(gentle melody) >> Welcome back to theCUBE's live coverage of AWS Summit in San Francisco, California. Events are back. AWS Summit in New York City this summer, theCUBE will be there as well. Check us out there. I'm glad to have events back. It's great to have of everyone here. I'm John Furrier, host of theCUBE. Dr. Matt Wood is with me, CUBE alumni, now VP of Business Analytics Division of AWS. Matt, great to see you. >> Thank you, John. It's great to be here. I appreciate it. >> I always call you Dr. Matt Wood because Andy Jackson always says, "Dr. Matt, we would introduce you on the arena." (Matt laughs) >> Matt: The one and only. >> The one and only, Dr. Matt Wood. >> In joke, I love it. (laughs) >> Andy style. (Matt laughs) I think you had walk up music too. >> Yes, we all have our own personalized walk up music. >> So talk about your new role, not a new role, but you're running the analytics business for AWS. What does that consist of right now? >> Sure. So I work. I've got what I consider to be one of the best jobs in the world. I get to work with our customers and the teams at AWS to build the analytics services that millions of our customers use to slice dice, pivot, better understand their data, look at how they can use that data for reporting, looking backwards. And also look at how they can use that data looking forward, so predictive analytics and machine learning. So whether it is slicing and dicing in the lower level of Hadoop and the big data engines, or whether you're doing ETL with Glue, or whether you're visualizing the data in QuickSight or building your models in SageMaker. I got my fingers in a lot of pies. >> One of the benefits of having CUBE coverage with AWS since 2013 is watching the progression. You were on theCUBE that first year we were at Reinvent in 2013, and look at how machine learning just exploded onto the scene. You were involved in that from day one. It's still day one, as you guys say. What's the big thing now? Look at just what happened. Machine learning comes in and then a slew of services come in. You've got SageMaker, became a hot seller right out of the gate. The database stuff was kicking butt. So all this is now booming. That was a real generational change over for database. What's the perspective? What's your perspective on that's evolved? >> I think it's a really good point. I totally agree. I think for machine learning, there's sort of a Renaissance in machine learning and the application of machine learning. Machine learning as a technology has been around for 50 years, let's say. But to do machine learning right, you need like a lot of data. The data needs to be high quality. You need a lot of compute to be able to train those models and you have to be able to evaluate what those models mean as you apply them to real world problems. And so the cloud really removed a lot of the constraints. Finally, customers had all of the data that they needed. We gave them services to be able to label that data in a high quality way. There's all the compute you need to be able to train the models. And so where you go? And so the cloud really enabled this Renaissance with machine learning. And we're seeing honestly a similar Renaissance with data and analytics. If you look back five to ten years, analytics was something you did in batch, your data warehouse ran an analysis to do reconciliation at the end of the month, and that was it. (John laughs) And so that's when you needed it. But today, if your Redshift cluster isn't available, Uber drivers don't turn up, DoorDash deliveries don't get made. Analytics is now central to virtually every business, and it is central to virtually every business's digital transformation. And being able to take that data from a variety of sources, be able to query it with high performance, to be able to actually then start to augment that data with real information, which usually comes from technical experts and domain experts to form wisdom and information from raw data. That's kind of what most organizations are trying to do when they kind of go through this analytics journey. >> It's interesting. Dave Velanta and I always talk on theCUBE about the future. And you look back, the things we're talking about six years ago are actually happening now. And it's not hyped up statement to say digital transformation is actually happening now. And there's also times when we bang our fists on the table saying, say, "I really think this is so important." And David says, "John, you're going to die on that hill." (Matt laughs) And so I'm excited that this year, for the first time, I didn't die on that hill. I've been saying- >> Do all right. >> Data as code is the next infrastructure as code. And Dave's like, "What do you mean by that?" We're talking about how data gets... And it's happening. So we just had an event on our AWS startups.com site, a showcase for startups, and the theme was data as code. And interesting new trends emerging really clearly, the role of a data engineer, right? Like an SRE, what an SRE did for cloud, you have a new data engineering role because of the developer onboarding is massively increasing, exponentially, new developers. Data science scientists are growing, but the pipelining and managing and engineering as a system, almost like an operating system. >> Kind of as a discipline. >> So what's your reaction to that about this data engineer, data as code? Because if you have horizontally scalable data, you've got to be open, that's hard (laughs), okay? And you got to silo the data that needs to be siloed for compliance and reason. So that's a big policy around that. So what's your reaction to data's code and the data engineering phenomenon? >> It's a really good point. I think with any technology project inside of an organization, success with analytics or machine learning, it's kind of 50% technology and then 50% cultural. And you have often domain experts. Those could be physicians or drug design experts, or they could be financial experts or whoever they might be, got deep domain expertise, and then you've got technical implementation teams. And there's kind of a natural, often repulsive force. I don't mean that rudely, but they just don't talk the same language. And so the more complex a domain and the more complex the technology, the stronger their repulsive force. And it can become very difficult for domain experts to work closely with the technical experts to be able to actually get business decisions made. And so what data engineering does and data engineering is, in some cases a team, or it can be a role that you play. It's really allowing those two disciplines to speak the same language. You can think of it as plumbing, but I think of it as like a bridge. It's a bridge between the technical implementation and the domain experts, and that requires a very disparate range of skills. You've got to understand about statistics, you've got to understand about the implementation, you got to understand about the data, you got to understand about the domain. And if you can put all of that together, that data engineering discipline can be incredibly transformative for an organization because it builds the bridge between those two groups. >> I was advising some young computer science students at the sophomore, junior level just a couple of weeks ago, and I told them I would ask someone at Amazon this question. So I'll ask you, >> Matt: Okay. since you've been in the middle of it for years, they were asking me, and I was trying to mentor them on how do you become a data engineer, from a practical standpoint? Courseware, projects to work on, how to think, not just coding Python, because everyone's coding in Python, but what else can they do? So I was trying to help them. I didn't really know the answer myself. I was just trying to kind of help figure it out with them. So what is the answer, in your opinion, or the thoughts around advice to young students who want to be data engineers? Because data scientists is pretty clear on what that is. You use tools, you make visualizations, you manage data, you get answers and insights and then apply that to the business. That's an application. That's not the standing up a stack or managing the infrastructure. So what does that coding look like? What would your advice be to folks getting into a data engineering role? >> Yeah, I think if you believe this, what I said earlier about 50% technology, 50 % culture, the number one technology to learn as a data engineer is the tools in the cloud which allow you to aggregate data from virtually any source into something which is incrementally more valuable for the organization. That's really what data engineering is all about. It's about taking from multiple sources. Some people call them silos, but silos indicates that the storage is kind of fungible or undifferentiated. That's really not the case. Success requires you to have really purpose built, well crafted, high performance, low cost engines for all of your data. So understanding those tools and understanding how to use them, that's probably the most important technical piece. Python and programming and statistics go along with that, I think. And then the most important cultural part, I think is... It's just curiosity. You want to be able to, as a data engineer, you want to have a natural curiosity that drives you to seek the truth inside an organization, seek the truth of a particular problem, and to be able to engage because probably you're going to some choice as you go through your career about which domain you end up in. Maybe you're really passionate about healthcare, or you're really just passionate about transportation or media, whatever it might be. And you can allow that to drive a certain amount of curiosity. But within those roles, the domains are so broad you kind of got to allow your curiosity to develop and lead you to ask the right questions and engage in the right way with your teams, because you can have all the technical skills in the world. But if you're not able to help the team's truth seek through that curiosity, you simply won't be successful. >> We just had a guest, 20 year old founder, Johnny Dallas who was 16 when he worked at Amazon. Youngest engineer- >> Johnny Dallas is a great name, by the way. (both chuckle) >> It's his real name. It sounds like a football player. >> That's awesome. >> Rock star. Johnny CUBE, it's me. But he's young and he was saying... His advice was just do projects. >> Matt: And get hands on. Yeah. >> And I was saying, hey, I came from the old days where you get to stand stuff up and you hugged on for the assets because you didn't want to kill the project because you spent all this money. And he's like, yeah, with cloud you can shut it down. If you do a project that's not working and you get bad data no one's adopting it or you don't like it anymore, you shut it down, just something else. >> Yeah, totally. >> Instantly abandon it and move on to something new. That's a progression. >> Totally! The blast radius of decisions is just way reduced. We talk a lot about in the old world, trying to find the resources and get the funding is like, all right, I want to try out this kind of random idea that could be a big deal for the organization. I need $50 million and a new data center. You're not going to get anywhere. >> And you do a proposal, working backwards, documents all kinds of stuff. >> All that sort of stuff. >> Jump your hoops. >> So all of that is gone. But we sometimes forget that a big part of that is just the prototyping and the experimentation and the limited blast radius in terms of cost, and honestly, the most important thing is time, just being able to jump in there, fingers on keyboards, just try this stuff out. And that's why at AWS, we have... Part of the reason we have so many services, because we want, when you get into AWS, we want the whole toolbox to be available to every developer. And so as your ideas develop, you may want to jump from data that you have that's already in a database to doing realtime data. And then you have the tools there. And when you want to get into real time data, you don't just have kinesis, you have real time analytics, and you can run SQL against that data. The capabilities and the breadth really matter when it comes to prototyping. >> That's the culture piece, because what was once a dysfunctional behavior. I'm going to go off the reservation and try something behind my boss' back, now is a side hustle or fun project. So for fun, you can just code something. >> Yeah, totally. I remember my first Hadoop projects. I found almost literally a decommissioned set of servers in the data center that no one was using. They were super old. They're about to be literally turned off. And I managed to convince the team to leave them on for me for another month. And I installed Hadoop on them and got them going. That just seems crazy to me now that I had to go and convince anybody not to turn these servers off. But what it was like when you- >> That's when you came up with Elastic MapReduce because you said this is too hard, we got to make it easier. >> Basically yes. (John laughs) I was installing Hadoop version Beta 9.9 or whatever. It was like, this is really hard. >> We got to make it simpler. All right, good stuff. I love the walk down memory Lane. And also your advice. Great stuff. I think culture is huge. That's why I like Adam's keynote at Reinvent, Adam Selipsky talk about Pathfinders and trailblazers, because that's a blast radius impact when you can actually have innovation organically just come from anywhere. That's totally cool. >> Matt: Totally cool. >> All right, let's get into the product. Serverless has been hot. We hear a lot of EKS is hot. Containers are booming. Kubernetes is getting adopted, still a lot of work to do there. Cloud native developers are booming. Serverless, Lambda. How does that impact the analytics piece? Can you share the hot products around how that translates? >> Absolutely, yeah. >> Aurora, SageMaker. >> Yeah, I think it's... If you look at kind of the evolution and what customers are asking for, they don't just want low cost. They don't just want this broad set of services. They don't just want those services to have deep capabilities. They want those services to have as low an operating cost over time as possible. So we kind of really got it down. We got built a lot of muscle, a lot of services about getting up and running and experimenting and prototyping and turning things off and turning them on and turning them off. And that's all great. But actually, you really only in most projects start something once and then stop something once, and maybe there's an hour in between or maybe there's a year. But the real expense in terms of time and operations and complexity is sometimes in that running cost. And so we've heard very loudly and clearly from customers that running cost is just undifferentiated to them. And they want to spend more time on their work. And in analytics, that is slicing the data, pivoting the data, combining the data, labeling the data, training their models, running inference against their models, and less time doing the operational pieces. >> Is that why the service focuses there? >> Yeah, absolutely. It dramatically reduces the skill required to run these workloads of any scale. And it dramatically reduces the undifferentiated heavy lifting because you get to focus more of the time that you would have spent on the operations on the actual work that you want to get done. And so if you look at something just like Redshift Serverless, that we launched a Reinvent, we have a lot of customers that want to run the cluster, and they want to get into the weeds where there is benefit. We have a lot of customers that say there's no benefit for me, I just want to do the analytics. So you run the operational piece, you're the experts. We run 60 million instant startups every single day. We do this a lot. >> John: Exactly. We understand the operations- >> I just want the answers. Come on. >> So just give me the answers or just give me the notebook or just give me the inference prediction. Today, for example, we announced Serverless Inference. So now once you've trained your machine learning model, just run a few lines of code or you just click a few buttons and then you got an inference endpoint that you do not have to manage. And whether you're doing one query against that end point per hour or you're doing 10 million, we'll just scale it on the back end. I know we got not a lot of time left, but I want to get your reaction on this. One of the things about the data lakes not being data swamps has been, from what I've been reporting and hearing from customers, is that they want to retrain their machine learning algorithm. They need that data, they need the real time data, and they need the time series data. Even though the time has passed, they got to store in the data lake. So now the data lake's main function is being reusing the data to actually retrain. It works properly. So a lot of post mortems turn into actually business improvements to make the machine learnings smarter, faster. Do you see that same way? Do you see it the same way? >> Yeah, I think it's really interesting >> Or is that just... >> No, I think it's totally interesting because it's convenient to kind of think of analytics as a very clear progression from point A to point B. But really, you're navigating terrain for which you do not have a map, and you need a lot of help to navigate that terrain. And so having these services in place, not having to run the operations of those services, being able to have those services be secure and well governed. And we added PII detection today. It's something you can do automatically, to be able to use any unstructured data, run queries against that unstructured data. So today we added text queries. So you can just say, well, you can scan a badge, for example, and say, well, what's the name on this badge? And you don't have to identify where it is. We'll do all of that work for you. It's more like a branch than it is just a normal A to B path, a linear path. And that includes loops backwards. And sometimes you've got to get the results and use those to make improvements further upstream. And sometimes you've got to use those... And when you're downstream, it will be like, "Ah, I remember that." And you come back and bring it all together. >> Awesome. >> So it's a wonderful world for sure. >> Dr. Matt, we're here in theCUBE. Just take the last word and give the update while you're here what's the big news happening that you're announcing here at Summit in San Francisco, California, and update on the business analytics group. >> Yeah, we did a lot of announcements in the keynote. I encourage everyone to take a look at, that this morning with Swami. One of the ones I'm most excited about is the opportunity to be able to take dashboards, visualizations. We're all used to using these things. We see them in our business intelligence tools, all over the place. However, what we've heard from customers is like, yes, I want those analytics, I want that visualization, I want it to be up to date, but I don't actually want to have to go from my tools where I'm actually doing my work to another separate tool to be able to look at that information. And so today we announced 1-click public embedding for QuickSight dashboard. So today you can literally as easily as embedding a YouTube video, you can take a dashboard that you've built inside QuickSight, cut and paste the HTML, paste it into your application and that's it. That's what you have to do. It takes seconds. >> And it gets updated in real time. >> Updated in real time. It's interactive. You can do everything that you would normally do. You can brand it, there's no power by QuickSight button or anything like that. You can change the colors, fit in perfectly with your application. So that's an incredibly powerful way of being able to take an analytics capability that today sits inside its own little fiefdom and put it just everywhere. Very transformative. >> Awesome. And the business is going well. You got the Serverless detail win for you there. Good stuff. Dr. Matt Wood, thank you for coming on theCUBE. >> Anytime. Thank you. >> Okay, this is theCUBE's coverage of AWS Summit 2022 in San Francisco, California. I'm John Furrier, host of theCUBE. Stay with us for more coverage of day two after this short break. (gentle music)

>> Live from Las Vegas, it's theCUBE covering AWS re:Invent 2018, brought to you by Amazon Web Services, Intel, and their ecosystem partners. >> And welcome back to Las Vegas. We are live here at AWS re:Invent along with Justin Moore and I'm John Wallis. I know when you travel these days, all you want is, you want it to work, right? >> Yeah. >> We just want to get there. Well, I'll tell you what, Phil Wood from EasyJet wants you to get there as well. As does Matt Leonard from CenturyLink. Gentlemen, glad to have you with us. >> Thanks very much. >> We appreciate it. >> EasyJet, a European-based carrier just north of London, so we're talking about air travel. You are, as we've just recently learned, you are a Catalyst Award winner from CenturyLink, there's a reason for that and that's a point of distinction. So Matt, if you would maybe take us through a little bit about what EasyJet did to earn that distinction. >> Sure, the Catalyst Award is an award that we give out in combination with VMware to kind of highlight customers that are doing new and exciting things with regard to digital transformation. We've been a provider of services and a partner with EasyJet for a long time and they've done some really cool things with regard to services they provide their end customers. And we play a very very small part of that. Two exciting things that are my personal favorites with regard to EasyJet is the Look and Book service. So within the application if you want to book a new trip you normally have to type in the airport that you want to go to, and you have to figure out what's the name of the airport, or the three-digit code. With the EasyJet application you can upload a picture and it has intelligence that's used to figure out that picture and what that landmark is and then what the nearest airport is. So that's pretty exciting. And the second exciting thing within the application >> is a trip in one tap. So you can basically justdial in how much money you want to spend for a trip, hit the Go button, in literally one tap it'll recommend a city, a hotel, and a fun and exciting thing that's happening during that duration of time. So for last minute travelers, my family's certainly one of those, we got a free period of time, one tap it'll tell you where to stay, how to get there with EasyJet and then what's exciting happening within that city. >> So I could put in, I say, I want to spend 300 dollars a ticket, and tap boom, and it'll say you can go to Brussels, you can go to Amsterdam but you can't make it to Dublin this weekend, right? Or whatever. I love that. So what has that done for your business in terms of, on a micro level and a macro level, what's it doing in terms of that interface and what's it mean to your business in general? >> As a business, we're 23 years old, so we started very much like a startup and we kind of came in at low-cost airline bracket. But now what we're renowned for is the convenience, and you've got two examples there where our customers love that because it's a convenient way. They don't have to do lots of searching, they can just take the photograph and they know exactly where they're going to go. And that's really what differentiates us is that convenience and the customer experience that we offer to all of our customers. We have a lot of customers. We have 90 million passengers a year. They come to us because they know not just that we give great value but that experience. So what it's done, it's made us grow. And that's literally how we continue to grow is to expand those customer services and Centurylink have been a part of that journey for over half of our tenure as an airline. >> It sounds like technology is actually right on the edge of driving that value for customers and making things easy. Like just the experience of being able to walk out and take a photo of something and say, I want to go here. I would like to go out and see if I can trick it by taking a photo of the Eiffel Tower out in the back here. >> We'll go and try it out in a bit. >> I'm confident. >> We'll see how it goes. That's making use of a whole bunch of technologies. It's got mobile technology in there, it's got image recognition, it's got machine learning. What else are you