looks like, I think if you look at event driven, serverless compute in Lambda. Lambda is a great example of of really ah, derivative plays teaching, which is we knew when we were building Lambda that as people became excited about that programming model it would cannibalize easy to in our core compute service. And there are a lot of companies that won't do that. And for us we were trying to build a business that outlasts all of us. And that's you know, it's successful over a long period of time, and the the best way I know to do that is to listen to what customers We're trying to solve an event on their behalf, even if it means in the short term you may cannibalize yourself. And so that's what we always think about is, you know, wherever we see an opportunity to provide a better customer experience, even if it means in the short term, make cannibalism revenue leg lambda with complete with easy to our over our surveillance with provisions or are we're going to do it because we're gonna take the long view, and we believe that we serve customers well over a long period of time. We have a chance to do >>that. It's a cannibalize yourself and have someone else do it to you, right? That's that's the philosophy. Alright, fine. I know you've got tight for time. We got a you got a hard stop, But let's talk about the vaccine because you know, you brought up in the keynote carrier was a featured thing. And look at the news headlines. Now you got the shots being administered. You're starting to see, um, hashtag going around. I got my shot. So, you know, there's a There's a really Momenta. Mit's an uplifting vibe here. Amazon's involved in this and you talked about it. Can you share the innovation? There can just give us an update and what's come out of that and this supply chain factor. The cold chain. You guys were pretty instrumental in that share your your thoughts. >>We've been really excited and privileged partner with companies who are really trying to change what's possible for all of us. And I think you know it started with some of the companies producing vaccines. If you look at what we do with Moderna, where they built their digital manufacturing sweet on top of us in supply chain, where they used us for computing, storage and data warehousing and machine learning, and and on top of AWS they built, they're Cove in 19 vaccine candidate in 42 days when it normally takes 20 months. I mean, that is a total game changer. It's a game changer for all of us and getting the vaccine faster. But also, you just think about what that means for healthcare moving forward, it zits very exciting. And, yeah, I love what carriers doing. Kariya is building this product on top of AWS called links, which is giving them end and visibility over the transportation and in temperature of of the culture and everything they're delivering. And so it, uh, it changes what happens not only for food, ways and spoilage, but if you think about how much of the vaccine they're gonna actually transport to people and where several these vaccines need the right temperature control, it's it's a big deal. And what you know, I think there are a great example to what carrier is where. You know, if you think about the theme of this ring and then I talked about in my keynote, if you want to survive as an organization over a long period of time, you're gonna have to reinvent yourself. You're gonna have to probably do it. Multiple times over and the key to reinventing his first building, the right reinvention culture. And we talk about some of those principles earlier, but you also have to be aware of the technology that's available that allows you to do that. If you look at Carrier, they have built a very, very strong reinvention culture. And then, if you look at how they're leveraging, compute and storage and I o. T at the edge and machine learning, they know what's available, and they're using that technology to reinvent what's what's possible, and we're gonna all benefit because of >>it. All right. Well, Andy, you guys were reinventing the virtual space. Three weeks, it went off. Well, congratulations. Great to go along for the ride with the cube virtual. And again. Thank you for, um, keeping the show alive over there. Reinvent. Um, thanks for your team to for including the Cube. We really appreciate the Cube virtual being involved. Thank you. >>It's my pleasure. And thanks for having me, John and, uh, look forward to seeing you soon. >>All right? Take care. Have a hockey game in real life. When? When we get back, Andy Jesse, the CEO of a W s here to really wrap up. Reinvent here for Cuba, Virtual as well as the show. Today is the last day of the program. It will be online for the rest of the year and then into next month there's another wave coming, of course. Check out all the coverage. Come, come back, It's It's It's online. It's all free Cube Cube stuff is there on the Cube Channel. Silicon angle dot com For all the top stories, cube dot net tons of content on Twitter. Hashtag reinvent. You'll see all the commentary. Thanks for watching the Cube Virtual. I'm John Feehery.

>>from around the globe. It's the Cube with digital coverage of AWS reinvent 2020 sponsored by Intel, AWS and our community partners. Welcome to the Virtual Cube and our coverage of aws reinvent 2020. I'm Lisa Martin. I'm joined by Ali Siddiqui, the chief product officer of BMC Software. We're gonna be talking about what BMC and A W s are doing together. Ali, it's great to have you on the Cube. Thank >>you, Lisa. Get great to be here and be part off AWS treatment. Exciting times. >>They are exciting times. That is true. No, never a dull moment these days, right? So all he talked to me a little bit. About what? A w what BMC is doing with AWS. Let's dig into what you're doing there on the technology front and unpack the benefits that you're delivering to customers. Great >>questions, Lisa. So at BMC, we really have a close partnership with AWS. It's really about BMC. Placido Blue s better together for our customers. That's what it's really about. We have a global presence, probably the largest, uh, off any window out there in this in our industry with 15 data centers, AWS data centers around the globe. We just announced five more in South Africa. Brazil Latin Um, a P J. A couple of them amia across the globe. Really? The presence is very strong with these, uh, data centers because that lets us offered local presence, Take care of GDP are and we have great certification. That is Aw, sock to fedramp. I'll four Haifa dram. We even got hip certifications as well as a dedicated Canada certifications for our customers. Thanks to our partnership, close partnership with the WS and on all these datas into the cross. In addition, for our customers, really visibility into aws seamless capability toe do multi cloud management is key and with a recent partnership with AWS around specifically AWS >>s >>S m, which gives customers cream multi cloud capabilities around multi cloud management, total visibility seamlessly in AWS and all their services whether it's easy toe s s s three sage maker, whatever services they have, we let them discover on syphilis. Lee give them visibility into that. >>That 360 degree visibility is really key to understand the dependencies right between the software in the services and help customers to optimize their investments in a W s assume correct. >>Exactly. With the AWS s s m and r E I service management integration. We really give deep visibility on the dependency, how they're being used, what services are being impacted and and really, AWS s system is a key, unique technology which we've integrated with them very, very happy with the results are customers are getting from it. >>Can you share some of those results? Operational efficiencies, Cost savings? Yeah, >>Yeah, least another great question. So when I look at the general picture off E I service management in the eye ops, which we run with AWS across all these global dinner senses and specifically with AWS S S M people are able to do customers. And this is like the talkto hyper scale, as we're talking about, as well as large telcos like Ericsson and and some of the leading, uh, industry retail Or or, you know, other customers we have They're getting great value because they're able to do service modeling, automatically use ascend to get true deep visibility seamlessly to do service discovery with for for for all the assets that they run or using our S service management in the eye ops capabilities. It really is the neck shin and it's disrupting the service idea Some traditional service management industry with what we offering now with the service management, AWS s, S M and other AWS Cloud needed capabilities such as sage Maker and AWS, Lex and connect that we leverage in our AI service management ai absolution. We recently announced that as a >>single >>unified platform which allows our customers to go on BMC customers and joined with AWS customers to go on this autonomous digital enterprise journey Uh, this announcement was done by our CEO of BMC. I'm in Say it in BMC Exchange recently, where we basically launched a single lady foundation, a single platform for observe ability, engagement with automation >>for the autonomous digital enterprise. I presume I'd like to understand to, from your perspective, this disruption that you're enabling. How is it helping your customers not just survive this viral disruption that we're all living with but be able thio, get the disability into their software and services, really maximize and optimize their cloud investments so that their business can operate well during these unprecedented times, meet their customer demands, exceed them and meet their customers. Where? There. How is this like an accelerator of that >>great question, Lisa. So when we say autonomous digital enterprise, this is the journey All our customers they're taking on its focus on three trips, agility, customer center, city and action ability. So if you think about our solutions with AWS, really, it's s of its management. AI ops enables these enterprises to go on this autonomous digital enterprise journey where they can offer great engagement to the employees. All CEOs really care about employee engagement. Happy employees make for more revenue for for those enterprises, as well as offer great customer experience for the customers. Uh, using our AI service management and AI ops combined. 80 found in this single platform, which we are calling 80 foundation. >>Yeah, go ahead. Sorry. >>No, go ahead, please. >>I was going to say I always look at the employee experience, and the customer experience is absolutely inextricably linked with the employee experience is hampered. That's bride default. Almost going to impact the customer experience. And right now, I don't know if it's even possible to say both the employee experience and the customer experience are even mawr essential to really get right because now we've got this. You know this big scatter That happened a few months ago with some companies that were completely 100% on site to remote being able, needing to give their employees access to the tools to do their jobs properly so that they can deliver products and services and solutions that customers need. So I always see those two employees. Customer experience is just inextricably linked. >>Absolutely. That's correct, especially in this time, even if the new pandemic these epidemics time, uh, the chief human resource offers. The CEOs are really thick focused on keeping the employees engaged and retaining top talent. And that's where our yes service management any other solution helps them really do. Use our digital assistance chat boards, which are powered by a W X and Lex and AWS connect and and and our integration with, uh, helix control them, which is another service we launched on AWS Helix Control them, which is our South version off a leading SAS product automation product out there, a swell as RP integrations we bring to the table, which really allows them toe take employing, give management to the next level And that's top of mind for all CEOs and being driven by line of business like chief human resource officers. Such >>a great point. Are you? Are you finding that mawr of your conversations with customers are at that sea level as they look to things like AI ops to help find you in their business that it's really that that sea level not concerned but priority to ensure that we're doing everything we can within our infrastructure, wherever where our software and services are to really ensure that we're delivering and exceeding customer expectations? That a very tumultuous time? >>Yes, What we're finding is, uh, really at the CEO level CEO level the sea level. It's about machine learning ai adopting that more than the enterprise and specifically in our capabilities when I say ai ops. So those are around root cause predictive I t. And even using ai NLP for self service for self service is a big part, and we offer key capabilities. We just did an acquisition come around, which lets them do knowledge management self service. So these are specific capabilities, predictability, ai ops and knowledge management. Self service that we offer that really is resonating very well with CEOs who are looking to transform their I T systems and in I t ops and align it with business is much better and really do innovation in this area. So that's what's happening, and it's great to see that we will do that. Exact capabilities that come with R E Foundation. The unified platform forms of ability and lets customers go on this autonomous digital enterprise journey without keeping capabilities. >>Do you see this facilitating the autonomous digital enterprise as as a way to separate the winners and losers of tomorrow as so much of the world has changed and some amount of this is going to be permanent, imagine that's got to be a competitive advantage to customers in any industry. >>We believe enterprises that have the growth mindset and and want to go into the next generation, and that's most of them. Toe, to be honest, are really looking at the ready autonomous digital price framework that we offer and work with our customers on the way to grow revenue to get more customer centric, increase employee engagement. That's what we see happening in the industry, and that's where our capabilities with 80 Foundation as well as Helix. Whether it's Felix Air Service management, he likes a Iot or now recently launched Helix Control them really enable them toe keep their existing, uh, you know, tools as well as keep their existing investments and move the ICTY ops towards the next generation off tooling and as well as increase employee engagement with our leading industry leading digital assistant chat board and and SMS management solution that that's what we see. And that's the journey we're taking with most of our customers and really, the ones with the growth mindset are really being distinguished as the front runs >>talk to me about some validation from the customer's perspective, the industry's perspective. What are you guys hearing about? What you're doing s BMC and with a w s >>so validation from customer that I just talked about great validation. As I said, talk to off the hyper skills users for proactive problem management. Proactive incident management ai ops a same time independent validation from Gardner we are back wear seven years and I don't know in a row So seven years the longest street in Gartner MQ for I t s m and we are a leader in that for seven years the longest run so far by any vendor. We are scoring the top in the top number one position in 12 of the 15 critical capabilities. As you know, Gardner, I d s m eyes really about the critical capability that where most customers look. So that's a big independent validation. Where we score 12 off the way were number one in 12 of the 15 capability. So that was the awesome validation from Gardner and I. D. S M. We also recently E Mei Enterprise Management Associates published a new report on AI Ops and BMT scored the top spot on the charts with Business impact and business alignment. Use cases categories for AI ops. So think about what that means. It's really about your business, right? So So we being the top of the chart for business impact and business alignment for ai ops radar report from Enterprise Management associated with a create independent validation that we can point toe off our solutions and what it is, really, because we partner very closely with our customers. We also got a couple of more awards than we want a lot more, but just to mention two more I break breakthrough, which is a nursery leading third party sources out there for chat boards and e i base chat board solution lamed BMC Helix Chat Board as the best chat board solution out there. Uh, SAS awards another industry analysts from independent from which really, uh really shows the how we're getting third parties and independents to talk about our solutions named BMC SAS per ticket and event management, which is really a proactive problem and proactive incident solution Revolution system as as the best solution out there for ticketing and event management. >>So a lot of accolades. A. Yes. It sounds like a lot of alcohol. A lot of validation. How do customers get How do you get started? So customers looking to come to BMC to really understand get that 3 60 degree visibility. How did they get started? >>Uh, well, they can start with our BMC Discovery, which integrates very tightly with AWS s s M toe. Basically get the full visibility off assets from network to storage toe aws services. Whether there s three. Uh, easy to, uh doesn't matter what services they did. A Kafka service they're using whatever. So the hundreds of services they're using weaken seamlessly do that. So that's one way to do that. Just start with BMC Helix Discovery. Thea Other one is with BMC Knowledge Management on BMC Self Service. That's a quick win for most of our customers. I ai service management, tooling That's the Third Way and I I, off stooling with BMC, Helix Monitor and AI ops that we offer pretty much the best in the industry in those that customers can start So the many areas, and now with BMC, control them. If they want to start with automation, that's a great way to start with BMC control them, which is our SAS solution off industry leading automation product called Controlling. >>And so, for just last question from a go to market perspective, it sounds like direct through BMC Channel partners. What about through a. W. S? >>Yes, absolutely. I mean again, we it's all about BMC and AWS better together we offer cloud native AWS services for our solutions, use them heavily, and I just mentioned whether that S S M or chat boards or any of the above or sage maker for machine learning I and customers can contact the local AWS Rep toe to start learning about BMC and AWS. Better together. >>Excellent. Well, Ali, thank you for coming on the program, talking to us about what BMC is doing to help your customers become that autonomous digital enterprise that we think up tomorrow. They're going to need to be to have that competitive edge. I've enjoyed talking to you >>same year. Thank you so much, Lisa. Really. It's about our customers and partnering with AWS. So very proud of Thank you so much. >>Excellent for Ali Siddiqui. I'm Lisa Martin and you're watching the Cube.

