(lively music) >> Good afternoon and welcome back to beautiful Las Vegas, Nevada, where we're here live from the show floor, all four days of AWS re:Invent. I'm Savannah Peterson, joined with my co-host Dave Vellante. Dave, how you doing? >> Good. Beautiful and chilly Las Vegas. Can't wait to get back to New England where it's warm. >> Balmy, New England this time of year in December. Wow, Dave, that's a bold statement. I am super excited about the conversation that we're going to be having next. And, you know, I'm not even going to tee it up. I just want to bring Dilip on. Dilip, thank you so much for being here. How you doing? >> Savannah, Dave, thank you so much. >> Hey, Dilip. >> Excited to be here. >> It's joy to have you. So, you have been working at Amazon for about 20 years. >> Almost. Almost. >> Yes. >> Feels like 20, 19 1/2. >> Which is very exciting. You've had a lot of roles. I'm going to touch on some of them, but you just came over to AWS from the physical retail side. Talk to me about that. >> Yup, so I've been to Amazon for 19 1/2 years. Done pricing, supply chain. I was Jeff Bezos technical advisor for a couple years. >> Casual name drop. >> Casual name drop. >> Savannah: But a couple people here for that name before. >> Humble brag, hashtag. And then I, for the last several years, I was leading our physical retail initiatives. We just walk out Amazon One, bringing convenience to physical spaces. And then in August, with like as those things were getting a lot of traction and we were selling to third parties, we felt that it would be better suited in AWS. And, but along with that, there was also another trend that's been brewing, which is, you know, companies have loved building on AWS. They love the infrastructure services, but increasingly, they're also asking us to build applications that are higher up in the stack. Solving key, turnkey business problems. Just walk out Amazon One or examples of that, Amazon Connect. We just recently announced supply chain, so now there's a bevy interesting services all coming together, higher up in the stack for customers. So it's an exciting time. >> It was interesting that you're able to, you know, transfer from that retail. I mean, normally, in historically, if you're within an industry, retail, manufacturing, automotive whatever. You were kind as locked in a little bit. >> Dilip: Siloed a little bit. Yeah, yeah, yeah. >> Because they had their own, your own value chain. And I guess, data has changed that maybe, that you can traverse now. >> Yeah, if you think about the things that we did, even when we were in retail, the tenants was less about the industries and more about how can we bring convenience to physical spaces? The fact that you don't like to wait in line is no more like likely, you know, five years from now than it is today. So, it's a very durable tenant, but it's equally applicable whether you're in a grocery store, a convenience store, a stadium, an airport. So it actually transcends any, and like supply chain, think of supply chain. Supply chain isn't, you know, targeted to any one particular industry. It has broad applicability. So these things are very, you know, horizontally applicable. >> Anything that makes my life easier, I'm down. >> Savannah: We're all here for the easy button. We've been talking about it a bit this week. I'm in. And the retail store, I mean, I'm in San Francisco. I've had the experience of going through. Very interesting and seamless journey, honestly. It's very exciting. So tell us a little bit more about the applications group at AWS. >> Yup. So as I said, you know, we are, the applications group is a combination of several services. You know, we have communication developer services, which is the ability to add simple email service or video and embed video, voice chat using a chime SDK. In a higher up in the stack, we are taking care of things that IT administrators have to deal with where you can provision an entire desktop with the workspaces or provide a femoral access to it. And then as you go up even higher up in the stack, you have productivity applications like AWS Wicker, which we just did GA, you know, last week in AWS Clean Rooms which we announced as a service in preview. And then you have, you know, Connect, which is our cloud contact center, AWS supply chain. Just walk out Amazon One, it just feels like we're getting started. >> Just a couple things going on. >> So, clean rooms. Part of the governance play, part of data sharing. Can you explain, you know, we were talking offline, but I remember back in the disk drive days. We were in a clean room, they'd show you the clean room, you couldn't go near it unless you had a hazmat suit on. So now you're applying that to data. Explain that concept. >> Yeah, so the companies across, you know, financial services or healthcare, advertising, they all want to be able to combine and pull together data`sets with their partners in order to get these collaborative insights. The problem is either the data's fragmented, it's siloed or you have, you know, data governance issues that's preventing them from sharing. And the key requirement is that they want to be able to share this data without exposing any of the underlying data. Clean rooms are always emerged as a solution to that, but the problem with that is that they're hard to maintain. They're expensive. You have to write complex privacy queries. And if you make a mistake, you risk exposing the same data that you've been, you know, studiously trying to protect. >> Trying to protect. >> You know, take advertising as an industry, as an example. You know, advertisers care about, is my ad effective? But it turns out that if you're an advertiser and let's say you're a Nike or some other advertiser and your pop, you know, you place an ad on the website. Well, you want to stop showing the ad to people who have already purchased the product. However, people who purchased the product,- >> Savannah: It happens all the time. >> that purchasing data is not accessible to them easily. But if you could combine those insights, you know, the publishers benefit, advertisers benefits. So AWS Clean Rooms is that service that allows you very easily to be able to collaborate with a group of folks and then be able to gain these collaborative insights. >> And the consumers benefit. I mean, how many times you bought, you search it. >> It happens all the time. >> They know. And like, I just bought that guys, you know? >> Yeah, no, exactly. >> Four weeks. >> And I'm like, you don't need to serve me that, you know? And we understand the marketing backend. And it's just a waste of money and energy and resources. I mean, we're talking about sustainability as well. I don't think supply chain has ever had a hotter moment than it's had the last two and a half years. Tell me more about the announcement. >> Yup, so super excited about this. As you know, as you said, supply chains have always been very critical and very core for companies. The pandemic exacerbated it. So, ours way of sort of thinking about supply chains is to say that, you know, companies have taken, over the years many, like dozens, like millions and millions of dollars of investment in building their own supply chains. But the problem with supply chains is that the reason that they're not as functional as they could be is because of the lack of visibility. Because they're strung together very many disparate systems, that lack of visibility affects agility. And so, our approach in it was to say that, well, if we could have folks use their existing supply chain what can we do to improve the investment on the ROI of what they're getting? By creating a layer on top of it, that provides them that insights, connects all of these disparate data and then provides them insights to say, well, you know, here's where you overstock, here's where you under stock. You know, this is the, you know, the carbon emission impact of being able to transfer something. So like rather without requiring people to re-platform, what's the way that we can add value in it? And then also build upon Amazon's, you know, years of supply chain experience, to be able to build these predictive analytics for customers. >> So, that's a good, I like that you started with the why. >> Yes. >> Right now, what is it? It's an abstraction layer and then you're connecting into different data points. >> Yes, that's correct. >> Injecting ML. >> Feel like you can pick in, like if you think about supply chain, you can have warehouse management systems, order management systems. It could be in disparate things. We use ML to be able to bring all of this disparate data in and create our unified data lake. Once you have that unified data lake, you can then run an insights layer on top of it to be able to say, so that as the data changes, supply chain is not a static thing. Data's constantly changing. As the data's changing, the data lake now reflects the most up-to-date information. You can have alerts and insights set up on it to say that, what are the kinds of things that you're interested in? And then more importantly, supply chain and agility is about communication. In order to be able to make certain things happen, you need to be able to communicate, you need to make sure that everyone's on the same page. And we allow for a lot of the communication and collaboration tools to be built within this platform so that you're not necessarily leaving to go and toggle from one place to the other to solve your problems. >> And in the pie chart of how people spend their time, they're spending a lot less time communicating and being proactive. >> That's correct. >> And getting ahead of the curve. They're spending more time trying to figure out actually what's going on. >> Yes. >> And that's the problem that you're going to solve. >> Well, and it ensures that the customer at the other end of that supply chain experience is going to have their expectations managed in terms of when their good might get there or whatever's going to happen. >> Exactly. >> I feel like that expectation management has been such a big part of it. Okay, I just have to ask because I'm very curious. What was it like advising Jeff? >> Quite possibly the best job that I've ever had. You know, he's a fascinating individual. >> Did he pay you to say that? >> Nope. But I would've, like, I would've done it for like, it's remarkable seeing how he thinks and his approach to problem solving. It is, you know, you could be really tactical and go very deep. You could be extremely strategic. And to be able to sort of move effortlessly between those two is a unique skill. I learned a lot. >> Yeah, absolutely. So what made you want to evolve your career at Amazon after that? 'Cause I see on your LinkedIn, you say, it was the best job you ever had. With curiosity? >> Yeah, so one of the things, so the role is designed for you to be able to transition to something new. >> Savannah: Oh, cool. >> So after I finished that role, we were just getting into our foray with physical stores. And the idea between physical stores is that, you and I as consumers, we all have a lot of choices for physical stores. You know, there's a lot of options, there's a lot of formats. And so the last thing we wanted to do is come up with another me too offering. So, our approach was that what can we do to improve convenience in physical stores? That's what resulted in just walk out to Amazon Go. That's what resulted in Amazon One, which is another in a fast, convenient, contactless way to pay using the power of your palm. And now, what started in Amazon retail is now expanded to several third parties in, you know, stadiums, convention centers, airports. >> Airport, I just had, was in the Houston airport and got to do a humanless checkout. >> Dilip: Exactly. >> And actually in Honolulu a couple weeks ago as well too. Yeah, so we're going to see more and more of this. >> Yes. >> So what Amazon, I think has over a million employees. A lot of those are warehouse employees. But what advice would you give to somebody who's somewhere inside of Amazon, maybe they're on AWS, maybe they're Amazon. What advice would you give somebody inside that's maybe, you know, hey, I've been at this job for five, six years, three, four years, whatever it is. I want to do something else. And there's so much opportunity inside Amazon, right? What would you advise them? >> My single advice, which is actually transferable and I use it for myself is choose something that makes you a little uncomfortable. >> Dave: Get out of your comfort zone. >> It's like, you got to do that. It's like, it's not the easiest thing to hear, but it's also the most satisfying. Because almost every single time that I've done it for myself, it's resulted in like, you don't really know what the answer is. You don't really know exactly where you're going to end up, but the process and the journey through it, if you experience a little bit of discomfort constantly, it makes you non complacent. It makes you sort of not take the job, sort of in a stride. You have to be on it to do it. So that's the advice that I would give anyone. >> Yeah, that's good. So something that's maybe adjacent and maybe not completely foreign to you, but also something that, you know, you got to go dig a little bit and learn. >> You're planning a career change over here, Dave? >> No, I know a lot of people in Amazon are like, hey, I'm trying to figure out what I want to do next. I mean, I love it here. I live by the LPS, you know, but, and there's so much to choose from. >> It is, you know, when I joined in 2003, there were so many things that we were sort of doing today. None of those existed. It's a fascinating company. And the evolution, you could be in 20 different places and the breadth of the kinds of things that, you know, the Amazon experience provides is timeless. It's fascinating. >> And, you know, you look at a company like Amazon, and, you know, it's so amazing. You look at this ecosystem. I've been around- >> Even a show floor. >> I've been around a lot of time. And the show floor says it all. But I've seen a lot of, you know, waves. And each subsequent wave, you know, we always talk about how many companies were in the Fortune 1000 and aren't anymore. And, but the leaders, you know, survive and they thrive. And I think it's fascinating to try to better understand the culture that enables that. You know, you look at a company like Microsoft that was irrelevant and then came back. You know, even IBM was on death store for a while and they come back and so they. And so, but Amazon just feels, you know, at the moment you feel like, "Oh wow, nothing can stop this machine." 