seeing at the show here at AWS, what are some of the technologies that you think will drive the next evolution of things, what's going to win you the next award? >> I think one of the things I've really been looking at is around data and around the personalization. So we talked about customer experience but our whole journey of taking a plane, taking a holiday, for example, it's from the moment you book it to the moment you get back. There's so many touch points during that and there's so much data that we can take from that. So I've been really interested in looking at how different organizations and how Amazon have been using data. I also think you can't come to a show like this without looking at machine learning and AI. We're using aspects of that in how we analyze our data, but that's certainly something I think's going to change the airline industry moving forward. >> How important is a partnership with someone like CenturyLink in making sure that you get the best use of these technologies? >> Matt talked about that they have a small part to play but you've got to understand that every single customer, every single search on our website goes through a network. In order for us to connect to our customers, be they booking a flight, be they on a flight, we've got to go through a reliable network. And the way I describe it, it needs to be effortless. It needs to just work. You mentioned that right at the beginning. But I also think as well for us to exploit technologies like the cloud, which is what we're starting to invest a lot more into, we need a partner who can help us on that journey. So again, that's where CenturyLink and the partnership we've got has been absolutely crucial. The things that we're doing with CenturyLink around making sure that we're only paying for our network for what we use. We're an airline. Our airports are seasonal so kind of traditional networks, what you'll end up doing is paying for bandwidth all year, when in the winter seasons if you're not flying there that's dead money. So it's simple things like that but that makes a huge difference towards my cost base perspective. >> And time of day, I assume that affects that as well? >> Absolutely. I mean, clearly in our summer periods we fly a lot, so time of day during the summer, there's not that many hours we don't fly. >> You get a lot of daylight over there, right? (laughter) >> But certainly in winter where we have our kind of summer destinations, it makes a big big difference. And that's cost we pass on to the customer as well which is massively important. >> What is it about the customer that you don't know? You talked about AI, what that could do for you down the road. How much information, how much data do you think you can extract from the customer to make that experience even better, and what do you need to know about them, and how will CenturyLink help you get there? >> You need to know everything. I mean, we know that we sell a hundred seventy million bacon sandwiches a year. Whether that's useful or not, but we know that. >> There's hungry people. >> That's a lot of bacon. >> It is a lot. But it means that we know the type of food that our customers want to eat, we know the top destinations, even knowing how long between booking a flight and actually flying. So we know from a price perspective and from a making sure our planes are full or making sure we're not overselling our flights. All of that information, there's just a wealth of data that you're getting out there. And it's not just customers. One of the big factors for us is safety. So we use our data now to analyze maintenance. So we have predicted maintenance around when's the right time to put in spare parts but also what's the most efficient time so that we're not disrupting the customer. So actually we may want to bring a maintenance cycle sooner so we can open up more routes for customers to fly when they want to. So it's very hard to answer that question cause every day we're coming up with new ideas or new bits of information that at the time we never thought we needed to know but that actually turns out to be an absolutely crucial part of our offer. >> That's not an unusual thing for most people in a world where there's this much dynamic, this much change going on. So what process do you run through to figure out, where should we be looking to find out the next set of optimizations? Or how do you discover what is the next thing that you should work on, like where does the idea for, maybe we should build this app. Where does that come from? >> I don't think there's one model. I think what's always been at the heart of EasyJet is innovation, and we've always focused on the customer. So we have a great loyalty scheme and our customers are very loyal. We have 75% loyalty with our customers which is phenomenal. We get a lot of feedback and that feedback drives a lot of the ideas that we push forward. So I think it's a mixture of our passion, it's a mixture of our experience, but I'd say that feedback from the customer, that drives a lot of the ideas that we do moving forward. >> From the CenturyLink perspective, you received certification for the MSP designation. >> Yup. >> Working in the travel business, what does that do, or how does that MSP certification translate over to learning about a different industry, to applying different approaches, unique approaches, because it's not one size fits all. They have very, very specific challenges that you're trying to address. >> Yeah, so on a broader sense, our mission with clients like EasyJet and customers interested in the cloud is really to connect, migrate, and then manage their workloads within the cloud. That's really what we're focused on. And there's certainly commonalities within verticals but every customer's different, and really assessing, starting with the customer, and that's a common thing that I think both EasyJet as well as CenturyLink and certainly Amazon have in common, really focused on that customer journey. One of the approaches that we take through a program called CustomerLink is put the customer right in the center of the team and we've applied the Agile methodology to that customer engagement process. So we do a standup meeting once every two weeks, we do sprints once every two weeks. A lot of our customers are part of that board that we use to activate the sprint and to define priorities and what actions are. So really pulling the whole team together across different departments, focusing on the customer first, and in many cases the customer's customer first cause a lot of your priorities are based on what your customers are after, and really making sure that we're working on the right activity in a very lean way, pulling away as much waste as possible that aren't contributing to adding value to the customer journey. >> And then from your side of the fence going forward, you've mentioned four or five general areas, you've said, we could improve here, we could look at this, we could look at that. How do you prioritize and say, okay, let's focus here now and then we'll move on. So if you had to focus now, or for the next twelve months, what would that be on? >> So we've actually just relaunched our strategy. At the heart we are an airline so our priority is about being number one or number two in all the primary airports. We've got to keep that. But we also recognize from the data that the amount of our customers who will book hotels or book further products through some of our partners that's something that we can actually capitalize on. So we're looking more into holidays now. Taking that customer centricity, and how do we make the end-to-end journey for our customers so including travel to and from airport and the whole day. So that's a priority for us. Continue building our customer loyalty. So as much as we pride ourselves on loyalty, we believe there's a lot more you can do. I think the airline loyalty schemes need to be shaken up a little bit more. If you look in the retail sector or things like that they're focusing on different things. It's no longer just the case of air miles. People want speedier boarding, or they want a better experience, better seating arrangements. So we're looking at our loyalty. And then also business. We talk about, we've got really good slots for when we fly planes. And they're slots that are competitive to a business traveler. So that's our three main areas, I would say, are business, holidays, and loyalty. >> Matt, you're going to be in business for a while. I think you're okay. If you could work on legroom, I'm sold. Matt and Phil, thank you for being with us. We appreciate the time. Join us here on theCUBE. You're watching our live coverage from Las Vegas at AWS re:Invent. (electronic music)

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

>> Announcer: Live from the Moscone Center it's theCUBE, covering AWS Summit San Francisco 2018. Brought to you by Amazon Web Services. >> Hello everyone, welcome to theCUBE here in San Francisco at Moscone West, theCUBE's exclusive coverage of Amazon Web Services Summit 2018. It's the first of their kickoff of their little satellite events, really about developers and training and educating people on Amazon Web Services products and services. Again theCUBE covers re:Invent, that's their big show, This is more of a, less of a sales and marketing but more of a really get down and dirty with the developers and practitioners. I'm John Furrier, with my cohost this week Stu Miniman all day today, wall to wall coverage. Stu, the keynotes just kicked off, Andy Jassy is not here, notable. Werner Vogels does all the summits so he's always been the headline. Last year Andy Jassy kind of did the keynote, fireside chat, we had that up on our YouTube channel, SiliconANGLE theCUBE, but here the story is all about SageMaker and the continued dominance of Amazon Web Services, and then again as we were speculating at re:Invent, and we've been saying on theCUBE, the maturization of Amazon Web Services is clear. Everyone knows the numbers, they're breaking out the reporting, they clearly got competitive forces for the first time in AWS's history, they have some serious competition upping their game. Microsoft nipping at their heels, Google putting out some open source tech, Oracle trying to throw FUD into the fire and say, change the rules and kind of keep the rules on their terms, so the competitive pressure. But at the end of the day there's a whole new era of modern software development, modern business applications and we're seeing it with things like cloud expansion, on-premise consolidation, hybrid-cloud, multi-cloud, decentralized infrastructure, blockchain AI, these are the themes, this is what developers want, this is what businesses are doing, let's analyze and discuss the keynotes. What's your thoughts? >> Yeah, so John, I mean, first of all, we watched the rolling thunder that is AWS just rolling through the entire industry, and now rolling all over the globe. So the AWS Summit, I think they actually had an idea about Summit in Singapore like, last night, and we're going to be covering a few of them. I was last year at the AWS New York City Summit, and I tell you, that New York City show alone was one of the best shows I went to all year. The amount of people, the excitement, what really differentiates as you said, the big re:Invent versus the summit, first of all, the summit, they tend to be a local audience, it's free for basically everybody to come in. So numbers are great, you know, we're in San Francisco, they going to 10, 15 thousand people here probably. Google Cloud Next was here last year in February and it feels almost the same amount of people here for a regional Amazon show. So the numbers are wow, the announcements, every day Amazon's running an announcement, so Doctor Werner Vogels, Doctor Matt Wood, get up stage, go through some of the usual we're dominating every industry and every service and everything there, but when you piece apart there's like, ooh, there's real announcements that are coming, things that jumped out, we talked about machine learning, Matt Wood talked about SageMaker is really growing super fast, people that I talked to that have been using it are loving it. They came out with SageMaker local, which means that I can develop it on my laptop and do it with that cool, you take ML with that cool, what was it, that deep lens, that they've got. It's how do I get these environments? Amazon isn't just about infrastructure cloud anymore, They've gone to paths, they're pushing to edge, they're doing all of these things. They had a whole ton of announcements, when they were already past the time that the keynotes were going to be done, Oh, you thought we're done, well security, security, security and secrets manager, firewall manager, there's so many services. The theme I've been looking at the past couple years, how do we keep up with all of this, even internally? You talk to Amazon people, they don't know everything that everyone's doing, because it's all those two pizza teams and how they're growing. >> And they always have to get all their sound bites in because they don't have a lot of time to get all that packed into one powerful punch. Just on a quick side note for the folks that watching knows theCUBE, we've been covering Amazon really since the beginning, since the re:Invent started, you know we've been covering data center infrastructure and big data, Hadoop and now beyond. You're starting to see coverage around blockchain and cryptocurrency. So again, we are expanding our coverage of the AWS ecosystem and cloud to include most of the major regional shows of AWS Summit, continuing to go deep into the AWS re:Invent and the community, we are also initiating coverage heavily on Google, Google Cloud Next, we'll be at their show and soon to be at Microsoft show, that's still to be determined with Microsoft that they will let us in, we're working on that, we think that's going to be good, but we'll be nailing and doubling down on the cloud coverage. So Stu, with that as a backdrop, people know we've been deep with Amazon, I've been called an Amazon fanboy many times, but the numbers are clear. I'm a Google fanboy, by the way, too, I love Google stuff. Microsoft I got to learn more about, obviously they have bundling and Office so they're a legacy player, Oracle a legacy player, so you got two legacy players, you got Amazon and Google, I would put them in two different categories, but then Alibaba in China trying to dip in as you got those, the real kind of cloud native companies, Google and Amazon on one end, you get the legacy players with Microsoft and Oracle and IBM on the other. So you have this really highly competitive environment. We're seeing for the first, or second time, Andy Jassy did it at re:Invent, but Werner Vogels put up the competitive slide. He said "This is what we're doing." And he showed the number of services that Amazon offers, vis a vis the competition, and he didn't actually call out the vendors but we kind of know, I put on my Twitter feed, you can see his number one, the second one's Microsoft. Google they put in the Google colors, that's obviously Google, and red is Oracle. Amazon is clearly dominating on the number of services available across the cloud. So when we've been squinting through the numbers on who's leading who, you've really got look at two perspectives. The broad range of available services and the number of customers using those services versus point solutions that might be one instance of the cloud. This is a new architecture, it's not the old waterfall model it's not the old six months to provision into it, mentioned that. This is like a highly competitive environment. So Stu I've got to ask you, how do you squint through that and look at the competition that Amazon has, obviously the numbers aren't great. But how should customers look at the competition, how are you looking at it, how is our team evaluating the competition? >> Well first of all John, it is not a zero sum game and it is very nuanced and complicated. And for most customers it's not a solution, it's many solutions and it's something that Amazon doesn't love, is that you talk about things like multi-cloud and they would say "Well, we have the "best service everywhere and we're the cheapest everywhere "and everyone's all in on us," well, when you get down to it, You know, I hate I have to defend a little bit, you say Microsoft and Oracle, legacy. Microsoft has business productivity applications. They are the leader in the space when you talk about... >> Yeah they're the leader in legacy applications. >> But you know, you start with the Microsoft Office Suite, and say what you will, it's still dominant out there, it's there. Microsoft gave enterprises the green light to go to SaaS, and they really helped drive that. >> John: Whoah, whoah, that's a direction. >> Yeah. >> John: But they're a legacy vendor, what you just said is that they're legacy. >> But Azure is doing quite well... >> John: Oracle's going to the cloud, are they legacy? >> Oracle's got a phenomenal team, have been building some really interesting things in cloud, but obviously no doubt about it, Amazon's leading, but when you talk to users and you say, okay, there's lots of reasons they might be using Azure for various pieces. Everybody is using AWS, except for those people, John, and you used the example, the ones that compete against Amazon and obviously that's a concern. Because today Amazon is competing against more and more companies, so that's a little bit... >> I'm not, I'm not down on the legacy, what I'm trying to point out is that IBM was clear about this, they were up front about it at IBM Think we were just at, which is, they're saying the legacy has to evolve. Doesn't mean legacy's going to die, I mean Microsoft clearly is going to the cloud, their stock's at like 90 plus, it was at 26 a few years ago so, Satya Nadella taking over from Ballmer. Clearly that's the direction Microsoft has to go, and they're doing it. Now, they're a legacy company doing cloud. Oracle, legacy company, doing cloud. IBM, legacy company, doing cloud. So that's necessarily a bad thing, I'm just saying vis a vis the competition I would put Google and I would put Amazon in a new, modern, non-legacy kind of world. >> Yeah, well okay, and you find one of the lines I love that Werner Vogels was talking about is we talked about AWS customers are builders, and he said builders have a bias for action. And I love that, because if you talk to companies, and you know, we've talked a lot on theCUBE, digital transformation, much more than a buzzword, John, I've not talked to anybody, that they're like, "Oh, kind of hogwash, you know, I'm just going to "keep doing the same thing I've been doing "for the last 10 years and I'll keep being successful." We understand that change needs to happen and it's not easy. So if you've got data scientists, if you've got, you know, understanding data, if you're embracing developers, Amazon has affinity with these groups, and that's why they build and they listen to their customers and there's new services and another thing, Amazon gets up on stage and it's not so much "Oh, here's the vision of where we're going," it's here's the stuff that we GAed that we already had you in the beta. Here's the new things, and they might give you a couple things in preview, but they iterate and move so fast. >> Yeah, checking the boxes on the product side, but... >> But much more than checking the boxes, they listen to their customers. >> Well, well of course, that's what they say, but we know they're doing that, but the thing, I mean checking the boxes, they're on the cadence of the Amazon releases, which we've talked about that. But fundamentally, Stu, I think the two big things and this is what I want to get your reaction to is, what's going on with Amazon, the consistent thing is that they lay out the preferred architecture of the modern stack and it's not the same architecture as the old way. Two, the SageMaker and machine learning and where AI is going, if you look at what Matt Wood discussed, SageMaker, my prediction, will surpass Aurora as the number one shipping service for Amazon in the history of their product. That thing is on a torrent pace, and the way they lay it out architecturally, they're not head figment, they're saying this is what we're doing, they lay out the architecture, and they're putting in the machine learning. So, to me, I love that. Now, all the other stuff that they're doing it's just the cadence of Amazon. More announcements, more services, general availability, they're moving the ball down the field, as Jeff Frick would say, matriculating the ball down the field. So your reaction to the modern architecture, and the SageMaker, machine learning for all developers. >> Yeah, absolutely, Amazon is setting the bar for how we think about architecture today. They're leaders in serverless, an area I've been hot on the last year or so. You know, Werner was up on stage talking about Ai Roba who I got the chance to interview last year. So absolutely they are the bar that everything is measured on in this industry. And if they're not, have the leading product in everything, they are close second and they have so many services that there is just this flywheel of not only services and customers and the new flywheel we talked about on theCUBE two years ago with Andy Jassy is data. John, I want to throw back at you a question. Amazon released something called AWS Secrets Manager. Do we trust Amazon with our secrets? Is the government coming after Amazon now? There's some of these macroeconomic things happening, you're hearing everything in Silicon Valley, what are you hearing lately? >> Well what I'm hearing is one, people are really kind of not happy with Amazon's success because it, you know, market share at the expense of other old guard or legacy vendors, and so that's taking it's toll. Oracle to me is the biggest company that's impacted most by Amazon. It's clear that a war of words is happening between Ellison and Jassy. Two, there's a big policy battle going on in D.C. I think Bloomberg broke a story that Oracle is trying to incite Trump to tackle Amazon proper, but and then Amazon is affected, Amazon Web Services is affected, because they have all that Department of Defense and the CIA deal, so you're seeing Amazon, Amazon Web Services for the first time dealing with competitive pressures that's old school tactics, which is policy formulation, and as they say in the policy game in D.C., Stu, the battle is won before it's even fought. This is new territory for Amazon, they really got to get their act together, and if I had to tell Andy Jassy any advice would be like look it, you got to start thinking chess game at this point, and understand that the competition is not going to roll over. We've said this on theCUBE many times. Oracle's not going to roll over, IBM's not going to roll over. Now, other companies, like Cloud Air who's down thirty percent on earnings, they're going to have to do a deal with Amazon, just like VMware did. So I think you have these big cloud players sucking the oxygen out of the room, and there are impacts. The growing startups who are pre-public companies or are public companies have to either join the ecosystem or find another partner. The major cloud players are going to fight tooth and nail for market share as stakes on the table is the future internet, it's basically everything in cloud that's going to extend to democratization around decentralization, the future of money, sovereignty, government, digital nations, internet of things, these are, it's a high stakes chess game and Amazon is now on new territory, and I think that to me is the big walkaway is that no one is going to let them take this uncontested. >> Yeah, John, look at this crowd. The expo hall is filling up, customers are still excited. The buzz that I hear is that Amazon, they listen, they still move really fast when they need to make changes, I remember a year ago when we were here for the Google event I was talking, it's like, ah, Google's got such better pricing for the small business and everything like that. A week later Amazon changed all of their pricing, billing by the microsecond, I talked back to some of my sources and they're like, "Yeah Amazon listened and totally flipped the game." >> Yeah, well Jassy, he... >> There are sustainable advantages, so difficult in the fast pace of change but Amazon is doing better than what Oracle used to do in the past, they were kind of like, we'd get the lead and kind of want the competition intact, with them with the old sailing analogy, Amazon doesn't worry about the competition, they listen to their customers, they're moving forward. >> Well, I think that they do, they don't admit it but they have to watch, they've got to look in their rear view mirror a little bit, but Stu, to end out the analysis I would say the following, my observation is this: Andy Jassy and his team are very customer-centric. He sat on theCUBE many times, so as an organization they're very process oriented, they'll listen to customers. But if you look at what's happening in the world today, is that in the old way, the way that Intuit laid it out that took months to provision the software, the old technology business model or venture architecture for a business was make a sound technology decision, and all the chits will fall in the right places. This is completely opposite now, if you look at what's going on with cloud and blockchain and cryptocurrency and decentralized applications, it's the business model that matters, the technology switching costs are now fungible with Lambda you're starting to see these sets of services that can be spun up in parallel. So the scale and flexibility of the platform, and Werner Vogels pointed this out on the keynote, this is fundamental. The decisions that are fatal to a company is the business model and the business logic, this is where the action is. That means it's not just a developer game any more, it's the CTO, it's the data scientists, and Werner Vogels laid that out and I think that to me was my big walkaway from today's keynote is that Amazon recognizes that it's not just about developers, make developers more productive, but bring all those people together to do the right for the business model, the business logic and applications. >> Yeah, John, we're always looking for what are those things that are slow down the company and the roadblocks, one thing Amazon I think did a great job they're out in front of GDPR, that are super hot topic out there, and they just say categorically, "We're ready for GDPR on all of our services," so full steam ahead, don't stop your spending, keep growing. >> Couldn't be a better time to be a theCUBE host to analyze and talk about the competition. Let's see how Amazon handles the competition, do they just keep pedal to the metal, or do they address it and play those 3D chess games? TheCUBE here in San Francisco for live coverage of AWS Summit 2018 in San Francisco, more coverage after this short break. We'll be right back. (techno music)