la from Las Vegas it's the cube covering AWS reinvent 2019 brought to you by Amazon Web Services and in care along with its ecosystem partners hey welcome back everyone cubes live coverage of eight of us reinvent 2019 this is the cube seventh year covering Amazon reinvent it's their eighth year of the conference and want to just shout out to Intel for their sponsorship for these two amazing sets without their support we would be able to bring our mission of great content to you I'm John Force to many men we're here with the chief of AWS the chief executive officer Andy chassis tech athlete and himself three our keynotes welcome to the cube again great to see you great to be here thanks for having me guys congratulations on a great show a lot of great buzz thank you a lot of good stuff your keynote was phenomenal you get right into you giddy up right into as you say three hours 30 announcements you guys do a lot but what I liked the new addition in the last year and this year is the band house man yeah they're pretty good they hit the Queen note so that keeps it balanced so we're going to work on getting a band for the cube awesome so if I have to ask you what's your walk-up song what would it be there's so many choices depends what kind of mood I'm in but maybe times like these by the Foo Fighters these are unusual times right now Foo Fighters playing at the Amazon intersect show they are Gandy well congratulations on the intersect you got a lot going on intersect is the music festival I'll get that in a second but I think the big news for me is two things obviously we had a one-on-one exclusive interview and you laid out essentially what looks like was gonna be your keynote it was transformation key for the practice I'm glad to practice use me anytime yeah and I like to appreciate the comments on Jedi on the record that was great but I think the transformation story is a very real one but the NFL news you guys just announced to me was so much fun and relevant you had the Commissioner of NFL on stage with you talking about a strategic partnership that is as top-down aggressive goals you could get yeah I have Roger Goodell fly to a tech conference to sit with you and then bring his team talk about the deal well you know we've been partners with the NFL for a while with the next-gen stats are they using all their telecasts and one of the things I really like about Roger is that he's very curious and very interested in technology in the first couple times I spoke with him he asked me so many questions about ways the NFL might be able to use the cloud and digital transformation to transform their various experiences and he's always said if you have a creative idea or something you think that could change the world for us just call me is it or text me or email me and I'll call you back within 24 hours and so we've spent the better part of the last year talking about a lot of really interesting strategic ways that they can evolve their experience both for fans as well as their players and the player health and safe safety initiative it's so important in sports and particularly important with the NFL given the nature of the sport and they've always had a focus on it but what you can do with computer vision and machine learning algorithms and then building a digital athlete which is really like a digital twin of each athlete so you understand what does it look like when they're healthy what and compare that when it looks like they may not be healthy and be able to simulate all kinds of different combinations of player hits and angles and different plays so that you can try to predict injuries and predict the right equipment you need before there's a problem can be really transformational so it was super excited about it did you guys come up with the idea it was the collaboration between there's really a collaboration I mean they look they are very focused on player's safety and health and it's it's a big deal for their you know they have two main constituents that the players and fans and they care deeply about the players and it's a it's a hard problem in a sport like football but you watch it yeah I gotta say it does point out the use cases of what you guys are promoting heavily at the show here of the stage maker studio which is a big part of your keynote where they have all this data right and they're dated hoarders they've the hoard data but they're the manual process of going through the data it was a killer problem this is consistent with a lot of the enterprises that are out there they have more data than they even know so this seems to be a big part of the strategy how do you get the customers to actually a wake up to the fact that they got data and how do you tie that together I think in almost every company they know they have a lot of data and there are always pockets of people who want to do something with it but when you're gonna make these really big leaps forward these transformations so things like Volkswagen is doing with they're reinventing their factories in their manufacturing process or the NFL where they're gonna radically transform how they do players health and safety it starts top-down and if they if the senior leader isn't convicted about wanting to take that leap forward and trying something different and organizing the data differently and organizing the team differently and using machine learning and getting help from us and building algorithms and building some muscle inside the company it just doesn't happen because it's not in the normal machinery of what most companies do and so it all wait almost always starts top-down sometimes it can be the commissioner or the CEO sometimes it can be the CIO but it has to be senior level conviction or it does get off the ground and the business model impact has to be real for NFL they know concussions hurting their youth pipelining this is a huge issue for them is their business model they they lose even more players to lower extremity injuries and so just the notion of trying to be able to predict injuries and you know the impact it can have on rules the impact it can have on the equipment they use it's a huge game changer when they look at the next 10 to 20 years all right love geeking out on the NFL but no more do you know off camera a 10 man is here defeated season so everybody's a Patriots fan now it's fascinating to watch you and your three-hour keynote Vernor in his you know architectural discussion really showed how AWS is really extending its reach you know it's not just a place for a few years people have been talking about you know cloud as an operation operational model it's not a destination or a location but I felt that really was laid out is you talked about breadth and depth and Verna really talked about you know architectural differentiation people talk about cloud but there are very there are a lot of differences between the vision for where things are going help us understand and why I mean Amazon's vision is still a bit different from what other people talk about where this whole cloud expansion journey but put over what tagger label you want on it but you know the control plane and the technology that you're building and where you see that going well I think that we've talked about this a couple times we we have two macro types of customers we have those that really want to get at the load level building blocks and stitch them together creatively and however they see fit to create whatever is in there in their heads and then we have this second segment of customers who say look I'm willing to give up some of that flexibility in exchange for getting 80% of the way they're much faster in an abstraction that's different from those low level building blocks in both segments of builders we want to serve and serve well and so we built very significant offerings in both areas I think when you look at micro services you know some of it has to do with the fact that we have this very strongly held belief born out of several years at Amazon where you know the first seven or eight years of Amazon's consumer business we basically jumbled together all of the parts of our technology and moving really quickly and when we wanted to move quickly where you had to impact multiple internal development teams it was so long because it was this big ball this big monolithic piece and we got religion about that and trying to move faster in the consumer business and having to tease those pieces apart and it really was a lot of the impetus behind conceiving AWS where it was these low-level very flexible building blocks that don't try and make all the decisions for customers they get to make them themselves and some of the micro services that you saw Verner talking about just you know for instance what we what we did with nitro or even what we do with firecracker those are very much about us relentlessly working to continue to to tease apart the different components and even things that look like low-level building blocks over time you build more and more features and all of a sudden you realize they have a lot of things that are they were combined together that you wished weren't that slowed you down and so nitro was a completely reimagining of our hypervisor and virtualization layer to allow us both to let customers have better performance but also to let us move faster and have a better security story for our customers I got to ask you the question around transformation because I think it all points to that all the data points you got all the references goldman-sachs on stage at the keynote Cerner and the healthcare just an amazing example because I mean this demonstrating real value there there's no excuse I talked to someone who wouldn't be named last night and then around the area said the CIA has a cost bar like this cost up on a budget like this but the demand for mission based apps is going up exponentially so there's need for the cloud and so seeing more and more of that what is your top-down aggressive goals to fill that solution base because you're also very transformational thinker what is your what is your aggressive top-down goals for your organization because you're serving a market with trillions of dollars of span that's shifting that's on the table a lot of competition now sees it too they're gonna go after it but at the end of the day you have customers that have that demand for things apps yeah and not a lot of budget increase at the same time this is a huge dynamic what's your goals you know I think that at a high level are top-down aggressive goals so that we want every single customer who uses our platform to have an outstanding customer experience and we want that outstanding customer experience in part is that their operational performance and their security are outstanding but also that it allows them to build and it build projects and initiatives that change their customer experience and allow them to be a sustainable successful business over a long period of time and then we also really want to be the technology infrastructure platform under all the applications that people build and they were realistic we know that that you know the market segments we address with infrastructure software hardware and data center services globally are trillions of dollars in the long term it won't only be us but we have that goal of wanting to serve every application and that requires not just the security operational performance but also a lot of functionality a lot of capability we have by far the most amount of capability out there and yet I would tell you we have three to five years of items on our roadmap that customers want us to add and that's just what we know today well and any underneath the covers you've been going through some transformation when we talked a couple years ago about how serverless is impacting things I've heard that that's actually in many ways glue behind the two pizza teams to work between organizations talk about how the internal transformations are happening how that impacts your discussions with customers that are going through that transformation well I mean there's a lot of a lot of the technology we build comes from things that we're doing ourselves you know and that we're learning ourselves it's kind of how we started thinking about microservices serverless - we saw the need we know we would have we would build all these functions that when some kind of object came into an object store we would spin up compute all those tasks would take like three or four hundred milliseconds then we spin it back down and yet we'd have to keep a cluster up in multiple availability zones because we needed that fault tolerance and it was we just said this is wasteful and that's part of how we came up with lambda and that you know when we were thinking about lambda people understandably said well if we build lambda and we build the serverless event-driven computing a lot of people who are keeping clusters of instances aren't going to use them anymore it's going to lead to less absolute revenue for us but we we have learned this lesson over the last 20 years at Amazon which is if it's something it's good for customers you're much better off cannibalizing yourself and doing the right thing for customers and being part of shaping something and I think if you look at the history of Technology you always build things and people say well that's gonna cannibalize this and people are gonna spend less money what really ends up happening is they spend spend less money per unit of compute but it allows them to do so much more that the ultimately long-term end up being you know more significant customers I mean you are like beating the drum all the time customers what they say we implement the roadmap I got that you guys have that playbook down that's been really successful for you yeah two years ago you told me machine learning was really important to you because your customers told what's the next tranche of importance for customers what's on top of mine now as you look at this reinvent kind of coming to a close replays tonight you had conversations your your tech a fleet you're running around doing speeches talking to customers what's that next hill from from my fist machine learning today there's so much I mean that's not it's not a soup question you know I think we're still in this in the very early days of machine learning it's not like most companies have mastered yet even though they're using it much more than they did in the past but you know I think machine learning for sure I think the edge for sure I think that we're optimistic about quantum computing even though I think it'll be a few years before it's really broadly useful we're very enthusiastic about robotics I think the amount of functions are going to be done by these robotic applications are much more expansive than people realize it doesn't mean humans won't have jobs they're just going to work on things that are more value-added I thought we're believers in augmented and virtual reality we're big believers and what's going to happen with voice and I'm also I think sometimes people get bored you know I think you're even bored with machine learning maybe already but yet people get bored with the things you've heard about but I think just what we've done with the chips you know in terms of giving people 40% better price performance in the latest generation of x86 processors it's pretty unbelievable and the difference in what people are going to be able to do or just look at big data I mean big date we haven't gotten through big data where people have totally solved it the amount of data that companies want to store process and analyze is exponentially larger than it was a few years ago and it will I think exponentially increase again in the next few years you need different tools the service I think we're not we're not for with machine learning we're excited to get started because we have all this data from the video and you guys got sage maker yeah we call it a stairway to machine learning heaven we start with the data move up what now guys are very sophisticated with what you do with technology and machine learning and there's so much I mean we're just kind of again in this early innings and I think that it was soaked before sage maker was so hard for everyday developers and data scientists to build models but the combination of sage maker and what's happened with thousands of companies standardizing on it the last two years Plus now sage maker studio giant leap forward we hope to use the data to transform our experience with our audience and we're on Amazon Cloud I really appreciate that and appreciate your support if we're with Amazon and Instant get that machine learning going a little faster for us a big that'll be better if you have requests so any I'm you talked about that you've got the customers that are builders and the customers that need simplification traditionally when you get into the you know the heart of the majority of adoption of something you really need to simplify that environment but when I think about the successful enterprise of the future they need to be builders yeah so has the model flipped if you know I normally would said enterprise want to pay for solutions because they don't have the skill set but if they're gonna succeed in this new economy they need to go through that transformation that yeah so I mean are we in just a total new era when we look back will this be different than some of these previous waves it's a it's a really good question Stu and I I don't think there's a simple answer to it I think that a lot of enterprises in some ways I think wish that they could just skip the low level building blocks and and only operate at that higher level abstraction it's why people were so excited by things like sage maker or code guru or Kendra or contact lens these are all services that allow them to just send us data and then run it on our models and get back the answers but I think one of the big trends that we see with enterprises is that they are taking more and more of their development in-house and they are wanting to operate more and more like startups I think that they admire what companies like Airbnb and Pinterest and slack and and you know Robin Hood and a whole bunch of those companies stripe have done and so when you know I think you go through these phases and errors where there are waves of success at different companies and then others want to follow that success and and replicate and so we see more and more enterprises saying we need to take back a lot of that development in-house and as they do that and as they add more developers those developers in most cases like to deal with the building blocks and they have a lot of ideas on how they can create us to creatively stitch them together on that point I want to just quickly ask you on Amazon versus other clouds because you made a comment to me in our interview about how hard it is to provide a service that to other people and it's hard to have a service that you're using yourself and turn that around and the most quoted line in my story was the compression algorithm there's no compression outliving for experience which to me is the diseconomies of scale for taking shortcuts yeah and so I think this is a really interesting point just add some color comments or I think this is a fundamental difference between AWS and others because you guys have a trajectory over the years of serving at scale customers wherever they are whatever they want to do now you got micro services it's even more complex that's hard yeah how about that I think there are a few elements to that notion of there's no compression algorithm I think the first thing to know about AWS which is different is we just come from a different heritage in a different background we sweep ran a business for a long time that was our sole business that was a consumer retail business that was very low margin and so we had to operate a very large scale given how many people were using us but also we had to run infrastructure services deep in the stack compute storage and database in reliable scalable data centers at very low costs and margins and so when you look at our our business it actually today I mean it's it's a higher margin business in our retail business the lower margin business and software companies but at real scale it's a it's a high-volume relatively low margin business and the way that you have to operate to be successful with those businesses and the things you have to think about and that DNA come from the type of operators that we have to be in our consumer retail business and there's nobody else in our space that does that you know the way that we think about cost the way we think about innovation and the data center and and I also think the way that we operate services and how long we've been operating services of the company it's a very different mindset than operating package software then you look at when you think about some of the issues and very large scale cloud you can't learn some of those lessons until you get two different elbows of the curve and scale and so what I was telling you is it's really different to run your own platform for your own users where you get to tell them exactly how it's going to be done but that's nothing really the way the real world works I mean we have millions of external customers who use us from every imaginable country and location whenever they want without any warning for lots of different use cases and they have lots of design patterns and we don't get to tell them what to do and so operating a cloud like that at a scale that's several times larger the next few providers combined is a very different endeavor and a very different operating rigor well you got to keep raising the bar you guys do a great job really impress again another tsunami of announcements in fact you had to spill the beans early with quantum the day before the event tight schedule I gotta ask you about the music festival because I think there's a really cool innovation it's the inaugural intersex conference yeah it's not part of replay which is the concert tonight right it's a whole new thing big music act you're a big music buff your daughter's an artist why did you do this what's the purpose what's your goal yeah it's an experiment I think that what's happened is that reinvent has gotten so big with 65,000 people here that to do the party which we do every year it's like a thirty five forty thousand person concert now which means you have to have a location that has multiple stages and you know we thought about it last year when we were watching it and we said we're kind of throwing like a four hour music festival right now there's multiple stages and it's quite expensive to set up that set for our partying we said well maybe we don't have to spend all that money for four hours in the rip it apart because actually the rent to keep those locations for another two days is much smaller than the cost of actually building multiple stages and so we we would try it this year we're very passionate about music as a business and I think we are I think our customers feel like we throw in a pretty good music party the last few years and we thought we were trying at a larger scale as an experiment and if you look at the economics the headliners real quick the Foo Fighters are headlining on Saturday night Anderson Park and the free Nashville free Nationals Brandi Carlile Shawn Mullins Willie Porter it's a good set Friday night it's back in Kacey Musgraves so it's it's a really great set of about 30 artists and we're hopeful that if we can build a great experience that people want to attend that we can do it it's scale and it might be something that you know both pays for itself and maybe helps pay for reinvent to overtime and you know I think that we're also thinking about it as not just a music concert and festival the reason we named it intersect is that we want an intersection of music genres and people and ethnicities and age groups and art and Technology all there together and this will be the first year we try it it's an experiment and we're really excited about I'm gone congratulations all your success and I want to thank you we've been seven years here at reinvent we've been documenting the history two sets now once-dead upstairs so appreciate a cube is part of reinvent you know you guys really are a part of the event and we really appreciate your coming here and I know people appreciate the content you create as well and we just launched cube 365 on Amazon Marketplace built on AWS so thanks for letting us cool build on the platform appreciate it thanks for having me guys Jesse the CEO of AWS here inside the cube it's our seventh year covering and documenting they're just the thunderous innovation that Amazon is doing they're really doing amazing work building out the new technologies here in the cloud computing world I'm John Force too many men be right back with more after this short break [Music]

>>LA Las Vegas. It's the cube covering AWS reinvent 2019 brought to you by Amazon web services and don't along with its ecosystem partners. >>Hey, welcome back to the cubes coverage of AWS reinvent 19 from Las Vegas. This is our third day of covering the event. Lots of conversations, two cube sets, as John would say, a Canon of cube content. Lisa Martin here with my esteemed colleague Justin. Um, and Justin, I have a couple of guests joining us. We've got to my left jus bhandarkar had a product for FreshWorks inc and Broughton Sahar VP and GM of machine learning services from Amazon. Gentlemen, welcome to the cube. You still have voices, which is very impressive after three days. A lot of practice it does or hiding out in quiet areas. Right? So Tay, Jess FreshWorks inc I souping on the website. Justin and I were talking before we went live, you guys have 150,000 of businesses using your technologies. I hadn't heard of FreshWorks, but it looks like it's about customer relationship management and customer experience. Tell our audience a little bit about what FreshWorks is and the technologies that you deliver. >>Okay. So we were founded in, uh, back in 2010. We were born in the cloud in the AWS cloud. Uh, and we started off as a, uh, customer support, uh, application. And we have grown on to now deliver a suite of customer engagement applications that include marketing and automation capabilities, CRM, uh, customer support and customer success. And so what we really are looking after is, is to deliver a value across the entire customer journey. Uh, you know, >>so there's been some big legacy CRMs around for a long time. What was the market opportunity back in 2010? The FreshWorks folks saw there's a gap here. We need to fill it. >>Yeah. We, well, we, uh, like any other startup, we decided to focus in one place and our focus, uh, was really around SMBs. We felt like SMBs were underserved and, uh, we felt like as rich as the technology is and the experiences have become, uh, we felt like we needed to democratize access to that. And because SMBs tended to have fewer resources and maybe, uh, in some cases weren't as tech savvy. Uh, we felt like they were kind of getting left behind. And so we wanted to step in there and make them whole and kind of offer them the same set of richness that you would expect, you know, for a large enterprise customer to have. And for that actually working in conjunction with AWS has been super important for us cause we have really been able to deliver on that promise. >>Maybe you can tell us a bit about the relationship between yourselves and FreshWorks. I believe the fresh works is built completely on AWS and always has been. >>Yeah. So how did that relationship begin and how has that grown as, as FreshWorks has grown into, into this massive company that you've become? >>Yeah, so, um, FreshWorks got off on AWS and then when we launched Sage maker and as you know, we have 700 tens of thousands of customers today doing the machine learning on AWS and on Sage maker. What customers have seen is that they get significant benefits in terms of features and developer productivity. And lower cost of ownership and FreshWorks saw that they could reduce that time to getting the models out by an order of magnitude. And their house was saying for example, that they used to take couple of days to get the models out to production. And by using Sage maker they were able to get it down to a couple of hours. And we have seen this happen with many other customers into it. For example, got down from six months to about a week. And just because of the productivity, performance and cost benefits that Sage makeup provides, you have seen the house FreshWorks and then many other companies, many of the customers more to AWS for the machine learning. >>Are they what are you using this machine learning to do? So you have all of these different models and we were talking a little bit before we went live about how you, how you use different models for different customers. But what are those models actually used to do? What service do they provide? >>Okay. So as you know, we have a set of these applications which are built around functional use cases. And so if you take a given customer, they might have multiple products from us and they might be doing multiple different use cases on us. And so you can quickly think of this as being, you know, maybe three to five specific use cases that require, you know, machine learning, you know, assistance. Uh, and so as a result, as we scale this up to the our entire, uh, set of customers, we now literally have thousands and thousands of these ML models that we have built, addressed, uh, geared to, uh, addressing specific pain points of that particular customer. Right? So it's all about catering the ML model for a specific use in a specific context. And then it's not only just about building it, uh, which, you know, obviously Sage Merker does a great job of helping us do that, but it's also about maintaining it over time and making sure that it stays relevant and fresh and so on. >>And again, working with AWS has been instrumental in for us to kind of stay ahead of that curve and make sure that we're continuing to drive accuracy and scale and simplicity into, uh, into, you know, into those particular use cases for customers. Then, you know, we released many features this year that makes this important. So one of the things that we have as part of Sage maker studio is a Sage make a model monitor that automatically monitors predictions and allows a customer to say, when are those predictions not being of the appropriate quality? And then we can send an allowance. So we are really building Sage maker out as a machine learning platform that they get all of the undifferentiated heavy lifting so that customers can really focus on what they need to do to build a model, train the model, and deploy the model. >>So in terms of your users, you mentioned too just the, the, the gap in the market back in 2010 was the small, the SMB space that probably something like a Salesforce or an Oracle was possibly too complex for an SMB. But now we're talking about emerging technologies, machine learning, AI. What is the appetite for the smaller, are you dealing with, I guess my question is a lot of SMBs that are born in the cloud companies, so smaller and more agile and more willing to understand and embrace technology versus legacy SMBs that might be, I don't want to say technology averse but not born within it. >>Yeah. So, so we, uh, we run through the entire gamut. So we obviously have, uh, you know, Silicon Valley based startups. We have more traditional companies around travel and hospitality and real estate and other, other verticals. Uh, and what we have really, really seen the commonality has been is that, uh, as good as the technology has become for AI and ML, uh, there is still some disparity in how people are able to consume it. Right. And if you have a lot of resources, a lot of skilled engineers, it is very easy for you to do that, thanks to all of the capabilities that are delivered by AWS. But in the other cases, uh, they do require more handholding specifically for those use cases that really impact them. Like how do I reduce my churn amongst customers? How do I maximize the chances of closing a deal? How do I make sure that the marketing campaign I run delivers on all of the, the objectives that I have? Right? So all of those things they re they need help. And so we are in there to kind of simplify that for them and leveraging all of the underlying technologies from AWS. We're able to deliver that together >>and going in from the beginning all in on AWS when AWS was only about four years old or so, right? Back in 2010. Um, talk to me about the opportunities that that is opened up for FreshWorks to evolve, you know, offer a suite of different solutions. Talk to us about Amazon and AWS is evolution and how quickly that they're evolving and developing new products and services as like fuel for FreshWorks business. >>Yeah. So really the big focus that we have always had is to deliver the right experiences that really impact end users. For those particular functional use cases around marketing, sales support and customer success, right? So as part of that, while we are focusing on on that experience, we also need to be focusing on delivering all of these services at scale, right? And with all the right security built in and all the right, uh, other, you know, tool set that that's built in. And so, so the synergy that we have found with between us and AWS is that we're able to rely on all of the right things for AWS to deliver upon. So they are also all about offering simple API APIs about making things scalable right from the get go about being extremely cost effective about uh, continuing to drive innovation. And these are all the things that drive us as well for our customers. And so it's been a very complimentary partnership from that respect is, you know, we kind of like go on this journey together and in our customer obsession is a key leadership principle. And so everything we do at AWS is really working back from the customer and making sure that we are really addressing all of the pain points. And making them successful? >>Well, because customer experience is a D it can be a deal breaker for companies, right? You think of you have a problem with your ISP and you call in or you go through social media or um, a chat bot and you can't get that problem resolved. As a consumer, you have so much choice to go to another vendor who might be able to better meet your needs or have the use the data to make sure they already know what's the problem. It's the same thing in the CRM space, right? If businesses don't have the right technologies to use the data to really know their customers, this customer's churn. And so it's really, we see CX as a driving force in any industry that if you can't get that right, customers are going to go, I'm going to go somewhere else because I have that choice. >>Yes. I mean customer expectations that you said have risen customer inpatients with bad experiences gone down. And one of the things that we have really focused on is as we go through this entire journey, we collect the data of that customer's journey. And we learn from it and we're able to visualize that for the sales person or the tech support person who's actually working with that customer. So they can actually see the journey of that customer. They visited the pricing page a couple of times, maybe they're interested to make a purchase or they visited the cancellation policy page. Okay, maybe I need to do something about that. Right. And so that is really been instrumental kind of in success success. And you know, what we are doing at AWS and Sage maker is making sure that all our customers get access to this technology. And that is where we start with how do we make machine learning accessible to all developers so that all of the experiences that we have gained at Amazon from investing in machine learning for the last 20 years, we take all of those learnings and make it available to our customers so they can apply machine learning for transforming their businesses. >>Yup. >>And that's exactly what it can be as transformational. Well gentlemen, thank you very much for joining Justin and me on the program talking to us about FreshWorks. What you guys are doing with Amazon and the opportunity to really dial up that CX experience with machine learning. We appreciate your time. >>Thank you. Thank you very much. >>All right. For my car is Justin Warren. I'm Lisa Martin and your Archie, the cube from AWS. Reinvent 19 from Vegas. Thanks.