'Cause everybody's trying to disrupt Amazon and then, you know, only the paranoid survive, all that stuff. But it's not like, past is not prologue, all right? So that's why I asked these questions. And you just said that a lot of the services today that although the ideas didn't even exist, I mean, walkout. I mean, that's just amazing. >> I think one of the things that Amazon does really well culturally is that they create the single threaded leadership. They give people focus. If you have to get something done, you have to give people focus. You can't distract them with like seven different things and then say that, oh, by the way, your eighth job is to innovate. It just doesn't work that way. It's like it's hard. Like it can be- >> And where were the energy come from that? >> Exactly. And so giving people that single threaded focus is super important. >> Frank Slootman, the CEO of Snowflake, has a great quote. He wrote on his book. He said, "If you got 14 priorities, you got none." And he asks,- >> Well said. >> he challenges people. If you had to give up everything and do only one thing for the next 365 days, what would that be? It's a really hard question to answer. >> I feel like as we're around New Year's resolution times. I mean when we thinking about that, maybe we can all share our one thing. So, Dilip, you've been with the the applications team for five months. What's coming up next? >> Well, as I said, you know, it feels like it's still day one for applications. If you think about the things, the news that we introduced and the several services that we introduced, it has applicability across a variety of horizontal industries. But then we're also feeling that there's considerable vertical applications that can be built for specific things. Like, it could be in advertising, it could be in financial services, it could be in manufacturing. The opportunities are endless. I think the notion of people wanting applications higher up in the stack and a little more turnkey solutions is also, it's not new for us, but it's also new and creative too. You know, AWS has traditionally been doing. >> So again, this relates to what we were sort of talking about before. And maybe, this came from Jazzy or maybe it came from Bezos. But you hear a lot, it's okay to be misunderstood or if we were misunderstood for a long time. So when people hear up the stack, they think, when you think about apps, you know, in the last 10 years it was taking on-prem and bringing it into the cloud. Okay, you saw that with CREM, email, CRM, service management, you know, data warehouses, et cetera. Amazon is thinking about this in a different way. It's like you're looking at the world saying, okay, how can we improve whatever? Workflows, people's lives, doing something that's not been done before? And that seems to be the kind of applications that you guys are thinking about building. >> Yeah. >> And that's unique. It's not just, okay, we're going to take something on-prem put it in the cloud. Been there, done that. That S-curve is sort of flattening now. But there's a new S-curve which is completely new workflows and innovations and processes that we really haven't thought about yet. Or you're thinking about, I presume. >> Yeah. Having said that, I'd also like to sort of remind folks that when you consider the, you know, the entire spend, the portion of workloads that are running in the cloud is a teeny tiny fraction. It's like less than 5%, like 4% or something like that. So it's a very, there's still plenty of things that can sort of move to the cloud. But you're right that there is another trend of where in the stack and the types of applications that you can provide as well. >> Yeah, new innovation that haven't well thought of yet. >> So, Dilip, we have a new tradition here on theCUBE at re:Invent. Where we're looking for your 30 minute Instagram reel, your hot take, biggest key theme, either for you, your team, or just general vibe from the show. >> General vibe from the show. Well, 19 1/2 years at Amazon, this is actually my first re:Invent, believe it or not. This is my, as a AWS employee now, as re:Invent with like launching services. So that's the first. I've been to re:Invent before, but as an attendee rather than as a person who's, you know, a contributing number of the workforce. >> Working actually? >> If you will. >> Actually doing your job. >> And so I'm just amazed at the energy and the breadth. And the, you know, from the partners to the customers to the diversity of people who are coming here from everywhere. I had meetings from people in New Zealand. Like, you know, the UK, like customers are coming at us from like very many different places. And it's fascinating for me to see. It's new for me as well given, you know, some of my past experience. But this is a, it's been a blast. >> People are pumped. >> People are pumped. >> They can't believe the booth traffic. Not only that quality. >> Right. All of our guests have talked about that. >> Like, yeah, you know, we're going to throw half of these leads away, but they're saying no, I'm having like really substantive conversations with business people. This is, I think, my 10th re:Invent. And the first one was mostly developers. And I'm like, what are you talking about? And, you know, so. Now it's a lot more business people, a lot of developers too. >> Yeah. >> It's just. >> The community really makes it. Dilip, thank you so much for joining us today on theCube. >> Thank you for having me. >> You're fantastic. I could ask you a million questions. Be sure and tell Jeff that we said hi. >> Will do. >> Savannah: Next time you guys are hanging out. And thank all of you. >> You want to go into space? >> Yeah. Yes, yes, absolutely. I'm perhaps the most space obsessed on the show. And with that, we will continue our out of this world coverage shortly from fabulous Las Vegas where we are at AWS re:Invent. It is day four with Dave Vellante. I'm Savannah Peterson and you're watching theCUBE, the leader in high tech coverage. (lively music)

(stirring music) >> Announcer: From Seattle, Washington, it's theCUBE. Covering AWS IMAGINE Nonprofit. Brought to you by Amazon Web Services. >> Hey, welcome back everybody. Jeff Frick here with theCUBE. We're in downtown Seattle, Washington, actually right on the waterfront. It has been a spectacular visit here for the last couple of days. And we're back in Seattle for AWS IMAGINE. We were here a couple weeks ago for AWS IMAGINE Education. This is a different version of the conference, really focused around government and nonprofits, and we're really excited to kick off our day with the guy coming right off the keynote who's running this, he's Dave Levy. He's the vice president for U.S. Government and Nonprofit for AWS. Dave, great to see you, and congrats on the keynote. >> Thank you, thanks for having me, too. We're really excited. >> Absolutely. So as you're talking about mission and purpose, and as I'm doing my homework for some of the topics we're going to cover today, these are big problems. I couldn't help but think of a famous quote from Jeff Hammerbacher from years ago, who said, "The greatest minds of my generation "are thinking about how to make us click ads." And I'm so happy and refreshed to be here with you and your team to be working on much bigger problems. >> Yeah, well thank you. We're very excited, we're thrilled with all the customers here, all the nonprofits, all the nongovernmental organizations, all of our partners. It's just very exciting, and there are a lot of big challenges out there, and we're happy to be a part of it. >> So it's our first time here, but you guys have been doing this show, I believe this is the fourth year. >> Its fourth year, yeah. >> Give us a little background on the nonprofit sector at AWS. How did you get involved, you know, what's your mission, and some of the numbers behind. >> Well, it's one of the most exciting part of our businesses in the worldwide public sector. And we have tens of thousands of customers in the nonprofit sector, and they are doing all sorts of wonderful things in terms of their mission. And we're trying to help them deliver on their mission with our technology. So you see everything from hosting websites, to doing back office functions in the cloud, running research and donor platforms, and so it's just a very exciting time, I think. And nonprofit missions are accelerating, and we're helping them do that. >> Yeah, it's quite a different mission than selling books, or selling services, or selling infrastructure, when you have this real focus. The impact of some of these organizations is huge. We're going to talk to someone involved in human trafficking. 25,000,000 people involved in this problem. So these are really big problems that you guys are helping out with. >> They're huge problems, and at Amazon, we really identify with missionaries. We want our partners and our customers to be able to be empowered to deliver on their mission. We feel like we're missionaries and we're builders at Amazon, so this is a really good fit for us, to work with nonprofits all over the world. >> And how did you get involved? We were here a couple weeks ago, talked to Andrew Ko. He runs EDU, he'd grown up in tech, and then one of his kids had an issue that drove him into the education. What's your mission story? >> Well, on a personal level, I'm just passionate about this space. There's so much opportunity. It's everything from solving challenges around heart disease, to research for cancer, patient care, to human trafficking. So all of those things resonate. It touches all of our lives, and I'm thrilled to be able to contribute, and I've got a fantastic team, and we've got amazing customers. >> Right. It's great. Did a little homework on you, you're a pretty good, interesting guy too. But you referenced something that I thought was really powerful, and somebody interviewing you. You talked about practice. Practice, practice, practice, as a person. And you invoked Amara's Law, which I had never heard for a person, which is we tend to overestimate what we can do in the short term, but we underestimate what we can do in the long term. And as these people are focused on these giant missions, the long term impacts can be gargantuan. >> Yeah, I think so. Like you said, we're tackling some huge problems out there. Huge, difficult problems. Migrations, diseases. And, you know, it takes a while to get these things done. And when you look back on a ten year horizon, you can really accomplish a lot. So we like to set big, bold, audacious goals at Amazon. We like to think big. And we want to encourage our customers to think big along with us. And we'll support them to go on this journey. And it may take some time, but I'm confident we can solve a lot of the big problems out there. >> But it's funny, there's a lot of stuff in social now where a lot of people don't think big enough. And you were very specific in your keynote. You had three really significant challenges. Go from big ideas to impact. Learn and be curious, and dive deep. Because like you said, these are not simple problems. These aren't just going to go away. But you really need to spend the time to get into it. And I think what's cool about Amazon, and your fanatical customer focus, to apply that type of a framework, that type of way of go to market into the nonprofit area, really gives you a unique point of view. >> I hope so. And we're doing a lot of really cool things here at the conference. We've got a Working Backwards session. One of the things about working backwards that's really interesting is the customer's at the center of that. And it all starts with the customer. I can't tell you how many times I've been in a meeting at Amazon where somebody has said, wait a second. This is what we heard these customers say, this is what we heard about their mission. And it's all about what customers want. So we're really excited that our customers here and our nonprofits here are going to be going through some of those sessions, and hopefully we can provide a little innovation engine for them by applying Amazon processes to it. >> For the people that aren't familiar, the working backwards, if I'm hearing you right, is the Amazon practice where you actually write the press release for when you're finished, and then work backwards. So you stay focused on those really core objectives. >> Yeah, that's right. It's start with your end state in mind and work backwards from there. And it starts with a press release. And certainly those are fun to write, because you want to know what you're going to be delivering and how you're going to be delivering it, and frankly how your customers and how your stakeholders will be responding. So it's a really great exercise, helps you focus on the mission, and sets up the stage for delivery in the future. >> It's funny, I think one of the greatest and easy simple examples of that is the Amazon Go store. And I've heard lots of stores, I've been it now a couple times up here, in San Francisco, and the story that I've heard, maybe you know if it's true or not, is that when they tried to implement it at first, they had a lot of more departments. And unfortunately it introduced lines not necessarily at checkout, but other places in the store. And with that single focus mission of no lines, cut back the SKUs, cut back the selection, and so when I went in it in San Francisco the other day, and it gave me my little time in the store, the Google search results? It was, I think, a minute and 19 to go in, grab a quick lunch, and then get back on my way. So really laser-focused on a specific objective. >> Yeah, and that's the point of the working backwards process. It's all about what customers want, and you can refine that and continue to refine that, and you get feedback, and you're able to answer those questions and solve those difficult problems. >> That's great. Well, Dave, thanks for inviting us here for the first time again. Congrats on the keynote, and we look forward to a bunch of really important work that your customers and your team are working on, and learning more about those stories. >> Thanks, we're thrilled. Very thrilled. >> All right. He's Dave, I'm Jeff. You're watching theCUBE. We're in Seattle at the AWS IMAGINE Nonprofit. Thanks for watching, we'll see you next time. (light electronic music)