(upbeat music) >> Hello and welcome back to theCUBE. We're here in Palo Alto, California. I'm your host, John Furrier with a special guest here in the studio. As part of our Cloud Native SecurityCon Coverage we had an opportunity to bring in Jon Turow who is the partner at Madrona Venture Partners formerly with AWS and to talk about machine learning, foundational models, and how the future of AI is going to be impacted by some of the innovation around what's going on in the industry. ChatGPT has taken the world by storm. A million downloads, fastest to the million downloads there. Before some were saying it's just a gimmick. Others saying it's a game changer. Jon's here to break it down, and great to have you on. Thanks for coming in. >> Thanks John. Glad to be here. >> Thanks for coming on. So first of all, I'm glad you're here. First of all, because two things. One, you were formerly with AWS, got a lot of experience running projects at AWS. Now a partner at Madrona, a great firm doing great deals, and they had this future at modern application kind of thesis. Now you are putting out some content recently around foundational models. You're deep into computer vision. You were the IoT general manager at AWS among other things, Greengrass. So you know a lot about data. You know a lot about some of this automation, some of the edge stuff. You've been in the middle of all these kind of areas that now seem to be the next wave coming. So I wanted to ask you what your thoughts are of how the machine learning and this new automation wave is coming in, this AI tools are coming out. Is it a platform? Is it going to be smarter? What feeds AI? What's your take on this whole foundational big movement into AI? What's your general reaction to all this? >> So, thanks, Jon, again for having me here. Really excited to talk about these things. AI has been coming for a long time. It's been kind of the next big thing. Always just over the horizon for quite some time. And we've seen really compelling applications in generations before and until now. Amazon and AWS have introduced a lot of them. My firm, Madrona Venture Group has invested in some of those early players as well. But what we're seeing now is something categorically different. That's really exciting and feels like a durable change. And I can try and explain what that is. We have these really large models that are useful in a general way. They can be applied to a lot of different tasks beyond the specific task that the designers envisioned. That makes them more flexible, that makes them more useful for building applications than what we've seen before. And so that, we can talk about the depths of it, but in a nutshell, that's why I think people are really excited. >> And I think one of the things that you wrote about that jumped out at me is that this seems to be this moment where there's been a multiple decades of nerds and computer scientists and programmers and data thinkers around waiting for AI to blossom. And it's like they're scratching that itch. Every year is going to be, and it's like the bottleneck's always been compute power. And we've seen other areas, genome sequencing, all kinds of high computation things where required high forms computing. But now there's no real bottleneck to compute. You got cloud. And so you're starting to see the emergence of a massive acceleration of where AI's been and where it needs to be going. Now, it's almost like it's got a reboot. It's almost a renaissance in the AI community with a whole nother macro environmental things happening. Cloud, younger generation, applications proliferate from mobile to cloud native. It's the perfect storm for this kind of moment to switch over. Am I overreading that? Is that right? >> You're right. And it's been cooking for a cycle or two. And let me try and explain why that is. We have cloud and AWS launch in whatever it was, 2006, and offered more compute to more people than really was possible before. Initially that was about taking existing applications and running them more easily in a bigger scale. But in that period of time what's also become possible is new kinds of computation that really weren't practical or even possible without that vast amount of compute. And so one result that came of that is something called the transformer AI model architecture. And Google came out with that, published a paper in 2017. And what that says is, with a transformer model you can actually train an arbitrarily large amount of data into a model, and see what happens. That's what Google demonstrated in 2017. The what happens is the really exciting part because when you do that, what you start to see, when models exceed a certain size that we had never really seen before all of a sudden they get what we call emerging capabilities of complex reasoning and reasoning outside a domain and reasoning with data. The kinds of things that people describe as spooky when they play with something like ChatGPT. That's the underlying term. We don't as an industry quite know why it happens or how it happens, but we can measure that it does. So cloud enables new kinds of math and science. New kinds of math and science allow new kinds of experimentation. And that experimentation has led to this new generation of models. >> So one of the debates we had on theCUBE at our Supercloud event last month was, what's the barriers to entry for say OpenAI, for instance? Obviously, I weighed in aggressively and said, "The barriers for getting into cloud are high because all the CapEx." And Howie Xu formerly VMware, now at ZScaler, he's an AI machine learning guy. He was like, "Well, you can spend $100 million and replicate it." I saw a quote that set up for 180,000 I can get this other package. What's the barriers to entry? Is ChatGPT or OpenAI, does it have sustainability? Is it easy to get into? What is the market like for AI? I mean, because a lot of entrepreneurs are jumping in. I mean, I just read a story today. San Francisco's got more inbound migration because of the AI action happening, Seattle's booming, Boston with MIT's been working on neural networks for generations. That's what we've found the answer. Get off the neural network, Boston jump on the AI bus. So there's total excitement for this. People are enthusiastic around this area. >> You can think of an iPhone versus Android tension that's happening today. In the iPhone world, there are proprietary models from OpenAI who you might consider as the leader. There's Cohere, there's AI21, there's Anthropic, Google's going to have their own, and a few others. These are proprietary models that developers can build on top of, get started really quickly. They're measured to have the highest accuracy and the highest performance today. That's the proprietary side. On the other side, there is an open source part of the world. These are a proliferation of model architectures that developers and practitioners can take off the shelf and train themselves. Typically found in Hugging face. What people seem to think is that the accuracy and performance of the open source models is something like 18 to 20 months behind the accuracy and performance of the proprietary models. But on the other hand, there's infinite flexibility for teams that are capable enough. So you're going to see teams choose sides based on whether they want speed or flexibility. >> That's interesting. And that brings up a point I was talking to a startup and the debate was, do you abstract away from the hardware and be software-defined or software-led on the AI side and let the hardware side just extremely accelerate on its own, 'cause it's flywheel? So again, back to proprietary, that's with hardware kind of bundled in, bolted on. Is it accelerator or is it bolted on or is it part of it? So to me, I think that the big struggle in understanding this is that which one will end up being right. I mean, is it a beta max versus VHS kind of thing going on? Or iPhone, Android, I mean iPhone makes a lot of sense, but if you're Apple, but is there an Apple moment in the machine learning? >> In proprietary models, here does seem to be a jump ball. That there's going to be a virtuous flywheel that emerges that, for example, all these excitement about ChatGPT. What's really exciting about it is it's really easy to use. The technology isn't so different from what we've seen before even from OpenAI. You mentioned a million users in a short period of time, all providing training data for OpenAI that makes their underlying models, their next generation even better. So it's not unreasonable to guess that there's going to be power laws that emerge on the proprietary side. What I think history has shown is that iPhone, Android, Windows, Linux, there seems to be gravity towards this yin and yang. And my guess, and what other people seem to think is going to be the case is that we're going to continue to see these two poles of AI. >> So let's get into the relationship with data because I've been emerging myself with ChatGPT, fascinated by the ease of use, yes, but also the fidelity of how you query it. And I felt like when I was doing writing SQL back in the eighties and nineties where SQL was emerging. You had to be really a guru at the SQL to get the answers you wanted. It seems like the querying into ChatGPT is a good thing if you know how to talk to it. Labeling whether your input is and it does a great job if you feed it right. If you ask a generic questions like Google. It's like a Google search. It gives you great format, sounds credible, but the facts are kind of wrong. >> That's right. >> That's where general consensus is coming on. So what does that mean? That means people are on one hand saying, "Ah, it's bullshit 'cause it's wrong." But I look at, I'm like, "Wow, that's that's compelling." 'Cause if you feed it the right data, so now we're in the data modeling here, so the role of data's going to be critical. Is there a data operating system emerging? Because if this thing continues to go the way it's going you can almost imagine as you would look at companies to invest in. Who's going to be right on this? What's going to scale? What's sustainable? What could build a durable company? It might not look what like what people think it is. I mean, I remember when Google started everyone thought it was the worst search engine because it wasn't a portal. But it was the best organic search on the planet became successful. So I'm trying to figure out like, okay, how do you read this? How do you read the tea leaves? >> Yeah. There are a few different ways that companies can differentiate themselves. Teams with galactic capabilities to take an open source model and then change the architecture and retrain and go down to the silicon. They can do things that might not have been possible for other teams to do. There's a company that that we're proud to be investors in called RunwayML that provides video accelerated, sorry, AI accelerated video editing capabilities. They were used in everything, everywhere all at once and some others. In order to build RunwayML, they needed a vision of what the future was going to look like and they needed to make deep contributions to the science that was going to enable all that. But not every team has those capabilities, maybe nor should they. So as far as how other teams are going to differentiate there's a couple of things that they can do. One is called prompt engineering where they shape on behalf of their own users exactly how the prompt to get fed to the underlying model. It's not clear whether that's going to be a durable problem or whether like Google, we consumers are going to start to get more intuitive about this. That's one. The second is what's called information retrieval. How can I get information about the world outside, information from a database or a data store or whatever service into these models so they can reason about them. And the third is, this is going to sound funny, but attribution. Just like you would do in a news report or an academic paper. If you can state where your facts are coming from, the downstream consumer or the human being who has to use that information actually is going to be able to make better sense of it and rely better on it. So that's prompt engineering, that's retrieval, and that's attribution. >> So that brings me to my next point I want to dig in on is the foundational model stack that you published. And I'll start by saying that with ChatGPT, if you take out the naysayers who are like throwing cold water on it about being a gimmick or whatever, and then you got the other side, I would call the alpha nerds who are like they can see, "Wow, this is amazing." This is truly NextGen. This isn't yesterday's chatbot nonsense. They're like, they're all over it. It's that everybody's using it right now in every vertical. I heard someone using it for security logs. I heard a data center, hardware vendor using it for pushing out appsec review updates. I mean, I've heard corner cases. We're using it for theCUBE to put our metadata in. So there's a horizontal use case of value. So to me that tells me it's a market there. So when you have horizontal scalability in the use case you're going to have a stack. So you publish this stack and it has an application at the top, applications like Jasper out there. You're seeing ChatGPT. But you go after the bottom, you got silicon, cloud, foundational model operations, the foundational models themselves, tooling, sources, actions. Where'd you get this from? How'd you put this together? Did you just work backwards from the startups or was there a thesis behind this? Could you share your thoughts behind this foundational model stack? >> Sure. Well, I'm a recovering product manager and my job that I think about as a product manager is who is my customer and what problem he wants to solve. And so to put myself in the mindset of an application developer and a founder who is actually my customer as a partner at Madrona, I think about what technology and resources does she need to be really powerful, to be able to take a brilliant idea, and actually bring that to life. And if you spend time with that community, which I do and I've met with hundreds of founders now who are trying to do exactly this, you can see that the stack is emerging. In fact, we first drew it in, not in January 2023, but October 2022. And if you look at the difference between the October '22 and January '23 stacks you're going to see that holes in the stack that we identified in October around tooling and around foundation model ops and the rest are organically starting to get filled because of how much demand from the developers at the top of the stack. >> If you look at the young generation coming out and even some of the analysts, I was just reading an analyst report on who's following the whole data stacks area, Databricks, Snowflake, there's variety of analytics, realtime AI, data's hot. There's a lot of engineers coming out that were either data scientists or I would call data platform engineering folks are becoming very key resources in this area. What's the skillset emerging and what's the mindset of that entrepreneur that sees the opportunity? How does these startups come together? Is there a pattern in the formation? Is there a pattern in the competency or proficiency around the talent behind these ventures? >> Yes. I would say there's two groups. The first is a very distinct pattern, John. For the past 10 years or a little more we've seen a pattern of democratization of ML where more and more people had access to this powerful science and technology. And since about 2017, with the rise of the transformer architecture in these foundation models, that pattern has reversed. All of a sudden what has become broader access is now shrinking to a pretty small group of scientists who can actually train and manipulate the architectures of these models themselves. So that's one. And what that means is the teams who can do that have huge ability to make the future happen in ways that other people don't have access to yet. That's one. The second is there is a broader population of people who by definition has even more collective imagination 'cause there's even more people who sees what should be possible and can use things like the proprietary models, like the OpenAI models that are available off the shelf and try to create something that maybe nobody has seen before. And when they do that, Jasper AI is a great example of that. Jasper AI is a company that creates marketing copy automatically with generative models such as GPT-3. They do that and it's really useful and it's almost fun for a marketer to use that. But there are going to be questions of how they can defend that against someone else who has access to the same technology. It's a different population of founders who has to find other sources of differentiation without being able to go all the way down to the the silicon and the science. >> Yeah, and it's going to be also opportunity recognition is one thing. Building a viable venture product market fit. You got competition. And so when things get crowded you got to have some differentiation. I think that's going to be the key. And that's where I was trying to figure out and I think data with scale I think are big ones. Where's the vulnerability in the stack in terms of gaps? Where's the white space? I shouldn't say vulnerability. I should say where's the opportunity, where's the white space in the stack that you see opportunities for entrepreneurs to attack? >> I would say there's two. At the application level, there is almost infinite opportunity, John, because almost every kind of application is about to be reimagined or disrupted with a new generation that takes advantage of this really powerful new technology. And so if there is a kind of application in almost any vertical, it's hard to rule something out. Almost any vertical that a founder wishes she had created the original app in, well, now it's her time. So that's one. The second is, if you look at the tooling layer that we discussed, tooling is a really powerful way that you can provide more flexibility to app developers to get more differentiation for themselves. And the tooling layer is still forming. This is the interface between the models themselves and the applications. Tools that help bring in data, as you mentioned, connect to external actions, bring context across multiple calls, chain together multiple models. These kinds of things, there's huge opportunity there. >> Well, Jon, I really appreciate you coming in. I had a couple more questions, but I will take a minute to read some of your bios for the audience and we'll get into, I won't embarrass you, but I want to set the context. You said you were recovering product manager, 10 plus years at AWS. Obviously, recovering from AWS, which is a whole nother dimension of recovering. In all seriousness, I talked to Andy Jassy around that time and Dr. Matt Wood and it was about that time when AI was just getting on the radar when they started. So you guys started seeing the wave coming in early on. So I remember at that time as Amazon was starting to grow significantly and even just stock price and overall growth. From a tech perspective, it was pretty clear what was coming, so you were there when this tsunami hit. >> Jon: That's right. >> And you had a front row seat building tech, you were led the product teams for Computer Vision AI, Textract, AI intelligence for document processing, recognition for image and video analysis. You wrote the business product plan for AWS IoT and Greengrass, which we've covered a lot in theCUBE, which extends out to the whole edge thing. So you know a lot about AI/ML, edge computing, IOT, messaging, which I call the law of small numbers that scale become big. This is a big new thing. So as a former AWS leader who's been there and at Madrona, what's your investment thesis as you start to peruse the landscape and talk to entrepreneurs as you got the stack? What's the big picture? What are you looking for? What's the thesis? How do you see this next five years emerging? >> Five years is a really long time given some of this science is only six months out. I'll start with some, no pun intended, some foundational things. And we can talk about some implications of the technology. The basics are the same as they've always been. We want, what I like to call customers with their hair on fire. So they have problems, so urgent they'll buy half a product. The joke is if your hair is on fire you might want a bucket of cold water, but you'll take a tennis racket and you'll beat yourself over the head to put the fire out. You want those customers 'cause they'll meet you more than halfway. And when you find them, you can obsess about them and you can get better every day. So we want customers with their hair on fire. We want founders who have empathy for those customers, understand what is going to be required to serve them really well, and have what I like to call founder-market fit to be able to build the products that those customers are going to need. >> And because that's a good strategy from an emerging, not yet fully baked out requirements definition. >> Jon: That's right. >> Enough where directionally they're leaning in, more than in, they're part of the product development process. >> That's right. And when you're doing early stage development, which is where I personally spend a lot of my time at the seed and A and a little bit beyond that stage often that's going to be what you have to go on because the future is going to be so complex that you can't see the curves beyond it. But if you have customers with their hair on fire and talented founders who have the capability to serve those customers, that's got me interested. >> So if I'm an entrepreneur, I walk in and say, "I have customers that have their hair on fire." What kind of checks do you write? What's the kind of the average you're seeing for seed and series? Probably seed, seed rounds and series As. >> It can depend. I have seen seed rounds of double digit million dollars. I have seen seed rounds much smaller than that. It really depends on what is going to be the right thing for these founders to prove out the hypothesis that they're testing that says, "Look, we have this customer with her hair on fire. We think we can build at least a tennis racket that she can use to start beating herself over the head and put the fire out. And then we're going to have something really interesting that we can scale up from there and we can make the future happen. >> So it sounds like your advice to founders is go out and find some customers, show them a product, don't obsess over full completion, get some sort of vibe on fit and go from there. >> Yeah, and I think by the time founders come to me they may not have a product, they may not have a deck, but if they have a customer with her hair on fire, then I'm really interested. >> Well, I always love the professional services angle on these markets. You go in and you get some business and you understand it. Walk away if you don't like it, but you see the hair on fire, then you go in product mode. >> That's right. >> All Right, Jon, thank you for coming on theCUBE. Really appreciate you stopping by the studio and good luck on your investments. Great to see you. >> You too. >> Thanks for coming on. >> Thank you, Jon. >> CUBE coverage here at Palo Alto. I'm John Furrier, your host. More coverage with CUBE Conversations after this break. (upbeat music)