>>LA from Las Vegas. It's the cube covering AWS reinvent 2019, brought to you by Amazon web services and along with its ecosystem partners. >>Oh, welcome back. Inside the sands, we continue our coverage here. Live coverage on the cube of AWS. Reinvent 2019. We're in day three at has been wall to wall, a lot of fun here. Tuesday, Wednesday now Thursday. Dave Volante. I'm John Walls and we're joined by Christian Rahman who was the founder and CEO of for Christian. Good morning to you. Good morning. Thanks for having afternoon. If you're watching on the, uh, on the East coast right now. Um, let's talk about sleep a little bit. I know you're all about data, um, but let's go ahead and introduce the company to those at home who might not be familiar with what your, your poor focus was. The primary focus. Absolutely. So athlete is a managed ETL as a service company. ETL is extract, transform, and load basically about getting data from different data sources, like different applications and databases into a place where it can be analyzed. >>Typically a data warehouse or a data Lake. So let's talk about the big picture then. I mean, because this has been all about data, right? I mean, accessing data, coming from the edge, coming from multiple sources, IOT, all of this, right? You had this proliferation of data and applications that come with that. Um, what are you seeing that big picture wise in terms of what people are doing with their data, how they're trying to access their data, how to turn to drive more value from it and how you serve all those masters, if you will. So there are a few trends that we see these days. One is a, you know, an obvious one that data warehouses are moving to the cloud, right? So, you know, uh, companies used to have, uh, data warehouses on premises and now they're in the cloud. They're, uh, cheaper and um, um, and more scalable, right? With services like a Redshift and snowflake in particular on AWS. Um, and then, uh, another trend is that companies have a lot more applications than they used to. You know, in the, um, in the old days you would have maybe a few data ware, sorry, databases, uh, on premises that you would integrate into your data warehouses. Nowadays you have companies have hundreds or even thousands of applications, um, that effectively become data silos, right? Where, um, uh, analysts are seeing value in that data and they want to want to have access to it. >>So, I mean, ETL is obviously not going away. I mean, it's been here forever and it'll, it'll be here forever. The challenge with ETL has always been it's cumbersome and it's expensive. It's, and now we have this new cloud era. Um, how are you guys changing ETL? >>Yeah. ETL is something that everybody would like to see go away. Everybody would just like, not to do it, but I just want to get access to their data and it should be very unfortunate for you. Right. Well, so we started, uh, we started athlete because we saw that ETL is not going away. In fact, with all the, uh, all these applications and all these needs that analysts have, it's actually becoming a bigger problem than it used to be. Um, and so, uh, what we wanted to do is basically take, take some of that pain out, right? So that companies can get to analyzing their data faster and with less engineering effort. >>Yeah. I mean, you hear this, you know, the typical story is that data scientists spend 80% of their time wrangling data and it's, and it's true in any situation. So, um, are you trying to simplify, uh, or Cloudify ETL? And if so, how are you doing that? >>So with, uh, with the growth in the number of data analysts and the number of data analytics projects that companies wants to take on the, the traditional model of having a few engineers that know how to basically make the data available for analysts, that that model is essentially now broken. And so, uh, just like you want to democratize, uh, BI and democratize analytics, you essentially have to democratize ETL as well, right? Basically that process of making the data ready for analysis. And, uh, and that is really what we're doing at athlete. We're, we're opening up ETL to a much broader audience. >>So I'm interested in how I, so I'm in pain. It's expensive. It's time consuming. Help me Christian, how, how can you help me, sir? >>So, so first of all, we're, we're, um, uh, at least specifically we're a hundred percent AWS, so we're deeply focused on, uh, Redshift data warehouses and S3 and good data lakes. Uh, and you know, there's tremendous amount of innovation. Um, those two sort of sets of technologies now, um, Redshift made a bunch of very cool announcements era at AWS reinvent this year. Um, and so what we do is we take the, uh, the infrastructure piece out, you know, so you can deploy athlete as a hosted service, uh, where we manage all the infrastructure for you or you can deploy it within your VPC. Um, again, you know, in a much, much simplified way, uh, compared to a traditional ETL technologies. Um, and then, you know, beyond that taking, uh, building pipelines, you know, building data pipelines used to be something that would take engineers six months to 18 months, something like that. But, um, but now what we, what we see is companies using athlete, they're able to do it much faster often, um, often an hours or days. >>A couple of questions there. So it's exclusively red shift, is that right? Or other analytic databases and make is >>a hundred percent AWS we're deeply focused on, on integrating well with, with AWS technologies and services. So, um, so on the data warehousing side, we support Redshift and snowflake. >>Okay, great. So I was going to ask you if snowflake was part of that. So, well you saw red shift kind of, I sort of tongue in cheek joke. They took a page out of snowflake separating compute and storage that's going to make customers very happen so they get happy. So they can scale that independently. But there's a big trend going on. I wonder if you can address it in your, you were pointing out before that there's more data sources now because of the cloud. We were just having that conversation and you're seeing the data exchange, more data sources, things like Redshift and snowflake, uh, machine intelligence, other tools like Databricks coming in at the Sage maker, a Sage maker studios, making it simpler. So it's just going to keep going faster and faster and faster, which creates opportunities for you guys. So are you seeing that trend? It's almost like a new wave of compute and workload coming into the cloud? >>Yeah, it's, it's super interesting. Companies can now access, um, a lot more data, more varied data, bigger volumes of data that they could before and um, and they want faster access to it, both in terms of the time that it takes to, you know, to, to bite zero, right? Like the time, the time that it takes to get to the first, uh, first analysis. Um, and also, um, and also in terms of the, the, the data flow itself, right? They, they not want, um, up to the second or up to the millisecond, um, uh, essentially fresh data, uh, in their dashboards and for interactive analysis. And what about the analytics side of this then when we were talking about, you know, warehousing but, but also having access to it and doing something with it. Um, what's that evolution looking like now in this new world? So lots of, um, lots of new interesting technologies there to, um, um, you know, on the, on the BI side and, um, and our focus is on, on integrating really well with the warehouses and lakes so that those, those BI tools can plug in and, and, um, um, and, and, you know, um, get access to the data straight away. Okay. >>So architecturally, why are you, uh, how are you solving the problem? Why are you able to simplify? I'm presuming it's all built in the cloud. That's been, that's kind of an obvious one. Uh, but I wonder if you could talk about that a little bit because oftentimes when we talk to companies that have started born in the cloud, John furrier has been using this notion of, you know, cloud native. Well, the meme that we've started is you take out the T it cloud native and it's cloud naive. So you're cloud native. Now what happens oftentimes with cloud native guys is much simpler, faster, lower cost, agile, you know, cloud mentality. But maybe some, sometimes it's not as functional as a company that's been around for 40 years. So you have to build that up. What's the state of ETL, you know, in your situation. Can you maybe describe that a little bit? How is it that the architecture is different and how address functionality? >>Yeah, I mean, um, so a couple of things there. Uh, um, you, you mentioned Redshift earlier and how they now announce the separation of storage and compute. I think the same is true for e-tail, right? We can, we can build on, um, on these great services that AWS develops like S three and, and, uh, a database migration service and easy to, um, elastic MapReduce, right? We can, we can take advantage of all these, all these cloud primitives and um, um, and, and so the, the infrastructure becomes operationally, uh, easier that way. Um, and, and less expensive and all, all those good things. >>You know, I wonder, Christian, if I can ask you something, given you where you live in a complicated world, I mean, data's complicated and it's getting more complicated. We heard Andy Jassy on Tuesday really give a message to the, to the enterprise. It wasn't really so much about the startups as it previously been at, at AWS reinvent. I mean, certainly talking to developers, but he, he was messaging CEOs. He had two or three CEOs on stage. But what we're describing here with, with red shift, and I threw in Databricks age maker, uh, elastic MapReduce, uh, your tooling. Uh, we just had a company on that. Does governance and, and builders have to kind of cobble these things together? Do you see an opportunity to actually create solutions for the enterprise or is that antithetical to the AWS cloud model? What, what are your thoughts? >>Oh, absolutely know them. Um, uh, these cloud services are, are fantastic primitives, but um, but enterprises clearly have a lot of, and we, we're seeing a lot of that, right? We started out in venture Bactec and, and, and got, um, a lot of, a lot of venture backed tech companies up and running quickly. But now that we're sort of moving up market and, and uh, and into the enterprise, we're seeing that they have a requirements that go way beyond, uh, beyond what, what venture tech, uh, needs. Right. And in terms of security, governance, you know, in, in ETL specifically, right? That that manifests itself in terms of, uh, not allowing data to flow out of, of the, the company's virtual private cloud for example. That's something that's very important in enterprise, a much less important than in, uh, in, in venture-backed tech. Um, data lineage. Right? That's another one. Understanding how data, uh, makes it from, you know, all those sources into the warehouse. What happens along the way. Right. And, and regulated industries in particular, that's very important. >>Yeah. I mean, I, you know, AWS is mindset is we got engineers, we're going to throw engineers at the problem and solve it. Many enterprises look at it differently. We'll pay money to save time, you know, cause we don't have the time. We don't have the resource, I feel like I, I'd like to see sort of a increasing solutions focus. Maybe it's the big SIS that provide that. Now are you guys in the marketplace today? We are. Yup. That's awesome. So how's that? How's that going? >>Yeah. Um, you mean AWS market? Yes. Yes. Uh, yeah, it's, it's um, um, that's definitely one, one channel that, uh, where there's a lot of, a lot of promise I think both. Um, for, for for enterprise companies. Yeah. >>Cause I mean, you've got to work it obviously it doesn't, just the money just doesn't start rolling in you gotta you gotta market yourselves. >>But that's definitely simplifies that, um, that model. Right? So delivering, delivering solutions to the enterprise for sure. So what's down the road for you then, uh, from, from ETL leaps perspectives here or at leaps perspectives. Um, you've talked about the complexities and what's occurred and you're not going away. ETL is here to say problems are getting bigger. What do you see the next year, 12, 18, 24 months as far as where you want to focus on? What do you think your customers are going to need you to focus on? So the big challenge, right is that, um, um, bigger and bigger companies now are realizing that there is a ton of value in their data, in all these applications, right? But in order to, in order to get value out of it, um, you have to put, uh, engineering effort today into building and maintaining these data pipelines. >>And so, uh, so yeah, so our focus is on reducing that, reducing those engineering requirements. Um, right. So that both in terms of infrastructure, pipeline, operation, pipeline setup, uh, and, and those kinds of things. So where, uh, we believe that a lot of that that's traditionally been done with specialized engineering can be done with great software. So that's, that's what we're focused on building. I love the, you know, the company tagged the perfect data pipeline. I think of like the perfect summer, the guy catching a big wave out in Maui or someplace. Good luck on catching that perfect data pipeline you guys are doing. You're solving a real problem regulations. Yeah. Good to meet you. That cause more. We are alive at AWS reinvent 2019 and you are watching the cube.

>>LA from Las Vegas. It's the cube covering AWS reinvent 2019 brought to you by Amazon web services and along with its ecosystem partners. >>Welcome back to the queue from Las Vegas. We are live at AWS reinvent 19 Lisa Martin with John furrier. We've been having lots of great conversations. John, we're about to have another one cause we always love to talk about customer proof in the putting. Please welcome a couple of guests. We have Rafael, director of analytics and data management from triple a life. Welcome. Thanks for having me. Really appreciate it. Our pleasure. And from Burkle anchor Jane, the SVP of cloud platforms. Welcome. Thank you. Thank you so much. Pleasure to be here. So here we are in this, I can't see of people around us as, as growing exponential a by the hour here, but awkward. Let's start with you give her audience an understanding of Merkel, who you are and what you do. >>Yeah, absolutely. So Marco is a global performance marketing agency. We are part of a dental agent network and a, it's almost about 9,000 to 10,000 people worldwide. It's a global agency. What differentiates Merkel from rest of the other marketing agencies is our deep roots and data driven approach. We embrace technology. It's embedded in all our, all our solutions that we take to market. Um, and that's what we pride ourselves with. So, um, that's basically a high level pitch about Merkel. What differentiates us, my role, uh, I lead the cloud transformation for Merkel. Um, uh, basically think of my team as the think tanks who bring in the new technology, come up with a new way of rolling out solutions product I solutions, uh, disruptive solutions, which helps our clients and big fortune brands such as triple life insurance, uh, to transform their marketing ecosystem. >>So let's go ahead and dig. A lot of folks probably know AAA life, but, but Raphael, give us a little bit of an overview. This is a 50 year old organization. >>So we celebrate our 50th 50 year anniversary this year. Actually, we're founded in 1969. So everybody life insurance, we endeavor to be the provider of choice for a AAA member. Tell them to protect what matters most to them. And we offer a diverse set of insurance products across just about every channel. Um, and um, we engage with Merkel, uh, earlier, the, um, in 2018 actually to, to, uh, to build a nice solution that allows us to even better serve the needs of the members. Uh, my role, I am the, I lead our analytics and data management work. So helping us collect data and manage better and better leverage it to support the needs of members. >>So a trip, I can't even imagine the volumes of data that you're dealing with, but it's also, this is people's data, right? This is about insurance, life insurance, the volume of it. How have you, what were some of the things that you said? All right guys, we need to change how we're managing the data because we know there's probably a lot more business value, maybe new services that we can get our on it or eyes >>on it. >>So, so that was, that was it. So as an organization, uh, I want to underscore what you said. We make no compromises when it comes to the safety of our, of our members data. And we take every step possible to ensure that it is managed in a responsible and safe way. But we knew that on, on the platform that we had prior to this, we weren't, we weren't as italics. We wanted to be. We would find that threaten processes would take spans of weeks in order to operate or to run. And that just didn't allow us to provide the member experience that we wanted. So we built this new solution and this solution updates every day, right? There's no longer multi-week cycle times and tumbler processes happen in real time, which allows us to go to market with more accurate and more responsive programs to our members. >>Can you guys talk about the Amazon and AWS solution? How you guys using Amazon's at red shift? Can he says, you guys losing multiple databases, give us a peek into the Amazon services that you guys are taking advantage of that anchor. >>Yeah, please. Um, so basically when we were approached by AAA life to kind of come in and you know, present ourselves our credentials, one thing that differentiated there in that solution page was uh, bringing Amazon to the forefront because cloud, you know, one of the issue that Ravel and his team were facing were scalability aspect. You know, the performance was, was not up to the par, I believe you guys were um, on a two week cycle. That data was a definition every two weeks. And how can we turn that around and know can only be possible to, in our disruptive technologies that Amazon brings to the forefront. So what we built was basically it's a complete Amazon based cloud native architecture. Uh, we leveraged AWS with our chip as the data warehouse platform to integrate basically billions and billions of rows from a hundred plus sources that we are bringing in on a daily basis. >>In fact, actually some of the sources are the fresh on a real time basis. We are catching real time interactions of users on the website and then letting Kimberly the life make real time decisions on how we actually personalize their experience. So AWS, Redshift, you know, definitely the center's centerpiece. Then we are also leveraging a cloud native ELT technology extract load and transform technology called. It's a third party tool, but again, a very cloud native technology. So the whole solution leverage is Python to some extent. And then our veil can talk about AI and machine learning that how they are leveraging AWS ecosystem there. >>Yeah. So that was um, so, uh, I anchor said it right. One thing that differentiated Merkel was that cloud first approach, right? Uh, we looked at it what a, all of the analysts were saying. We went to all the key vendors in this space. We saw the, we saw the architecture is, and when Merkel walked in and presented that, um, that AWS architecture, it was great for me because if nausea immediately made sense, there was no wizardry around, I hope this database scales. I was confident that Redshift and Lambda and dynamo would this go to our use cases. So it became a lot more about are we solving the right business problem and less about do we have the right technologies. So in addition to what Ankur mentioned, we're leveraging our sort of living RNR studio, um, in AWS as well as top low frat for our machine learning models and for business intelligence. >>And more recently we've started transition from R to a Python as a practitioner on the keynote today. Slew a new thing, Sage maker studio, an IDE for machine learning framework. I mean this is like a common set. Like finally, I couldn't have been more excited right? That, that was my Superbowl moment. Um, I was, I was as I was, we were actually at dinner yesterday and I was mentioning Tonker, this is my wishlist, right? I want AWS to make a greater investment in that end user data scientists experience in auto ML and they knocked it out of the park. Everything they announced today, I was just, I was texting frat. Wow, this is amazing. I can't wait to go home. There's a lot of nuances to, and a lot of these announcements, auto ML for instance. Yeah. Really big deal the way they did it. >>And again, the ID who would've thought, I mean this is duh, why didn't we think about this sooner? Yeah. With auto ML that that focus on transparency. Right. And then I think about a year ago we went to market and we ended up not choosing any solutions because they hadn't solved for once you've got a model built, how do you effectively migrated from let's say an analyst who might not have the, the ML expertise to a data science team and the fact that AWS understood out of the gate that you need that transparent all for it. I'm really excited for that. What do you think the impacts are going to be more uptake on the data science side? What do you think the impact of this and the, so I think for, I think we're going to see, um, that a lot of our use cases are going to part a lot less effort to spin up. >>So we're going to see much more, much faster pilots. We're going to have a much clearer sense of is this worth it? Is this something we should continue to invest in and to me we should drive and I expect that a lot, much larger percentage of my team, the analysts are going to be involved in data and data science and machine learning. So I'm really excited about that. And also the ability to inquire, to integrate best practices into what we're doing out of the gate. Right? So software engineers figured out profiling, they figured out the bugging and these are things that machine learners are picking up. Now the fact that you're front and center is really excited. Superbowl moment. You can be like the new England Patriots, 17 straight AFC championship games. Boston. Gosh, I could resist. Uh, they're all Seattle. They're all Seattle here and Amazon. I don't even bring Seattle Patriots up here and Amazon, >>we are the ESPN of tech news that we have to get in as far as conversation. But I want to kind of talk a little bit, Raphael about the transformation because presumably in, in every industry, especially in insurance, there are so many born in the cloud companies that are a lot, they're a lot more agile and they are chasing what AAA life and your competitors and your peers are doing. What your S establishing with the help of anchor and Merkel, how does this allow you to actually take the data that you had, expand it, but also extract insights from maybe competitive advantages that you couldn't think about before? >>Yeah, so I think, uh, so as an organization, even though we're 50 years old, one of the things that drew me to the company and it's really exciting is it's unrelated to thrusting on its laurels, right? I think there's tremendous hunger and appetite within our executive group to better serve our members and to serve more members. And what this technology is allowed is the technology is not a limiting factor. It's an enabling factors. We're able to produce more models, more performant models, process more of IO data, build more features. Um, we've managed to do away with a lot of the, you know, if you take it and you look at it this way and squeeze it and maybe it'll work and systematize more aspects of our reporting and our campaign development and our model development and the observability, the visibility of just the ability to be agile and have our data be a partner to what we're trying to accomplish. That's been really great. >>You talked about the significant reduction in cycle times. If we go back up to the executive suite from a business differentiation perspective, is the senior leadership at AAA understanding what this cloud infrastructure is going to enable their business to achieve? >>Absolutely. So, so our successes here I think have been instrumental in encouraging our organization to continue to invest in cloud. And uh, we're an active, we're actively considering and discussing additional cloud initiatives, especially around the areas of machine learning and AI. >>And the auger question for you in terms of, of your expertise, in your experience as we look at how cloud is changing, John, you know, educate us on cloud cloud, Tuto, AI machine learning. What are, as, as these, as businesses, as industries have the opportunity to for next gen cloud, what are some of the next industries that you think are really prime to be completely transformed? >>Um, I'm in that are so many different business models. If you look around, one thing I would like to actually touch upon what we are seeing from Merkel standpoint is the digital transformation and how customers in today's world they are, you know, how brands are engaging with their customers and how customers are engaging with the brands. Especially that expectations customer is at the center stage here they are the ones who are driving the whole customer engagement journey, right? How all I am browsing a catalog of a particular brand on my cell phone and then I actually purchased right then and there and if I have an issue I can call them or I can go to social media and log a complaint. So that's whole multi channel, you know, aspect of this marketing ecosystem these days. I think cloud is the platform which is enabling that, right? >>This cannot happen without cloud. I'm going to look at, Raphael was just describing, you know, real time interaction, real time understanding the behavior of the customer in real time and engaging with them based on their need at that point of time. If you have technologies like Sage maker, if you have technologies like AWS Redship you have technologies like glue, Kinesis, which lets you bring in data from all these disparate sources and give you the ability to derive some insights from that data in that particular moment and then interact with the customer right then and there. That's exactly what we are talking about. And this can only happen through cloud so, so that's my 2 cents are where they are, what we from Merkel standpoint, we are looking into the market. That's what we are helping our brands through to >>client. I completely agree. I think that the change from capital and operation, right to no longer house to know these are all the sources and all the use cases and everything that needs to happen before you start the project and the ability to say, Hey, let's get going. Let's deliver value in the way that we've had and continue to have conversations and deliver new features, new stores, a new functionality, and at the same time, having AWS as a partner who's, who's building an incremental value. I think just last week I was really excited with the changes they've made to integrate Sage maker with their databases so you can score from the directly from the database. So it feels like all these things were coming together to allow us as a company to better off on push our aims and exciting time. >>It is exciting. Well guys, I wish we had more time, but we are out of time. Thank you Raphael and anchor for sharing with Merkel and AAA. Pleasure. All right. Take care. Or John furrier. I am Lisa Martin and you're watching the cube from Vegas re-invent 19 we'll be right back.