>> from Cambridge, Massachusetts. It's the Cube covering M I T. Chief data officer and information quality Symposium 2019. Brought to you by Silicon Angle Media. >> Welcome back >> to M I. T. Everybody watching the Cube, The leader in live tech coverage. My name is Dave Volonte here with Paul Guillen. My co host, Tom Davenport, is here is the president's distinguished professor at Babson College. Huebel? Um, good to see again, Tom. Thanks for coming on. Glad to be here. So, yeah, this is, uh let's see. The 13th annual M I t. Cdo lucky. >> Yeah, sure. As this year. Our seventh. I >> think so. Really? Maybe we'll offset. So you gave a talk earlier? She would be afraid of the machines, Or should we embrace them? I think we should embrace them, because so far, they are not capable of replacing us. I mean, you know, when we hit the singularity, which I'm not sure we'll ever happen, But it's certainly not going happen anytime soon. We'll have a different answer. But now good at small, narrow task. Not so good at doing a lot of the things that we do. So I think we're fine. Although as I said in my talk, I have some survey data suggesting that large U. S. Corporations, their senior executives, a substantial number of them more than half would liketo automate as many jobs as possible. They say. So that's a little scary. But unfortunately for us human something, it's gonna be >> a while before they succeed. Way had a case last year where McDonald's employees were agitating for increasing the minimum wage and tThe e management used the threat of wrote of robotics sizing, hamburger making process, which can be done right to thio. Get them to back down. Are you think we're going to Seymour of four that were maybe a eyes used as a threat? >> Well, I haven't heard too many other examples. I think for those highly structured, relatively low level task, it's quite possible, particularly if if we do end up raising the minimum wage beyond a point where it's economical, pay humans to do the work. Um, but I would like to think that, you know, if we gave humans the opportunity, they could do Maur than they're doing now in many cases, and one of the things I was saying is that I think companies are. Generally, there's some exceptions, but most companies they're not starting to retrain their workers. Amazon recently announced they're going to spend 700,000,000 to retrain their workers to do things that a I and robots can't. But that's pretty rare. Certainly that level of commitment is very rare. So I think it's time for the companies to start stepping up and saying, How can we develop a better combination of humans and machines? >> The work by, you know, brain Nelson and McAfee, which is a little dated now. But it definitely suggests that there's some things to be concerned about. Of course, ultimately there prescription was one of an optimist and education, and yeah, on and so forth. But you know, the key point there is the machines have always replace humans, but now, in terms of cognitive functions, but you see it everywhere you drive to the airport. Now it's Elektronik billboards. It's not some person putting up the kiosks, etcetera, but you know, is you know, you've you've used >> the term, you know, paid the cow path. We don't want to protect the past from the future. All right, so, to >> your point, retraining education I mean, that's the opportunity here, isn't it? And the potential is enormous. Well, and, you know, let's face it, we haven't had much in the way of productivity improvements in the U. S. Or any other advanced economy lately. So we need some guests, you know, replacement of humans by machines. But my argument has always been You can handle innovation better. You can avoid sort of race to the bottom at automation sometimes leads to, if you think creatively about humans and machines working as colleagues. In many cases, you remember in the PC boom, I forget it with a Fed chairman was it might have been, Greenspan said, You can see progress everywhere except in the product. That was an M. I. T. Professor Robert Solow. >> OK, right, and then >> won the Nobel Prize. But then, shortly thereafter, there was a huge productivity boom. So I mean is there may be a pent up Well, God knows. I mean, um, everybody's wondering. We've been spending literally trillions on I t. And you would think that it would have led toe productivity, But you know, certain things like social media, I think reduced productivity in the workplace and you know, we're all chatting and talking and slacking and sewing all over the place. Maybe that's is not conducive to getting work done. It depends what you >> do with that social media here in our business. It's actually it's phenomenal to see political coverage these days, which is almost entirely consist of reprinting politicians. Tweets >> Exactly. I guess it's made life easier for for them all people reporters sitting in the White House waiting for a press conference. They're not >> doing well. There are many reporters left. Where do you see in your consulting work your academic work? Where do you see a I being used most effectively in organizations right now? And where do you think that's gonna be three years from now? >> Well, I mean, the general category of activity of use case is the sort of someone's calling boring I. It's data integration. One thing that's being discussed a lot of this conference, it's connecting your invoices to your contracts to see Did we actually get the stuff that we contracted for its ah, doing a little bit better job of identifying fraud and doing it faster so all of those things are quite feasible. They're just not that exciting. What we're not seeing are curing cancer, creating fully autonomous vehicles. You know, the really aggressive moonshots that we've been trying for a while just haven't succeeded at what if we kind of expand a I is gonna The rumor, trawlers. New cool stuff that's coming out. So considering all these new checks with detective Aye, aye, Blockchain new security approaches. When do you think that machines will be able to make better diagnoses than doctors? Well, I think you know, in a very narrow sense in some cases, that could do it now. But the thing is, first of all, take a radiologist, which is one of the doctors I think most at risk from this because they don't typically meet with patients and they spend a lot of time looking at images. It turns out that the lab experiments that say you know, these air better than human radiologist say I tend to be very narrow, and what one lab does is different from another lab. So it's just it's gonna take a very long time to make it into, you know, production deployment in the physician's office. We'll probably have to have some regulatory approval of it. You know, the lab research is great. It's just getting it into day to day. Reality is the problem. Okay, So staying in this context of digital a sort of umbrella topic, do you think large retail stores roll largely disappeared? >> Uh, >> some sectors more than others for things that you don't need toe, touch and feel, And soon before you're to them. Certainly even that obviously, it's happening more and more on commerce. What people are saying will disappear. Next is the human at the point of sale. And we've been talking about that for a while. In In grocery, Not so not achieve so much yet in the U. S. Amazon Go is a really interesting experiment where every time I go in there, I tried to shoplift. I took a while, and now they have 12 stores. It's not huge yet, but I think if you're in one of those jobs that a substantial chunk of it is automata ble, then you really want to start looking around thinking, What else can I do to add value to these machines? Do you think traditional banks will lose control of the payment system? Uh, No, I don't because the Finn techs that you see thus far keep getting bought by traditional bank. So my guess is that people will want that certainty. And you know, the funny thing about Blockchain way say in principle it's more secure because it's spread across a lot of different ledgers. But people keep hacking into Bitcoin, so it makes you wonder. I think Blockchain is gonna take longer than way thought as well. So, you know, in my latest book, which is called the Aye Aye Advantage, I start out talking by about Tamara's Law, This guy Roy Amara, who was a futurist, not nearly as well known as Moore's Law. But it said, You know, for every new technology, we tend to overestimate its impact in the short run and underestimated Long, long Ryan. And so I think a I will end up doing great things. We may have sort of tuned it out of the time. It actually happens way finally have autonomous vehicles. We've been talking about it for 50 years. Last one. So one of the Democratic candidates of the 75 Democratic ended last night mentioned the chief manufacturing officer Well, do you see that automation will actually swing the pendulum and bring back manufacturing to the U. S. I think it could if we were really aggressive about using digital technologies in manufacturing, doing three D manufacturing doing, um, digital twins of every device and so on. But we are not being as aggressive as we ought to be. And manufacturing companies have been kind of slow. And, um, I think somewhat delinquent and embracing these things. So they're gonna think, lose the ability to compete. We have to really go at it in a big way to >> bring it. Bring it all back. Just we've got an election coming up. There are a lot of concern following the last election about the potential of a I chatbots Twitter chat bots, deep fakes, technologies that obscure or alter reality. Are you worried about what's coming in the next year? And that that >> could never happen? Paul. We could never see anything deep fakes I'm quite worried about. We don't seem. I know there's some organizations working on how we would certify, you know, an image as being really But we're not there yet. My guess is, certainly by the time the election happens, we're going to have all sorts of political candidates saying things that they never really said through deep fakes and image manipulation. Scary? What do you think about the call to break up? Big check. What's your position on that? I think that sell a self inflicted wound. You know, we just saw, for example, that the automobile manufacturers decided to get together. Even though the federal government isn't asking for better mileage, they said, We'll do it. We'll work with you in union of states that are more advanced. If Big Tak had said, we're gonna work together to develop standards of ethical behavior and privacy and data and so on, they could've prevented some of this unless they change their attitude really quickly. I've seen some of it sales force. People are talking about the need for data standard data protection standards, I must say, change quickly. I think they're going to get legislation imposed and maybe get broken up. It's gonna take awhile. Depends on the next administration, but they're not being smart >> about it. You look it. I'm sure you see a lot of demos of advanced A I type technology over the last year, what is really impressed you. >> You know, I think the biggest advances have clearly been in image recognition looking the other day. It's a big problem with that is you need a lot of label data. It's one of the reasons why Google was able to identify cat photos on the Internet is we had a lot of labeled cat images and the Image net open source database. But the ability to start generating images to do synthetic label data, I think, could really make a big difference in how rapidly image recognition works. >> What even synthetic? I'm sorry >> where we would actually create. We wouldn't have to have somebody go around taking pictures of cats. We create a bunch of different cat photos, label them as cat photos have variations in them, you know, unless we have a lot of variation and images. That's one of the reasons why we can't use autonomous vehicles yet because images differ in the rain and the snow. And so we're gonna have to have synthetic snow synthetic rain to identify those images. So, you know, the GPU chip still realizes that's a pedestrian walking across there, even though it's kind of buzzed up right now. Just a little bit of various ation. The image can throw off the recognition altogether. Tom. Hey, thanks so much for coming in. The Cube is great to see you. We gotta go play Catch. You're welcome. Keep right. Everybody will be back from M I t CDO I Q In Cambridge, Massachusetts. Stable, aren't they? Paul Gillis, You're watching the Cube?