(calm music) >> Okay, welcome back everyone to theCUBE's live coverage here on the floor at Moscone south in San Francisco California for AWS summit, 2022. This is part of their summit conferences, not re:Invent it's kind of like becoming like regional satellite, mini re:Invents, but it's all part of education developers. Of course theCUBE's here. We're going to be at the AWS summit in New York city, only two this year. And this summer check us out. Of course, re:MARS is another event we're going to be going to so check us out there as well. And of course re:Invent at the end of the year and re:Inforce the security conference in Boston. So, Sarbjeet Johal, our next guest here. CUBE alumni, CUBE influencer, influencer in the cloud industry. Sarbjeet great to see you. Thanks for coming on. Oh, by the way, we'll be at Boston re:Inforce, re:Invent in December, re:MARS which is the robotics AI show, and of course the summit here in San Francisco and New York city, the hot areas. >> That's cool. >> Great to see you. >> Good to see you too. >> Okay. I got a lot of data to report. You've been on the floor talking to people. What are you finding out? What's the report? >> The report is actually, I spoke to three people from AWS earlier. As said one higher up guy from the doctor, Casey Tan. He works on French SaaS chips and he gave me a low down on how that thing works. And there's a systolic arrays TPUs, and like a lot of insider stuff >> Like deep Silicon chip stuff. >> Yes. And that they're doing some great stuff there. And of course that works for us at scale and for cloud guys it's all about scale. If you're saving pennies at that scale, you're saving millions and maybe hundreds of millions at some point. Right? So that was one. And I also spoke to the analytics guys and they gave me some low-down on the Glue announcements. How the big data processing is happening at AWS and how they are now giving you the ability where your infrastructure hugs your demand. So you're not wasting any sources. So that was a number one complaint with the Glue from AWS. So that was one. And then I did the DeepRacing race and my timings were like number 78. So. >> You got some work to do. You download your machine learning module. >> No, I will do that and then play with it. Yes. I will train one. >> You like a simulation too? >> Yeah. Yeah. I will do that simulation, yes. >> What else? Anything jump off the page for you. What's the highlight if you could point at something? Did anything pop up at you in this event with AWS? Was there any aha moment or something that just jumps off the page? >> I think it was mainly sort of incremental to be honest with you. And the one thing-- >> Nothing earth shattering >> Nothing earth shattering and that at the summit it's like that, you know, like it but they are doing new announcements of like almost every day with new services. So I would go home and read on that but there are some patterns that we are seeing emerging and there are some folks very active on Twitter. Mark in recent just did very controversial kind of tweet couple of days back. That was, that was hard. >> Was he shit posting again? >> Shit posting. Yeah. He was shit posting actually, according to actually I saw Corey as well on the floor, Corey and Rodrigo. And, and-- >> Did you see Corey's interview with me? We were talking about shit posting 'cause he wrote in this newsletter. Mark and recently Elon Musk, they're all kind of like they're really kind of active on Twitter with a lot of highly intelligent snarkiness. >> They're super intelligent and they know the patterns, they know the economics and technology. Super smart guys and yeah. Who is in control, there was a move from the middle seat and social media kind of side of things where people are controlling the narratives and who controls the narrative. Is it billionaires? Is it government? We see that. >> Well I mean, it's interesting seeing the power. I mean, I call it the revenge of the nerds. You got the billionaires who are looking at the political screw-ups that Facebook and others have done. And by not being clear and it's hard, it's a hard problem to solve. I don't really want to be in their seat. Even Andy Jassy is the CEO of AWS. What is he? I mean, he's dealing with problems that for some people would be their worst part of like they could ever dream of scenario. He's dealing with that at breakfast. And then throughout his day, he's got all kinds of Amazon's so big and Apple and you got Google and you got the fan companies. So, you know, at some point tech is now so part of society, it's not just the nerds from California. It's tech is in everything now. So it's a societal impact. And so there's consequences for stuff. And so you're starting to see this force for good that's come from the sustainability angle. You're going to start to see force for good with technology as it relates to people's lives. And we had Mapbox on the CUBE and they provide all this navigation and Gareth the guy who runs that division, he talks about dark kitchens, dark stores. So just they're re-engineering the supply chain of delivery. So we all been to restaurants and seen people there from picking up food delivery. Why are they going to the retail? So dark kitchens are just basically depots for supplying the 10 menus that everyone orders from. That's a change of a structural change in the industry. So that's jumped out at me, Matt Wood spoke to me about serverless impact to the analytics team. And again, structural changes, technical and culture. Right? So, so you're starting to see to me more and more of the two themes of some technology change, architectural change, system change and culture thinking. And you know, we had a 20 year old guest on here who was first worked at Amazon web services when he was 16. >> Wow. >> Graduated high school early and went into Amazon. He's like, I love tools. So people love tools. Hardware is coming back. Right? So I mean Sarbjeet this is crazy. >> It's crazy. >> What's going on. >> It's crazy actually. Remember the nine year old kid at re:Invent 2019. Karthick was the name if I remember, but I spoke to him and he was crazy. He was AWS certified and kids are playing with this technology in their high schools. >> It's awesome. >> And even in their elementary schools now. >> They can get their hands on it quicker. They don't need to go in full class for a year. They can self-teach, they can do side projects they can launch a side hustle, they can stand up a headless retail outlet, who knows what they can do if you got the Lego blocks. This is what I love about the cloud, you can really show something fast and then abandon it. >> Actually, I think it is all enabled through cloud. Like the accessibility of technology has gone like exponentially, like wildfire. Like once you have access to the cloud just all you need is connection to the internet. After that you have the VMs. and you have the serverless, there's zero cost to you. And things are thrown at you. Somebody who was saying that earlier here like we have said that many times it's like that's how the drug dealer, you know, sell the drug. Like sniff it, it's free, >> First is free. >> So they're doing it. Yes. >> We say that about theCUBE. >> And from the, I see cloud from two different angles, like we all do. And like, I try to sort of force myself to look at it from the both angles. There's the supplier side and the buyer side or the consumer side on the other side. Right? So from the supplier side, it's a race for talent to build it, number one, then number two is race for talent to train them. So we saw the numbers and millions being shown today at the keynote again. And Google is showing those numbers as well. Like how many millions they are training like 25 to 30 million people within next two, three years. It's crazy numbers. >> Sarbjeet I got to say so if I have to look at what jumped off the page for me on this event, was couple things and this is kind of weird nuanced stuff but I'll just try to explain it as best I can. Number one, we're going to see more managed services like DevOps managed services. As DevOps teams grow, talent is a problem. And Kubernetes obviously is growing and got to get that right. It's not easy to be a Kubernetes, you know slinging clusters around with Kubernetes. It's hard. I think that's got to get easier. So I think the path to easy is going to be some sort of abstraction service layer. And I think the smart people are going to have this layer will manage it and then provide that as a service, number one. Number two is this notion of a systems design thinking around elements, whether it's storage or maps for like Mapbox and around these elements they have to have a systematic effect of other things. You can't just, if it changes, it's going to have consequences that's what systems do. So, tooling being built around these elements and they have to have hardened APIs that is clear. People who are trying to be "cloud native" need to get this right. And you have to have the tooling in and around the the element and then have APIs to connect and then glue up. So it's interesting. Clearly those things are happening and multiple conversations, people were teasing that out. And then obviously the super cloud was coming in. >> Is there. >> Mapbox is basically a super cloud. They're like what snowflake is for data analytics. They are for-- >> MongoDB is another one. >> MongoDB's got Atlas. I mean, MongoDB was criticized for years. Doesn't scale. Remember the old lamp stack days, they were preferred. They're document, they nailed it with document. The document aspects of data, but they were always getting criticized. They can't scale. And they just keep scaling. But now with Atlas, they're on AWS. It's just, auto scale. So that's killer for MongoDB. So I think their stock price is undervalued my opinion but you know, I don't give legal advice. >> I think that the whole notion of-- >> Or financial advice. >> The multicloud, right? So for a multicloud to kill that complexity of multicloud, we have to go to the what Dave Vellante and you guys say super cloud, right? Another level of abstraction on top of infrastructure provider by AWS, Google cloud, Azure. So that's where we're going. >> Well, Dave and I debate this right, he bundles multi-cloud in there and most people think that's what he's saying but I'm saying multi-cloud is a reality. I mean, multi-cloud means you're going to have multiple clouds. They're just not you're not sharing workloads across those clouds. It's like not the same workload. That's not going to yet happen. I run Azure because I have 365, that's it. I run Amazon for everything else. That's kind of the use case. But to me, super cloud is building on top of AWS or Azure where you leverage their CapEx and create differentiated value. It's your own cloud without all the CapEx but it's got to be like super integrated and the benefit's got to be so good that it seems like pennies to your point earlier. >> Yeah. >> And the economics to the applications in it are just so obvious and they got to be they got to be so big for the application developer. So that's to me is super cloud. And then of course having the connected tissue to manage the transit around multiple clouds. >> Yeah. I think they have it too. I totally agree with you. But another thing is from having the developer background I think the backward compatibility is a huge issue in cloud. >> Yeah. I agree. >> It's a lot of technical debt being built and I hear that, I'm hearing that more and more. I think that we have to solve as industry as like these three main players have to solve that problem. So that's one big thing, actually. I'm very like after, you know, like to talk about it and all that stuff. So yeah. It's another thing is another pattern actually to all the cloud naysayers out there, right? Is that those are the people who come from the hardware background. So I've seen another pattern out there. So I'm trying to synthesize, who are these people who bash cloud all the time? I'm pro-cloud of course everybody knows that. >> We know you're pro, we're all pro cloud. We're totally biased. We love cloud >> Actually. No, I've seen both sides. I've seen both sides. I've worked at EMC, VMware, I worked at Oracle cloud as well. And then, and before that I have written a lot of software. A software developer is pro-cloud. A typical hardware ops guy or girl, they are pro on-prem or pro hybrid and all that. Like they try to keep it there. >> I think first of all, I have opinion on this. I think, I think you're right. But how hardware is coming back, if you look at how cloud is enabling hardware, it's retro, it's designed for the cloud. So hardware's going to offload, either accelerate stuff and offload stuff from the software guide. So look at DeepRacer it's hardware. Now it's a car. You've got the silicon and the chips. So the chips you're talking about. Those aren't chips for service and the data center. They're just chips to make the software in the cloud run better. >> Sarbjeet: Well scale. >> So scaling. And so I think we're going to see a Renaissance in hardware. It's going to look different. It's going to act different. So we're watching this. I mean, you brought up the idea of having a CUBE hardware box. >> Yeah. It's a great idea. >> It's a good idea. DM me and tell me it's a bad idea or good idea. I'll blame Sarbjeet for that. But what else have you learned? >> What else have learnt actually it's basically boils down to economics at the end of the day. It's about moving fast. It's about having developer productivity, again going back the cloud naysayers. It's like, why did you build a bike? Remember Steve Job used to say that, "computer is the bicycle for the human minds." >> Yes. >> Right. So cloud is the bicycle for the enterprises. They makes them move faster. 'So I think that's-- >> All right. We're closing down. We're going to hold on until they pull the plug on theCUBE literally. Sarbjeet great to see you on there. Check 'em out on Twitter. Great event. Good to see you, great report. Thank for sharing. Sarbjeet Johal here on theCUBE, taking over our community site I hear, right? Now you going to work-- >> I'm there. I'm always there. >> Great to have you on. I'm going to work on some new things with theCUBE. Really appreciate working with us. Thanks a lot. >> I really appreciate you guys giving me this platform. It's an amazing platform. Thank you very much. >> That's all right. We'll be back. That's it for our coverage of AWS summit 2020 here live on the floor. Events are back. Hybrid's back. We get theCUBE studios in Palo Alto in Boston. Re:invent at the end of the year but we're going to the summit in New York city. In the summer, we got re:Inforce in Boston the security conference. Re:MARS which is the robotics IML conference. And of course the big summit New York and San Francisco we're there of course. Share thecube.net for all the action. I'm John for your host with Sarbjeet here. Closing out the show. Thanks for watching. (Calm music)

(upbeat music) >> Okay, welcome back everyone to theCUBE's coverage of AWS Summit 2022, here at Moscone Center live on the floor, I'm John Furry host of theCUBE, all the action day two, remember AWS Summit in New York City is coming in the summer. We'll be there as well. Got a great guest Murli Murli who's the VP and GM of Cloud Native Business Unit Portworx, been in theCUBE multiple times. We were just talking about the customer he had on Ford from Detroit, where kubernetes will be this year. >> That's right. >> Great to see you. >> Yeah, same here, John. Great to see. >> So, what's the update? Quickly this, before we get into the country, give the update on what's going on in the company, what's happening? >> Well, you know, we've been acquired by Pure Storage it's well over a year. So we've had one full year of being inside of Pure. It's been wonderful, right? So we've had a great ride so far, The products have been renewed. We've got a bunch of integrations with Pure. We more than doubled our business and more than doubled our head count. So things are going great. >> I always had a, congratulations by the way. And I was going to ask about the integration but before I get there, yeah, we've been always like play some jokes on theCUBE and because serverless is so hot, I've been using storage lists and actually saw a startup yesterday had the word networking lists in their title. So this idea of like making things easier, but me, I mean serverless of this is basically servers that make it easier. >> Yeah, yeah >> So this is kind of where we see Cloud Native going. Can you share your thoughts on how Pure and Portworx are bringing this together? Because you can almost connect the dots in my mind. So say specifically what is the Cloud Native angle with Pure? >> Yeah. So look, I'll kind of start by being captain an obvious, I guess. Just sort of stating some obvious stuff and then get to what I hope will be a little bit more new and interesting. So the obvious stuff to start with is just the fact that Cloud Native is exploding. Containers are exploding. It's kind of a well known fact that 85% of the enterprise organizations around the world are pretty much going to be deploying containers, if not already in the next couple of years, right? So one it's really happening. The, buzz is now, it's not just in the future, the hype is now. The second part of that is it's really part of that is things are going production. 56% of these organizations are in production already. And that's the number is going to climb to 80 fairly quickly. So not only is this stuff being deployed as being deployed in sort of fairly mission critical, especially Greenfield applications. So that's kind of one, right? Now, the second thing that we're seeing is as they go in into production, John, the migraines are starting, right? Customer migraines, right? It's always happens in stuff that they have not looked around the corner and anticipated. So one of them is, again, a fairly obvious one is as they go into production, they need to be able to kind of recover from some oops that happens, right? And the kinds of think about this, right? John, this stuff is rapidly changing, right? Look at how many versions of kubernetes come out on a regular basis. On top of that, you got all these app, virgins, new database virgins, new stuff, vendors like us, ourselves have new virgins. So with all these new virgins, when you put it all together the stack, sometimes misbehave. So you got to kind of, "Hey, let me go recover." Right? You have outages. So essentially the whole area of data protection becomes a lot more critical. That's the migraine that people are beginning to get now, right? They can feel the migraine coming on. The good news is this is not new stuff. People know on- >> John: The DevOps. >> Yeah. Well, and in fact it is that transition from DevOps to ITOps, right? People know that they're going into production, that they need backup and data protection and disaster recovery. So in a way it's kind of good news, bad news, the good news is they know that they need it. The bad news is, it turns out that it's kind of interesting as they go Cloud Native, the technology stack has changed. So 82% of customers who are kind of deploying Cloud Native are worried about data protection. And in fact, I'll go one step further 67% of those people have actually kind of looked at what they can get from existing vendors and are going, "Hey, this is not it. This is not going to do my stuff for me." >> And by the way, just to throw a little bit more gas on that fire is ransomware attacks. So any kind of vulnerability opening? Maybe make people are scared. >> Murli: Absolutely. >> So with- >> Murli: Its a board level topic, right? >> Yeah, and then you bring down the DevOps, which is we all know the innovation formula launch in iterate, pivot, iterate, pivot, then innovation you get the formula, all your metrics, but it's a system. >> Correct. >> Storage is part now of a system when you bring Cloud Native into it, you have a consequence if something changes. >> Murli: Correct. >> So I see that. And the question I have for you is, where are we in the stability side of it? Are we close to getting there and what's coming out to help that, is it more tooling? Because the trend is people are building tools around their Cloud Native thing. I was just talking to MongoDB and they got a database, now that's all tooling. Vertically integrate into the asset or the product, because it integrates with APIs, right? So that makes total sense. >> So I think there's kind of again, a good news, bad news there, right? There's a lot of good news, right? In the world of containers and kubernetes what are some of the good news items, right? A lot of the APIs have settled down have been defined well, CNCF has done a great job promoting that, right? So the APIs are stable, right? Second, the product feature set, have become more stable, particularly sort of the the core kubernetes product security kind of stuff, right? Now what's the bad news. The bad news is, while these things are stable they are not ready for scale in every case yet, right? And when you integrate at scale, so and typically the tipping point is around 20 to 30 nodes, right? So typically when you go beyond 20 to 30 nodes then the stuff starts to come a apart, right? Like, the wheels come off of the train and all of that. And that's typically because there's a lot of the products that were designed for DevOps, are not well suited for ITOps. So really there is a new- >> And the talent culture. >> Exactly. >> Talent and culture sometimes aren't ready or are changing. >> So it's a whole bunch of people trying to use kind of a maturing product set with skill sets that are pretty low, right? So when we get into production, then other factors come into play, high availability, right? Security, you talk about ransomware, disaster recovery backup. So these are things that are sort of, I would say not 101 problems, but 201 problems, so right? This is natural as we go to that part of the thing. And that's the kind of stuff that, Portworx and Pure Storage have been kind of focused on solving. And that's kind of been how we've made our mark in the industry, right? We've helped people really get to production on some of these different points. >> Expectation on both companies have been strong, high quality, obviously performance on Pure side from day one, just did a great job with the products. Now, when you go into Cloud Native you have now this connection okay. To the customer, again I think huge point on the changing landscape. How do you see that IT to DevOps emerging? Because the trend that we're seeing is, abstracting way the complexities of management. So I won't say managed services are more of a trend, they've always been around but the notion of making it easier for customers. >> Yep, absolutely right. >> Super important. So can you guys share what you guys are doing to make it easier because not everyone has a DevOps team. >> Yeah, so look, the number one way things are made more easy, is to make it more consumable by making it as a service. So this is one of the things, here we are, at AWS Summit, right? And delighted to be here by the way. And we have a strategic alliance with with AWS, and specifically, what we're here to announce really is that we're announcing a backup as a SaaS product. Coming up in a few weeks we're going to be giing running on AWS as a service integrated with AWS. So essentially what happens is, if you have a containerized set of applications you're deploying it on EKS, ECS, AWS, what have you. We will automatically provide the ability for that to be backed up scaled and to be very, very container granular, very app specific, right? Yeah, so it's designed specifically for kubernetes. Now here's the kind of key thing to say, right? Backup's been around for a long time. You've interviewed, tons of backup people in the past. But traditional backup is just not going to work for kubernetes. And it's very simple if you think about it, John. >> John: And why is that? >> It's a very simple thing, right? Traditional backup focuses on apps and data, right? Those are the two kind of legs of that. And they create catalogs and then do a great job there. Well, here's, what's happened with Cloud Native. You have a thing inserted in the middle called kubernetes. So when you take a snapshot, I'm now kind of going into a specific kind of, world of storage, right? When you take a snapshot, what Portworx does is we take a 3D snapshot. What you really need to recover, from a backup situation where, you want to go back to the earlier stage to be kubernetes specific, you need a app snapshot, snapshot of the kubernetes spec, pod spec, And third of snapshot of the data. Well, traditional, backup folks are not taking that middle snapshot. So we do a 3D snapshot and we recover all three which is really what you need to be able to kind of like get backed up, get recovered in minutes. >> Okay and so the alternative to not doing that is what? What will happen? >> You To do that, to do your old machine level backup? So what happens with traditional backups are typically VM level or machine level, right? So you're taking a snapshot of the whole kind of machine and server or VM setup and then you recover all of that, and then you run kubernetes on that and then you try to recover it- >> John: To either stand everything up again. >> Yeah, yeah. >> John: Pretty much. >> Yeah. Whereas, what do most people want to do? This is a very different use case, by the way, right? How does this work? What people are doing for kubernetes is they're not doing archival kind of backup. What they're doing is real time, right? You're running an ops. Like I said, you got an oops, "Hey, a new release for one of the new databases then work right? Boom! I want to just go back to like yesterday, right? So how do I do that? Well, here you can just go back for that one database, one app, and recover back to that. So it's operational backup and recovery as opposed to archival backup and recovery. So for that, to be able to recover in seconds, right? You need to be, he kind of want integrated with AWS which is what we are. So it's integrated, it's automated, and it's very, very container granular. And so these three things are the things that make it sort of, very specific way. >> I love the integration story. 