>>law from Las Vegas. It's the two covering a ws re invent 2019. Brought to you by Amazon Web service is and in along with its ecosystem partners, >>everyone welcome to the Cubes Live coverage in Las Vegas For AWS Reinvent 2019 starts Seventh year of the Cube coverage. Watching the big wave of Amazon continue to pound the pound the beach with more announcements. I'm John Ferrier instructing the seal for the new ways with my partner, David Dante, our next guest. Brian Hall, vice president. Product market for all of AWS >>Brian. Thanks for coming on. The Cube is >>really a pleasure to be here. We've had ready, eh? We've >>had many conversations off camera around opportunities, innovation and watching Andy Jackson Kino, which is a marathon. Three hours, 30 announcements. He's hit his mark. Live music, well done. But he got a ton of stuff in there. Let's unpack the key points. Tell us what you think people should pay attention to. Of all the announcements, one of the three major or one of the major areas that are that stand out that are most notable that you wanna highlight. >>Okay, I'll give you I'll give you four areas that I think are most notable from the keynote. First is we continue to be very focused on how do we give the deepest and broadest platform for all the different things people want to be able to do with computing. And we had a big announcements around new instance instances of easy to that air based on custom design silicon that that we built one of them is called IMF one. These are instances that are focused on machine learning inference. Where it turns out, up to 90% of the cost for machine learning often is. And so we have. We have a brand new set of instances reduce costs by up to 90% for people doing inference in the cloud. We also last year announced a armed chip that we developed called Graviton, and we announced today grab it on two and that their new instances that are running on gravity on thio, including our general purpose computer instances, are compute intensive instances and high memory instances, and people will get up to 40% price performance improvement by using the instances that are based on the >>method of the messages faster more inexpensive. But also there's an architectural shift going on with Compute Way. Heard that with the I. O. T. And the Outpost stuff where computer is moving to the data because moving around is well recognized and now affirmed its expensive. Yeah, this is a big part of it. You got local zone. What's that local zone? Was it a local >>s? So they're kind of two ways that we're addressing that the first is but making it so that our infrastructure is closer to customers. We have outposts for customers that want to run a WS in their own environments. We announced today local zones which are essentially taking the computer storage database capabilities and putting it closer to metro areas where people want to have a single digit Leighton see for applications when going to the clouds over video rendering for gaming and like, that's gonna be very helpful. Is >>that gonna be like a regional point of presence was gonna be installed, Eleni, on any premise anyone wants, I could put my >>outpost can be put in any environment where you have the right power network infrastructure. Local zones are managed by Amazon, so I don't have to have it. I don't have to manage any data center. Anything. I could just choose to deploy to an environment that is geographically very >>smaller than a region. >>Small isn't an ability. Oh, yeah, >>Right. Okay. That's like a mini zone. Yeah, and and so what about the the availability component? It's sort of up to the customer to figure that out There >>it is connected to a region. So, for instance, we're releasing in Los Angeles with availability now, and that's connected to the US West region. So all of the data backup redundancy application duplication of people want to be able to do could do be done, do the region. >>All right, So graviton processor got onto those early press reports that leaked out prior to reinvent. I noticed that didn't match kind of what was announced. Just clarify what the grab it on ship is doing. What was the key? Grab it on a piece of the news here >>s O gravitas to is a arm based process lor designed and built by a W s. It is powering three different instance. Types are for those who know the types the see instances am instances and are instances on dhe available starting today with M six, which is one of our general purpose computing platforms. And so it gives up to 40% better price performance. And there's a whole ecosystem of platforms and APS Little run unarmed today. >>Are you pushing the envelope on computer? Which is great you continue to do That's the core of jewels of AWS, which we love and storage and everything else. Warm story. I get that a second, but I want your thoughts on the stage maker. A lot of time was spent on stage maker kind of levels of the stack infrastructure, machine, learning stage maker and tools. And a I service is. But the big announcement was this new I d frame environments, not a framework. You're taking an environment like an i d for all the different frameworks. Where did this come from? How I mean so obvious. Now, looking back that no one has this this was a big party announcement. You explain this. >>Yeah. So what you're referring to is sage Makers studio. One of the things that people have really liked about sage maker is it takes the whole process of building a model training a model ended up deploying a model and gives you the steps to do it, but there it hasn't been brought together into one environment before. And so sage maker Studio is a integrated development environment for machine learning that lets you spin up. No books. Run experiments test how your models performing. Deploy your model of detective. Your model is drifting all from one place, which gives me essentially a single dashboard for my whole machine learning work. Look, what do >>you think the impact's gonna be on this? Because if I'm just looking at that obvious awesomeness, it's like, OK, that means anyone can get start using machine learning, you know, be a guru or a total math. >>That's that's fundamentally a lot of what we're doing is trying to reduce the barrier for developers or anyone who has who has a desire to start using machine learning to be able to do that and say, you maker studios just another way that we're doing it. Another one we announced on Monday or on Sunday night, of course, a machine learning powered musical keyboard. Everyone knew that was coming right? That's that's just a example like Deep Racer, where we're taking machine learning. We're making it immediately practical and even fun. And then giving people a way to start experimenting does that they'll eventually become developers who are using machine learning for much >>things. Have a question. As you simplify machine learning, people are concerned about explain ability. You guys, I think, have some ways of helping people understand what's going on inside the algorithm. So that's not a pure black box. Is that correct interpretation? >>It is. It is way announced. Today s age maker experiments, which is one of the one of the things about machine learning, is your kind of constantly tuning the different variables that you're using in your model tow. Understand what works? What doesn't. That's all black box. It's really hard to tell with sage major studio and experiments in particular. Now I can see how models perform differently based on tweaking variables, which starts making it much easier to explain what's happening. >>I think you guys got it right, and he laid out the databases. Multiple databases pick your database. It's okay that multiple databases just create some abstracted layers on top. I totally agree with that philosophy and I think that's gonna be a nice haven for opportunity. We agree. >>Used to be that because so much of running a database was all of the operational expertise it took that you wouldn't wanna have too many databases because that's that many database administrators and people doing the undifferentiated heavy lifting now with the cloud. If you have a data set that's better suited for something like a uh uh, workload in Cassandra, we announced the Manage Cassandra service today. You can just been up that service, load your data and start going. And so it creates a lot more opportunity >>talk about quantum because I know you guys yesterday, which is always a signal from Amazon and didn't make the keynote cut, but a ray relevant quantum announcement, the joke was, is gonna be a quantum supremacy messaging. But no, is more of a humble approach from you guys is more. Hey, we're gonna put some quantum out there setting expectations on the horizon, not over playing your hand on that. But you also have an institute with Caltech humble academic thing going on. What's the quantum inside Inside conversation like an Amazon? What's the what's going on with you. What can we expect? >>We're really excited about what quantum computing's going to be able to do for customers, and we say a lot of Amazon on many things. It's date one, which means it's really early. When we look at Quantum somewhere between zero and one, we're not quite sure where. So just live saying it's really early days. And so what we're doing is providing a platform, a partnership with Caltech, to advance the state of the art and then also a Quantum Solutions lab to help customers start to experiment. To figure out how might. This enabled me to solve problems that I couldn't do before >>you? No one can ask. So Andy talked in a keynote about most of the spend is still on. So the early days of cloud were about, you know, infrastructures of service, storage, computer networking, and it seems like we're entering This era of this data is really sort of the driver where you're applying analytics and machine learning. Data's everywhere, and it seems to be driving sort of new forms of compute. It's not just in this sort of stovepipe anymore. You see that you see that sort of new emergence of new compute were close. >>Yeah. Yeah, we definitely do. And in particular, the way that people are starting to use data lakes, which is essentially a way of saying, Hey, I have my data and one place in a bunch of different formats. And I want different analytical tools, different machine learning tools, different applications toe all be able to build on that same data. And once you do that, you start unlocking opportunities for different application developers, different lines of business to take advantage of it. Brian, >>Thanks for coming on The Cube. Really appreciate your VP of all product. Mark. You get the keys to the kingdom, you kind of see what's going on. Take us home and finish the exit interview out by by talking about the best. Now that Jesse Safer last. The best for last was the outpost G A and the five G wavelength with CEO of Arise on. Yeah, I mean, that's gonna bring five G to stadiums for drones, immersive experiences. I mean, that's a big vision. Yeah, I think it's home >>people. People are rightfully excited about five G for having faster connections, but the thing that we're also very excited about is the fact that all these devices will have much lower laden see and the ability to run interactive applications that having a W s with AWS wavelength hosted with the five G providers is gonna give developers chances to melt. >>Brian Hall with With AWS I'm John David Lot. They were here on the Cube studios, sponsored by Intel's Our Signature sponsors of the Intel's Cube Studios. When it's to a shoutout for Intel to them for supporting our mission, bringing the best content from events and extracting the signal from the noise will be back with more after this short break.