>> Live, from Las Vegas, it's theCUBE, covering AWS re:Invent 2018. Brought to you by Amazon Web Services, Intel, and their ecosystem partners. >> Welcome back to Las Vegas, as we continue our coverage here on theCUBE of AWS re:Invent, day two of our three days of coverage, happy Wednesday to you, wherever you might be watching. We're joined by Yaron Haviv, who is the founder and CPO of iguazio, and Yaron, thanks for joining us here on theCUBE, once again. >> Thank you, hi. >> For folks at home who might be watching or at their office and not familiar with iguazio, tell us a little bit about the history of the company, what you saw as the need, as the founder, and what your primary focus is. >> So our key focus is delivering advanced services, the same one that you see in the Cloud, high-performance for real time analytics, essentially what we've seen as a gap, you have all the Cloud services in the Cloud, but when you're fanning into an Edge or an on-prem environment, you're usually consuming, like IT, VAMs, et cetera. So what we are doing, we're matching the same level of services, we provide serverless functions, AI as a service, and manage databases that can run, either in the Cloud or on-prem, or in federated Edge environment. So one consistent application development environment brought where we are. >> So, on the AI side, you mentioned that, as you're looking at your client base, your customers, and you're introducing this concept now, right? For those who aren't there yet. What do you sell them on, if you will? Or what do they want to know, what don't they understand, you think, generally speaking? >> Yeah, so in AI and ML, there are a lot of companies solving that problem, okay? Where we master is the notion of real-time AI, okay? What people are looking, is into embedding AI into business applications. Okay? The traditional notion is, you have a data lake, you throw all the data, and then your data sign, just go learn stuff, create nice, you know, desk-origin tableau. Great. So what? You know? What people really want is to build recommendation engines. You know, someone is logging into a website, he gets recommendations, so that requires very short latency of response, okay? You are doing front-detection and financial applications, so you're freighting a lot of data. You need to make decisions now, okay? You're doing cyber security analysis, so you're feeding data from routers and firewalls and switches, and you need react immediately to whatever is happening. You think about retail stores, things like Amazon Go. Cameras examining your behavior et cetera, you need to respond very very quickly. And now this is a much harder problem to deliver AI in real time, than it is in a sort-of a data-science workbench or just a batching notion. And traditionally, the way people address that problem is by profiling, creating sort-of a, every time, I'm going to see something very similar to that, I'm going to go to a database, pull, compare, and contrast, but the problem is that you need more and more multi-environment analysis on objects that keep on updating. You know, my location keeps on changing. If I'm going to stand in front of this store, I need to get this advertisement, or if I've just done some purchase with my card, and the bank knows my GPS location, it can cross-correlate that, and know if it's a fraud or not, okay? So there are more inputs going into the decision. This is where we master is, the ability to ingest lots of data in real time, cross-correlate that, in real time, to generate what's called feature vector. It's all those things that make up a decision. Run the decision, based on the traditional AI and deep learning algorithms, and they act on it. Whether it's response to customer requests or, you know, block some firewall, or whatever. And our focus is time to action. And the way we are implementing it, is using two major components. One is, real time serverless functions, which is an open-source we're promoting, called nuclio. A second is a real-time database, extremely high performance, it attaches to those functions and allow and help stitching the data and calculating and getting the results. So that's the general thing we're doing. >> So that idea of the serverless functions with nuclio, that's really about bringing, what you're used to in the Cloud, and bringing that out into the Edge. Which, I think, we were talking before, and that's I think a focus for a lot of developers who, I want to use all of the things I'm used to in the Cloud, where it's, I can just consume them as services, and it's quite easy to deal with. But then I come back into the on-site or on onto the Edge in this kind-of hybrid Cloud model, I don't actually have access to all of those things anymore. And I want to. >> Right, and it's even beyond that, because, you the Lambda came from more of like, WebHooks, Seoscases, et cetera. Extremely not concurrent, extremely low performance. You're talking about hundreds of milliseconds of latencies, you know, you're talking about, like, thousand invocations per second, you know? That's sort-of the concurrency, single-threaded applications. We're talking about real-time applications, you know. Hundreds of thousands of events per second. We're talking about latency in the range of milliseconds response time, that you have to respond. So we had to build a different serverless. Something that's real-time, something that has real-time access to data, et cetera. So that's originally where nuclio came in. And then, we started seeing pull from customers, saying, yes, but you're also a multi-Cloud serverless. And I can run your serverless on a laptop for debugging. I can run it on a mini Edge appliance, because this is my enforcement point. I can run it on-prem, because, you know, I'm stuck with some old gear in my on-prem application, and this is what started making nuclio very popular in lots of getup starts et cetera. And the fact that we're provided as a fully managed platform you know, it's open-source, consume it, whatever, but when you're using our managed platform, you get security, integration with active directory, integration with data, logging, monitoring. So, it really provides an alternative to Lambda, where you need high concurrency and everywhere. You know, Edge, Cloud, on-prem, but also high performance, high concurrency for those new workloads of real-time analytics. >> Yeah, so what are some of things that customers are using the platform to develop on? Like, could you give us an example of someone who's using some of these serverless functions for real-time application? Yeah, so, one of the applications is a, we do a lot of work with the network operators. You know, Verizon is one of our investors, but also working with different, other tel-cos. So we're doing real-time network monitoring, across all their firewalls and network equipment et cetera, to predict the network behavior. So, if there's going to be a failure, is it a cyber-security attack right now, things like that. The next level that they went into doing is actually a remediation. It's essentially re-routing the networks to bypass faults automatically, based on the predicted behaviors. Or, you know, stopping some attacks as they occur. So that's one use case. Another use case, in financial services and many other places, is predictive network operations. It's monitoring, again, behavior of services et cetera, like in trading platforms. And knowing that there is going to be a latency spike that's going to impact the trading, and essentially going and fixing that, in order to not lose millions of dollars of trades. Or real time tick analytics, you know? Until now, all the financial applications were very sort-of event driven, and complex event driven, not incorporating deep learning, things like that. Now, I think that there are many variants. You know, the, your president, you know, is going to tweet something about some company, and then it's going to impact the buyover or with stock. So, the current high-frequency trading algorithms are not designed for that, okay? Now, if you build all those serverless functions that listens on Twitter and Muse and all those things, and they can start cross-correlating that information to a much smarter decision. They fit in the real-time decision of buying and selling stocks into a lot more intelligent decision, you can make more money, okay? Another application, retailers, okay? We're working with locations where they have a thousand cameras in a single supermarket, because they just inspect the shelves to look into inventory levels, and eventually they're going to like, an Amazon Go model, where they actually want to know, to track what you're buying et cetera. So a thousand cameras in a store, you cannot shape all that bandwidth to the Cloud. And this is where it comes to a federated application model. Where, as a developer, the guys that are Cloud-born, or Cloud-first, they know containers, they know APIs, they know that stuff. They don't know how to build a box that sits in a store, okay? This is the other world of VMs and Venix, they don't care about that, they want APIs. They want Lambda functions, Dynamo, et cetera. So what we're providing is a mechanism where they can develop in the Cloud, test, simulate, run CICD pipelines, push our defects to the store, to actually go and do the work. And there we have strong partnerships with at least a couple of the major Cloud providers. We have co-ceiling agreements with Azure, we're working with Google, and, I assume, Amazon will be next, but those two, we have a strong relations with already. >> Alright, before we cut you loose, just gimme your idea about the show in general here, from what you've seen, and kind of how you feel about the conversations that you're a part of. >> Yeah, I was very busy talking to customers all day, so I haven't had a lot of time. I think interesting announcements, you know, they've made announcements with VMware, I'm still trying to figure out, what have they announced. You know, again, we spoke about the fact that the whole idea of Cloud is about service obstructions. Not virtual machines, not Kubernetes containers. It's about using APIs, using serverless functions, using AI workbenches that you can develop this new logic. If I'm going to use this VMware on-prem with Amazon, am I going to get all the SageMaker, Lambda, all that on-prem, or just more of a tactical thing, like Azure Stack, like, we're bringing UVMs, we're calling it Cloud, you know, just for marketing's sake. Is that a real Cloud services platform, okay? I think it aligns with what we're seeing now with the Kubernetes, I think we had some discussion about it. You know, IBM buys Reddit, you know, Cisco collaborates with Amazon, VMware buys Apptio. Kubernetes is containers, it's infrastructure. We speak to customers, we show them what we do serverless, you know AI workbenches, databases, service. That's the interesting part. That eliminates IT. If you're putting Kubernetes, it perpetuates IT. Now they need to take Kubernetes, tie it to their security system, build Spark on top of a container et cetera. Now that is a lot of IT and dev ops work involved. But many customers need agility. The reason they're going to Cloud, is not to use VMs, you know? It's to be able to take some Lambda function, some pre-bagged services, glue them together, and really come fast to market with an application. >> So what we really want to do is just to Cloud all the things. I think? (group chuckles) Cloud all the things. >> Mission accomplished. Yaron, thanks for being with us. We appreciate the time you're on theCUBE. Good to see you, sir. >> Thank you. >> Alright, back with more, here at AWS re:Invent. You're watching it live, and we're on theCUBE. (techno music)

>> Narrator: Live from the Wynn Hotel in Las Vegas, it's theCUBE covering Magento Imagine 2018. Brought to you by Magento. >> Hey, welcome back to theCUBE. We are continuing our coverage live from the Wynn Las Vegas at Magento Imagine 2018. We've had a really exciting day talking about commerce and how it's limitless and changing dramatically. Joining me next is Sucharita Kodali, the vice president and principal analyst at Forrester. Sucharita, it's great to have you on theCUBE. >> Thanks for having me, Lisa. >> So commerce is limitless. We've been hearing this thematically all day. You primarily are working with retailers on their digital strategies. And you've been doing this for a long time. Let's talk about the evolution that you've seen in the retail space with everybody expecting to have access to whatever they want to buy in their pockets. >> Right, right, right. I would say, so I've been working in the retail industry for the last two decades. I've been an analyst for the last 10 plus years. I've really seen a number of changes. And if I had to just summarize the biggest changes, one is just the inventory across different retail channels. So, that's definitely been a huge huge one. It's like, how do you, how do you order online, but then fulfill the item from a physical store or fulfill the item from another store? So those are, that's basically the digital transformation of retailers. Those are investments that companies like WalMart and Target have really been doubling down on and focusing on. The second big change is Amazon. And they single-handedly have transformed the retail industry. They have increased consumer expectations. And what Amazon's also done is reinvented retail as a business model. Because it is no longer about just selling product and being profitable selling that product. Amazon actually is not profitable with a lot of the items that it sells. It makes money in other ways. And it is probably what I would describe as America's first retail conglomerate. And that becomes a really interesting question for other companies to compete, do you have to become a retail conglomerate? Then, the third big change is just brand selling direct to consumer. I remember when I started at Forrester, my very first project was with a large consumer electronics company that asked, Well, should we even sell directly to consumers? There's channel conflict and issues with our distributors. And now, that's not even a factor. It's sort of table stakes you have to sell direct to consumer. And that's probably where we'll continue to see a lot of retail sales in the future. >> So the Amazon model, we expect to be able to get whatever we want whenever we want it, have it shipped to us either at home or shipped to us so we can go pick it up at a store. It's really set the bar. In fact, they just announced the other day that a hundred million Amazon Prime members. I know people that won't buy something if it's not available through Prime. But I think this morning the gentleman that was on main stage from Amazon said at least 50% of their sales are not products they sell, they're through all of the other retailers that are using Amazon as a channel as part of their omni-channel strategy. If you think of a retailer from 20 years ago, how do they leverage your services and expertise and advice to become omni-channel? Because as today, you said essentially it's table stakes for companies to have to sell to consumers. >> Yeah, yeah. There are so many questions that really require, I call it destroying the retail orthodoxies. And retail has historically been about buyers and merchandisers buying goods. There's the old expression in retail, You stack 'em high and watch 'em fly. And that is just where buyers would, Take a company like Toys R Us, they would basically take what Mattel and Hasbro told them to buy. They would buy a ton of it, put it in stores. And because there was less competition back in the '80s, consumers actually would buy that merchandise. And unfortunately, the change for retailers is that consumers have so much more choice now. There's so such more innovation. There are small entrepreneurs who are creating fabulous products, consumer tastes have changed. And this old paradigm of Mattel and Hasbro, or kind of fill in the blank with whatever vendors and suppliers, pushing things is no longer relevant. So, there was just an article in the journal today about how Hasbro sales were down by double digits because Toys R Us is now going to go out of business. So those are the kinds of things that retailers who did not adjust to those changes, they are the ones that really suffer. They don't find ways to develop new inventory, they don't find new channels for growth, and they don't protect their own. They don't build a moat around their customers like Amazon has done, or they don't find ways to source inventory creatively. That's where the problems are. >> You think that's more of a function of a legacy organization; having so much technology that they don't know how to integrate it all together? What do you think are some of the forcing functions old orthodoxies that companies that don't do it well are missing? >> Yeah, it's a lot of it is just in the old ways of doing business. So, a lot of it is being heavily dependent, for instance, on buyers and merchandisers buying things. I mean, one of the biggest innovations that Amazon realized was that, look you can sell things without actually owning the inventory. And that is, their entire, what we call the third party marketplace, and that is just so simple. But if you were to ask a buyer at a major retailer a decade or two ago, "Why do you have to buy the inventory?" their response would be, Well, you have to buy the inventory, that's just the way it is. And it's like, well