'Cause I think that's the big mega trend we're seeing now is is that integrating in. And, but again, it's a systems concept. It's not standalone storage, detached storage. >> Murli: Exactly. >> It's always, even though it might be decoupled a little bit it's glued together through say- >> John, you said it right. The easy button is for the system, right? Not for the individual component. Look, all of us vendors in this ecosystem are going around framing, having a being easy. But when we say that, what do we mean? We mean, oh, I'm easy to use. Well that doesn't help the user. Who's got to put all this stuff together. So it's really kind of making that stack work. >> This is easy to use, but it made these things more complex. This is what we do in the enterprise solve complexity with more complexity. >> Putting the problem to the other guy. Yeah. So it's that end to end ease of use is kind of what I would say, is the number one benefit, right? One it's container specific and designed for kubernetes. And second, it really, really is easy. >> Well, I really like the whole thing and I want to get your thoughts as we close out, what should people know about Pure and Portworx's relationship now and in the Amazon integration, what's the new narrative the north Star's still the same? High performance store, backup, securely recover and deliver the data in whatever mechanism we can. That north Star's clear, never changes, which is great. I feel love about Pure and Cloud Native. It's just taking the blockers away- >> I think the single biggest thing I would say, is all of these things, what we're turning into it is as a service offering. So if we're going to backup as a service our Portworx product now is going to be the Portworx enterprise Pure Storage product is going to be offered as a service. So with, as a service, it's easy to consume. It's easy to deploy. It's fully automated. That's the kind of the single biggest aha! Especially for the folks who are deploying on AWS today, AWS is well known for being easy to use. It's kind of fully automated. Well here, now you have this functionality for Cloud Native workloads. >> Final question, real quick, customer reaction so far, I'm assuming marketplace integration, buying terms, join selling, go to market? >> So yeah, it is integrated billing and all of that is part of that kind of offering, right? So when we say easy, it's not just about being easy to use it's about being easy to buy. It's being easy to expand all of that and scaling. Yeah. And being able to kind of automatically or automagically as I like to say, scale it, right? So all of that is absolutely part of it, right? So it is really kind of... It's not about having the basics anymore. We've been in the market now for six, seven years, so right? We have sort of an advanced offering that not only knows what customer want but anticipates what ones can expect and that's a key difference. >> I was talking to Dr. Matt Wood real quick. I know we got to wrap up on the schedule, but earlier today about AI and business analytics division's running and we were talking about serverless and the impact of serverless. And he really kind of came down the same lines where you are with the storage and the cloud data which is, "Hey, some people just want storage and the elastic leap analytics without all the under the cover stuff." Some people want to look under the covers, fine whatever choice. So really two things, so. >> Yeah, yeah. All the way from you can buy the individual components or you can buy the as a service offering, which just packages it all up in a on easy to consume kind of solution, right? >> Final, final question. What's it like at Pure everything going well, things good? >> We love it, man. I'll tell you these folks have welcomed us with open arms. And look, I've been acquired twice before. And I say this, that one of the key linchpins to a successful integration or acquisition is not just the strategic intent that always exists but really around a common culture. And, we've been blessed. I think the two companies have a strong common culture of being customer first, product excellence, and team wins every time. And these three things kind of have pulled us together. It's been a pleasure. >> One of the benefits of doing the queue for 13 years is that you get the seats things. Scott came on the queue to announce Pure Storage on theCUBE, cuz he was a nobody else. There was, oh, you're never going to get escape Velocity, EMC's going to kill, you never owned you. Nope. >> Well, we're talking about marketplaces and theCUBE is the marketplace of big announcements, John. So this is, delighted- >> Announcements. >> Yeah. Yeah. Well that was the AWS announcement. Yeah. So that's, that is big >> Final words, share the audience. What's what to expect in the next year for you guys? What's the big come news coming down? What's coming around the corner? >> I think you can expect from from Pure and Portworx the as a service set of offerings around, HADR backup, but also a brand new stuff, keep an eye out. We'll be back with John. I hope that talking about this is data services. So we have a Portworx data service product that is going to be announced. And it's magic. It's allowing people to deploy databases in a very, very, it's the easy button for database deployment. >> Congratulations on all your success. The VP and General Manager of the Cloud Native Business Unit. >> You make it sound bigger than it actually is, John. >> Thanks for coming on. Appreciate it. >> Thanks. >> Okay theCUBE coverage be back for more coverage. You're watching theCUBE here, live in Moscone on the ground at an event AWS Summit 2022. I'm John Furrier. Thanks for watching. (upbeat music)

>>Okay, welcome back everyone. Cube's coverage here. Live on the floor at AWS summit, 2022, an in person event in San Francisco. Of course, AWS summit, 2022 in New York city is coming up this summer. The cube will be there as well. Make sure you check us out then too, but we day two of coverage had a great guest here. I Han VP of developer relations, Mongo DB, formally of AWS. We've been known each other for a long time doing, uh, developer relations at Mongo DB. Welcome to the queue. Good to see >>You. Thank to be here. Thanks for inviting me, John. It's great >>To, so Mongo DB is, um, first of all, stocks' doing really well right now. Businesswise is good, but I still think it's undervalue. A lot of people think is, is a lot more going huge success with Atlas. So congratulations to the team over there. Um, what's the update? What's the relationship withs, you know, guys have been great partners for years. What's the new thing. Yeah. >>So MongoDB Atlas obviously runs on several different major cloud providers, but AWS is the largest partner that we work with in the public cloud. So the majority of our Atlas workloads for our customers are running on the AWS platform. And just earlier this year, we announced a new strategic collaboration agreement with AWS. That's gonna further strengthen and deepen that partnership that we have with them. >>What's the main product value right now on the scale on, on Atlas, what's the drive in the revenue momentum. >>So, I mean, you know, there's a huge trend in the industry towards cloud managed databases, right? You look back 10, 15 years ago when we first met, most customers were only and operating their own data infrastructure, either running it in their own data centers, or maybe if they were really early using the primitives that cloud providers like AWS offered to run their databases in the cloud when Amazon launched RDS back in 2009, I think it was, we started to see this trend towards cloud managed databases. We followed that with our own Atlas offering back in 2016. And as Andy jazzy from AWS would say very often it's offloading that UND differentiated, heavy lifting, allowing developers to focus on building applications. They don't have to win and operate the data infrastructure. We do it for them, and that has proven incredibly popular amongst our customers. You know, Atlas route right now is growing at 50, sorry, 85% car year on year growth. >>You know, um, I've been following MongoDB for a long, long time. I mean, going back to the lamp stack days, you know, and you think about what Mongo has done as a product because of the developer traction, you know, Mongo can't do this, just keeps getting better every year. And, and the, I think the stickiness with developers is a real big part of that. Can you your view there cuz you're in VE relations. I mean, developers all love Mongo. They're teaching in school. People are picking up a side hustles, they're coding on it, using it all everywhere. I mean it's well known. >>There's a few different reasons for that. I think the main one is the, the document orientated model that we use, the document data models that are used by Mongo DB, just a net way for developers to work with data. And then, uh, we've invested in creating 16 first party drivers that allow developers using various different programming languages, whether that's JavaScript or Python or rust to integrate MongoDB, natively and idiomatic with their software. So it's very, very easy for a developer to pick up MongoDB, grab one of these drivers from their package manager of their choice and then build applications that natively manipulate data inside MongoDB, whether that's MongoDB Atlas or our enterprise edition on their own premises. They get a very consistent and very easy to, I easy to use developer experience with our, with our platform. >>Talk about the go to market with AWS. You guys also have a tightly coupled relationships. There's been announcements there recently. Uh, what's changing most right now that people should pay attention to. Well, >>The first thing is there's a huge amount of technical integration between MongoDB and AWS services. And that's the basis for many of our customers choosing to run Mon Mongo DB on AWS. We're active in 23 AWS regions around the world. And there's many other integration points as well, like cryptographic protection of Mongo MongoDB, stored data using Amazon cryptographic services, for example, or building serverless applications with AWS Lambda and MongoDB servers. So there's a ton of technical integration. Yeah, but what we started to work on now is go to market integration with AWS as well. So you can buy Mongo DB Atlas through AWS's marketplace. You can use the payer, you go offering to pay for it with your AWS bill. And then we're collaborating with AWS on migrations and other joint go to market activities as well. That >>Means get incentives, the sales people at AWS. >>Of course our moreover I mean, it's just really easy for customers, really easy for developers to consume. Yeah, they don't need to contract with MongoDB. They can use their existing AWS contracting, their existing discounting relationships and pre purchasing arrangements with AWS to consume Atlas. >>It's the classic meet the customers where they >>Are exactly right. Meet the developer where they are and meet the customers where they are now with this new model as well. >>Yeah. I love marketplace. I think it's been great. You know, even with its kind of catalog and vibe, I think it's gonna get better and better, uh, over there teams doing good work. Um, and it's easy to consume. That's key. >>Yeah. Super easy. Reduce that friction and make it real easy for developers to adopt this. Right. >>Talk about some of the top customers that you guys share with AWS. What are some of the customers you guys have together and what the benefits of the >>Relationship joint references that we talk about? A lot, one of them is Shutterfly. So in the photographic products area, they built a eCommerce offering with MongoDB and AWS. The second is seven 11 with seven 11. We're doing a lot in the mobile space. So edge applications, we've got a feature in MongoDB Atlas that allows you to synchronize data with databases on mobile devices. Those can be phones point of sale devices or handheld devices that might be used in the parcel industry, for example. So seven 11 using us in that way. And then lastly with Pitney Bowes, we've got a big digital transformation project with Pitney Bowes where they've reimagined their, uh, postage and packaging services, delivering those to their customers, using MongoDB as a data store as well. >>I wanna get in some of the trends, you've got a great per you know, you know, Mongo from Amazon side and now you're there. Um, Mongo's, as you pointed out has, has been around for a long time. What are some of the stats? I mean, how many customers, how many countries? Well, it's pretty massive >>Mind. We've got almost quarter of a billion downloads today, 240 million MongoDB downloads since we launched the first product <laugh>, we've got 33,000 active customers that are using MongoDB Atlas today and we're running well over a million free tier clusters on MongoDB Atlas across all of the different providers where we operate the service as well. So these numbers are, you know, mind blowing in terms of scale. Uh, but of course at the core of that is operational excellence. Customers love Mongo DBS because they don't have to operate it themselves. They don't have to deal with fairly conditions. They don't have to deal with scaling. They don't have to deal with deployment. We all, we do all of those things as part of the service offering and customers get an endpoint that they can use with their applications to store and retrieve data reliably. And with consistently high perform, >>You know, it's, you know, in the media, something has to be dead. Someone's the death of the iPhone, the death of this, nothing that really dies. Mongo DB has always been kind of like talked about, well, it doesn't scale on the high end. Of course, Oracle was saying that, I mean, all the, all the big database vendors were kind of throwing darts at, at Mongo, uh, DB, uh, but it kept scaling. Atlas is a whole nother. Could you just unpack that a little bit more? Why is it so important? Because scale is just, I mean, it's, it's horizontal, but it's also performant. >>Exactly. Right. So with, uh, Mongo DB's document access model that I've described already, you break some of the limitations that exist inside traditional relational databases. So, you know, they don't scale well, if you've got high concurrent and see of data access, and they're typically difficult and expensive to scale because you need to share data. Once you grow beyond individual cluster nodes, and you'll know that all relational databases suffer from these same kinds of issues with non relational systems, no SQL systems like MongoDB, you have to think a little bit more about design at the beginning. So designing database to cater for the different access patterns that you have, but in return for that upfront preparation, that design work, you get near limitless, scalability and performance will scale nearly linearly with that scalability as well. So very much more high performance, very much more simplicity for the developer as their database gets larger and their cluster gets larger to support it. >>Yeah. You know, Amazon web service has always had an a and D jazz. We talk to us all the time, every interview I've done with Swami and Matt wood or whoever on the team and executive levels always said the same thing. There's not one database to rule the world, right? Obvious you're talking about Oracle, but even within AWS customers, they're mixing and matching databases based on use cases. So in distributed environment, they're all working together. So, um, you guys fit nicely into that. So how does that, >>I think strategy slightly counterbalances that so, you know, they would say use the specific tool for the specific task that you have in hand. Yeah. What we try to focus on is creating the simple and most effective developer experience that we can, and then supporting different facets to the product in order to allow developers to different use cases. A really good example with something like MongoDB Atlas search. So we integrated Apache Luine into MongoDB Atlas. Customers can very simply apply Apache Luine search indexes to the data that they've got in MongoDB. And then they can interact with that search data using the same drivers as an API. Yeah, yeah. That they use for regular queries. So if you want to run search on your application data, you don't need a separate open search or elastic search cluster, just turn on MongoDB Atlas search and use that, that search facet. So it's interest and we have other capabilities that it's >>Vertically integrating inside within Mongo, >>Correct? Yes. That's better. Yeah. With the guy, all of creating a really simple and effective developer experience, boosting developer productivity and helping developers get more done in less time. >>You mentioned serverless earlier, what's the serverless angle with AWS when Mongo, >>Is there one? Yeah. So we have MongoDB serverless currently in preview, uh, has the same kind of characteristics that you would, or the characteristics that you would expect from a serverless data base. So consumption based model, you provision an endpoint and that will scale elastically in accordance with your usage and you get billed by consumption units so much like the serverless paradigm that we've seen delivered by AWS, the same kind of model for Mongo, DB, Atlas serverless. >>What, what attracted you to Mongo DBS? So you knew them before, or you moved over there. Um, what's going on there? What's the culture like right now? Oh, >>The culture's great. I mean, it's a much smaller company than AWS where I was before, you know, it's a very large organization. And one of the things that I really like about MongoDB is, as I've said earlier, we can serve the different use cases that a developer might have with a single product, with different aspects, to it, different facets to it. Uh, and it's a really great conversation to have with a, with a developer, with a developer customer, to be able to offer one thing that helps them solve five or six different problems that have traditionally been quite hard for them to wrestle with quite difficult for them to, to deal with. And then we've got this focus on developer experience through these driver packages that we have as well. So it's really great to have as a developer relations pro have that kind of tooling in my kit bag that can help developers become more effective. >>Talk about tooling, cuz you know, I always have, uh, kind of moments where I waffle between more. I love platforms, tools are being over overused, too many tools tool with the tool, you know, the expressions, but we're seeing from developers, the ones that don't want to go into the hood, we serverless plays beautifully. Yep. They want tools. They do. And, and the, the new engineering developers that are coming outta college and universities, they love tools. >>Yeah. And we actually have quite a few of those built into Mongo, DB Atlas. So inside Mongo, DB Atlas, we've got things like an index optimizer, which will suggest the best way that you might index your data for better perform months inside MongoDB, running on Atlas, we've got a data Explorer, which is much like another product that we've got called MongoDB compass that allows you to see and manipulate the data that you have stored within your database natively within the Atlas interface. Uh, and then we also have, uh, whole slew of different metrics, monitoring capabilities built into the platform as well. So these are aspects of Atlas that developers can take advantage of. And then over on the client side, visual studio code plugins. Yeah. So you can manipulate and operate with data directly inside visual studio code, which is obviously the most common and popular IDE out there today, as well as integration with things like infrastructure is code tools. So we support cloud formation for provisioning. We have CDK constructs inside. Yeah. The CDK construct library. We also have a lot of customers using Terraform to provision MongoDB across both AWS and other providers. So having that developer tooling of course is super important. Yeah. Aspect of the developer experience, trying to >>Build out deploying observability is a big one. How does that fit in? Cuz you knew need to talk and not only measure everything here, but talk to other systems. >>Yeah. So we recently announced a provider for Prometheus and Grafana. So we can emit metrics into those providers. Obviously CNCF projects, very common and popular inside customers that are running on Kubernetes. We've got a Kubernetes operator for MongoDB Atlas as well. Good. So you can provision MongoDB Atlas from within Kubernetes as well as having our own native metrics directly within Atlas as well. >>Ian you're crushing it. You got all the, the data, the fingertips. Are you gonna be at Cuban this year? Uh, >>I will be, but some of our team members will definitely be there. >>Yeah, we'll be at, uh, EU. The cube will be there. Great. Thanks for coming on. Appreciate the insight final world. I'll give you the last word. Tell the audience what's going on. What's at Mongo DB. What should they pay attention to? If they've used Mongo and are aware of it? What's the update. What's >>The so you should come to MongoDB world actually in New York at the beginning of June, June 7th, the ninth in the Javit center in New York. Gonna have our own show there. And of course we'd love to see you there. >>Okay. Cube comes here day two of eight, us summit, 2020, this Cub I'm John for your host. Stay with us more. Our coverage as day two winds down. Great coverage.