la from Las Vegas it's the cube covering AWS reinvent 2019 brought to you by Amazon Web Services and Vinum care along with its ecosystem partners hello everyone welcome to the cube we're here live in Las Vegas for AWS reinvent 2019 I'm John Farrar your host is silicon Angles flagship the cube we're extract a signal noise leader in event coverage with day Volante my co-host and justin warren tech analysts Forbes contributor guru of cube host guys keynote for J&E jassie first of all I don't know how he does it he's just like continues hissing Marc loved the live music in there but a slew of announcements this is a reinvention of AWS you can tell that they're just essentially trying to go the next level on what the cloud means how they're gonna bring it to customers and you know they've been criticized for you know kind of nut I won't say falling behind I could say Microsoft's been probably praised more for catching up and it's been a lot of discussion around that the loss of the Jedi contract variety of enterprise wins Microsoft has the field Salesforce Google's just kind of retooling but Amazon clearly the leader with a little pressure for the first time in the rearview mirror they've got someone on their on their tail win and Microsoft's far back but this isn't a statement from from chassis and Amazon of okay you want to see the Jets we're gonna we're gonna turn on the Jets and blow pass everybody Jesse gets cocky self Justin what do you think yeah so a lot of signaling to enterprise that it's safe to come here it's this is where you can have everything that you need to get everything that you need done you can get all of it in one place so there there is a real signal there to say Enterprise if you want to do cloud there's only one place to do cloud enterprise customers they tried out some big names Goldman Sachs not a small enterprise they had all the classic born in the cloud but you know we put out this concept on I'm on our Silicon angle post called reborn in the cloud almost born-again enterprise you start to see the telegraphing of what their core message is which is transform just don't kick the tires and fall into the Microsoft trap go with em is on and transform your business model transform your miss not just run IT a better way than before well yeah I mean I'm impressed they got two CEOs the CEO of Goldman Sachs David Solomon the CEO of Cerner coming to the show it's kind of rare that the CEO of your customer comes to the show I guess the second thing I'd say is you know Amazon is not a rinse and repeat company at these shows although they are when it comes to shock and awe so they ticked the Box on shock and awe but you're right John they're talking a lot about transformation I sort of think of it as disruption here's what I would say to that Amazon has a dual disruption agenda one is its disrupting the horizontal technology stack and 2 its disrupting industries it wants to be the platform of which startups in particular but also incumbents can disrupt industries and it's in their DNA because it's in Amazon's DNA and I think it's the last thing I'll say as Amazon is the reach a Amazon retailers the you can buy anything here store and now to your point Justin Amazon Web Services is you can get AWS anywhere at the edge and a little mini data centers that they're built on outpost and of course in the cloud all right I want to get you guys reactions a couple things I saw and I want to just analyze the keynote one as we saw Jesse come out with the transformation message that's really more of their posture to the market you should be transforming we're gonna take Amazon as a center of gravity and push it out to the edge without post so kind of a customer company posture there on the industry then you had the announcements and I thought that the sage maker studio was pretty robust a lot of data and announcements so you had the transformation message a lot of core data and then they kind of said hey we're open we got open source databases we got kubernetes and multiple flavors a couple steers from the Twitter crowd on that one and then finally outpost with the edge where they're essentially you know four years ago Dave they said no more data centers in ten years now they're saying we're gonna push Amazon to the your datacenter so you know a posture for the company a lot of data centric data ops almost program and build I'm also DevOps feel to it what's your reaction to that I think the most interesting part for me was the change there was a bit of a shift there I think he made the statement of rather than bringing the data to the computer we want to bring the compute to the data and I think that's that's acknowledging reality that data has gravity and it's very difficult for enterprises particularly if you've already invested a lot in building a data Lake so being able to just pick that up and then move it to any cloud nothing let alone AWS just moving that around is is a big effort so if you're going to transform your business you have to kind of rethink completely how you address some of these issues and one of that would be well what if rather than let's just pick everything up and move it to cloud what if we could actually do something a little bit better than that and we can pick and choose what we want to suit our particular solution and your point Dave I think that's where Amazon strength comes from is it they are the everything store so you can buy whatever you want be at this tiny little piece that only five companies need or the same thing that everyone else on the planet needs you can come and buy everything from us and that's what I think they're trying to signal to an organization that says look if you want to transform and you're concerned that it'll be difficult to do we've got you we've got something here that will suit your needs and we will be able to work with you to transform your business and we're seeing you know Amazon years ago we wouldn't talk about hybrid and now they're going really all-in on hybrid and it's not outpost is no longer just this thing they're doing with VMware it's now a fundamental piece of their infrastructure for the edge and I think the key point there is the the edge is going to be one with developers and Amazon is essentially bringing its development platform to the edge without posts as the the underpinning and I like the strategy much much better than I like what I'm seeing from some of the guys like HP and Dell which is they're throwing boxes you know over the fence with really without a strong developer angle your thoughts I mean my my big takeaway was I think this is key knows about a next-generation shift on the business model but that's the transformation he didn't come out and say it I said it in my post but I truly believe if you're not born in the cloud or reborn in the cloud you'll probably be out of business and as a startup were to ask them of the VCS this question how do you go after and target some of those people who aren't gonna be reborn in the cloud to have the scale advantage but the data announcements was really the big story here because we look at DevOps infrastructure as code programming infrastructure we've seen that that that's of now an established practice now you start to see this new concept around data ops some people call it AI ops whatever but Dana now the new programmability it's almost a devops culture - data and I think what got my attention the most was the IDE for stage maker which kind of brings in this cool feature of what everyone was which is I want machine learning but I can't hire anybody and I got to make I got a democratized machine learning I got to make application developers get value out of the data because the apps need to tap the data it's got to be addressable so I think this is a stake in the ground for the next five to ten years of a massive shift from increasing the DevOps mission to add a layer making that manageable multiple databases he's totally right on that it's not one database if you want time series for real-time graph for you know network constructs it's pick your database you know that shouldn't be it inhibitor at all I think the data story is real that's the top story in my mind the data future what that's going to enable and then the outpost is just a continuation of Amazon realizing that the center of the cloud is not the end game it's just the center of gravity and I think you gonna start to see edge become really huge I mean I count ten into ten purpose-built databases now and jesse was unequivocal he said you gotta have the right database tool for the right job you're seeing the same thing with their machine learning and AI tools it's been shocking dozens and dozens of services each with their own sort of unique primitives that give you that flexibility and so where you can disagree with the philosophy but their philosophy is very clear we're gonna go very granular and push a lot of stuff out there I think there's two bits at play there that I can see you know I think you're right on the data thing and something that people don't quite realize is that modern data analysis is programming like it's code your data scientists know how to code so there was a lot of talk there about notebooks going in there like they love their notebooks they love using different frameworks to solve different problems and they need to be able to use for this one I need tens of flow for another one I might need MX net yeah so if you couple that that idea that we need to it's all about the data and you couple that with developers and AWS knows developers really really well so you've got modern enterprises lot wanting to do more with the data that they have the age or business problem of I've got all this information I need to process I need to do be out bi I need to do data analysis and you couple that with the Pala that iws has with developers I think it's a pretty strong story then you know in my interview with Jesse I asked him the question and I stole the line from Steve Moe Mulaney from aviatrix you take the tea out of cloud native it's cloud naive and I think what I've been seeing is a lot of customers have been naive about what cloud is and it's actually been buying IT and so they really don't are not sensitive to the capabilities message so I asked Jeff see I'm like you got these capabilities that's cool if you want to go to the store and buy everything or look at everything and buy what you want and construct and transform check no problem I buy that however some customers just want a package solution and Amazon has not always been great on having something packaged for customers so he kind of addressed that and this might be an Achilles heel for Amazon as Microsoft has such entrenched sales sales presence that they might be pushing a solution that frankly customers might not care about capabilities we did see one bit where there was a little bit of a nudge towards is fees and and systems integrators and I think that that really for me is there needs to be a lot more work done by Amazon there because that's what Enterprise me enterprise is used to dealing with systems integrators that will help them to use the raw materials that ados provides to solve that promote you said there are two segments of developers and customers one that wants all the low level building blocks and others want simpler faster results with abstractions aka packaging so they're going down the road but again they're not shy don't like hey we're just going to continue to build we're not going to try to move off our trajectory they're gonna stay with adding more power and frankly some digs at snowflake I fought with red shift and I thought the dig to the kubernetes community with we code our own stuff wink wink we don't have to slow down was a nice jab at the CN CF I thought because he's saying hey you know what we're not in committees deciding features which is the customers and implementing them so a kind of a jab well sure that's gonna rapid a I would say the snowflake is sort of a copycat separating compute from stores that's what snowflakes has been doing forever but he did take direct jabs at IBM Oracle and obviously Microsoft with with Windows so I like to see that you know usually Jessie doesn't do that it's good take the gloves so much so many announcements out there you got to go to silk and angled comm will have all the stories but one of the top stories coming into the reinvent that we didn't hear anything about but if you squint through and connect the dots on Jessie's keynote it is pretty evident what the strategy is and that's multi-cloud so I'll see multi-cloud is a word that Amazon is not using at all onstage as you can tell they don't really they're in well they're one cloud they don't really care about the other clouds but their customers do so guys multi cloud is a legit conversation how they get multi cloud is debatable acquisition sprawl by the end of the day multiple clouds is reality I think Jessie was kind of predicting and laying down some early narratives around the multi cloud story by saying hey we have more capabilities we're faster we're doing more stuff so I think he's trying to cede the base on the concept of hey if you want to go look at other clouds try to go apples to apples NIT that other than that he didn't really address at all multi-cloud what do you guys think about multi cloud yeah what it's pretty much that if you're gonna have multiple clouds at least one of them's gonna be AWS so they're gonna get some of your money if we came a bi can't get all your money I'll get at least get some of your money that's reasonable but I think part of the multi cloud conversation is that enterprises are actually trying to clarify their existing way of doing things so cloud isn't a destination it's not like a it's not a physical location it's a state of mind it's a way of operating things an enterprise that that's that's the transformation part that enterprises are trying to do so transform the way that they operate themselves to be more cloud like so part of the multi cloud piece I think that people are kind of missing is well it's not just Amazon or some of its competitors its existing on-site infrastructure and making that into a cloud which i think is where something like outpost becomes a really strong proposition and I've said a million times multiplied cloud is more of a symptom than it is a strategy that'll start to change they will see an equilibrium there you know right cloud for the right job but today it's a problem that CIOs are asked being asked to clean up the crime scene all right let's wrap up by summarizing the keynote each of you guys give me your take on I'll start I think this was a inflection point for AWS and Jesse in the sense of they now know they have to go the next gen loud it's Amazon enterprise it's data it's outpost it's all these things it's truly next-gen I think this is going to be all about data it's all gonna be about large-scale infrastructure and data scaling and with edge and outpost I think is really an amazing move for them in the sense that's gonna probably put in motion another five to ten years of continuing architectural reshipping and I think that if you're not born in the cloud or reborn in the cloud you're gonna be naive to the fact that you're not gonna have the capabilities to be success when I think that's going to be an opportunity for entrepreneurs and for companies pivoting into enterprises so I think this goes will go might go down as one of the most pax keynotes but I think it'll look back as one of the instrumental transitions for Amazon so I think he did a good job beginning and to rush 30 announcements in three hours marathon but overall I thought he did a great job I think I would agree Jesse always does a good job he's giving a message to you know CEOs as opposed to the CIO and he had two CEOs on stage I thought there was quite a gap between you know that message of transformation and then sort of geeking out on all the new services so there's still some work to be done there but I think it's a lot of developers in the audience I'm seeing them tell your boss to get on the train it's a very hard keynote to serve both audiences but so it's a start but there's a lot of work to be done there Justin yeah I agree with that I think this is probably one of the first keynotes maybe last year but certainly this year there's like AWS is very serious about enterprise and is trying to talk to enterprise a lot more than it ever has it still talks to developers but we didn't see anywhere near as much interesting in kind of the startup ecosystem it's like no no cloud is for serious companies doing serious work and I think that we're just going to see Amazon talking about that more and more and more because that's where all the money is yeah next-generation cloud new architectures all about the enterprise guys this is the cube opening day for three days of wall-to-wall coverage keynote analysis from Andy Jessie and Amazon Andy Jessie will be on Thursday at 3 o'clock we got a lot of top Amazon executives will who'll help us open and unpack all these to make mega announcements stay with us for more cube coverage and go to Silicon angle comm cube net for the videos be back back after this short break [Music]

(upbeat music) >> Hi everybody, welcome back to London. You're watching The Cube, and we have a special coverage here of the pre-day at AWS headquarters in London. I'm Dave Vellante and The Cube, we go out to the events, we extract the signal from the noise. Alastair Allen is here, the chief technical officer of Healthcare Kainos Software. It's a Belfast based company, publicly traded company. Alastair, welcome to The Cube. Great to see you, thanks for coming out. You were downstairs earlier addressing the audience, we're gonna talk about that. But first of all, tell us about Kainos. >> So Kainos, Belfast based company, formed in the late '80's a spin out of Queens University in Belfast. We've grown to now over 1300 people and we build digital technology to help people work faster, smarter and better. There's two things we do. We provide digital services, bespoke services for public and private sector organizations across the world, and we provide digital platforms for work day customers and also for healthcare organizations. >> So, when you say digital platforms. What exactly do you mean by that? Tell our audience. >> So, our digital platforms in healthcare is something that we can talk about. So platforms to enable both hospitals to digitize their workflow and also regions, so CCG's , STP's within the NHS. To bring information together using a platform and normalizing that data and making it available to clinicians and patients. >> And this is, your flagship product is called Evolve. Correct? >> Correct. >> And you're one of the sort of founders or inventors of Evolve. Tell us more about Evolve. >> So Evolve, originated just over ten years ago, our first customer was Ipswich Hospital and Ipswich had a big problem with paper, with a large medical records library and they asked us to come in and help them digitize that and make it available in an easy to view, accessible format for their clinicians. >> So tell me more about that. So you digitize it, you take all this mounds of paper and what does that do? Other than reduce the amounts of paper. Does it make it searchable? >> Yeah, we index the content, we apply metadata whenever we capture it, trying to make it accessible for clinicians. I think when you digitize paper , the one good thing paper had going for it was you could pick it up and it was tactile. So we've done a lot of work to try and make it mobile, make it accessible, make it searchable and increasingly now with some of the services that AWS provide, we're able to look at taking that even further and getting more information out of that content. >> Add some color to that. So how has the AWS cloud affected your ability to deliver these capabilities to your customers? >> Well I think, the breathe and depth of services that AWS provide, enables us to be able to innovate quickly, to use services like I've mentioned like comprehend medical. That take the heavy lifting, away from us and helps us focus on delivering better applications for our customers. >> So part of what you do, is you architected the software that's running on the cloud. Can you talk a little bit about the architecture? What you guys have built. Presumably the cloud allows you to scale. >> Alastair Allen: Yeah. >> And take advantage of more innovations. But discuss the architecture if you would. >> So, the product that I originally talked about in 2009 and about four years ago in 2015, we decided to re-platform for the cloud. And that was in response to a number of problems that we were seeing in the market. And moved to patient centered care, a drive to try and standardize care away from the variable nature that was there and also to get away from closed silos of information. And we decided at that point to create our platform natively in the cloud and using the services of Amazon web services. So we created a microservices based architecture that runs in multi-candidate cloud native way. With a AWS. That allows us to adopt disciplines like continuous delivery and cultures like DeVops. We've been able to release value quickly and often to our customers. >> So it was a total rewrite of the platform? >> Yes. So we started again from scratch and we developed that using the modern cloud services. And we've used that then for all use cases as well so we've moved beyond just settings within a hospital. And been able to take that beyond the walls of a hospital, out into the community, into primary care, mental health. And delivering solutions like that, across regions within the NHS, to join up information. Where before clinicians would simply not have had access to those. >> In a sense you're migrating your existing install base to the cloud based platform, as I presume it's a SAS based platform. Is that right? >> So, Evolve Integrated Care is a platform it's a SAS based platform. So we run it, we monitor it, we maintain it and we deliver that as a service to our customers. >> And so your existing customers now have an opportunity to migrate and how does that all work? >> Yeah, so we're talking to our existing customers, how they can leverage the cloud based platform and the breathe of different services that it provides. We very much see an opportunity for helping to digitize a hospital. So how do you optimize the flow of patients through a hospital and making sure that clinicians have access to the information. Many of customers have hundreds of applications, information spread across their estate, bringing that together and orchestrating the workflow for particular pathways or particular conditions. >> Plus they have to manage their own infrastructure, I presume. >> Absolutely, and we want to build applications quickly, they want to focus on delivering healthcare. They don't want to focus on managing ten and server rooms within their hospitals. So, our move to the cloud really came about because of our customers telling us that they're struggling to manage this infrastructure. They wanted us to take some of that burden away from them and to help them with some of their security challenges, availability challenges. Quite often their local infrastructure was not very resilient. And by moving to AWS, we were able to use native cloud services to address many of those challenges. >> So you're taking away that heavy lifting for them. AWS takes it away for you. >> Alastair Allen: Yeah. >> In a large regard as well. While your engineers can obviously program the infrastructure. But how have you seen the customers that have moved and taken advantage of this. What has it done for their business specifically? What's the impact? >> So, what I think, it frees up people within their organization to scale up in other areas to do other things. It frees up physical space as well in many cases. It takes away risks and we've all heard of some of the recent security incidents. Wanna Cry was a huge thing in the NHS not so long ago. Coming around from just simple things like not patching servers and work stations. So, by taking on that responsibility we're freeing up those hospital systems to focus on what they do best. >> How do they do that? Do they kind of retrain folks? What's that been like? I presume it wasn't frictionless but it's an opportunity for people to advance their careers. Do you have any visibility on how your customers have handled that? >> To be honest, not a huge amount. It has, I agree, there has been some friction there. It's not always an easy journey, there's a whole mindset change of what people used to do before and the types of activity that they'll do tomorrow. And it's something that our customers are still on a journey on. And so we're quite early on in that process. >> But I would say to folks in the IT community of your expertise's of managing storage arase, there's probably a better future for you if you can move up the stack and learn more about applications , data, machine intelligence. >> Absolutely, higher up the value chain and getting closer to the user, closer to the customer. >> I mean, that's where the difference is. And it's particularly in healthcare right? You try to balance the cost of healthcare, everybody's aware of the rising cost of healthcare with the patient outcomes. And technology is a way to address that problem. Isn't it? >> Absolutely, and I think never before. I think it's just a great time to work in health IT. We've now got access to some fantastic services the rise of artificial intelligence, the machine learning has never before been so available. And really having organizations such as ourselves to really solve those problems that our customers have and introduce those efficiencies and ultimately better patient outcomes. >> So how are you using the data that lives in Evolve, I presume you're looking at applying artificial intelligence and the like, talk about that. But also, how do you ensure security, privacy, etcetera? >> So, a couple things on data, I think one of the things we've done recently is the adoption of the FHIR standard within healthcare and all the data that we aggregate from the various clinical systems, we normalize that down into a single FHIR data profile and that really helps us then have a common data model that our application can use. But that's only the start, that creates the potential then to use that for secondary usage, such as publishing health data analytics and ultimately machine learning. And we're looking at a number of errors in machine learning, I think there are some ethical challenges there to be aware of and we've started with a recent examples of understanding how we can use machine learning to try and get that structured data out of the documents, that's something that we're working on with data with the AWS team at the minute, to leverage a lot of that scanned content that we have and evolve and be able to create the structured outcome. Really to make it easier for clinicians to find information within the medical record. >> So the AWS reinvent last fall, you know Sage Maker was of course buzzing. Is that something that you're looking at? >> It's something, so we haven't used it in Evolve so far but within Kainos we have an AI practice and we have a group of guys that are focused on the AI capability. Evaluating those tools, working with AWS and helping us understand how we can use that technology to solve the problems of our customers. >> Yeah, it's early days. So you talk about helping solve the problems of the customers. Summarize for us the key problems that you see machine intelligence, AI solving. >> I think there's probably different categories of how you could use it. There's the diagnostic sort of use case where you could use AI to help process imagery, to help with the diagnostic process. There's being able to add personalization to whether that be to patients or to clinicians, helping to provide insight into whatever the use case may be and all the use cases similar to that. >> Last words, so you're addressing the pre-day healthcare reform that's going on here at AWS. What's that like, what's going on downstairs, what did you tell the audience? >> Yeah, great day. So we had a group of healthcare professionals across the NHS in Ireland, very interesting group. We spoke this morning, I spoke with our customer Gloucester CC chief and we talked about the shared care record solution that we've delivered into Gloucester. So that's bringing information together for over 600,000 patients across the region and providing information in a single joined up view that was not available before. So great feedback, great interaction, lots of questions afterwards so looking forward to going back down and chatting some more to the group. >> Excellent. Hard to do that without the cloud I would imagine , accommodating all of the 600,000 customers right. >> Not possible. >> Alastair thanks so much for coming to The Cube. >> Thanks, Dave. >> Appreciate having you. Alright, thanks for watching everybody. Keep it right there, we'll be back with our next guest. You're watching The Cube from AWS headquarter in London. We'll be right back. (upbeat music)