why? Why don't you try to find a new way to do business? And they never did. But it took Amazon to figure that out. And the great irony of why so many retailers continue to struggle is that Amazon has exposed the playbook on how to sell inventory without owning it. And so few retailers to this day have adopted that approach. And that's the great irony I think, is that that's the most profitable part of Amazon's business is that third party marketplace. And every retailer I've talked to is like, Oh, it's really hard. We can't do that. But, the part of Amazon's business that everyone is looking to imitate is their fast shipping. Which, is the most expensive part of their business. Amazon is only able to afford the fast free shipping because of the third party marketplace. Other retailers want to get the fast free shipping without the marketplace. And it just doesn't make any sense. And that's really the heart of the challenge is that they just don't think about alternative business models. They don't want to change the way that they've historically run their businesses. And some of this could mean that merchants are not as powerful in organizations. And maybe that's part of the pushback is that, there could be a lot of people who lose jobs. The future will be robo-buyers and financial services you have robo-advisors, why not robo-planners in retail? >> So one of the keys then, of eliminating some of the old orthodoxies for merchants is to be able to pivot and be flexible. But it has to start from where in an organization from a digital strategy perspective? Where do you help an organization not fall into the Toys R Us bucket? >> Yeah, I think a lot of it does have to start with merchandising and putting in some interesting digital tools to help merchants be more flexible. So, you want to flex to supply and demand. And some of that comes with integrating marketplaces into your own experience. Some of it can be investing in 3D printers that can make things that are plastic or metals based on demand. That's something that I always wondered why Toy R Us didn't, for instance, make Fidget Spinners on demand. Why did you have to get them with a six month leave time from China, it never made any sense. You can scale service, so use technology to match great store associates with a customer who may have a question. And you don't have to be in the same store. It can be a Facetime call with somebody who is far away. But very few retailers do that. And finally, the last bit is really to look at new alternative business models and finding new ways of making money beyond just selling inventory. >> That's really key because there are so many oppurtunities when companies go omni-channel of not just increasing sales and revenue, but also reducing attrition, making the buying process simple and seamless. Everybody wants one click, right? >> Right. >> Super seamless, super fast, and relevant. It's got to be something if you're going to attract my business, you need to be able to offer something where you know me to a degree. >> Absolutely. >> Or know what it is I might have a propensity to buy. >> Absolutely. And that's the entire area of personalization. And that personalization can be anything from a recommendation that I give you. It can be proactively pushing a recommendation. That's what companies like Stitch Fix do is I tell you what I want and then they send you a box in the mail of things I think you would like and oh, by the way are your size and within your budget. It can be customization. One of Nike's most successful parts of their business is their Nike ID program which allows you to customize shoes according to colors and different sort of embellishments that you may like. And that's exactly the kind of thing that more retailers need to be looking at. >> What are some of the trends maybe that a B2B organization might be able to love or some of the conveniences that we have as consumers and we expect in terms of-- Magento, I was looking on their website the other day and a study that they've done suggests 93 percent of B2B buyers want to be able to purchase online. So, new business models, new revenue streams, but it really is a major shift of sales in marketing to be able to deliver this high velocity low touch model. What are some of the things that a business like a Magento, could learn from say a Nike with how they have built this successful omni-channel experience? >> Well, interestingly I think one of the most important things to recognize is that every B2B buyer is also a B2C buyer. And their expectations are set by their experiences in B2C. So, if you have everything from all of the information at your fingertips, all of that information is optimized for mobile devices. You have different ways to view that information, you have all of your loaded costs, like shipping, or tax, or if there's cross-border. All of the information related to the time to ship, any customs and duties, all of that needs to be visible because in any experience that you have with say a site like Amazon, you're going to get that information. So, the expectation is absolutely there to have it in any situation whether it's B2B or whether it's buying components or kind of very long tail items. That's basically the cost of doing business at this point, is that you have to deliver all of the information that the customer wants and needs. And if you don't, the customer is just going to opt to go purchase that product at whatever destination offers it. >> Somewhere else. >> And somebody will. That's the challenge when you have 800 thousand Plus eCommerce sellers out there selling every product imaginable in the both B2B and B2C landscape. >> So, on the data side there's so much data out there that companies have any type of business to be able to take advantage of that. I know that there's, BI has so much potential. Are you hearing retailers start to embrace advanced analytics techniques, AI machine learning, Where are they with starting to do that? I know that some eyeglass companies have virtual reality augmented reality type of apps where you can kind of try on a pair of frames. Where are you seeing advanced analytics start to be successful and help retailers to be able to target buyers that might say, oh, I can't try that on? No, I want to go somewhere that I can touch and feel it. >> Yeah, well, it's emerging still. I mean, retailers have a lot of data. I think they're trying to figure out where is it most useful. And one of the places where it is incredibly useful is in the backend with fraud management. So, after retailers were forced to put in chip cards as a payment form, what you started to see was more of the fraud shifting to eCommerce. I just had two credit cards that had to be shut off because of E-commerce fraud. But that is where you see the fraudsters going to. And what you see as a result of that is some innovators in that space technology companies really leveraging machine learning, AI, other advanced data techniques to identify fraudulent transactions and to better help retailers eliminate or reduce the percent of transactions that have to then be charged back. So, that's probably one of the most promising areas. There are others that are emerging. We're seeing more visual recognition technologies. House for instance, is excellent at that and Pinterest too. If there's part of an image you like you can click on it or you can tap it and see other images like that. And that's incredibly difficult. And it was even more difficult 10-15 years ago, but it's becoming easier. There's the voice element, voice to text or text to voice. I think that the best applications they're often in customer service, there are so many interactions that happen anywhere in a consumer facing world. It doesn't even have to be within retail. You can think about the complaints to the airline industry or to a bank. And a lot of it falls into a black hole. You always hear that oh, This call may be recorded, but it is really difficult to go back and transcribe that. And to really synthesize that into major themes. And what ML in particular can do is to basically pull out those themes, it can automate all of that, and can give insights as to what you could be doing, what you should be doing, what are the opportunities that you may not have even known existed. So there are definitely emerging places. I mean even a visual recognition, so we talked about House and Pinterest. Another great example is the computer vision that you have in the Amazon Go stores. And there's a robot that the Wal Mart stores are now testing to go find if there are gaps in the inventory that need to be filled. Or if something is running low or out of stock. So there are definitely some interesting applications, but it's still early days for sure. >> So last question, we've got to wrap here, but, we're in April 2018, what are some of the, your top three recommendations for merchants, as they prepare for say Black Friday coming up in what, six or eight months. What are you top three recommendations for merchants to be successful and be able to facilitate a seamless online offline experience? >> Well, we always have kind of imbalances between supply and demand, and that's where I do think things like third party sellers, third party marketplaces are huge. So to be able to leverage that is certainly one opportunity. Another is to think creatively about promotions. In Japan they have these promotions called Fukubukuro promotions, and it's basically like grab bags of like all the left over inventory. But then they basically put it into mystery bags where you can buy it for half off. And consumers line up around the block at stores to go buy these grab bags. Because they also have also like a gamified approach where, you know, one of out 10 of the bags will have like an Ipad or some really high value item. So people really like these things, and they have trading parties. So just new ways of having promotions beyond just the typical door busters that retailers think about. And then kind of third I think is just try to pace out the demand. One of the big issues in E-commerce has been just the burst in demand that always happen in December. And that creates a lot of problems from the standpoint of actually shipping the orders. So the more that you can pull those transaction forward into November, the better off you are from a fulfillment and supply chain standpoint. >> Alright Sucharita thank you so much for stopping by theCUBE >> Thanks Lisa >> And sharing your insights on the trends and what's going on in the commerce and E-commerce space. Really enjoy talking with you. >> Nice to talk to you too. >> We want to thank you for watching. You're watching theCUBE live from Magento Imagine 2018, I'm Lisa Martin. Stick around, I'll be back with my next guest after a short break. (upbeat music)

>> Announcer: Live from the Moscone Center, it's theCUBE covering AWS Summit San Francisco, 2018, brought to you by Amazon Web Services. (techy music playing) >> Welcome back, I'm Stu Miniman joined by my cohost Jeff Frick, and this is theCUBE's live coverage of AWS Summit San Francisco. We are thrilled to welcome back to the program Sandy Carter, who's a vice president with Amazon Web Services. Been with the company about a year. We've had you on the program many times, but first time since you've been at AWS, so... >> That's right, I'm celebrating my year yesterday with Amazon Web Services. >> Stu: And no cake, all right. >> I had a cake yesterday, actually, cake and champagne, by the way. (laughing) >> Sandy, we always love to hear, you know, you talk to so many customers, you know, bring us back for a little bit. What brought you to AWS, what's exciting to your customers when you're talking to them today? >> Well, you know, I really love innovation, I love being innovative, and you know, bar none Amazon is the most innovative company out there today, but really what brought me to Amazon was their focus on the customer, really "obsession" on the customer. When they say obsession they really mean obsession. They work backwards from the customer. We really have this big, big thrust. In fact, one of my favorite stories is when I first came to Amazon we'd be in these meetings and people would say, "Well, what does Low Flying Hawk think about this," or "What does Low Flying Hawk think about that," and I was like, "Who is Low Flying Hawk?" Well, he's a person who would give comments on a forum and just a person who wasn't even spending millions of dollars with Amazon but just had a lot of big clout. We actually just opened a building named Low Flying Hawk, believe it or not. >> Jeff: Have you identified this person? >> They do know who he is, yes. (laughing) But it's really, it just symbolizes the focus that Amazon has on the customer and why that's so important. >> And Sandy, at re:Invent you actually, you spoke to the analyst, I was listening to the session. It's not just kind of, people think AWS they think public cloud. You work for Amazon, it's everything kind of across what you think of Amazon.com, AWS, everything from drones and using Kindles and everything like that. Can you give us a little bit of kind of that pan view of how Amazon looks at innovation? >> Yeah, so it's really interesting. Amazon is very methodical in the way that we innovate, and what we do is we really try to understand the customer. We work backwards from the customer, so we do a press release first, we do frequently asked questions next, and then we do a narrative-- >> You're saying you do an internal press release, yes, yes. >> Yeah, internal press release. Internal frequently asked questions, and then we review a six-page document, no PowerPoints whatsoever, which enables us to debate and learn from each other and just iterate on the idea that makes it better and better and better so that when we come out with it it's a really powerful idea and powerful concept, something that the customers really want. >> So, we'll ask you what you're doing now, but one more kind of transition question, what was your biggest surprise? You know, there's a lot of kind of mystery from people on the outside looking in in terms of culture, and we know it's car driving and innovative growing like crazy company, not only in business but in terms of people. What was your biggest surprise once you kind of got on the inside door? >> My biggest surprise was just how incredibly encouraging and supportive the team is at AWS. My boss is Matt Garman, he's been supportive since day one, you know, Andy, they just cheer you on. They want you to do well and I've really never been at a company that everybody's really pulling for you to be successful, not political infighting but really pulling for you to be successful. So, that's really was the biggest surprise to me, and then that customer obsession. Like, it's not customer focus, it really is customer obsession. >> Right, I think it's so well illustrated by the, again not AWS, but Amazon with the store, right, with no cash register, no people. >> Sandy: Amazon Go. >> To think about that-- >> Sandy: Yeah. >> From the customer point of view is nobody likes to stand in line at the grocery store, so it's such a clean illustration of a customer centric way to attack the problem. >> And I love that because what we did is we opened up the beta first for employees, so we would go in and play with it and test it out, and then we opened it up in Seattle and we would give customer tours. Now it's open to the public in Seattle, so it just again shows you that iterative process that Amazon uses and it's super cool, have you guys been? >> Jeff: Have not been. >> Ugh, in fact, my daughter went in. She put on a mask, she was going to fool the system but it wasn't fooled. All the ML and all the AI worked brilliantly. >> I love how everyone loves to get so creative and try to, you know, get through the system, right, try to break the system. >> I know, but my daughter, that's what I would figure for sure. (laughing) >> So, what are you working on now? You've been there a year, what are you working on? >> So, we are innovating around the enterprise workload, so we know that a lot of startups and cloud native companies have moved to the cloud, but we're still seeing a lot of enterprises that are trying to figure out what their strategy is, and so, Stu and Jeff, what I've been working on is how do we help enterprises in the best way possible. How can we innovate to get them migrated over as fast as possible? So for instance, we have Windows that runs on AWS. It's actually been running there longer than with any other vendor and we have amazing performance, amazing reliability. We just released an ML, machine learning OMI for Windows so that you can use and leverage all that great Windows support and applications that you have, and then you guys saw earlier I was talking to VMware. We know that a lot of customers want to do hybrid cloud on their journey to going all-in with the cloud, and so we formed this great partnership with VMware, produced an offering called VMware Cloud on AWS and we're seeing great traction there. Like Scribd's network just talked about how they're using it for disaster recovery. Other customers are using it to migrate. One CIO migrated 143 workloads in a weekend using that solution. So, it just helps them to get to that hybrid state before they go all-in on the cloud. >> So, are they, I was going to say, are they building a mirror instance of what their on-prem VMware stack is in the Amazon version? Is that how they're kind of negotiating that transition or