>>From around the globe. If the cube with digital coverage of AWS reinvent 20, 20 special coverage sponsored by AWS worldwide public sector. >>Okay. Welcome back everyone. To the cubes, virtual coverage of AWS reinvent 2020, this is the cube virtual. I'm your host John farrier with the cube. The cube normally is there in person this year. It's all virtual. This is the cube virtual. We're doing the remote interviews and we're bringing in commentary and discussion around the themes of re-invent. And this today is public sector, worldwide public sector day. And the theme from Teresa Carlson, who heads up the entire team is to think big and look at the data. And I wanted to bring in a special cube alumni and special guests. Alan Cohen. Who's a partner at data collective venture capital or DCVC, um, which we've known for many, many years, founders, Matt OCO and Zachary Bogue, who started the firm, um, to over at about 10 years ago. We're on the really the big data wave and have grown into a really big firm thought big data, data, collective big ideas. That's the whole purpose of your firm. Alan. You're now a partner retired, retired, I mean a venture capitalist over at being a collective. Great to see you. Thanks for coming on. >>Great to see you as well. John, thanks for being so honest this morning. >>I love to joke about being retired because the VC game, it's not, um, a retirement for you. You guys made, you made some investments. Data collective has a unique, um, philosophy because you guys invest in essentially moonshots or big ideas, hard problems. And if I look at what's going on with Amazon, specifically in the public sector, genome sequencing now available in what they call the open data registry. You've got healthcare expanding, huge, you got huge demand and education, real societal benefits, uh, cybersecurity contested in space, more contention and congestion and space. Um, there's a lot of really hard science problems that are going on at the cloud. And AI are enabling, you're investing in entrepreneurs that are trying to solve these problems. What's your view of the big ideas? What are people missing? >>Well, I don't know if they're missing, but I think what I'd say, John, is that we're starting to see a shift. So if you look at the last, I don't know, forever 40, 50 years in the it and the tech industry, we took a lot of atoms. We built networks and data warehouses and server farms, and we, we kind of created software with it. So we took Adam's and we turned them into bets. Now we're seeing things move in the other direction where we're targeting bits, software, artificial intelligence, massive amount of compute power, which you can get from companies like, like AWS. And now we're creating better atoms. That means better met medicines and vaccines we're investor, um, and a company called abs Celera, which is the therapeutic treatment that J and J has, um, taken to market. Uh, people are actually spaces, a commercial business. >>If it's not a science fiction, novel we're investors in planet labs and rocket labs and compel a space so people can see right out. So you're sitting on your terrorists of your backyard from a satellite that was launched by a private company without any government money. Um, you talked about gene sequencing, uh, folding of proteins. Um, so I think the big ideas are we can look at some of the world's most intractable issues and problems, and we can go after them and turn them into commercial opportunities. Uh, and we would have been able to do that before, without the advent of big data and obviously the processing capabilities and on now artificial intelligence that are available from things like AWS. So, um, it's kind of, it's kind of payback from the physical world to the physical world, from the virtual world. Okay. >>Pella space was featured in the keynote by Teresa Carlson. Um, great to tie that in great tie in there, but this is the kind of hard problems. And I want to get your take because entrepreneurs, you know, it reminds me of the old days where, you know, when you didn't go back to the.com, when that bubble was going on, and then you got the different cycles and the different waves, um, the consumer always got the best kind of valuations and got the most attention. And now B to B's hot, you got the enterprise is super hot, mainly because of Amazon >>Sure. Into the Jordash IPO. Obviously this morning, >>Jordache IPO, I didn't get a phone call for friends and family and one of their top customers. They started in Palo Alto. We know them since the carton Jordache, these are companies that are getting massive, uh, zoom. Um, the post pandemic is coming. It's going to be a hybrid world. I think there's clear recognition that this some economic values are digital being digitally enabled and using cloud and AI for efficiencies and philosophy of new things. But it's going to get back to the real world. What's your, it's still hard problems out there. I mean, all the valuations, >>Well, there's always hard problems, but what's different now. And from a perspective of venture and, and investors is that you can go after really hard problems with venture scale level of investments. Uh, traditionally you think about these things as like a division of a company like J and J or general electric or some very massive global corporation, and because of the capabilities that are available, um, in the computing world, um, as well as kind of great scientific research and we fund more PhDs probably than any other, uh, any other type of background, uh, for, for founders, they can go after these things, they can create. Uh, we, uh, we have a company called pivot bio, uh, and I think I've spoken to you about them in the past, Sean, they have created a series of microbes that actually do a process called nitrogen fixation. Um, so it attaches the nitrogen to the roots of corn, sorghum and wheat. >>So you don't have to use chemical fertilizer. Well, those microbes were all created through an enormous amount of machine learning. And where did that machine learning come from? So what does that mean? That means climate change. That means more profitable farmers. Uh, that means water and air management, all major issues in our society where if we didn't have the computing capabilities we have today, we wouldn't have been able to do that. We clearly would have not been able to do that, um, as a venture level of investments to get it started. So I think what's missing for a lot of people is a paucity of imagination. And you have to actually, you know, you actually have to take these intractable problems and say, how can I solve them and then tear it apart to its actual molecules, just the little inside joke, right? And, and then move that through. >>And, you know, this means that you have to be able to invest in work on things. You know, these companies don't happen in two or three years or five years. They take sometimes seven, 10, 15 years. So it's life work for people. Um, but though, but we're seeing that, uh, you know, that everywhere, I mean, rocket lab, a company of ours out of New Zealand and now out of DC, which we actually launched the last couple of space, um, satellites, they print their rocket engines with a 3d printer, a metal printer. So think about that. How did all that, that come to bear? Um, and it started as a dangerous scale style of investments. So, you know, Peter Beck, the founder of that company had a dream to basically launch a rocket, you know, once a year, once a month, once a week, and eventually to once a day. So he's effectively creating a huge, um, huge upswing in the ability of people to commercialize space. And then what does space do? It gives you better observability on the planet from a, not just from a security point of view, but from a weather and a commerce point of view. So all kinds of other things that looked like they were very difficult to go after it now starts to become enabled. Yeah. >>I love the, uh, your investment in Capella space because I think that speaks volumes. And one of the things that the founder was talking about was getting the data down is the hard part. He he's up, he's up there now. He can see everything, but now I've got to get the data down because say, say the wildfires in California, or whether, um, things happening around the globe now that you have the, uh, the observation space, you got to get the data down there. This is the huge scale challenge. >>Well, let me, let me, let me give you something. That's also, so w you know, we are in a fairly difficult time in this country, right? Because of the covert virus, uh, we are going to maybe as quickly as next week, start to deliver, even though not as many as we'd like vaccines and therapeutics into this virus situation, literally in a year, how did all these things, I mean, obviously one of the worst public health crisis of our lifetimes, and maybe, you know, uh, of the past century, uh, how did that happen? How did it all day? Well, you know, some, I mean, the ability to use, um, computing power in, in assistance, in laboratory, in, in, uh, in, um, development of, of pharmaceutical and therapeutics is a huge change. So something that is an intractable problem, because the traditional methods of creating vaccines that take anywhere from three to seven years, we would have a much worse public health crisis. I'm not saying that this one is over, right. We're in a really difficult situation, but our ability to start to address it, the worst public health crisis in our lifetime is being addressed because of the ability of people to apply technology and to accelerate the ability to create vaccines. So great points, absolutely amazing. >>Let's just, let's just pause that let's double down on that and just unpack that, think about that for a second. If you didn't, and then the Amazon highlight is on Andy Jesse's keynote carrier, which makes air conditioning. They also do refrigeration and transport. So one IOT application leveraging their cloud is they may call it cold chain managing the value chain of the transport, making sure food. And in this case vaccine, they saw huge value to reduce carbon emissions because of it does the waste involved in food alone was a problem, but the vaccine, they had the cold, the cold, cold, cold chain. Can you hear me? >>Maybe this year, the cold chain is more valuable than the blockchain. Yeah. >>Cold don't think he was cold chain. Sounds like a band called play. Um, um, I had to get that in and Linda loves Coldplay. Um, but if you think about like where we are to your point, imagine if this hit 15 years ago or 20 years ago, um, you know, YouTube was just hitting the scene 20 years ago, 15 years ago, you know, so, you know, that kind of culture, we didn't have zoom education would be where we would be Skyping. Um, there's no bandwidth. So, I mean, you, you know, the, the bandwidth Wars you would live through those and your career, you had no bandwidth. You had no video conferencing, no real IOT, no real supply chain management and therapeutics would have taken what years. What's your reaction to, to that and compare and contrast that to what's on full display in the real world stage right now on digital enablement, digital transformation. >>Well, look, I mean, ultimately I'm an optimist because of what this technology allows you to do. I'm a realist that, you know, you know, we're gonna lose a lot of people because of this virus, but we're also going to be able to reduce a lot of, um, uh, pain for people and potentially death because of the ability to accelerate, um, these abilities to react. I think the biggest and the, the thing that I look for and I hope for, so when Theresa says, how do you think big, the biggest lesson I think we're going to we've learned in the last year is how to build resilience. So all kinds of parts of our economy, our healthcare systems, our personal lives, our education, our children, even our leisure time have been tested from a resilience point of view and the ability of technology to step in and become an enabler for that of resilience. >>Like there isn't like people don't love zoom school, but without zoom school, what we're going to do, there is no school, right? So, which is why zoom has become an indispensable utility of our lives, whether you're on a too much, or you've got zoom fatigue, does it really matter the concept? What we're going to do, call into a conference call and listen to your teacher, um, right in, you know, so how are you going to, you're going to do that, the ability to repurpose, um, our supply chain and, you know, uh, we, we, we see this, we're going to see a lot of change in the, in the global supply chain. You're going to see, uh, whether it's re domestication of manufacturing or tightening of that up, uh, because we're never going to go without PPE again, and other vital elements. We've seen entire industries repurposed from B2B to B to C and their ability to package, deliver and service customers. That is, those are forms of resilience. >>And, and, and, and taking that to the next level. If you think about what's actually happening on full display, and again, on my one-on-one with Andy Jassy prior to the event, and he laid this out on stage, he kind of talks about this, every vertical being disrupted, and then Dr. Matt wood, who's the machine learning lead there in Swami says, Hey, you know, cloud compute with chips now, and with AI and machine learning, every industry, vertical global industry is going to be disrupted. And so, you know, I get that. We've been saying that in the queue for a long time, that that's just going to happen. So we've been kind of on this wave of horizontal, scalability and vertical specialization with data and modern applications with machine learning, making customization really high-fidelity decisions. Or as you say, down to the molecule level or atomic level, but this is clear what, what I found interesting. And I want to get your thoughts because you have one been there, done that through many ways of innovation and now investor leading investor >>Investor, and you made up a word. I like it. Okay. >>Jesse talks about leadership to invent and reinvent. Can't fight gravity. You've got to get talent hungry for invention, solve real-world problems. Speed. Don't complexify. That's his message. I said to him, in my interview, you need a wartime conciliary cause he's a big movie buff. I quote the godfather. Yeah. Don't you don't want to be the Tom Hagen. You don't want to be that guy, right? You're not a wartime. Conciliary this is a time there's times in companies' histories where there's peace and there's wartime, wartime being the startup, trying to find its way. And then they get product market fit and you're growing and scaling. You're operating, you're hiring people to operate. Then you get into a pivot or a competitive situation. And then you got to get out there and, and, and get dirty and reinvent or re-imagine. And then you're back to peace. Having the right personnel is critical. So one of the themes this year is if you're in the way, get out of the way, you know, and some people don't want to hold on to hold onto the past. That's the way we did it before I built this system. Therefore it has to work this way. Otherwise the new ways, terrible, the mainframe, we've got to keep the mainframe. So you have a kind of a, um, an accelerated leadership, uh, thin man mantra happening. What is your take on this? Because, >>Sorry. So if you're going to have your F R R, if you're going to, if you are going to use, um, mob related better for is I'll share one with you from the final season of the Soprano's, where Tony's Prado is being hit over the head with a bunch of nostalgia from one of his associates. And he goes, remember, when is the lowest form of conversation and which is iconic. I think what you're talking about and what Andy is talking about is that the thing that makes great leadership, and what I look for is that when you invest in somebody or you put somebody in a leadership position to build something, 50% of their experience is really important. And 50% of it is not applicable in the new situation. And the hard leadership initiative has to understand which 50 matters in which 50 doesn't matter. >>So I think the issue is that, yeah, I think it is, you know, lead follow or get out of the way, but it's also, what am I doing? Am I following a pattern for a, for a, for an, a, for a technology, a market, a customer base, or a set of people are managing that doesn't really exist anymore, that the world has moved on. And I think that we're going to be kind of permanent war time on some level we're going to, we're going to be co we're because I think the economy is going to shift. We're going to have other shocks to the economy and we don't get back to a traditional normal any time soon. Yep. So I, I think that is the part that leadership in, in technology really has to, would adopt. And it's like, I mean, uh, you know, the first great CEO of Intel reminded us, right. Then only the paranoid survive. Right. Is that it's you, some things work and some things don't work and that's, that's the hard part on how you parse it. So I always like to say that you always have to have a crisis, and if there is no crisis, you create the crisis. Yeah. And, you know, >>Sam said, don't let a good crisis go to waste. You know? Um, as a manager, you take advantage of the crisis. >>Yeah. I mean, look, it wouldn't have been bad to be in the Peloton business this year. Right, too. Right. Which is like, when people stayed home and like that, you know, you know, th that will fade. People will get back on their bikes and go outside. I'm a cyclist, but you know, a lot more people are going to look at that as an alternative way to exercise or exercising, then when it's dark or when the weather is inclement. So what I think is that you see these things, they go in waves, they crest, they come back, but they never come back all the way to where they were. And as a manager, and then as a builder in the technology industry, you may not get like, like, like, okay, maybe we will not spend as much time on zoom, um, in a year from now, but we're going to still spend a lot of time on zoom and it's going to still be very important. >>Um, what I, what I would say, for example, and I, and looking at the COVID crisis and from my own personal investments, when I look at one thing is clear, we're going to get our arms around this virus. But if you look at the history of airborne illnesses, they are accelerating and they're coming every couple of years. So being able to be in that position to, to more react, more rapidly, create vaccines, the ability to foster trials more quickly to be able to use that information, to make decisions. And so the duration when people are not covered by therapeutics or vaccines, um, short, and this, that is going to be really important. So that form of resilience and that kind of speed is going to happen again and again, in healthcare, right. There's going to be in, you know, in increasing pressure across that in part of the segment food supply, right. I mean, the biggest problem in our food supply today is actually the lack of labor. Um, and so you have far, I mean, you know, farmers have had a repurpose, they don't sell to their traditional, like, so you're going to see increased amount of optimization automation and mechanization. >>Lauren was on the, um, keynote today talking about how their marketplaces collected as a collective, you know, um, people were working together, um, given that, given the big ideas. Well, let's, let's just, as we end the segment here, let's connect big ideas. And the democratization of, I mean, you know, the old expression Silicon Valley go big or go home. Well, I think now we're at a time where you can actually go big and stay and, and, and be big and get to be big at your own pace because the, the mantra has been thinking big in years, execute plan in months and execute weekly and month daily, you know, you can plan around, there's a management