>> From our studios in the heart of Silicon Valley. HOLLOWAY ALTO, California It is a cube conversation. >> Hello and welcome to Cube conversations. I'm James Kabila's lead analyst at Wicked Bond. Today we've got an excellent guest. Who's a Cube alumnus? Par excellence. It's your own Haviv who is the founder and CEO of a guajillo. Hello. You're wrong. Welcome in. I think you're you're coming in from Tel Aviv. If I'm not mistaken, >> right? Really? Close the deal of any thanks from my seeing you again. >> Yeah. Nice to see you again. So I'm here in our Palo Alto studios. And so I'm always excited when I can hear your own and meet with your room because he always has something interesting in new to share. But what they're doing in the areas of cloud and serve earless and really time streaming analytics And now, data science. I wasn't aware of how deeply they're involved in the whole data Science pipelines, so ah, your own. This is great to have you. So my first question really is. Can you sketch out? What are the emerging marketplace requirements that USA gua Si are seeing in the convergence of all these spaces? Especially riel time streaming analytics edge computing server lis and data science and A I can you give us a sort of ah broad perspective and outlook on the convergence and really the new opportunities or possibilities that the convergence of those technologies enable for enterprises that are making deep investments. >> Yeah, so I think we were serving dissipated. What's happening now? We just call them different names will probably get into into this discussion in a minute. I think what you see is the traditional analytics and even data scientist Science was starting at sort of a research labs, people exploring cancer, expressing, you know, impact. Whether on, you know, people's moved its era. And now people are trying to make real or a Y from a guy in their assigned, so they have to plug it within business applications. Okay, so it's not just a veil. A scientist Inning the silo, you know, with a bunch of large that he got from his friends, the data engineer in the scan them and Derrickson Namesake runs to the boss and says, You know what? You know, we could have made some money in a year ago. We've done something so that doesn't make a lot of impact on the business, where the impact on the business is happening is when you actually integrate a I in jackpot in recommendation engines in doing predictive analytics on analyzing failures and saving saving failures on, you know, saving people's life. Those kind of use cases. Doctors are the ones that record a tighter integration between the application and the data and algorithms that come from the day I. And that's where we started to think about our platform. Way worked on a real time data, which is where you know, when you're going into more production environment of not fatal accident. Very good, very fast integration with data. And we have this sort of fast computation layer, which was a one micro services, and now everyone talks about micro services. We sort of started with this area, and that is allowing people to build those intelligent application that are integrated into the business applications. And the biggest challenges they see today for organizations is moving from this process of books on research, on data in a historical data and translating that into a visit supplication or into impact on business application. This is where people can spend the year. You know, I've seen the tweet saying with build a machine learning model in, like, a few weeks. And now we've waited eleven months for the product ization. So that artifact, >> Yes, that's what we're seeing it wicked bomb. Which is that A. I is the heart of modern applications in business and the new generation of application developers, in many ways, our data scientists, or have you know, lovers the skills and tools for data science. Now, looking at a glass zeros portfolio, you evolve so rapidly and to address a broader range of use cases I've seen. And you've explained it over the years that in position to go, as well as being a continuous data platform and intelligent edge platform, a surveillance platform. And now I see that you're a bit of a data science workbench or pipeline tooling. Clever. Could you connect these dots here on explain what is a guajillo fully >> role, Earl? Nice mark things for this in technology that we've built, OK, just over the years, you know, people, four years when we started, So we have to call it something else. Well, that I thought that analytic sort of the corporate state of science. And when we said continued analytics, we meant essentially feeding data and running, some of them speaking some results. This is the service opposed to the trend of truth which was dating the lady Throw data in and then you run the batch that analytic and they're like, Do you have some insight? So continue statistics was served a term that we've came up with a B, not the basket. You know, describe that you're essentially thinking, needing from different forces crunching it, Prue algorithms and generating triggers and actions are responsible user requests. Okay on that will serve a pretty unique and serve the fireman here in this industry even before they called it streaming or in a real time, data science or whatever. Now, if you look at our architecture are architecture, as I explained before, is comprised of three components. The first event is a real time, full time model database. You know, you know about it really exceptional in his performance and its other capabilities. The second thing is a pursue miss engine that allows us to essentially inject applications. Various guys, initially we started with application. I sense you do analytics, you know, grouping joining, you know, correlating. And then we start just adding more functions and other things like inference, saying humans recognitions and analysis. It's Arab is we have dysfunction engine. It allows us a lot of flexibility and find the really fast for the engine on a really fast data there endure it, remarkable results and then this return calling this turn this micro assume it's finger serve Ellis who certainly even where have the game of this or service gang. And the third element of our platform is a sense she having a fully manage, passed a platform where a ll those micro services our data and it threw a self service into face surfing over there is a mini cloud. You know, we've recently the last two years we've shifted to working with coronaries versus using our own A proprietary micro spurs does or frustration originally. So we went into all those three major technologies. Now, those pit into different application when they're interesting application. If you think about edge in the engine in serving many clouds, you need variety of data, sources and databases. With you, no problem arose streaming files. Terra. We'LL support all of them when our integrated the platform and then you need to go micro services that developed in the cloud and then just sort of shift into the enforcement point in the edge. And you need for an orchestration there because you want to do suffer upgrades, you need to protect security. So having all the integrated separated an opportunity for us to work with providers of agin, you may have noticed our joint announcement with Google around solution for hedge around retailers and an i O. T. We've made some announcement with Microsoft in the fast. We're going to do some very interesting announcement very soon. We've made some joint that nonsense with Samsung and in video, all around those errands, we continue. It's not that we're limited to EJ just what happens because we have extremely high density data platform, very power of fish and very well integrated. It has a great feat in the India, but it's also the same platform that we sell in. The cloud is a service or we sell two on from customers s so they can run. The same things is in the clouds, which happens to be the fastest, most real time platform on the Advantage service. An essential feature cannot just ignore. >> So you're wrong. Europe. Yeah, Iguazu is a complete cloud, native development and run time platform. Now serve earless in many ways. Seems to be the core of your capability in your platform. New Cleo, which is your technology you've open sourced. It's bill for Prem bays to private clouds. But also it has is extensible to be usable in broader hybrid cloud scenarios. Now, give us a sense for how nuclear and civilised functions become valuable or useful for data science off or for executing services or functions of data of the data science pipeline kick you connect the dots of nuclear and data science and a I from the development standpoint >> church. So So I think you know, the two pillars that we have technology that the most important ones are the data. You know, we have things like twelve batons on our data engine is very high performance and nuclear functions, and also they're very well integrated because usually services stateless. So you know, you you end up. If you want to practice that they have some challenges with service with No, no, you can't. You stay for use cases. You can mount files. You have real time connections to data, so that makes it a lot more interesting than just along the functions. The other thing, with no clothes that is extremely high performance has about two hundred times faster than land. So that means that you can actually go and build things like the stream processing and joins in real time all over practice, their base activities. You can just go and do collectors. We call them those like things. Go fetch information from whether services from routers for the X cybersecurity analysis for all sorts of sensors. So those functions are becoming like, you know, those nanobots technology of off the movies is that you just send them over to go and do things for you, whether it's the daily collection and crunching, whether it's the influencing engines, those things that, for example, get a picture of very put the model, decide what's in the picture, and that this is where we're really comes into play. They nothing important you see now an emergence off a service patterns in data science. So there are many companies that do like mother influencing as a service city what they do, they launch an end point of your eleven point and serve runs the model inside you send the Vector America values and get back in the Americans and their conversion. It's not really different and service it just wait more limited because I don't just want to send a vector off numbers because usually I understand really like a geo location of my cellphone, which are user I D. And I need dysfunction to cross correlated with other information about myself with the location. Then came commendation of which a product they need to buy. So and then those functions also have all sorts of dependency exam on different packages. Different software environment, horribles, build structures, all those. This is really where service technologies are much more suitable now. It's interesting that if you'LL go to Amazon, they have a product called Sage Maker. I'm sure yes, which is dinner, then a science block. Okay, now sage mint for although you would say that's a deal use case for after Onda functions actually don't use Amazon London functions in sage maker, and you ask yourself, Why aren't they using Lambda Stage Maker just telling you, you know you could use Lambda is a blue logic around sage maker. And that's because because London doesn't feed the use case. Okay, because lambda doesn't it is not capable of storing large content and she learning miles could be hundreds of megabytes or Landa is extremely slow. So you cannot do hi concurrency influencing with will land the function so essentially had to create another surveillance and college with a different name. Although if they just would have approved Landa, maybe it was one or a Swiss are So we're looking, We've took it, were taken the other approach We don't have the resources that I have so we created a monster virus Engine one servant attention does batch Frost is saying scream processing, consort, lots of data, even rocketeer services to all the different computation pattern with a single engine. And that's when you started taking all this trend because that's about yeah, we need two version our code. We need to, you know, record all our back into dependencies. And although yes, service doesn't so if we just had to go and tied more into the existing frameworks and you've looked at our frantically product called Tokyo Jupiter, which is essentially a scientist, right, some code in his data's passport book and then in clicks. One command called nuclear Deploy, it automatically compiles, is their science artifact in notebooks, that server and converted into a real hand function that can listen in on your next city. People can listen on streams and keep the scheduled on various timing. It could do magic. So many other things. So, and the interesting point is that if you think about their scientists there, not the farmers, because they should be a scientist on this's means that they actually have a bigger barrier to write in code. So if you serve in this framework that also automates the law daughter scaling the security provisioning of data, the versions of everything in fact fantasies, they just need to focus on writing other them's. It's actually a bigger back for the book. Now, if you just take service into them, Epstein's and they will tell you, Yeah, you know, we know how to explain, Doctor. We know all those things, so they're very their eyes is smaller than the value in the eyes of their scientists. So that's why we're actually seeing this appeal that those those people that essentially focus in life trying math and algorithms and all sorts of those sophisticated things they don't want to deal with. Coding and maintenance are refreshed. And by also doing so by oppression analyzing their cool for service, you can come back to market. You can address calle ability to avoid rewriting of code. All those big challenges the organizations are facing. >> You're gonna have to ask you, that's great. You have the tools to build, uh, help customers build serve Ellis functions for and so forth inside of Jupiter notebooks. And you mentioned Sage Maker, which is in a WS solution, which is up in coming in terms of supporting a full data science tool chain for pipeline development. You know, among teams you have a high profile partnerships with Microsoft and Google and Silver. Do you incorporate or integrator support either of these cloud providers own data science workbench offerings or third party offerings from? There's dozens of others in this space. What are you doing in terms of partnerships in that area? >> Yeah, obviously we don't want to lock us out from any of those, and, you know, if someone already has his work bench that I don't know my customers say they were locking me into your world back in our work when things are really cool because like our Jupiter is connected for real time connections to the database. And yes, serve other cool features that sentir getting like a huge speed boost we have. But that's on A with an within vigna of round Heads and Integration, which reviews are creating a pool of abuse from each of one of the data scientist running on African essentially launch clubs on this full of civilians whose off owning the abuse, which are extremely expensive, is you? No. But what we've done is because of her. The technology beside the actual debate engine is open source. We can accept it essentially just going any sold packages. And we demonstrate that to Google in danger. The others we can essentially got just go and load a bunch of packages into their work match and make it very proposed to what we provide in our manage platform. You know, not with the same performance levels. Well, functionality wise, the same function. >> So how can you name some reference customers that air using a guajillo inside a high performance data science work flows is ah, are you Are there you just testing the waters in that market for your technology? Your technology's already fairly mature. >> That says, I told you before, although you know, sort of changed messaging along the lines. We always did the same thing. So when we were continuous analytics and we've spoken like a year or two ago both some news cases that we Iran like, you know, tell cooperators and running really time, you know, health, a predictive health, monitoring their networks and or killing birds and those kind of things they all use algorithms. You control those those positions. We worked with Brian nailing customers so we can feed a lot of there there in real time maps and do from detection. And another applications are on all those things that we've noticed that all of the use cases that we're working with involved in a science in some cases, by the way, because of sort of politics that with once we've said, we have analytics for continuous analytics, we were serving send into sent into the analytic schools with the organization, which more focused on survey data warehouse because I know the case is still serve. They were saying, and I do. And after the people that build up can serve those data science applications and serve real time. Aye, aye. OK, Ianto. Business applications or more, the development and business people. This is also why we sort of change are our name, because we wanted to make it very clear that we're aren't the carnage is about building a new applications. It's not about the warehousing or faster queries. On a day of Eros is about generating value to the business, if you ask it a specific amplification. And we just announced two weeks in the investment off Samsung in Iguazu, former that essentially has two pillars beyond getting a few million dollars, It says. One thing is that they're adopted. No cure. Is there a service for the internal clouds on the second one is, we're working with them on a bunch of us, Della sighs. Well, use case is one of them was even quoted in enough would make would be There are no I can not say, but says she knows our real business application is really a history of those that involves, you know, in in intercepting data from your sister's customers, doing real time on analytics and responding really quickly. One thing that we've announced it because of youse off nuclear sub picture. We're done with inferior we actually what were pulled their performance. >> You're onto you see if you see a fair number of customers embedding machine learning inside of Realtor time Streaming stream computing back ones. This is the week of Flink forward here in San San Francisco. I I was at the event earlier this week and I I saw the least. They're presenting a fair amount of uptake of ml in sight of stream computing. Do you see that as being a coming meet Mainstream best practice. >> Streaming is still the analytics bucket. OK, because what we're looking for is a weakness which are more interactive, you know, think about like, uh, like a chatterbox or like doing a predictive analytic. It's all about streaming. Streaming is still, you know, it's faster flow data, but it's still, sir has delay the social. It's not responses, you know. It's not the aspect of legacy. Is that pickle in streaming? Okay, the aspect of throughput is is higher on streaming, but not necessarily the response that I think about sparks streaming. You know, it's good at crossing a lot of data. It's definitely not good at three to one on would put spark as a way to respond to user request on the Internet S O. We're doing screaming, and we see that growth. But think where we see the real growth is panic to reel of inches. The ones with the customer logs in and sends a request or working with telcos on scenarios where conditions of LA car, if the on the tracks and they settled all sorts of information are a real time invent train. Then the customer closer says, I need a second box and they could say No, this guy needs to go away to that customer because how many times you've gotten technician coming to your house and said I don't have that more exactly. You know, they have to send a different guy. So they were. How do you impact the business on three pillars of business? Okay, the three pillars are one is essentially improving your china Reducing the risk is essentially reducing your calls. Ask him. The other one is essentially audio, rap or customer from a more successful. So this is around front and application and whether it's box or are doing, you know our thing or those kind of us kisses. And also under you grow your market, which is a together on a recommendation in at this time. So all those fit you if you want, have hey, I incorporated in your business applications. In few years you're probably gonna be dead. I don't see any bits of sustained competition without incorporating so ability to integrate really real data with some customer data and essentially go and react >> changes. Something slightly you mentioned in video as a partner recently, Of course, he announced that few weeks ago. At their event on, they have recently acquired Melon ox, and I believe you used to be with Melon Axe, so I'd like to get your commentary on that acquisition or merger. >> Right? Yes, yes, I was VP Data Center man Ox. Like my last job, I feel good friends off off the Guider, including the CEO and the rest of the team with medicines. And last week I was in Israel's with talk to the media. Kansas. Well, I think it's a great merger if you think about men in Ox Head as sort of the best that breaking and storage technology answer Silicon Side and the video has the best view technologies, man. It's also acquired some compute cheap technologies, and they also very, very nice. Photonics technologies and men are today's being by all the club providers. Remiss Troll was essentially only those technical engagement would like the seizures and you know the rest of the gas. So now VP running with the computation engine in and minerals coming, we serve the rest of the pieces were our storage and make them a very strong player. And I think it's our threatens intel because think about it until they haven't really managed to high speed networking recently. They haven't really managed to come with Jiffy use at your combat and big technology, and so I think that makes a video, sort of Ah, pretty. You know, vendor and suspect. >> And another question is not related to that. But you're in Tel Aviv, Israel. And of course, Israel is famous for the start ups in the areas of machine learning. And so, especially with a focus on cyber security of the Israel, is like near the top of the world in terms of just the amount of brainpower focused on cyber security there. What are the hot ML machine? Learning related developments or innovations you see, coming out of Israel recently related to cybersecurity and distributed cloud environments, anything in terms of just basic are indeed technology that we should all be aware of that will be finding its way into mainstream Cloud and Cooper Netease and civilised environments. Going forward, your thoughts. >> Yes, I think there are different areas, you know, The guys in Israel also look at what happens in sort of the U. S. And their place in all the different things. I think with what's unique about us is a small country is always trying to think outside of the box because we know we cannot compete in a very large market. It would not have innovation. So that's what triggers this ten of innovation part because of all this tippy expects in the country. And also there's a lot of cyber, you know, it's time. I think I've seen one cool startup. There's also backed by our VC selling. Serve, uh, think about like face un recognition, critical technology off sent you a picture and make it such that you machine learning will not be able to recognize Recognize that, you know, sort of out of the cyber attack for image recognition. So that's something pretty unique that I've heard. But there are other starts working on all the aspects on their ops and information in our animal and also cyber automated cyber security and hope. Curious aspect. >> Right, Right. Thank you very much. Your own. This has been an excellent conversation, and we've really enjoyed hearing your comments. And Iguazu. It was a great company. Quite quite an innovator is always a pleasure to have you on the Cube. With that, I'm going to sign off. This is James Kabila's with wicked bond with your own haviv on dh er we bid You all have a good day. >> Thank you.