how does that work? >> So, with VMware they don't have to refactor, so they can just go straight over. With Microsoft workloads what we're seeing a lot of times is maybe they'll bring a sequel app over and they'll just do a lift and shift, and then once they feel comfortable with the cloud they'll go to Aurora, which as you've found was the fastest growing service that AWS has ever had, and so we see a lot of that, you know, movement. Bring it over, lift and shift, learning and you know, if you think about it, if you're a large enterprise one of your big challenges is how do I get my people trained, how do I get them up to speed, and so we've done... Like, we've got a full dot net stack that runs on AWS, so their people don't even have to learn a new language. They can develop in Visual Studio and use PowerShell but work on AWS and bring that over. >> You know, Sandy, bring us inside your customers because the challenge for most enterprises is they have so many applications. >> Sandy: Yeah. >> And you mentioned lift and shift. >> Sandy: Yeah. >> You know, I know some consultant's out there like, "Lift and shift is horrible, don't do it." It's like, well, there's some things you'll build new in the cloud, there's some things you'll do a little bit, and there's some stuff today lift and shift makes sense and then down the road I might, you know, move and I've seen, you know, it was like the seven Rs that Amazon has as to do you re-platform, refactor-- >> That's right. >> You know, all that and everything, so I mean, there's many paths to get there. What are some of the patterns you're hearing from customers? How do they, how is it easier for them to kind of move forward and not get stuck? >> Well, we're seeing a lot of data center evacuations, so those tend to be really fast movement and that's typically-- >> Jeff: Data center evacuation-- >> Yeah, that's what-- >> I haven't heard that one. >> Yeah, that's what, evacuation, they've got to get out of their data center buyer for a certain date for whatever reason, right? They had a flood or a corporate mandate or something going on, and so we are seeing those and those are, Stu, like lift and shift quickly. We are seeing a lot of customers who will create new applications using containers and serverless that we talked about today a lot, and that's really around the innovative, new stuff that they're doing, right. So, Just Eat, for instance, is a large... They do online food service out of the UK. I love their solution because what they're doing is they're using Alexa to now order food, so you can say, "Alexa, I want a pizza delivered "in 20 minutes, what's the best pizza place "that I can get in 20 minutes?" Or "I want sushi tonight," and Alexa will come back and say, "Well, it's going to take "an hour and a half, you had sushi two days ago. "Maybe you want to do Thai food tonight." (laughing) And so it's really incredible, and then they even innovated and they're using Amazon Fire for group ordering. So, if there's a big football game or something going on they'll use Amazon Fire to do that group ordering. All that is coming in through Alexa, but the back end is still Windows on AWS. So, I love the fact that they're creating these new apps but they're using some of that lift and shift to get the data and the training and all that moving and grooving, too. >> Yeah, what do you, from the training standpoint, how, you know, ready are customers to retrain their people, you know, where are there shortages of skillsets, and how's Amazon, you know, helping in that whole movement? >> Well, training is essential because you've got so many great people at enterprises who have these great skills, so what we see a lot of people doing is leveraging things like dot net on AWS. So, they actually... They have something they know, dot net, but yet they're learning about the cloud, and so we're helping them do that training as they're going along but they still have something very familiar. Folks like Capital One did a huge training effort. They trained 1,000 people in a year on cloud. They did deep dives with a Tiger Team on cloud to get them really into the architecture and really understanding what was going on, so they could leverage all those great skills that they had in IT. So, we're seeing everything from, "I got to use some of the current tools that I have," to "Let me completely move to something new." >> And how have you, you've been in the Bay Area also for about a year, right, if I recall? >> Actually, I just moved, I moved to Seattle. >> Jeff: Oh, you did make the move, I was going to say-- >> I did. (laughing) >> "So, are they going to make you move up north?" >> I did because I was-- >> You timed it in the spring, not in November? >> I did, there you go. (laughing) When it's nice and sunny, but it's great. >> Exactly. >> It's great to live in Seattle. Amazon has such a culture that is in person, you know, so many people work there. It's really exhilarating to go into the office and brainstorm and whiteboard with people right there, and then our EBCs are there, so our executive briefing center is there, so customers come in all the time because they want to go see Amazon Go, and so it's really an exciting, energizing place to be. >> Yeah, I love the line that Warner used this morning is that AWS customers are builders and they have a bias for action. So, how do you help customers kind of translate some of the, you know, the culture that Amazon's living and kind of acting like a startup for such a large company into kind of the enterprise mindset? >> That's a great question, so we just proposed this digital innovation workshop. We are doing this now with customers. So, we're teaching them how to work backwards from the customer, how to really understand what a customer need is and how to make sure they're not biased when they're getting that customer need coming in. How to do, build an empathy map and how to write that press release, that internal press release and think differently. So, we're actually teaching customers to do it. It's one of our hottest areas today. When customers do that they commit to doing a proof of concept with us on AWS on one of the new, innovative ideas. So, we've seen a lot of great and exciting innovation coming out of that. >> All right, well, Sandy Carter, so glad we could catch up with you again. Thanks for bringing discussion of innovation, what's happening in the enterprise customers to our audience. For Jeff Frick, I'm Stu Miniman, we'll be back will lots more coverage here, you're watching theCUBE. (techy music playing)

(lively music) >> Hello, and welcome to a special CUBE Conversation here in Palo Alto studios of theCUBE, I am John Furrier, the co-founder of SiliconANGLE Media and also the co-host of theCUBE. We are here with Blake Morgan, who is the futurist, author, speaker, around the concept of customer experience, and has a great new book out called, More is More. Blake, Welcome to theCUBE Conversation. >> Thank you John. >> Thanks for coming in. So I love that it is a hard cover book, the book is great, it feels good, the pages, it's a really good read, but it's got a lot of meaty topics in there. So let's just jump in, what's the motivation for the book? Why the book? Why More is More? >> So I have been in the contact center space for over 10 years and basically everyone under the sun is a customer and we all know what it feels like to have a bad customer experience. Have you had a bad customer experience ever? >> John: Oh yes. >> Yeah, right. >> So there is no shortage of work to be done in this space. I think now it's a great time to be in customer experience because there is more awareness about what it actually means. So, I wrote the book to basically provide some kind of definition and to really help people understand, What is customer experience?. Is it customer service? No, it's not. So what does it mean? How can businesses improve customer experience and what do they need to know to get started? >> How about the evolution? Because you know digital has really changed the game. You are seeing cloud computing, machine learning, AI techniques, bots certainly. I mean Twitter came out over ten years ago. I remember when Comcast Cares came out, you know that was a revolution. It was this one guy who decided to be on Twitter. We saw that beginning of that, that trend, where you can now serve and touch folks with customer service and experience, but then again, the blinds between customer experience and customer experience is blurring. Now those multiple channels, do you send them a Snapchat? Do you Instagram? All kinds of new things are emerging, so how do you define, as a frame, the customer experience in this new context? >> Yeah, you're right, there are so many channels. It's really overwhelming for a lot of businesses. So I think it is important to really cut out the noise to think about, Who are you as a business?, and Who is your customer?. What does your customer need? And I really encourage businesses to make their life harder to make it easier on the customer, because in so many situations, companies make it easier on themselves and make it harder on their customers. For example, say you do tweet a company, they might tell you, Hey, now you need to call us and repeat yourself or Now you need to send us an email. Well that's not easy for me as the customer. So it's really all about making customers' lives easier and better. That's the name of the game. >> So what was the findings in the book, when you did the research for the book, what was the core problem that companies are facing? Was it understanding customer experience? Was it the re imagining of customer experience? Was it just a strategic imperative? What was the problem that you uncovered that was the core to this new customer experience equation? >> So a lot of people equate customer experience with customer service and that's a big problem because for most companies, customer service is a cost center. It's not a revenue generating arm of the business. It's not exciting, it's not a money maker, it's not marketing or sales, and so that is really what people think of, when they think of customer experience. But the book is based on this DO MORE framework and DO MORE is basically represents as an acronym. Each piece of the six piece framework represents a different piece of where customer experience lives. So the first D is design something special. The second, I'm not going to read you every, I'm not going to bore you every single word, but the second is about loving your employees, so that is a part of it too. So culture, modernizing with technology, obsessing over your customers, having a culture of customer centricity and embracing innovation and disruption. So these are all varying pieces of DO MORE, which really helps companies understand, it's not simply something that sits in the contact center. For example, let's say you've got your laptop here, and you love your laptop, but your experience of the laptop is not only shaped by, say you have to contact the call center, it is also shaped by how that laptop was built and how about those people who built the laptop. Were they fighting at work with each other? Did they like their jobs? Did they like their boss? Honestly, that's going to impact your experience. >> Yeah, was it a sweat shop. >> Was it a sweat shop? There you go. >> I mean there's all kind of issues about social good too kind of comes into it with that. >> It actually does, I write a lot about social good in my book and some really great CEOs today get that social good is important, like the CEO of Patagonia or Marc Benioff. I mean you can just rattle off so many examples of stuff that he's doing, whether it is equal pay for woman, or his huge house in Hawaii where he's housed monks, to help them when one of the monks had cancer actually. Salesforce is constantly doing good for it's employees and for the community at large. >> Take me through your view on how executives should think about customer experience with all the digital transformation, because a lot of business models are shifting, you are seeing mobile apps, changing the financial services market, because now the app is the teller. So you have three kinds of companies out there, you've got the customer service oriented company, like a Zappos, or you've got a tech company like Google, but they are all about product innovation. Then you've got companies like Apple and others, that are like the big brand and culture personalities, so you've got these three different kind of companies as an example, each one might have a different view on customer experience. How do you tie, how does an executive figure out how to match the more into their DNA? >> That's a fantastic question. I think it's important to have somebody accountable to it, whether it's a Chief Customer Officer or your CMO, because the CEO is ultimately responsible, however, the CEO has their hand in so many things, it's not scalable for them to be so involved on a granular level, on customer centric metrics and so on and so forth throughout the organization. So I would encourage a company to actually hire somebody who is accountable, who creates even tiger teams across the organization with these customer centric metrics in mind, so everybody is working together and they know their job, no matter if they are HR or finance or marketing or customer service, that their metrics, their performance metrics, are tied back to the customer satisfaction. >> I know you do a lot of talks and you do a lot of speeches out there and events, what's the common question that you get? I mean what are people really struggling with or what are they interested in, what are some of the things that you are hearing when you are out on the road giving talks? >> I think it's hard to actually put some of these practices, I think it's actually hard to put some of these ideas into practice. For example, I recently gave a talk at a large technology company down here in San Jose and I presented some pretty wild ideas about actually the energy for influencing change. So how do we keep that high level of stamina with our employees when it's just quite hard to sometimes even keep up. I remember I gave this speech, I talked about a lot of very eccentric ideas about self-management, like when you are a worker you need to take care of yourself because the corporation is never going to give you a pass to let's say, rest, or do what you need to do to feel good, to be good at work. I noticed some of the people in the audience were all texting each other and afterwards someone came up to me and said, you know we are all texting each other because you say these things and the speech was purchased by the leader of the company, however, when it comes to actually working here, that is not really the vibe here, that's not the culture. So I think that a lot of, even the best companies today, still struggle every single day with some of these ideas, because when you DO MORE, when you work harder than others, it's tiring, it can take it's toll on employees. So how do you keep people fresh? >> So fatigue is a huge issue. >> Fatigue, yes. It is an issue. >> So how do they solve that? Because again, that is an experience and the employees itself represent brands. >> Yeah. >> So what are some of the solutions for that? >> Yeah so it's normal that people in these big companies feel fatigued when they are working harder for the customer, but it is really important for people to just manage themselves because no one is going to give you permission to take ten minutes to go for a walk, take ten minutes to go meditate, so it's really about management providing the room for employees to breathe and also modeling it as an example, if leaders just worked 24/7, it's all about the grind, the grind, the grind, that's not a healthy culture, so they need to push their people, but also give them some kind of safety that they can take care of themselves as well. >> So talk about the book target. Who is the ideal candidate for the book? Who are you writing the book for? What do you hope to accomplish for the reader and the outcome? >> So I write for Forbes and Harvard Business Review and Hemispheres Magazine, I have a lot of different types of readers because customer experience really affects everybody in business. So it could be the CMO, it could be the Chief Customer Officer, it could be the CEO, in fact the CEO of 1-800-Flowers wrote the foreword for my book, Chris McCann. So this book is really relevant for a wide variety of people who are interested in making their company more competitive. >> That's a great point, so let's trill down on that, customer experience just doesn't end in a department, we've seen this in IT, information technology, it's a department that becomes now pervasive with cloud computing, you see social media out there, so customer experience has multiple touch points, hence the broad appeal, how should someone think about being the customer experience champion? Because you always have the champions that kind of drives the change, so you've got change agents and you have kind of to me, the pre-existing management in place, what's the human role in this? Because remember, you have machines out there, you have bots, and all those machine learning technology out there, it's important that the human piece is integral to this, right? I mean what's your view on the role of the person? >> Yeah I'm not anti-technology, I'm not anti-bot, I am excited about the Amazon Go cashier-less stores, Amazon Go stores, but I do feel that technology can help us without totally replacing us. I think that we need thoughtful people in charge of these technologies to lead us, to make smart decisions, but you can't just let the technology go. I think that can be really scary. We've definitely seen so many TV shows about this, you can't blink without seeing another TV show about robots taking over the world. >> So it's a concern. What's the biggest thing you've learned from the book? What was the key learnings for you, personally, when you wrote this book? >> Well, writing a book, there is a lot of learning. I actually had my daughter, I was pregnant while I wrote this book and so I think for me to be totally candid, it was a lesson in patience and working through that period for me being pregnant. So I was like giving birth to the book and an actual baby. To be totally truthful, that was my learning. >> You got a lot more than the book. >> Blake: Laughing >> Well, congratulations, how old is the baby? >> She's sixteen months. >> Congratulations, awesome. >> Thank you. >> Well thanks for coming in and sharing about More is More, Blake Morgan, futurist author on the customer experience, More is More, it's theCUBE Conversation and really an impactful thought because customer experience transcends not just a department, it really is a mindset, it's about culture, it's about a lot of things, and it's certainly in the digital revolution, it's really going to be fundamental. Thanks for sharing your thoughts. >> Blake: Thanks so much. >> Appreciate it. I am John Furrier here in the Palo Alto studios for CUBE Conversation, thanks for watching. (lively music)