technique potentially to leverage cloud and AI to really think about bit the big idea. Uh, if I'm a manager, whether I'm in public sector or commercial or any vertical industry, I can still have that big idea that North star and then work backwards and figure that out. >>That sounds to the Amazon way. What's your take on how people should be. What's the right way to think about executing down that path so that someone who's say trying to re-imagine education. And I know a, some people that I've talked to here in California are looking at it and saying, Hey, I don't need to have silos students, faculty, alumni, and community. I can unify them together. That's an idea. I mean, execution of that is, you know, move all these events. So they've been supplying siloed systems to them. Um, I mean, cause people want to interact online. The Peloton is a great example of health and fitness. So there's, there's everyone is out there waiting for this playbook. >>Yeah. Unfortunately I, I had the playbook. I'd mail it to you. Uh, but you know, I think there's a couple of things that are really important to do. Maybe good to help the bed is one where is there structural change in an industry or a segment or something like that. And sorry to just people I'm home today, right? It's, everybody's running out of the door. Um, and you know, so I talked about this structural change and you, we talked about the structural change in healthcare. We talked about kind of maybe some of the structural change that's coming to agriculture. There's a change in people's expectations and how they're willing to work and what they're willing to do. Um, you, as you pointed out the traditional silos, right, since we have so much information at our fingertips, um, you know, people's responsibility as opposed to having products and services to deliver them, what they're willing to do on their own is really changed. >>Um, I think the other thing is that, uh, leadership is ultimately the most important aspect. And we have built a lot of companies in the industry based on forms of structural relations industry, um, background, I'm a product manager, I'm a sales person, I'm a CEO, I'm a finance person. And what we're starting to see is more whole thinking. Um, uh, particularly in early stage investors where they think less functionally about what people's jobs are and more about what the company is trying to get done, what the market is like. And it's infusing a lot more, how people do that. So ultimately most of this comes down to leadership. Um, uh, and, and that's what people have to do. They have to see themselves as a leader in their company, in their, in the business. They're trying to build, um, not just in their function, but in the market they're trying to win, which means you go out and you talk to a lot more people. >>You do a lot, you take a lot fewer things for granted. Um, you read less textbooks on how to build companies and you spend more time talking to your customers and your engineers, and you start to look at enabling. So the, we have made between machine learning, computer vision, and the amount of processing power that's available from things like AWS, including the services that you could just click box in places like the Amazon store. You actually have to be much more expansive in how you think about what you can get done without having to build a lot of things. Cause it's actually right there at your fingertips. Hopefully that kind of gets a little bit to what you were asking. >>Well, Alan, it's always great to have you on and great insight and, uh, always a pleasure to talk candidly. Um, normally we're a little bit more boisterous, but given how terrible the situation is with COVID while working at home, I'm usually in person, but you've been great. Take a minute to give a plug for the data collective venture capital firm. DCVC you guys have a really unique investment thesis you're in applied AI, computational biology, um, computational care, um, enterprise enablement. Geospatial is about space and Capella, which was featured carbon health, smart agriculture transportation. These are kind of like not on these are off the beaten path of like traditional herd mentality of venture capital. You guys are going after big problems. Give us an update on the firm. I know that firm has gotten bigger lately. You guys have >>No, I mean the further firm has gotten bigger, I guess since Matt, Zach started about a decade ago. So we have about $2.3 billion under management. We also have bio fund, uh, kind of a sister fund. That's part of that. I mean, obviously we are, uh, traditionally an early stage investor, but we have gone much longer now with these additional, um, um, investment funds and, and the confidence of our LPs. Uh, we are looking for bears. You said John, really large intractable, um, industry problems and transitions. Uh, we tend to back very technical founders and work with them very early in the creation of their business. Um, and we have a huge network of some of the leading people in our industry who work with us. Uh, we, uh, it's a little bit of our secret weapon. We call it our equity partner network. Many of them have been on the cube. >>Um, and these are people that work with us in the create, uh, you know, the creation of this. Uh, we've never been more excited because there's never been more opportunity. And you'll start to see, you know, you're starting to hear more and more about them, uh, will probably be a couple of years of report. We're a household name. Um, but you know, we've, we we're, we're washing deal flow. And the good news is I think more people want to invest in and build the things that we've. So we're less than itchy where people want to do what we're doing. And I think some of the large exits that starting to come our way or we'll attract more, more great entrepreneurs in that space. >>I really saw the data models, data, data trend early, you saw a Realty impacted, and I'll say that's front and center on Amazon web services reinvent this year. You guys were early super important firm. I'm really glad you guys exist. And you guys will be soon a household name if not already. Thanks for coming on. Right, >>Alan. Thanks. Thank you. Appreciate >>It. Take care. I'm John ferry with the cube. You're watching a reinvent coverage. This is the cube live portion of the coverage. Three weeks wall to wall. Check out the cube.net. Also go to the queue page on the Amazon event page, there's a little click through the bottom and the metadata is Mainstage tons of video on demand and live programming there too. Thanks for watching.

>>from around the >>globe. It's the Cube with digital coverage of AWS reinvent 2020. Special coverage sponsored by AWS Worldwide Public sector. >>Right. Hello and welcome to the Cube. Virtual our coverage of aws reinvent 2020 with special coverage of the public sector experience. This is the day when we go through all the great conversations around public sector in context to reinvent great guest will for Justin, head of A W s ai and machine learning enablement and partnership with AWS Wilfred. Thanks for joining us. >>Thanks, John. Thanks for having me on. I'm pretty excited to be part of this cube interview. >>Well, I wish we could be in person, but with the pandemic, we gotta do the remote. But I want to get into some of the things you're working on. The A I m l Rapid Adoption Assistance Initiative eyes a big story. What is? What is it described what it is. >>So we launched this artificial intelligence slash machine learning rapid adoption assistance for all public sector partners who are part of the AP in network in September 2020. Onda. We launched this in response to the president's Executive water called the American Year Initiative. So the rapid adoption assistant what it provides us. It provides a direct scalable on automated mechanism for all the public sector partners to reach out to AWS experts within our team for assistance in building and deploying machine learning workloads on behalf of the agencies. So for all all the partners who are part off, this rapid adoption assistance will go through a journey with AWS with my team and they will go through three different faces. The first face will be the envisioning face. The second phase would be the enablement face on the third would be the bill face, as you know, in the envisioning face will dive deeply The use case, the problem that they're trying to solve. This is where we will talk about the algorithms and framework on. We will solidify the architecture er on validate the architecture er on following that will be an enablement face where we engage with the partners trained their technical team, meaning that it will be a hands on approach hands on on keyboard kind of approach where we trained them on machine learning stack On the third phase would be the bill face on the partners leverage the knowledge that they have gained through the enablement and envisioning face, and they start building on rolling out workloads on behalf of the agencies. So we will stay with them throughout the journey on We will doom or any kind of blockers be technical or business, so that's a quick overview off a more rapid adoption assistance program. >>It's funny talking to Swami over the years and watching every year at reinvent the A I. M L Portfolio. Dr Matt Wood is always doing something new. This year is no exception. Even Mawr Machine Learning and AI in the In the News on this rapid adoption assistant initiative sounds like it's an accelerant. Um, so I get all that, But I want to ask you, what problem does it solve for the customer? Or Amazon is because there's demand. There's too much demand. People wanna go faster. What problem does this initiative this rapid adoption of a I machine learning initiative solved? >>So as you know, John, artificial intelligence and related technologies like deep learning and machine learning can literally transform the way agencies operate. They can enable them to provide better services, quicker services and more secure services to the citizens of this country. And that's the reason the president released an executive water called American Initiative on it drives all the government agencies, specifically federal agencies, to promote artificial intelligence to protect and improve the security and economy of the nation. So if you think about it, the best way to achieve the goal is to enable the partners toe build workloads on behalf of agencies, because when it comes to public sector, most of the workloads are delivered by partners. So the problem that we face based on our interaction with the partners is that though the partners have been building a lot off applications with AWS for more than a decade, when it comes to artificial intelligence, they have very limited resources when it comes to deep learning and machine learning, right, like speech recognition, cognitive computing, national language frosting. So we wanted exactly address that. And that's the problem you're trying to solve by launching this rapid adoption assistance, which is nothing but a dry direct mechanism for partners to reach our creative, these experts to help them to build those kind of solutions for the government. >>You know, it's interesting because AI and machine learning it's a secret sauce for workload, especially modern workloads. You mentioned agencies and also public sector. You know, we've seen Certainly there's been pandemic a ton of focus on moving faster, right? So getting those APS out quickly ai drives a lot of that, so totally get it. Um, I think it's an accelerant great program. It just makes a lot of sense. And I know you guys have been going in tow by vertical and kind of having stage making all these other tools kind of be specialized within those verticals. So it makes a ton of sense. I get it, and it is a great, great initiative and solve the problem. The question I have is who gets access to this, right? Is it just agencies you mentioned? Is it all public sector? Could you just clarify who can apply to this program? >>Yes, it is a partner focused program. So all the existing partners, though it is going to affect the end agencies, were trying to help the agency's through the partners. So all the existing AP in partners who are part of the PSP program, we call it the public sector partner program can apply for this rapid adoption assistance. So you have been following John, you have been following AWS and AWS partners on a lot of partners have different kind of expertise on they. They show that by achieving a lot of competencies, right, it could be technical competencies like big data storage and security. Or it could be domain specific competencies like public safety education on government competency. But for a playing this program, the partners don't need to have any kind of competency, and all they have to have is they have to be part of the Amazon Partner Network on they have to be part of the public sector partner program. That is number one Second. It is open toe all partners, meaning that it is open toe. Both technology partners, as well as consulting partners Number three are playing is pretty simple, John, right? You can quickly search for a I M or rapid adoption assistance on a little pop up a page on a P network, the partners have to go on Phil pretty basic information about the workload, the problem that they're trying to solve the machine learning services that they're planning to use on a couple of other information, like contact information, and then our team reaches out to the partner on help them with the journey. >>So real. No other requirements are prerequisites. Just part of the partner program. >>Absolutely. It is meant for partners. And all you have to do is you have to be a part off 18 network, and you have to be a public sector apartment. >>Public sector partner makes sense. I mean, how you're gonna handle the demand. I'm sure the it's gonna be a tsunami of interest, because, I mean, why wouldn't someone take advantage of this? >>Yep. It is open to all kinds of partners because they have some kind of prerequisites, right? So that's what I'm trying to explain. It is open to all partners, but we have since it is open to existing partners, we kind of expect the partners toe understand the best practices off deploying a machine, learning workloads, or for that case, any kind of workload which should be scalable, land secure and resilient. So we're not going to touch? Yeah, >>Well, I wanna ask you what's what's the response been on this launch? Because, you know, I mean to me, it just makes it's just common sense. Why wouldn't someone take advantage of it? E. Whether responses partner or you have domain expertise or in a vertical just makes a lot of sense. You get access to the experts. >>The response has been great. As I said, the once you apply the journey takes six weeks, but already we just launched it. Probably close toe. Two months back in September 2nd week of September, it is almost, uh, almost two months, and we have more than 15 partners as part of this program on dykan name couple of partners say, for example, we worked with delight on We Are. We will be working on number of work clothes for the Indy agencies through delight. And there are other couple of number of other partners were making significant progress using this rapid adoption assistance that includes after associates attained ardent emcee on infinitive. So to answer your question, the response has been great so far. >>So what's the I So I gotta ask, you know, one of things I thought that Teresa Carlson about all the time in Sandy Carter is, you know, trying to get the accelerant get whether it's Fed ramp and getting certifications. I mean, you guys have done a great job of getting partners on board. Is there any kind of paperwork? What's the process? What should a partner expect to take advantage of that? I'm sure they'll be interest beyond just the launch. What's what's involved? What zit Web bases it check a form? Is that a lot of hoops to jump through? Explain what? What? The process >>is. Very interesting question. And it probably is a very important question from a part of perspective, right? So since it is offered for a peon partners, absolutely, they should have already gone through the AP in terms and conditions they should have. Already, a customer agreement or advanced partners might have enterprise agreement. So for utilizing this for leveraging this rapid adoption assistance program, absolutely. There's no paperwork involved. All they have to do is log into the Web form, fill up the basic information. It comes to us way, take it from there. So there is no hard requirements as long as you're part of the AP network. And as long as you're part of the PSP program, >>well, for great insight, congratulations on a great program. I think it's gonna be a smash hit. Who wouldn't wanna take? I know you guys a lot of goodness there with Amazon Cloud higher level services with a I machine learning people could bring it into the table. I know from a cybersecurity standpoint to just education the range of, um, workloads is gonna be phenomenal. Obviously military as well. Eso totally cool. Love it. Congratulations. Like my final question is, um, one about the partner. So I'm a partner. I like this. Say I'm a partner. I jump in Easy to get in. Walk me through What happens? I mean, I signed some paperwork. You check the boxes, I get involved, I get, like, a rep. Do I do things? Do I? What happens to me? Walk me down the path of execution. What's expectation of what will happen? >>I'll explain that in two parts, John. Right? One is from a partner journey perspective and then from AWS perspective. What? What we expect out off partners, right? So, from a experience perspective, as long as they fill out, fill out the web form on, fill out the basic information about the project that they're trying to work. It comes to us. The workflow is automated. All the information is captured on the information comes to my team on. We get back to the partners within three days, but the journey itself can take from 6 to 8 weeks because, as I mentioned during the envisioning case, we try to map the problem to the solution. But the enablement phases the second phase is where it can take anywhere from 2 to 3 weeks because, as I mentioned, we focused on the three layers of the machine learning stack for certain kind of partners. They might be interested in sage maker because they might want to build a custom machine learning model. But for some of the partners, they want the argument that existing applications using S. R or NLP or nL you so we can focus on the high level services. Or we can train them on stage makers so it can take anywhere between 2 to 3 weeks or 3 to 4 weeks. And finally, the build phase varies from partner to partner on the complexity of the work. Lord at that point were still involved with a partner, but the partner will be taking the lead on will be with them to remove any kid of Glaucus being technical or, uh, business couple of Yeah, well, I just >>want to say the word enablement in your title kind of speaks volumes. This isn't about enabling customers. >>It is all about enabling the in customers through partners. So we focus on enabling partners. They could be business big system integrators like Lockheed's or Raytheon's or Delight. Or it could be nimble in small partners. Or it could be a technology partner building an entire pass or SAS service on behalf of the government agencies. Right or that could help the comment agencies in different verticals. So we just enabled the in the agency's through the partners. And the focus of this program is all about partner enablement. >>Well, for just ahead of a does a i machine learning enablement in partnership, part of public sector with a W. S. This is our special coverage. Well, for thanks for coming on being a cube virtual guest. I wish we could be in person, but this year it's remote. This is the cube virtual. I'm John for a year. Host of the Cube. Thanks for watching. >>Thanks a lot, John.