live from Las Vegas it's the cube covering AWS reinvent 2018 brought to you by Amazon Web Services Intel and their ecosystem partners okay welcome back and we're live here in Las Vegas day three last interview of the day three days of wall-to-wall coverage two sets here at AWS reinvent 2018 our sixth year we've been at every reinvent except for the first one and it's been great to watch the rise I'm Jeff we're with Dean Volante we're here with Andy Jesse the CEO of AWS started this as it working backwards document years ago twelve years ago 12 half year zine years ago was when the document was written and we've launched 12 and a half years ago great to see you thanks for spending time I know you're super busy congratulations we met last week you couldn't really talk about it but boy there was so much more payload in the announcements than they were before are you happy with the results certainly three our keynote was taxing what's good impression when the keynote was over but ya know we're thrilled with it and most importantly the reason we're thrilled is because our customers are thrilled I think they just couldn't believe how much we delivered this week you know well over 100 capabilities and they were super excited about you know the storage announced was the thing is when you have millions of customers any announcement you make is going to be popular with thousands of customers so some people walked up to me and said oh I know it's not sexy but I love the storage announcements I needed the file systems I wanted that glacier deep archive some customers love the database releases with lots of customers that were excited about the machine learning piece and you know the another unsexy one where the enterprise abstractions just to make it so much easier for that type of builder who wants more prescriptive guidance to be able to get started quicker and then you know people are pretty excited that outpost too so it has you a question I'll talk Amazon speak now what what areas of the show do you feel you raise the bar this year on what was that what would you point to bar raising moments announcements well you know I think each year one of the things I like about reinvent and that we work hard on is we'd like to have we don't really want it to be a corporate event we wanted to be quirky and we want it to be authentic and we want you know we want our community to fit to have fun here while they also learn so you know Midnight Madness is for instance something we do every you know we've done the last couple years and we try radically different things and so I thought that Tatanka eating contest raised the bar is again this year was the second year in a row that we said again as political World Records and you know I thought I really liked Peter De Santis --is and Myrna Vogel's keynotes on Monday and today respectively I thought they both were fantastic and you know keep raising the bar are you over a year and you know so they're you know we're hoping I too will be something that people feel like raise the bar year over year what the house band synchronicity was quite good too you know yeah I tell you that that fan is terrific and you know and I think that again all those things I mentioned are part of what we you know think makes the event fun and quirky and different but the most important thing by far is the learning of the education and then our customers excited about not just the platform but we launched so many things do they feel like it helps them do their job better well while we're on the raising bar we've got a prop here this is the the deep racer deep the deep racer machine learning it's a toy for testing and the question comes up how old do you have to be to use this and I said hey if your kid can code machine learning good for them but talk about this because this is kind of interesting because it's fun but where'd this come from you know it came from last year when we release sage maker and we were making machine learning so much easier for everyday developers of scientists we said what can we do to give people hands-on experience because you learn things better if you actually try it and so we tried to help developers get more experience to computer vision by having a deep lens you know video camera and that was wildly popular and so as we were thinking about this year making reinforcement learning available as easily as we are in sage maker which we think is a huge potential game-changer grant Forsman learning the team kept thinking about it's great but nobody knows enforcement learning and nobody has experienced with it how can we give them experience what are ways they can get hands-on experience and that's how the deep racer car came up which is really making it simple where they can just give us a reward function with a line of Python strip and then Sage Maker will automatically train an RL algorithm and then they get to play it to the car and then race against one another and when we watched how competitive it was getting inside our own house on these RL infused cars racing each other we figured other people might find a compelling as well and I couldn't believe how many people participate yesterday yeah and then I don't know if you saw it three burners right before burners keynote the finals were really exciting to like the fact that there were some imperfections were actually made it more compelling to watch and so we had a racer Cup coming up - I met play 19 competitive yes that's going to happen yeah today was the accelerated version of the first ever deep racer League championship Cup but next year will be a full season at our 20 AWS summits the top winners in the in the deep racers you see bracer League races at each of those summits plus the top 10 vote getters in points from those summits will come here and compete for the championship Cup now you and I talked about a new persona last week when we met but now the announcements pretty clear now why this points to a whole new persona developer you got eSports on the twitch side booming heat sports is changing the game and in the whole digital sports category robotics space you got a satellite announcement this is a genre changed in digital culture and you see the AI stuff and machine learning how does the web services stack play in this new world where AI is now a service it's a whole nother paradigm shift what's your thoughts on all that well you know I mean all those areas that were continuing to expand into our areas that our customers are asking us to help them with and where there are huge opportunities for customers but where it's hard I mean if you look at space as an example if you've to interact with a satellite it's it's expensive to have to have all those satellites set up you know and those drown ground antennas set up and then you have to program them and then and you actually have to pay this fixed price instead of on-demand customers so why can't you give us access those satellites the way we consume AWS and then if you can have the ground antennas where when the data comes down from the satellite it's basically on the same premises as your AWS region so we can store the data and process the data analyze it and take action that is very compelling so that that just felt like a natural fit you know and the same thing with robotics I think that robotics is one of the most underrated areas of Technology I think robots will do all kinds of things for us at work and in a home and the tools out there to make it easy to build robotic applications and to do the simulation to deploy them and then have them work with the rest of your applications and infrastructure have been pretty primitive and so robot maker is I agree with you I think you look at the younger generation too even at the high school elementary level people are gravitating towards robotics robotics clubs are booming that maker culture goes through a whole nother level with robotic congratulation you know it's funny we had the youngest person to ever pass the AWS certification exam is a kid named Karthik nine years old passed and he was here this week actually and I got a chance to meet with him today and I said well after the certification what are you doing he said well I'm building a robot you know I'm feeling Ruben he said now with your launch of deep racer I want to try and find a way to to have the deep racer car be the eyes and the camera and the reinforcement learning for my robot nine years old yes it's gonna be a different generation with what they build John and I were talking this morning Andy at our open about you're making it harder for the critics used to be self-service only it criticized your open source contributions the hybrid strategy your turn a tick in the box is on all those outpost was I think surprised a lot of people it didn't so much surprise us that you were moving in that direction but I wonder if you could sort of talk about some of those key initiatives I know it's customer driven but wow the the TAM expansion the the customer value that you're bringing it's like a whole new era that you're entering yeah you know everything we build is you guys know we talk about all the time it's just it's driven by what customers want and so we just started over the last six months you know and really by virtue of having this partnership with VMware where we have a lot of enterprise engagement as they're moving to the cloud using VMware cloud and AWS we had a bunch of customers say it's really great I'm moving most my application of the cloud but there's some that aren't moving for a while because they got to be close to selling on-premises and I want to use AWS for this I don't want a different environment can you just find a way to put some services like compute and storage on-premises and hardware but I want to actually use the same control plan I'm going to use for the rest of AWS and I wanted to easily connect with the rest of my applications in AWS and we had you know we didn't like as you and I talked about a week or two ago we just have not like the model that's been out there so far to do this because it's you know the control plane is different the api's are different the tools are different the hardware is different the functionality is different and customers don't like it's why it's not getting much traction and we didn't want to pursue it if we didn't think it was going to be useful but we had this concept we were working on with a couple customers where they wanted compute and storage on-premises but they wanted to have that connect with all the other applications in the AWS cloud and so we have this idea that maybe this local set of compute and storage would be like a far zone from an availability zone they were using and we started thinking about that and we thought there was much more generalized idea which became outposts and so the thing that I think people are gonna love about that is for the applications that can't move easily because they need to be close slang on-premises you get AWS like real AWS compute real AWS Storage Analytics database sage maker will be in there as well but it's the same api's same control playing the same tools the same hardware we use in our data centers and it will easily connect through the same control plane to the rest of AWS the rest of the services and the rest of their applications there so and it provides a platform for a whole host of new services down I mean every customer meeting I've had in the last we made the announcement people are excited about I want to ask you guys are talking about all the innovation and new areas and we're seeing an expansion of the AWS distinct brand and things like TV advertising statcast I wonder what's behind that can you address that yeah it's a good question I mean there's kind of two different types of I'll call it TV advert Swartz we're doing one is straight-up advertising one is less so which is you know the one that less so is that a number of the sports leagues are really interested in and actually pretty sophisticated in using cloud computing and analytics and machine learning if you look at Major League Baseball now NFL and Formula One and they want to make the user experience and the viewer experience so much better and so they're building on top of AWS and then we like the ability of helping them showcase the capabilities that they're you know both the customer experience and the ml and AI capabilities then there's just a straight-up advertising them that we've been trying we tried a little bit of it last q4 and you know it's always very difficult to quantitatively measure tvf but we have a lot of ways that we try to triangulate that and we were really surprised and what looked like the positive numbers we saw for both TV as well as the outdoor media and things like in the airports and things like that and so we decided we would try it again this q4 and you know I think I would call us right now still experimenting yeah and it's very much kind of what Amazon does which is we try different things to see what resonates the see Whitefield says so so far so good and we expect to keep experimenting I I think that's a good call because the brand lift is probably there I'll see impressions get reach vehicle but you guys are in a rising tide market we're hearing co-creation VMware co-creating deep meaningful partnerships you always talk about that so it's kind of this success model of innovation to reimagine the satellite Lockheed Martin a partnership this seems to be a new way to do business in this rising tide how are you guys getting the word out education people want to know more this is a big kind of movement yeah well you know I think that if you looked at the first several years of AWS I was always surprised when I would go see enterprises and they would have no idea that Amazon was doing anything in the cloud even though we had the only cloud offering at the time so I think if you compare where we were a few years ago to today there's you know gigantic awareness relatively speaking but I still think that there are so many majority of workloads still live on premises I mean we have a twenty seven billion dollar revenue run rate business it's growing forty six percent year-over-year and yet we're still at the early stages of the meet of enterprise of public sector adoption in the u.s. you go outside the US where there twelve to thirty six months behind depending on the country in the industry and sometimes it feels like you know like Groundhog's Day well you guys are doing regions out there Italy as was announced yeah you're expanding very fast globally can you talk about that real quick yeah it's it's a you know we've had customers from 192 countries using AWS for many many years but they've been using AWS in regions outside of their country usually because there are a lot of workloads that could stand that latency and where the data doesn't have to be on natural soil but increasingly if you want to help customers get done what they want to and serve the broader array of their applications you have to have regions in their country both so that they have lower latency to their end users and because the data sovereignty laws which are getting really more rigid rather than more flexible let me ask you a question about competition you you said I can't members on the cube or in person there's no compression reach out gorilla for experience and time elastic economies with scale when you have copycat people trying to copy Amazon how do you talk about some of those things that are those diseconomies of scale what are the points that customers should look at when they say okay I got someone else is talking cloud Amazon's got years of experience ahead of the competition more services what do you talk about what do you point to you it's not about slimming the competition but what is the diseconomy of scale to try to match the trajectory of Amazon yeah it's it's a bunch of things you know first of all it's operational performance you know a lot of the hardest lessons you learn and operating of scale only happen when you get to that level of scale and you know there's some events that we see sometimes elsewhere we look at that and then we read the post-mortem we say oh yeah 2011 you know we remember they went through that I don't wish it on anybody but when you have a business at several times larger than the next or providers combined you just said a different level of scale and you've learned lessons earlier I also think that the reason that we continue to have both so much more functionality and innovate at a faster clip and seem to get capabilities that customers want is because we have so many more customers than anybody else you know a lot of times and this is happening all week to where customers will say to me I can't believe that you knew that I wanted that and I always say it's because you told us yeah it's not like we're Nostradamus you've told us that and so when you have so many more customers and when they feel free to give you feedback and when you've built good mechanisms like we have to get that feedback from the field to the product builders it means there's this real flywheel of getting you know getting more customers leads to more feedback leads to more features leads to better functionality where there's a network effect from being on the platform with all those other customers and all those industries I wonder if you could add some color to a premise that we've put forth on your edge strategy so what you guys you know we do a lot of these shows and a lot of the IOT and edge strategies that we've seen from traditional IT players what you call the old guard have fallen flat in our opinion because it's a top-down approach it reminds us of the Windows Phone it just didn't work and it's not going to work as their operations technologies people we see what you've announced here as a Bottoms Up approach you developing an application platform to build secure and manage apps for those folks right at the edge I wonder if you could add some color to that and some thoughts on your edge strategy yeah I mean again for us if we don't have some top-down strategy that you know that I think is grandiose it's just what customers want and so we have so many customers who have all these devices at the edge and all these assets at the edge and they said to us well the first problem I have I want to get this data into the cloud and then I want to do analytics item we say ok well how can we help they say well the first thing is I don't even know how to translate this data from the device protocol to just being able to operate in the cloud so that's the first problem we go solve well then people say ok now I can get it in but I actually I need security like you know if you look at the amount of security options for these edge devices it's a new field you know let that dine attack that took a lot of the internet down a couple years ago came from you know a device on the edge and so that's why you know we built you know a security capability and people say well okay now you've made it so I can run devices but if I'm gonna run thousands of devices I need a way to manage all those devices of scale and we build telling to manage two devices and people say well ok it's great that I can do it and device is big enough that have a CPU but what about when they don't have a CPU you know they have just a microcontroller and that's why we built the our toss piece and you know the list kind of keeps going people so this is great now that I get all this data in the cloud I can take all these analytics actions but on my device sometimes I don't want to make the round trip to the cloud so can you give me a way to use the same programming model and and pick which triggers I want to take action with cloud versus those that want to take on the device itself which was what green grass was so all of those pieces is not some kind of top-down master plan as much as we know that customers have all these devices the edge that they want to use that data analyze that data take action on that data and send it back in multiple ways and you have you have the cloud platform to give them the services to make the tools the right tools for the right job yeah that's the main team yeah so I got to ask you about one of the big controversies that we don't think that's that controversial but the chips that you announced new Amazon Web Services front microprocessors the chips yeah do two of them talk about them and Intel's also a partner a lot of people are talking about this in the press yeah Intel Amazon chips well that annapurna acquisition is Norton they bear fruit was 2015 I think yeah early it really the annapurna team is fantastic and they've added a huge amount of value to AWS and Amazon as a whole you know the first thing I would say is that Intel is a very deep partner of AWS and will be for a long time I mean that that's not changing and we've been a long thought that they were gonna be lots of different processors out there and and different ones that did different things at different price points and so like a lot of other companies we've been interested in arm for a long time and for a while it wasn't mature enough and the technology is matured and we found a way in in building our own ARM chip with graviton where we think we can allow customers to run a lot of their scale out generalize were close but up to 45 percent less expensively and so when you find a value proposition that compelling for customers you need to do it and you know as I mentioned in the keynote yesterday when we were talking about inference we feel like a lot of the world has been solvent for training and not solvent as much for inference yet and we've made training so much easier with the things that we've built in AWS over the last couple years but inferences where most of the cost is gonna be and so elastic inference we think it you know will allow people to be much more efficient in how they use them for use and how they spend money but when you've got the type of workloads at scale and productions that use whole GPUs or that need that low latency where you need it on the hardware of a chip that's optimized for inference they is faster that's more cost effective that's high throughput we can get hundreds of tops on it and thousands to you ban them together he's gonna totally change the game for imprison and so that was something that wasn't easy for us to find elsewhere and when we have team fortunately they could build it and it's the combination of the elastic service of inference with the chip that makes the difference it specialism there so it's not like I mean you can use each on their own and we expect they'll be a bunch of customers who will use each on their own but there will be an opportunity to use those in combination that will be very powerful it comes down to really deeply understanding the customer problem again at night training versus inference and everybody talks about the training right the the technical challenge you got a child is the internet and tells gonna make a lot of money as it stands expanding market banding so they'll get their share the chips get taped out their con a couple year to three year life cycles and everything starts anew every time somebody's building a new chip so I think it's actually great for customers of all sorts that there's multiple processors that are possible but we will have a deep relationship with Intel forever I think so I want to talk about one of the cool demos you did on stage not a lot you did customer did f1 that was a super cool I love that imagery because it said an analogy of high performance competitive racing that can be applied to this play sports anything and the level of accuracy that they need in the real time time series kind of encapsulates a lot of the cloud value talk about the f1 analytic thing are you guys gonna sponsor these events there's a relationship there give us what the picture of what's going on there you have a deep relationship with Formula One where they're using our platform to to do their all their digital properties as well as their analytics and machine learning and it was super cool to see Ross demo the way that they're changing the user experience for for viewers and you know it's it's it's an amazing sport you know it's not watched as much maybe in the US but outside the US that is the motorsport and the way that they're changing the experience the way that they're able to assess what's happening with drivers and with cars and then predict what's actually happening and make the viewer feel like they're actually either in the cockpit or actually in the pit itself with it with the crew is it's really exciting and it's non err to be a partner so you do some events they'll get the cube they're these these big time again there's a tech angle now and everything it's a plug for you to be at the they have one event cloud demócrata you're hitting now new industries I mean this is the thing right I mean it's disrupting every industry I mean what aren't you disrupting I mean what areas do you see that yet aren't coming online to the cloud I don't see industry segments at this point that aren't moving to the cloud I would have told you 18 to 24 months ago that I felt like financial services was moving a lot more slowly than then I thought they should or you know probably healthcare also was a little bit slower but both of those industry segments are moving very aggressively well it's taking longer they're high-risk industries and the digital transformation has it occurred fast enough but it's coming and there's regulatory pieces that they legitimately have to sort through and you know we have just if you look at financial services as an example we have a pretty significant team that does nothing but work with our partners to help them with the regulatory bodies because what we find is when we go with a customer to a regulator and show them a real use case and then how it will be done in a DOP is the regulator says oh well that's more secure that you do on-premises and so it's just an education process and you know I think that's been helpful in it and I'll get final questions for you what have you observed here at reinvent Houston glad people talking so you get a lot of feedback actually to clopped two-part question because I was asked the final final question so I'll just get it out front what are people missing of all the announcements you've had a lot of signal in there a lot of a lot of announcements what are what is something that you've observed that you think should be amplified that people might have not overlooked but like you feel like it's more important to sign the light on we'll start with that one well you know it's a little hard for me to tell this moment just because there have been so many in such a short amount of time and and if we just look a little bit at the coverage it seems and if I take just as inputs they comments and and the questions from customers it's been pretty broadly understood and people are pretty excited and as I said different segments have kind of their favorite areas but I feel like people are pretty excited by the breadth of capabilities you know I think that if I pick two in particular I would say that people are still in the machine learning space people are blown away by how much we provided are all three layers of the stack I think people are still getting their heads around which layer of the stack am I gonna participate at you know I mean the one that probably has the most potential for most companies is that middle layer because most companies have gobs of data and there are jewels in that data and if you can enable their developers their everyday developers to be able to build models and get at the predictive value and add value that has huge impact for companies moving forward but most modern companies with technology functions will use all three layers of the stack and so just getting their arms around which layers of the stack they should take advantage of first and having the personnel to be able to do it and we're making that much easier with things like sage maker and then you know I think if you look at the blockchain space I think that that is just one of those spaces that has a huge amount of buzz people talk a lot about it exactly sure sometimes what they're gonna do but but I also think that a lot of people said to us that breaking those into those two real customer jobs to be done and then having a great solution that does each of those jobs really well is not only something that AWS does all the time that makes it easier for them but it also made it easier for a lot of them to understand that a lot of customers said to us you know that qld be that ledger database with a single trust of central authority for my supply chain that's what I need for my supply chain I don't need all the complexity of a blockchain framework and then there were a lot of other people said oh yeah that is what I want I wanted to decentralize trust between peers but I just needed a way easier way to manage hyper ledger fabric and etherium so I think those are two that people like are so interested and still figuring out how to use as expansively as I think they hope they will Andy thanks so much for your time and I want to just say watching you guys in the past six years has been a fun journey together but watching the execution you guys have done an amazing job of keeping your eye on the ball and being humble but being proud and loud at the same time so congratulations and you know guns blaring in 2019 what's your top pray all right besides listening to the customers what's your top 20 19 we know you listen to cut oh my gosh we have so many things that we're doing in 2019 but you know we have a lot of delivery in front and in front of us I mean as much as we launched 140 unique things over the last six to eight business days and yet I tell you to stay tuned the rest of 2018 we have more coming and then in nineteen you'll you should expect to see more few capabilities more database capabilities more machine learning capabilities more analytics capability look a lot I could spend all night John we don't need it we don't need a post reinvent post you know traumatic announcements syndrome because just to digest it all yeah it's a lot of work looking forward to seeing how enterprises continue to make to to kind of manage their hybrid approach as they're as they're making this trend transition from on-premises to the cloud how many continue to jump on to VMware cloud an AWS how many jump onto outpost so I think that that transition and helping customers do that easily is something on here of course we'll be commentating and pontificating on that for the next year thanks for your time I really should have me and I appreciate that you guys come at regular pay our pleasure okay winding down that's the last interview here wall to wall covers two cents 110 interviews in the books we'll have 500 video assets total blog post on Sylvia angle calm that's reinvent closing down 2018 thanks for watching [Music]