>> Announcer: Live from Las Vegas, it's theCUBE. Covering AWS re:Invent 2017. Presented by AWS, Intel and our ecosystem of partners. >> Hey, welcome back everyone. We're live here in Las Vegas. It's theCUBE's exclusive coverage of AWS. Amazon Web Services re:Invent 2017. Amazon web Services annual conference, 45,000 people here. Five years in a row for theCUBE, and we're going to be continuing to cover years and decades after, it's on a tear. I'm John Furrier, my co-host Stu Miniman. Exciting science, one of the biggest themes here is AI, IoT, data, Deep Learning, DeepLens, all the stuff that's been really trending has been really popular at the show. And the person behind that Amazon is Swami. He's the Vice President of Machine Learning at AWS, among other things, Deep Learning and data. Welcome to theCUBE. >> Stu: Good to see you. >> Excited to be here. >> Thanks for coming on. You're the star of the show. Your team put out some great announcements, congratulations. We're seeing new obstruction layers of complexity going away. You guys have made it easy to do voice, Machine Learning, all those great stuff. >> Swami: Yeah. >> What are you most excited about, so many good things? Can you pick a child? I don't want to pick my favorite child among all my children. Our goal is to actually put Machine Learning capabilities in the hands of all developers and data scientists. That's why, I mean, we want to actually provide different kinds of capabilities right from like machine developers who want to build their own Machine Learning models. That's where SageMakers and n21 platform that lets people build, train and deploy these models in a one-click fashion. It supports all popular Deep Learning frameworks. It can be TensorFlow, MXNet or PyCharm. We also not only help train but automatically tune where we use Machine Learning for Machine Learning to build these things. It's very powerful. The other thing we're excited about is the API services that you talked about, the new obstruction layer where app developers who do not want to know anything about Machine Learning but they want to transcribe their audio to convert from speech to text, or translate it or understand the text, or analyze videos. The other thing coming from academia where I'm excited about is I want to teach developers and students Machine Learning in a fun fashion, where they should be excited about Machine Learning. It's such a transformative capability. That's why actually we built a device meant for Machine Learning in a hands-on fashion that's called DeepLens. We have developers right on re:Invent where from the time they take to un-box to actually build a computer with an application to build Hotdog or Not Hotdog, they can do it in less than 10 minutes. It's an amazing time to be a developer. >> John: Yeah. >> Stu: Oh my God, Swami. I've had so many friends that have sat through that session. First of all, the people that sit through it they get like a kit. >> Swami: That's awesome. >> Stu: They're super excited. Last year it was the Ecodot and everybody with new skills. This year, DeepLens definitely seems to be the one that all the geeks are playing with, really programing stuff. There's a bunch of other things here, but definitely some huge buzz and excitement. >> That's awesome, glad to hear. >> Talk about the culture at Amazon. Because I know in covering you guys for so many years and now being intimate with a lot of the developers in your teams. You guys just don't launch products, you actually listen to customers. You brought up Machine Learning for developers. What specifically jumped out at you from talking to customers around making it easier? It was too hard, was it, or it was confined to hardcore math driven data scientists? Was it just the thirst and desire for Machine Learning? Or you're just doing this for side benefits, it's like a philanthropy project? >> No, in Amazon we don't build technology because it's cool. We build technology because that's what our customers want. Like 90 to 95% of our roadmap is influenced by listening to customers. The other 5 to 10% is us reading between the lines. One of the things I actually ... When I started playing with Machine Learning, having built a bunch of database storage and analytics products. When I started getting into Deep Learning and various things I realized there's a transformative capability of these technologies. It was too hard for developers to use it on a day to day fashion, because these models are too hard to build and train. Our data now, the right level of obstruction. That's why we actually think of it as in a multi-layered strategy where we cater to export practitioners and data scientists. For them we have SageMaker. Then for app developers who do not want to know anything about Machine Learning they say, "I'll give you an audio file, transcribe it for me," or "I'll give you text, get me insights or translate it." For them we actually we actually provide simple to use API services, so that they can actually get going without having to know anything about what is TensorFlow or PyCharm. >> TensorFlow got a lot of attention, because that really engaged the developer community in the current Machine Learning, because we're like, "Oh wow, this is cool." >> Swami: Yeah. >> Then it got, I won't say hard to use, but it was high end. Are you guys responding to TensorFlow in particular or you're responding to other forces? What was the driver? >> In amazon we have been using Machine Learning for like 20 years. Since the year of like 1995 we have been leveraging Machine Learning for recommendation engine, fulfillment center where we use robots to pick packages and then Elixir of course and Amazon Go. One of the things we actually hear is while frameworks like TensorFlow or PyCharm, MXNet or PyCharm is cool. It is just too hard for developers to make use of it. We actually don't mind, our users use Cafe or TensorFlow. We want the, to be successful where they take from idea to product shell. And when we talk to developers, this process took anywhere from 6 to 18 months and it should not be this hard. We wanted to do what AWS did to IT industry for compute storage and databases. We want to do the same for Machine Learning by making it really easy to get started and consumer does in utility. That was our intel. >> Swami, I wonder if you can tell us. We've been talking for years about the flywheel of customers for Amazon. What are the economies of scale that you get for the data that you have there. I think of all the training of all the Machine Learning, the developers. How can you leverage the economies of scale that Amazon has in all those kind of environments? >> When you look at Machine Learning, Machine Learning tends to be mostly the icing on the cake. Even when we talk to the expert professors who are the top 10 scientists in the world, the data that goes into the Machine Learning is going to be the determining factor for how good it is in terms of how well you train it and so forth. This is where data scientists keep saying the breath of storage and database and analytics offerings that exist really matter for them to build highly accurate models. When you talk about not just the data, but actually the underlying database technology and storage technology really is important. S3 is the world's most powerful data leg that exists that is highly secure, reliable, scalable and cost effective. We really wanted to make sure customers like Glacier Cloud who store high resolution satellite imagery on S3 and glacier. We wanted them to leverage ML capabilities in a really easy one-click fashion. That's important. >> I got to ask you about the roadmap, because you say customers are having input on that. I would agree with you that that would be true, because you guys have a track record there. But I got to put the dots that I'm connecting in my mind right now forward by saying, you guys ... And telegraphing here certainly heard well, Furner say it and Andy, data is key and opening up that data and we're seeing New Relic here, Sumo Logic. They're sharing anonymous data from usage, workloads really instructive. Data is instructive for the marketplace, but you got to feed the models on the data. The question for you is you guys get so much data. It's really a systems management dream it's an application performance dream. You got more use case data. Are you going to open that up and what's the vision behind it? Because it seems like you could offer more and more services. >> Actually we already have. If you look at x-rays and service that we launched last year. That is one of the coolest capabilities, even I am a developer during the weekends when I cool out. Being able to dive into specific capabilities so one of the performance insights where is the borderline. It's so important that actually we are able to do things like x-raying into an application. We are just getting started. The Cloud transformed how we are building applications. Now with Machine Learning, what is going to happen is we can even do various things like ... Which is going to be the borderline on what kind of datasets. It's just going to be such an amazing time. >> You can literally reimagine applications that are once dominant with all the data you have, if you opened it up and then let me bring my data in. Then that will open up a bigger aperture of data. Wouldn't that make the Machine Learning and then AI more effective? >> Actually, you already can do similar things with Lex. Lex, think of it as it's an automatic speech recognition natural language understanding where we are pre-trained on our data. But then to customize it for your own chat bots or voice applications, you can actually add your own intents and several things and we customize it underlying Deep Learning model specific to your data. You're leveraging the amount of data that we have trained in addition to specifically tuning for yours. It's only going to get better and better, to your point. >> It's going to happen, it's already happening. >> It's already happening, yeah. >> Swami, great slate of announcements on the Machine Learning side. We're seeing the products get all updated. I'm wondering if you can talk to us a little bit about the human side of things. Because we've seen a lot of focus, right, it's not just these tools but it's the tools and the people putting those together. How does Amazon going to help the data scientists, help retrain, help them get ready to be able to leverage and work even better with all these tools? >> Machine Learning, we have seen some amazing usage of how developers are using Machine Learning. For example, Mariness Analytics is a non-profit organization that its goal is to fight human trafficking. They use recognition which is our image processing. They do actually identify persons of interest and victims so that they can notify law enforcement officer. Like Royal National Institute of Blind. They actually are using audio text to speech to generate audio books for visually impaired. I'm really excited about all the innovative applications that we can do to simply improve our everyday lives using Machine Learning, and it's such in early days. >> Swami, the innovation is endless in my mind. But I want to get two thoughts from you, one startup and one practitioner. Because we've heard here in theCUBE, people come here and saying, "I can do so much more now. "I've got my EMR, it's so awesome. "I can do this solving problem." Obviously making it easy to use is super cool, that's one. I want to get your thoughts on where that goes next. And two, startups. We're seeing a lot of startups retooling on Cloud economics. I call it post-2013 >> Swami: Yeah. >> They don't need a lot of money, they can hit critical mass. They can get market product, market fit earlier. They can get economic value quicker. So they're changing the dynamics. But the worry is, how do I leverage the benefit of Amazon? Because we know Amazon is going to grow and all Clouds grow and just for you guys. How do I play with Amazon? Where is the white space? How do I engage, do I just ...? Once I'm on the platform, how do I become the New Relic or slunk? How can I grow my marketplace and differentiate? Because Amazon might come out with something similar. How do I stay in that cadence of growth, even a startup? >> If you see in AWS we have tens of thousands of partners of course, right from ISV, SIs and whatnot. Software industry is an amazing industry where it's not like winner take all market. For example, in the document management space, even though we have S3 and WorkDocs, it doesn't mean Dropbox and Box are not successful either, and so forth. What we provide in AWS is the same infrastructure for any startup or for my team, even though I build probably many of the underlying infrastructure. Nowadays for my AI team, it's literally like a startup except I probably stay in an AWS building, but otherwise I don't get any internal APIs, it's the same API so easy to S3. >> John: It's a level playing field. >> It's a level playing field. >> By the way, everyone should know, he wrote DynamoDB. As an intern or was that ...? (Swami laughs) And then SQS, rockstar techy here, so it's great to have. You're what we call a tech athlete. Great to have you on. No white space, just go for it. >> Innovation is the key. The key thing, what we have seen amazing startups who have done exceptionally well is they intently listen to customers and innovate and really look for what it matters for their customers and go for it. >> The biggest buzz of the show from your group. What's your biggest buzz from the show here? DeepLens? >> DeepLens has been ... Our idea was to actually come up with a fun way to learn Machine Learning. Machine Learning, it used to be, even until recently actually as well as last week, it was actually an intimate thing for developers to learn while there is, it's all the buzz. It's not really straight forward for developers to use it. We thought, "Hey, what is a fun way for developers "to get engaged and build Machine Learning?" That's why we actually can see DeepLens so that you can actually build fun applications. I talked about Hotdog, Not Hotdog. I'm personally going to be building what I call as a Bear Cam. Because I live in the suburbs of Seattle where we actually have bears visiting our backyard digging our trash. I want to actually have DeepLens with a pre-train model that I'm going to train to detect bears. That it sends me a message through SQS and SNS so I get a text. >> Here's an idea we want to do, maybe your team can build it for us. CUBE Cam, we put the DeepLens here and then as anyone goes by, if they're a Twitter follower of theCUBE they can send me a message. (John and Swami laughing) Swami, great stuff. Deep Learning again, more goodness coming. >> Swami: That's awesome. >> What are you most excited about? >> In Amazon we have a phrase called, "It's Day One." Even though we are a 22-year-old company, I jokingly tell my team that, "It's day one for us, "except we just woke up and we haven't even "had a cup of coffee yet." We have just scratched the surface with Machine Learning, there is so much stuff to do. I'm super excited about this space. >> Your goals for this year is what? What's your goals? >> Our goals for this year was to put Machine Learning capabilities in the hands of all developers of all skill levels. I think we have done pretty well so far I think. >> Well, congratulations Swami here on theCUBE. Vice president of Machine Learning and a lot more, all those applications that were announced Wednesday along with the Deep Leaning and the AI and the DeepLens all part of his innovative team here at Amazon. Changing the game is theCUBE doing our part bringing data to you, video and more coverage. Go to Siliconangle.com for all the stories, Wikibon.com for research and of course theCUBE.net. I'm John Furrier and Stu Miniman. Thanks for watching, we'll be right back.