(upbeat music) >> Live from Las Vegas, it's theCUBE covering AWS re:Invent 2018. Brought to you by Amazon Web Services, Intel, and their ecosystem partners. >> Okay, welcome back everyone. Live coverage here in Las Vegas for Amazon re:Invent 2018. Day three, we're winding down over 150 videos. We'll have over 500 clips. Losing the voice. Dave Vellante, my co-host. Suzi analyst tech that we're going to extract theCUBE insights, James Kobielus. David Floyer from Wikibon. Jim you've been prolific on the blogs, Siliconangle.com, great stories. David you've got some research. What's your take? Jim, you're all over what's going on in the news. What's the impact? >> Well I think what this years re:Invent shows is that AWS is doubling down on A.I. If you look at the sheer range of innovative A.I. capabilities they've introduced into their portfolio, in terms of their announcements, it's really significant. A. They have optimized tense or flow for their cloud. B. They now have an automated labeling, called Ground Truth, labeling capability that leverages mechanical turf, which has been an Amazon capability for a while. They've also got now the industries first, what's called reinforcement learning plug-in to their data science tool chain, in this case Sage Maker, reinforcement learning is becoming so important for robotics, and gaming, and lots of other applications of A.I., and I'm just scratching the surface. So they've announced a lot of things, and David can discuss other things, but I'm seeing the depth of A.I. Their investment in it shows that they've really got their fingers on what enterprises are doing, and will be doing to differentiate themselves with this technology over the next five to ten years. >> What's an area that you see that people are getting? Clearly A.I. What areas are people missing that's compelling that you've observed here? >> When you say people are missing, you mean the general...? >> Journalists. >> Oh. >> Audience. There's so much news. >> Yeah. Yeah. >> Where are the nuggets that are hidden in the news? (laughing) What are you seeing that people might not see that's different? >> Getting back to the point I was raising, which is that robotics is becoming a predominant application realm for A.I. Robotics, outside the laboratory, or outside of the industrial I.O.T., robots are coming into everything, and there's a special type of A.I. you build into robots, re-enforcement learning is a big part of it. So I think the general, if you look at the journalists, they've missed the fact that I've seen in the past couple of years, robotics and re-enforcement learning are almost on the verge of being mainstream in the space, and AWS gets it. Just the depth of their investments. Like Deep Racer, that cute little autonomous vehicle that they rolled out here at this event, that just shows that they totally get it. That will be a huge growth sector. >> David Floyer, outpost is their on premises cloud. You've been calling this for I don't know how many years, >> (laughing) Three years. >> Three years? >> Yeah. What's the impact? >> And people said, no way Foyer's wrong (laughing). >> So you get vindication but... >> And people, in particular in AWS. (laughing) >> So you're right. So you're right, but is it going to be out in a year? >> Yeah, next in 2019. >> Will this thing actually make it to the market? And if it does what is the impact? Who wins and who loses? >> Well let's start with will it get to the market? Absolutely. It is outposts, AWS Outposts, is the name. It is taking AWS in the cloud and putting it on premise. The same API's. The same services. It'll be eventually identical between the two. And that has enormous increase in the range, and the reach that AWS and the time that AWS can go after. It is a major, major impact on the marketplace, puts pressure on a whole number of people, the traditional vendors who are supplying that marketplace of the moment, and in my opinion it's going to be wildly successful. People have been waiting that, wanting that, particularly in the enterprise market. They reasons for it are simple. Latency, low latency, you've got to have the data and the compute very close together. Moving data is very, very expensive over long distances, and the third one is many people want, or need to have the data in certain places. So the combination is meeting the requirements, they've taken a long time to get there. I think it's going to be, however wildly successful. It's going to be coming out in 2019. They'll have their alpha, their betas in the beginning of it. They'll have some announcements, probably about mid 2019. >> Who's threatened by this? Everybody? Cisco? HP? Dell? >> The integration of everything, storage, networking, compute, all in the same box is obviously a threat to all suppliers within that. And their going to have to adapt to that pretty strongly. It's going to be a declining market. Declining markets are good if you adapt properly. A lot of people make a lot of money from, like IBM, from mainframe. >> It's a huge threat to IBM. >> You're playing it safe. You're not naming names. (laughing) Okay, I'll rephrase. What's your prediction? >> What's my prediction on? >> Of the landscape after this is wildly successful. >> The landscape is that the alternatives is going to be a much, much smaller pie, and only those that have volume, and only those that can adapt to that environment are going to survive. >> Well, and let's name names. So who's threatened by this? Clearly Dell, EMC, is threatened by this. >> HP. >> HP, New Tanix, the VX rat guys, Lenovo is in there. Are they wiped out? No, but they have to respond. How do they respond? >> They have to respond, yeah. They have to have self service. They have to have utility pricing. They have to connect to the cloud. So either they go hard after AWS, connecting AWS, or they belly up to Microsoft >> With Azure Stack, >> Microsoft Azure. that's clearly going to be their fallback place, so in a way, Microsoft with Azure Stack is also threatened by this, but in a way it's goodness for them because the ecosystem is going to evolve to that. So listen, these guys don't just give up. >> No, no I know. >> They're hard competitors, they're fighters. It's also to me a confirmation of Oracle's same same strategy. On paper Oracle's got that down, they're executing on that, even though it's in a narrow Oracle world. So I think it does sort of indicate that that iPhone for the enterprise strategy is actually quite viable. If I may jump in here, four things stood out to me. The satellite as a service, was to me amazing. What's next? Amazon with scale, there's just so many opportunities for them. The Edge, if we have time. >> I was going to talk about the Edge. >> Love to talk about the Edge. The hybrid evolution, and Open Source. Amazon use to make it easy for the enterprise players to complete. They had limited sales and service capabilities, they had no Open Source give back, they were hybrid deniers. Everything's going to go into the public cloud. That's all changed. They're making it much, much more difficult, for what they call the old guard, to compete. >> So that same way the objection? >> Yeah, they're removing those barriers, those objections. >> Awesome. Edge. >> Yeah, and to comment on one of the things you were talking about, which is the Edge, they have completely changed their approach to the Edge. They have put in Neo as part of Sage Maker, which allows them to push out inference code, and they themselves are pointing out that inference code is 90% of all the compute, into... >> Not the training. >> Not the training, but the inference code after that, that's 90% of the compute. They're pushing that into the devices at the Edge, all sorts of architectures. That's a major shift in mindset about that. >> Yeah, and in fact I was really impressed by Elastic Inference for the same reasons, because it very much is a validation of a trend I've been seeing in the A.I. space for the last several years, which is, you can increasingly build A.I. in your preferred visual, declarative environment with Python code, and then the abstraction layers of the A.I. Ecosystem have developed to a point where, the ecosystem increasingly will auto-compile to TensorFlow, or MXNet, or PyTorch, and then from there further auto-compile your deployed trained model to the most efficient format for the Edge device, for the GP, or whatever. Where ever it's going to be executed, that's already a well established trend. The fact that AWS has productized that, with this Elastic Inference in their cloud, shows that not only do they get that trend, they're just going to push really hard. I'm making sure that AWS, it becomes in many ways, the hub of efficient inferencing for everybody. >> One more quick point on the Edge, if I may. What's going on on the Edge reminds me of the days when Microsoft was trying to take Windows and stick it on mobile. Right, the windows phone. Top down, I.T. guys coming at it, >> Oh that's right. >> and that's what a lot of people are doing today in IT. It's not going to work. What Amazon is doing see, we're going to build an environment that you can build applications on, that are secure, you can manage them from a bottoms up approach. >> Yeah. Absolutely. >> Identifying what the operations technology developers want. Giving them the tools to do that. That's a winning strategy. >> And focusing on them producing the devices, not themselves. >> Right. >> And not declaring where the boundaries are. >> Spot on. >> Very very important. >> Yep. >> And they're obviously inferencing, you get most value out of the data if you put that inferencing as close as you possibly can to that data, within a camera, is in the camera itself. >> And I eluded to it earlier, another key announcement from AWS here is, first of all the investment in Sage Maker itself is super impressive. In the year since they've introduced it, look at they've already added, they have that slide with all the feature enhancements, and new modules. Sage Maker Ground Truth, really important, the fully managed service for automating labeling of training datasets, using Mechanical Turk . The vast majority of the costs in a lot of A.I. initiatives involves human annotators of training data, and without human annotated training data you can't do supervised learning, which is the magic on a lot of A.I, AWS gets the fact that their customers want to automate that to the nth degree. Now they got that. >> We sound like Fam boys (laughing). >> That's going to be wildly popular. >> As we say, clean data makes good M.L., and good M.L. makes great A.I. >> Yeah. (laughing) >> So you don't want any dirty data out there. Cube, more coverage here. Cube insights panel, here in theCUBE at re:Invent. Stay with us for more after this short break. (upbeat music)

live from New York it's the cube covering AWS summit New York 2018 hot GUI Amazon Web Services and its ecosystem partners hello and welcome back here live cube coverage in New York City for AWS Amazon Web Services summit 2018 I'm John Fourier with Jeff Rick here at the cube our next guest is dr. Matt wood general manager of artificial intelligence with Amazon Web Services keep alumnae been so busy for the past year and been on the cubanía thanks for coming back appreciate you spending the time so promotions keep on going on you got now general manager of the AI group AI operations ai automation machine learning offices a lot of big category of new things developing and a you guys have really taken AI and machine learning to a whole new level it's one of the key value propositions that you guys now have for not just a large enterprise but down to startups and developers so you know congratulations and what's the update oh well the update is this morning in the keynote I was lucky enough to introduce some new capabilities across our platform when it comes to machine learning our mission is that we want to be able to take machine learning and make it available to all developers we joke internally that we just want to we want to make machine learning boring we wanted to make it vanilla it's just it's another tool in the tool chest of any developer and any any data data scientist and we've done that this idea of taking technology that is traditionally only within reached a very very small number of well-funded organizations and making it as broadly distributed as possible we've done that pretty successfully with compute storage and databases and analytics and data warehousing and we want to do the exact same thing for the machine learning and to do that we have to kind of build an entirely new stack and we think of that stack in in three different tiers the bottom tier really for academics and researchers and data scientists we provide a wide range of frameworks open source programming libraries the developers and data scientists use to build neural networks and intelligence they're things like tend to flow and Apache mx9 and by torch and they're really they're very technical you can build you know arbitrarily sophisticated says most she open source to write mostly open source that's right we contribute a lot of our work back to MX net but we also contribute to buy torch and to tend to flow and there's big healthy open source projects growing up around you know all these popular frameworks plus more like chaos and gluon and horror boredom so that's a very very it's a key area for for researchers and academics the next level up we have machine learning platforms this is for developers and data scientists who have data they see in the clout although they want to move to the cloud quickly but they want to be able to use for modeling they want to be able to use it to build custom machine learning models and so here we try and remove as much of the undifferentiated heavy lifting associated with doing that as possible and this is really where sage maker fits in Cersei's maker allows developers to quickly fill train optimize and host their machine learning models and then at the top tier we have a set of AI services which are for application developers that don't want to get into the weeds they just want to get up and running really really quickly and so today we announced four new services really across those their middle tier in that top tier so for Sage maker we're very pleased to introduce a new streaming data protocol which allows you to take data straight from s3 and pump it straight into your algorithm and straight onto the computer infrastructure and what that means is you no longer have to copy data from s3 onto your computer infrastructure in order to be able to start training you just take away that step and just stream it right on there and it's an approach that we use inside sage maker for a lot of our built-in algorithms and it significantly increases the the speed of the algorithm and significantly of course decreases the cost of running the training because you pay by the second so any second you can save off it's a coffin for the customer and they also it helps the machine learn more that's right yeah you can put more data through it absolutely so you're no longer constrained by the amount of disk space you're not even constrained by the amount of memory on the instance you can just pump terabyte after terabyte after terabyte and we actually had another thing like talked about in the keynote this morning a new customer of ours snap who are routinely training on over 100 terabytes of image data using sage maker so you know the ability to be able to pump in lots of data is one of the keys to building successful machine learning applications so we brought that capability to everybody that's using tensorflow now you can just have your tensor flow model bring it to Sage maker do a little bit of wiring click a button and you were just start streaming your data to your tents upload what's the impact of the developer time speed I think it is it is the ability to be able to pump more data it is the decrease in time it takes to start the training but most importantly it decreases the training time all up so you'll see between a 10 and 25 percent decrease in training time some ways you can train more models or you can train more models per in the same unit time or you can just decrease the cost so it's a completely different way of thinking about how to train over large amounts of data we were doing it internally and now we're making it available for everybody through tej matrix that's the first thing the second thing that we're adding is the ability to be able to batch process and stage make them so stage maker used to be great at real-time predictions but there's a lot of use cases where you don't want to just make a one-off prediction you want to predict hundreds or thousands or even millions of things all at once so let's say you've got all of your sales information at the end of the month you want to use that to make a forecast for the next month you don't need to do that in real-time you need to do it once and then place the order and so we added batch transforms to Sage maker so you can pull in all of that data large amounts of data batch process it within a fully automated environment and then spin down the infrastructure and you're done it's a very very simple API anyone that uses a lambda function it's can take advantage of this again just dramatically decreasing the overhead and making it so much easier for everybody to take advantage of machine load and then at the top layer we had new capabilities for our AI services so we announced 12 new language pairs for our translation service and we announced new transcription so capability which allows us to take multi-channel audio such as might be recorded here but more commonly on contact centers just like you have a left channel on the right channel for stereo context centers often record the agent and the customer on the same track and today you can now pass that through our transcribed service long-form speech will split it up into the channels or automatically transcribe it will analyze all the timestamps and create just a single script and from there you can see what was being talked about you can check the topics automatically using comprehend or you can check the compliance did the agents say the words that they have to say for compliance reasons at some point during the conversation that's a material new capability for what's the top surface is being used obviously comprehend transcribe and barri of others you guys have put a lot of stuff out there all kinds of stuff what's the top sellers top use usage as a proxy for uptake you know I think I think we see a ton of we see a ton of adoption across all of these areas but where a lot of the momentum is growing right now is sage maker so if you look at a formula one they just chose Formula One racing they just chose AWS and sage maker as their machine learning platform the National Football League Major League Baseball today announcer they're you know re offering their relationship and their strategic partnership with AWS cream machine learning so all of these groups are using the data which just streams out of these these races all these games yeah and that can be the video or it can be the telemetry of the cars or the telemetry of the players and they're pumping that through Sage maker to drive more engaging experiences for their viewers so guys ok streaming this data is key this is a stage maker quickly this can do video yeah just get it all in all of it well you know we'd love data I would love to follow up on that so the question is is that when will sage maker overtake Aurora as the fastest growing product in history of Amazon because I predicted that reinvent that sage maker would go on err is it looking good right now I mean I sorta still on paper you guys are seeing is growing but see no eager give us an indicator well I mean I don't women breakout revenue per service but even the same excitement I'll say this the same excitement that I see Perseids maker now and the same opportunity and the same momentum it really really reminds me of AWS ten years ago it's the same sort of transformative democratizing approach to which really engages builders and I see the same level of the excitement as levels are super super high as well no super high in general reader pipe out there but I see the same level of enthusiasm and movement and the middle are building with it basically absolutely so what's this toy you have here I know we don't have a lot of time but this isn't you've got a little problem this is the world's first deep learning in April were on wireless video camera we thought it D blends we announced it and launched it at reinvent 2017 and actually hold that but they can hold it up to the camera it's a cute little device we modeled it after wall-e the Pixar movie and it is a HD video camera on the front here and in the base here we have a incredibly powerful custom piece of machine learning hardware so this can process over a billion machine learning operations per second you can take the video in real time you send it to the GPU on board and we'll just start processing the stream in real time so that's kind of interesting but the real value of this and why we designed it was we wanted to try and find a way for developers to get literally hands-on with machine learning so the way that build is a lifelong learners right they they love to learn they have an insatiable appetite for new information and new technologies and the way that they learn that is they experiment they start working and they kind of spin this flywheel where you try something out it works you fiddle with it it stops working you learn a little bit more and you want to go around around around that's been tried and tested for developers for four decades the challenge with machine learning is doing that is still very very difficult you need a label data you need to understand the algorithms it's just it's hard to do but with deep lens you can get up and running in ten minutes so it's connected back to the cloud it's good at about two stage makeup you can deploy a pre-built model down onto the device in ten minutes to do object detection we do some wacky visual effects with neural style transfer we do hot dog and no hot dog detection of course but the real value comes in that you can take any of those models tear them apart so sage maker start fiddling around with them and then immediately deploy them back down onto the camera and every developer on their desk has things that they can detect there are pens and cups and people whatever it is so they can very very quickly spin this flywheel where they're experimenting changing succeeding failing and just going round around a row that's for developers your target audience yes right okay and what are some of the things that have come out of it have you seen any cool yes evolutionary it has been incredibly gratifying and really humbling to see developers that have no machine learning experience take this out of the box and build some really wonderful projects one in really good example is exercise detection so you know when you're doing a workout they build a model which detects the exerciser there and then detects the reps of the weights that you're lifting now we saw skeletal mapping so you could map a person in 3d space using a simple camera we saw security features where you could put this on your door and then it would send you a text message if it didn't recognize who was in front of the door we saw one which was amazing which would read books aloud to kids so you would hold up the book and they would detect the text extract the text send the text to paly and then speak aloud for the kids so there's games as educational tools as little security gizmos one group even trained a dog detection model which detected individual species plug this into an enormous power pack and took it to the local dog park so they could test it out so it's all of this from from a cold start with know machine learning experience you having fun yes absolutely one of the great things about machine learning is you don't just get to work in one area you get to work in you get to work in Formula One and sports and you get to work in healthcare and you get to work in retail and and develop a tool in CTO is gonna love this chief toy officers chief toy officers I love it so I got to ask you so what's new in your world GM of AI audition intelligence what does that mean just quickly explain it for our our audience is that all the software I mean what specifically are you overseeing what's your purview within the realm of AWS yeah that's that's a totally fair question so my purview is I run the products for deep learning machine learning and artificial intelligence really across the AWS machine learning team so I get I have a lot of fingers in a lot of pies I get involved in the new products we're gonna go build out I get involved in helping grow usage of existing products I get it to do a lot of invention it spent a ton of time with customers but overall work with the rest of the team on setting the technical and pronto strategy for machine learning at AWS when what's your top priorities this year adoption uptake new product introductions and you guys don't stop it well we do sync we don't need to keep on introducing more and more things any high ground that you want to take what's what's the vision I didn't the vision is to is genuinely to continue to make it as easy as possible for developers to use Ruggiero my icon overstate the importance or the challenge so we're not at the point where you can just pull down some Python code and figure it out we're not even we don't have a JVM for machine learning where there's no there's no developer tools or debuggers there's very few visualizers so it's still very hard if you kind of think of it in computing terms we're still working in assembly language and you're seen learning so there's this wealth of opportunity ahead of us and the responsibility that I feel very strongly is to be able to continually in crew on the staff to continually bring new capabilities to mortar but well cloud has been disrupting IT operations AI ops with a calling in Silicon Valley and the venture circuit Auto ml as a term has been kicked around Auto automatic machine learning you got to train the machines with something data seems to be it strikes me about this compared to storage or compared to compute or compared to some of the core Amazon foundational products those are just better ways to do something they already existed this is not a better way to do something that are exists this is a way to get the democratization at the start of the process of the application of machine learning and artificial intelligence to a plethora of applications in these cases that is fundamentally yeah different in it just a step up in terms of totally agree the power to the hands of the people it's something which is very far as an area which is very fast moving and very fast growing but what's funny is it totally builds on top of the cloud and you really can't do machine learning in any meaningful production way unless you have a way that is cheap and easy to collect large amounts of data in a way which allows you to pull down high-performance computation at any scale that you need it and so through the cloud we've actually laid the foundations for machine learning going forwards and other things too coming oh yes that's a search as you guys announced the cloud highlights the power yet that it brings to these new capabilities solutely yeah and we get to build on them at AWS and at Amazon just like our customers do so osage make the runs on ec2 we wouldn't we won't be able to do sage maker without ec2 and you know in the fullness of time we see that you know the usage of machine learning could be as big if not bigger than the whole of the rest of AWS combined that's our aspiration dr. Matt would I wish we had more time to Chad loved shopping with you I'd love to do a whole nother segment on what you're doing with customers I know you guys are great customer focus as Andy always mentions when on the cube you guys listen to customers want to hear that maybe a reinvent will circle back sounds good congratulations on your success great to see you he showed it thanks off dr. Matt would here in the cube was dreaming all this data out to the Amazon Cloud is whether they be hosts all of our stuff of course it's the cube bringing you live action here in New York City for cube coverage of AWS summit 2018 in Manhattan we'll be back with more after this short break