(upbeat music) >> Man: From the Computer History Museum in the heart of Silicon Valley, it's theCUBE! Covering Food IT. Fork to farm. Brought to you by Western Digital. >> Welcome back everybody, Jeff Frick here with theCUBE. We're in Mountain View, California at the Computer History Museum at Food IT, a really interesting conference about 350 people talking about the impacts of IT and technology in the agricultural space. Everything from farming, through to how you shop, how you consume, and what happens to the waste that we all, unfortunately, throw away way too much. We're excited to have our next guest, Mike Wolf, he's the creator and curator of The Spoon and the Smart Kitchen Summit. Mike, welcome! >> Hey, thanks for having me, I'm excited! >> Absolutely! So first off, before we jump in, what do you think of the show here? >> It's great! It's very focused on agriculture and the food chain, which is crucial. I focus a lot on the kitchen, when food gets to our homes, what we do with it, but this is where it all starts, so it's really important. >> It's so much stuff going on-- >> Yeah. >> With the kitchen and food preparation with all these services that will-- >> Yeah. >> Either bring you your meal, or they'll bring you pre-portioned and uncooked meals. So let's talk about a little bit, what is the Smart Kitchen Summit, and what is The Spoon? >> So I focused on the smart home a lot over my career. I've written a book on how to network your home, but about four or five years ago I noticed no one's really talking about how we're going to recreate the kitchen. We've focused from a digital home perspective on the living room. We saw the Netflix revolution, over-the-top, we've seen huge market value creation in the living room. But the kitchen was kind of left behind. So I said, let's start a conversation, let's focus on how we can recreate cooking in the kitchen. And the Smart Kitchen Summit is entering it's third year, it's kind of become the premier event about how technology will reshape how we get food, bringing her home, how we cook it, and how we eat it. >> Well it's funny though, because people would always say, you know, "I have the iPad on the front of my fridge, "it'll tell me when it's time to go get milk." So clearly, that's a pretty-- >> Yeah. >> Pretty low... Not of real significant use in this case, I would imagine, there's a lot more to it than that. >> Yeah, I think tablets and screens, and connecting to things with apps is like five percent of what's interesting. If you look at the refrigerator, the internet refrigerator, I was just talking to an LG guy, they created the first internet refrigerator in 2000, and it was $20,000, and no one bought it, 'cause everyone said "Why would I want to "connect my refrigerator "to the internet?" >> Right, right. >> Well, I kind of think we're at this point where now it becomes interesting. We can maybe have the fridge understand what our food is. The fridge itself is kind of a... The family bulletin board, so why not put a big screen on there if it's only a couple extra hundred dollars? >> Right. >> And so I think there's all sorts of ways in which we're getting food, like you said, new ways like Blue Apron, Cooking By Numbers services, new ways to cook food that are coming from the professional kitchen, like sous vide, high-precision cooking technology that's democratized for technology, and things like automated beer brewing appliances. I've always wanted a beer, brew beer, but my wife said "No way, you're going to have "the smelly..." >> Right. >> "Beer coming in my house." But I can use technology to make this automated and easy? I'm one of those guys that say "Let's do that." Then I can brag to my friends that I've actually made beer at home. >> Right, right. >> So. >> Well, it's funny 'cause we saw this other thing in the kitchen not that long ago, right? Where everybody had to have a Wolf, and it was kind of this, you know, kind of professionalize your kitchen with all these really heavy-duty, you know... >> Yeah. >> Appliances, that really, most people probably don't need a Wolf so they can keep their flambe at the perfect temperature-- >> Yeah. >> For extended periods of time. >> Yeah. >> So what are some of these things that are coming down the line that people haven't really thought of that you see as you study this phase? >> Well, so our research shows that everyone, almost every age group is using more digital technology in the kitchen, and that's iPhones, smart phones, and tablets, because what they're doing is looking for what they're going to have for dinner. So that starts the process of digitization in the kitchen, and so you've seen almost for 15, into 17, years now services like Allrecipes and Yummly creating kind of this digital recipe services. Now, we've also seen, really one of the most popular videos on the internet, BuzzFeed Tasty was the biggest video publisher for many months this year, doing a couple billion views a year, per month of these simple cooking videos. So... >> Right. >> A lot of it is very much generational. So millennials are grabbing on to these how-to-cook, you know, how-to-cook videos. They're very interested in cooking, but the definition of cooking is changing, so what they're seeing is the worrying about cooking through online, but also maybe applying cooking technology in a new way. Whether that's a very simple cooking appliance, like a sous vide circulator, or maybe an air fryer, or if you want to go high-end something, like a June Oven. So if you look forward, starting to add artificial intelligence, image recognition, and these type of technologies to the cooking process could make things a lot easier and make things faster, and kind of give you cooking super powers that you may otherwise not have. >> Right. It's so interesting! It continues to be a trend over and over, that it's kind of the hollowing of the middle, right? You are either you don't ever cook, right? >> Yeah. >> Everything is DoorDash, or however you get your... The meal. Or you kind of get to these specialty items where you're way into it as a hobby and, I mean, those videos, the cooking videos-- >> Yeah. >> Are fascinating to me, the popularity of those things. >> Yeah. >> But if you're kind of stuck in the middle, in the no-man's-land of what we think of maybe as a traditional kitchen, that's probably not a great place to be. >> Yeah, I think, you know, I'm that... I'm a different archetype depending on the day of the week, right? I may be in the middle of the week, and I'm tired, I have kids, I don't want to cook. Maybe something that automates my cooking maybe makes it easy with food delivery, it's fully cooked. That would be a great idea! But maybe on the weekend, I want to become, like, a maker, and really, like I say, the only maker space in the home, right now, besides the garage, is the kitchen. It's where I'm actually using my hands to make stuff. And I think that's great nowadays when we're all spending so much time in front of screens, moving around ones and zeros with our mouses, I think... Our research shows that people want to cook, but the definition of cooking is changing. So they may be assembling salads, or, and they're buying something from Costco and they're calling that cooking. But I think if we can have technology that allows us to actually make stuff in the home, where it's fresh and tastes good, it's healthy, and we feel like we're rewarding a craft, I think there's a lot of people who would want that. >> That's so interesting, that it's makers and craftsmanship, and you think back to kind of the traditional, beautiful cookbooks, right? That people would buy, maybe to actually use, maybe just 'cause they want to be associated with that type of activity and those types of photographs and stuff. So it's a very different way to think about it, as a maker versus, you know, just got to get the food out for the kids, I'm tired on a Thursday night at 6 p.m. >> Yeah, sometimes it's just sustenance, right? That's why packaged food is great. We like these protein bars. They're expensive, but they provide everything in one in, like, a flat piece of food. But at the same time, there's a whole food movement. Ever since John Mackey founded Whole Foods back in the early 80's, until the time that Amazon acquired it, the customer base has been growing. What I think is interesting is we can potentially see the democratization of better quality food. As you see, the decentralization of processed food, right? So over the past 100 to 200 years, all the technology around food has been towards centralized processing, and putting it into cans, making it... But what happens is you take all the nutritional value out of it. >> Right. >> But if you can start to think about bringing fresher food in the home, at a lower cost through optimized value chains, like what maybe Amazon can do with Whole Foods. Maybe that brings fresher food to the home at a lower cost, or it gets beyond the five to ten percent of the consumer, which is buying from Whole Foods. >> Right. >> It's a high-end type of retail channel, right? But I think everyone wants better food, so I think that's where I think technology could play a process. >> Well, just specifically, what are you thoughts on the Amazon acquisition of Whole Foods, and the impact of that? Not only for those two companies, specifically, but as a broader impact within the industry? >> I am excited for what Amazon could do with this technology. I live in Seattle, so I've been watching they're, what I would call lab experiments with Amazon Go, which is this recreation of the grocery store, this idea of walk in, walk out, don't ever talk to the cashier, that's really fascinating. Then you get Whole Foods, which is a pretty traditional retailer, even though it's kind of created the organic food movement in a lot of ways. I think bringing Amazon technology into theirs is really exciting, but I also think it validates the need for physical store fronts. I think Amazon's been trying to do online delivery, rolling trucks at your home for ten years. They've been working on Amazon for us for ten years, and they haven't been really... They haven't really reached massive scale. So I think this validates the idea of you need physical store fronts. Those physical store fronts may look very different in ten years, but the fact that Amazon is going to need that as a distribution point, as a point of presence in different neighborhoods, I think is fascinating. >> Alright, well, Mike we're almost out of time. I'll give you the last word. Where should people go to get more information about what you're up to? >> Yeah, go to TheSpoon.tech if you want to see our writing, podcast, and the future of food and cooking. And if you want to come to our event, go to SmartKitchenSummit.com. >> Alright, he's Mike Wolf, I'm Jeff Frick, you're watching theCUBE from Food IT. A lot of really interesting stuff. Again, it's all the way from the farm, the germination of the seeds, all the way through to what you eat, how you eat, and what you do with the stuff you don't. So thanks a lot Mike. >> Yeah, thanks! >> Alright, I'm Jeff Frick, you're watching theCUBE. We'll be right back after this short break. Thanks for watching. (upbeat music)