>> Announcer: Live from Las Vegas, it's theCUBE covering AWS re:Invent 2019. Brought to you by Amazon Web Services and Intel along with its ecosystem partners. >> Okay, welcome back to theCUBE, everyone. We're live in Las Vegas for AWS exclusive coverage of Amazon Web Services re:Invent 2019. I'm John Furrier with Stuart Miniman. Want to thank Intel for sponsoring our two sets. Shout-out to them for the sponsorship bringing great content to you from SiliconANGLE. Our next guest is with the NFL, and Andy Jassy just consummated a deal here in Las Vegas with Roger Goodell, the commissioner of the National Football League, on a new strategic initiative to use next gen stats, Amazon cloud, that whole data infrastructure with the NFL to change the profile and posture for safety and for all the athletes. And the guest here, we have Dr. Jeff Crandall, who's the chairman of the NFL Engineering Committee. Thanks for coming on. >> Thanks for having me. >> So I saw your guys' speech up there as part of the announcement with Dr. Matt Wood and a fellow NFL executive. This is a really cool initiative, because the NFL, you guys have a lot of data geeks there. You have an enormous amount of data. We see stat tasks and next gen stats from Amazon on TV. There's been a lot of advertising dollars doing that. Pretty cool. You're taking it to a next level. Explain the program you're doing. It's got $300 million in funding behind it. You started three years ago. Take a minute to explain. >> Sure, I think one of the things, it's $100 million, but-- >> Okay. >> Not quite $300 million yet. But if you look at it, it was part of an initiative the league developed to say what could they do about safety. I think part of the thing that not everyone recognizes is what the NFL does for safety and innovation, how much effort they put into that. So I'm part of an engineering effort called the engineering road map, and really what we want to do there is we thought there was an opportunity to transform the space for head protection by us putting our understanding in, creating tools, we could help those in the market develop better equipment and better protect our players. >> And so one of the things I'm learning is that you guys have tons of data, and I learned a fun stat that the fastest runner this year was running at, what, 22 miles an hour. >> Jeff: Yeah. >> But you guys are collecting a lot of data from the equipment, surface, everything. Can you explain some of the insight into the data collection, specifically the amount and diverse types. >> Yeah, that's one of the things that we've learned is in order to make effective interventions, you really have to have a handle on the breadth of what's going on on field, and in order to do that, it's a very fast, dynamic game, so you'd have to have a number of data sources coming in. You need to know about the game itself, the plays, the position, the particular play types. You need to know about the players. What speed, what's their position, what orientations, what routes. And then their environment, the surfaces, the equipment. What helmets are they wearing, what shoes. So we're trying to say what we have for extrinsic factors, what's in the environment, and then the intrinsic factors, what do the players themselves experience for factors. >> We know that data can be a differentiator, but it sounds like data like this will help the entire ecosystem. Can you speak a little bit to the medical community, the equipment manufacturers, the teams that have to build new stadiums. We've interviewed some of the architects that put a lot of technology into the stadiums themselves. So how does that data flow happen? >> Yeah, that's one of the things. We have so much data that we're able to create sort of an evaluation of what's happening to the players and what they're experiencing. And I think very few other sports, even, or very few other applications have that level of quantification. And so what we're looking at is how do each of those factors contribute to how players train, how players perform, how players are injured. And so by having that, we can come up with something we've called the digital athlete, which is essentially a virtual representation. And through that virtual representation, we start to understand how any of these factors influence the dimensions of performance and injury. It scales broadly to anything where the body would be stressed or loaded or trained. Any of those applications could benefit from what we're doing. >> So your simulation, that's a digital twin in parlance of IT nerds here. >> Sure. >> But this is a really killer idea because you can do many simulations that the cloud will provide, right? >> Jeff: Sure. >> I mean, and you got video to match it. So talk about that dynamic. 'Cause you got video and you got data points. >> Jeff: Yeah. >> They're kind of working together. >> Exactly. And so I think if you want to take the digital twin analog, I mean, one of the unique things about that is that you can have this virtual representation and you get this continuous input of feeds from sensors, from video, and you start to refine what that digital twin looks like, whether it's a player or whether it's a mechanical device. And the more feeds and the more data you have and the more time goes on, the better represented that is. And so that's really what we're gearing towards. >> Yeah, one of the things I abstracted out of the presentation was honestly, the head injuries, helmet, that's clear. That's got to get done. You're working hard around that. But there was a mention of lower body injuries, as well. So it's not just head. There's other things you guys are thinking about. Can you expand on what that might look like and how you guys are thinking about it? >> Sure, I mean, obviously we want to make sure whole body, head to toe, we're protecting the players the best we can. I think if you look from an injury frequency or an injury burden standpoint, time lost for players, lower limb is one of the major injuries in that calculation. And so what we're doing is we've been working on concussions and helmets for the last three or four years. We've been working on cleats and turf for a long time. We're starting to curate that data, and that will go into our digital twin, digital athlete platform. >> It's like they're having LIDAR. It's like when my car backs up and stops, maybe when there's a rollover coming over, an alert kicks the leg around the right spot. But this is what, the kind of thing you guys are thinking about, the rule changes and the innovation and safety is, you can actually make direct impact. So there was a rule change on kickoffs. >> Jeff: Sure. >> Talk about that dynamic, 'cause this is kind of a teaser of where things might go, right? >> Yeah, exactly. I think if you look at injury prevention, they obviously talk about where can you change? You can do it with engineering, you can do it with education, or you can do it with enforcement or rules. And what we've learned is that we can take the data we're gathering and do data-driven initiatives on any of those. We've done a kickoff rule that was informed by data. We've done a use of helmet, leading with the helmet rule. So I think the same underlying data leads to any of these application areas. >> And the results just on the numbers. You guys quoted some stats. What was the reduction in concussions last year? >> Last year there was a reduction in games of 29% for concussions. >> Awesome. >> That's great. I've been watching a lot of the 100-year football anniversary here, and it's evident how much technology has been having an impact. Gives us a little bit of how the AWS and NFL, where we'll see that going in the coming decades and beyond. >> So I think historically, we've had very strong medical, we've had very strong engineering. What we've been seeing, though, is we've been doing a lot of stuff manually. It's been very labor-intensive. We've had some wins and successes, as you just heard from last year, but now we're looking to scale and accelerate that. So building on what AWS brings to the table in terms of their data analytics, their cloud computing. We believe we can do a better job understanding what's happening on field and lead to interventions and innovations much more quickly, much more broadly than currently exist. >> For the folks watching, we're here at an IT show or cloud show. You guys are immersed in data, so you're leaning on AWS for a lot of the expertise on scale, machine learning. They got a lot of goodness in their portfolio, but you guys have the data. So for other companies that are looking at this transformation with the top-down leadership model that you guys have, what have you learned? What is some of the scar tissue you might have from the process you've been through? Any observations or learnings you could share around the order of magnitude, approaches. Is there some paths that you'd recommend? >> Well, I think one of the things we've learned is there's a hard way and there's a more efficient way. We've had as many as 17 people looking at videos, and it led us to believe, we've looked at more than 100,000 helmet impacts manually. There's got to be a better way. And so we actually spent two years talking with tech companies, exploring what was out there, before we came to this AWS partnership. So I think when we look at the future and look at the opportunities, I would say where we were bounded previously and we were looking at maybe an immediate horizon, now what we've said is let's wipe the slate clean. Let's see where we want to end up far into the future. Let's look at what we would build, something to be scalable that we could leverage. >> And this is a pretty significant announcement, 'cause Roger Goodell was here with Andy Jassy. So it's not just a tech deal. This is a bigger play here. >> Jeff: Yeah. >> Can you give some insight into the strategic impact of the AWS-NFL piece? >> So AWS has had a relationship with the league, and one of the primary things they've done is the next gen sport, the next gen stats, rather, tracking player motion on field. You know, you've seen a lot of the stats that come up in games. And so there was an idea how we could take data, leverage it. That was more for fan engagement. But that very same information, we've looked at collisions. We take next gen stats data with two players coming together. What's the closing velocity? What are the closing angles? And so I think what you've seen is how you can take this wealth of data in the NFL and by taking those that are sort of best in class and innovators with the data analytics and machine learning, what else can you extract from the data that may not have been evident without sort of a broader computing platform. >> You know, a lot of people look at the NFL. They see the big networks who cover the sport for the fan experience. There's kind of a nerd culture going on with NFL and the fan base. We've been hearing feedback all the time about theCUBE becoming a broadcaster for NFL. Has that been kicked around at Roger's level yet? (Jeff laughs) Has it gotten there? >> Well, I was thinking of doing digital twins of you guys. (John laughs) I was just sizing it up. But I'm not sure we're quite there yet. >> Dr. Crandall, thank you so much for coming on. Congratulations. What a great initiative. You guys are being transparent, forthright with your research. It's open. Congratulations. It's a good step. >> Great. My pleasure. Appreciate it. >> Thanks for coming on. I'm John Furrier, Stuart Miniman, with the NFL here as part of the big announcement on Thursday with Andy Jassy and the commissioner of the NFL, Roger Goodell, it's theCUBE getting you all the action here at re:Invent. We'll be right back with more after this short break. 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>> Announcer: Live from the Moscone Center. It's theCUBE! Covering AWS Summit San Francisco 2018. Brought to you by Amazon Web Services. (upbeat techno music) >> Hey, welcome back, everyone. We're live here in San Francisco. This is theCUBE's exclusive coverage of AWS Amazon Web Services Summit 2018 with my co-host Stu Miniman. We have two great guests. Hot startup from SingleFx, the CEO, Karthik Rau, and the CTO, Arijit Mukherji. Welcome to theCUBE. Good to see you again. >> Karthik: Yeah, great to see you again. Thanks for having us. >> So, we've been following you guys. You've been out five years. Two years in stealth, three years ago you launched on theCUBE. >> Karthik: Right here on theCUBE. >> We see you at AWS and VMware. Cloud's changed a lot. So, let's get an update. Karthik, take a minute to explain where you guys are at now company-wise, employees, traction momentum, product. Where are you guys at now? >> Karthik: Yeah, absolutely. So, SignalFx, first of all, let me tell you what we do. SignalFx is a realtime streaming operational intelligence solution. Basically, what that means is we collect monitoring data, operational data across the entire cloud environment, from the infrastructure all the way up to the applications. And we apply realtime analytics on that data to help people be a lot more proactive in their monitoring of these distributed environments. We launched the company in 2015. We come ... I'll let Arijit talk about our origins. We came out of Facebook. And we had a lot of experience building this to Facebook. In the past three years, we've been building up our company aggressively. We've now got hundreds of customers including several large Fortune 500 accounts, large web scale accounts like Acquia and HubSpot and Yelp and KAYAK. And we're over 100 employees now, about 120 employees. And yeah, doing great. >> So, Werner Vogels, the CTO, laid out on stage plus a great Matt Wood conversation about machine learning but the real thing that Werner laid out was the old way, the web server, multi-tier architecture stack kind of thing going on there to a more cloud DevOps horizontally scalable where sets of servers that could be spawned in parallel creates a new kind of operating model but also creates challenges around what to instrument. You know, as we would joke, someone left the lights on, implying EC2s been running. And all these kinds of things are going on. And you mentioned some of the Facebook kind of challenges. People were building their own scale. What have you guys learned and how does that apply today's modern infrastructure? What are some of the threshold challenges that companies are facing when they say, one, already there or I want to get there? How do you guys look at the main issues? >> Karthik: Do you want to take that? >> Yeah, so monitoring modern environments and infrastructure is actually quite a challenge. There's obviously a few things going around. One, as you mentioned, is the variety, the sheer variety of things. No longer just the three-tier architecture I have cloud services. I have containers. I have lambdas. I have my own applications. I have the cloud infrastructure itself that all needs to be monitored. And things are also becoming far more numerous. So, there's just many more of everything, right? And so, making sense of that space is becoming a big challenge. And our company was founded on the idea that monitoring is becoming an analytics problem. So, it's no longer about looking at individual servers or applications instances. It's more about making sense holistically over what's going on and being able to combine different types of data from different systems together to provide you with that high level view and that's the kind of functionality that we at SignalFx have been trying to provide. >> What are some of the data flows volumes look like. Cause I've heard multiple people talk about either Facebook or in open compute environments where there's just so much data coming in from the instrumentation that no human could actually get their arms around it. And you need to supplement it with machine learning and intelligence. I mean, is that something that you're seeing? What are some of the -- >> Yes, so actually what we see is different prospects or customers will be in different stages of a spectrum where maybe they were in a stage one where they're sort of using traditional architectures and then moving to these more modern systems. And as they get more modernized themselves, their use cases or the ways they wanted to do monitoring also gets more advanced. And so, we see the whole spectrum of it, as you mentioned. And so, understanding analytically how what we're is doing is great. But then you also want to take the human out of things as much as possible, right? >> Yeah. >> And make things more automated. And you want to look at the data and how things are behaving to learn from existing patterns to find outlines. So, that's really a very interesting challenge. And what I look at what we can do as a company going forward, like all the technological stuff that we can invest in, it's quite interesting. >> Yeah, Karthik, take us inside your customers. How does this modern monitoring, how does it change their business? How does it impact things like feedback loops and DevOps and everything that customers are having to deal in this kind of ever changing environment? >> Yeah, well I'll give you an example. There's a Fortune 500 company. They do product launches. And this is one of our customers and their product launches drive so much traffic that they do 80% of their business in the first two minutes of a product launch. And this is not at all uncommon in today's economy. And they're leveraging a lot of modern technologies, container architectures, serverless function architectures to spin up a bunch of capacity during these launches. And they were effectively flying blind most of the time. Because most of the traditional systems management monitoring solutions are not designed, A, to handle that volume. But, B, to handle the instant discovery requirements of if you're going to do 80% of your business in the first two minutes. So, the challenge is you're always playing defense. You're reacting to issues. And you're mostly flying blind. By leveraging SignalFx, they're getting realtime visibility, realtime discovery of these components as they're coming up. We're the only solution that can do that. So, literally within seconds of spinning up all of these containers, they're getting live streams into their dashboards, and live analytics, and live alerts. And what that's enabled them to do is be a lot more aggressive and effectively doing a lot more of these launches. So, that's driving their business and it's helping them drive their digital strategy forward. >> And microservices is really enabling you guys to be more relevant. Because truly the signal from the noise is where all these services reporting to? >> Karthik: Yeah. >> You talk about container madness. >> Karthik: There are two fundamental problems. So, one there's an architecture shift. And that's driving massive amounts of volume. You have physical machines that will live for three years in a data center. Divide it up into VMs, 10, 20 VMs per server. That'll maybe live for a few months. To now every process running in it's own container that might live for a few minutes. So, you have a massive exponential explosion in the number of components. But that's not the only problem. I was part of an architectural shift at VMware for a number of years. We weren't just affecting an architecture change. What's happening now is there's a cultural change and a process change that's happening as well. Because with containers, your development team can push changes directly out into a production environment. And what you're finding is you're going from sequential product development to parallel product development and a massive exponential increase in the number of code pushes. The only way you can operationalize that is you have to have realtime visibility in everything that's happening. Otherwise, the left arm doesn't know what the right arm is doing. >> John: And you need prescriptive and predictive analytics. >> Exactly. And you need predictive analytics to identify there's something unusual here. It's not a problem yet. But this is highly unusual and maybe it's your canary release. We need to do a code push. So, you want to roll it back. So, having that level of predictiveness becomes absolutely critical. >> Yeah, you mentioned realtime. We used to argue what really is realtime. And it was usually well in time to react to what the customer needs. What does realtime mean to your customers? Architecturally, is there something you do different to kind of understand what that means? >> Arijit: Yeah, so we actually fundamentally took a very different approach when we build a product. Where, typically, monitoring our metrics, monitoring was done with what we call a store and create or a batch-like architecture where you store all the data points that are coming in, then you create from it to any other use cases. While what we build at SignalFx is a fully end-to-end streaming architecture which is realtime. And what we mean by realtime is like two to three seconds between a data point coming through us and it's firing an alert or showing up in your chart. So, that's the kind of realtime. And it requires us to do lots of innovations up and down the stack. And we've built a lot of IP. We've got now patterns. And more are coming because the approach we took was quite novel. Different from-- >> John: You guys got a great management team. And looking at what you guys have done. I've been impressed with you guys. I want to just ask, Karthik, you mentioned about all these parallel processes that are going on. Totally agree. The process change, operationalizing an all new cultural way to create software manage the data. I mean, it really is the perfect storm for innovation. But also, it could literally screw people up. So, I got to ask you, who are you targeting for your customer? Who is the person that you talk to? Assuming it's kind of DevOps, so it's more like a cloud architect. Who do you target? Who do you sell to? Who's the buyer? Who uses your service? >> Karthik: Well, we see ... Every enterprise we see following a very similar journey. So, the first stage is, typically, you're just getting familiar with cloud. And you're probably just lifting and shifting enterprise workloads into the cloud. Probably experimenting with big data on the cloud. You're not yet doing microservices or containers or DevOps. And for them, we're still selling largely to classic IT. There just trying to get better visibility into their digital environment, you know, they're cloud environment. But then, what ends up happening is they very quickly get to what we call basically chaos. It's stage two. And it has a lot of parallels to shadow IT. What happened with SAS, where you have hundreds of different SAS tools is happening all over again with cloud but you've got hundreds or thousands of different operational tools. Different ways of doing monitoring, logging, security. And every team is doing it's own thing. And so, that's a big problem for enterprises who are trying to build best practices across their broader team. In that place, we're typically selling to departments because they don't have a centralize strategy yet. But what we find is the organizations at maturity have figured out that it's important to have certain centralized core services. And that doesn't mean they're forced on the end users. But they provide best practices around monitoring, logging, and such. And just make it easy for them to use those solutions. So, that's almost a new IT organization. It's platform engineering -- >> John: Is that a cloud architect? >> Platform engineering team, infrastructure engineering team, and they are effectively building best practices around the new stack not the traditional stack. >> So, you are or aren't targeting department level? Are you are? >> We sell to departments. But we also sell to the teams that are standardizing across the entire organization. >> So, cloud architects, for instance? >> Depends on the stage of the cloud journey. >> Or company. >> And the company, exactly. >> From an architectural standpoint, you talked that there's virtualization, there's containers, now serverless. How do you even figure out what to monitor in serverless? How fast is that changing? And how is that impacting your road map? >> So, serverless brings a very interesting challenge because they are very, very ephemeral. Like they're ephemeral in some sense. So, we realize there are two things. One is serverless, there's a reason why things are moving faster. It's because you want to be able to move faster. But then you also need to be able to monitor faster. It's no good monitoring serverless at five minutes later, for example. So, one of the things we invested in was how to get metrics, etc. and telemetry from these serverless environments in a very fast fashion. And that's something that we've done. The second thing we are doing that really works for this environment is afterall it's not about how many times a serverless function ran, it's about the value that it's providing the application that's running on it. And by focusing on a platform that let's you send these application metrics in great detail and then be able to monitor and analyze them, I think really amplifies the value in some sense. So, those are the two ... >> John: And talk about the ecosystems. One of the things I want to ask you guys because we've been seeing a collision between a lot of the different clouds. Clients want multicloud. Well, obviously, we're here at Amazon. They believe they should be the only cloud. But I think most customers would look at either legacy systems with some instrumentation and operational data to edge of the network, for instance. I mean, look at the edge of the network. That's just an extension of the data center depending on how you look at it. So, how do you guys view that kind of direction where customer says, "Hey, you know, I got a cloud architect. We're on Amazon. Of course, we have some old Microsoft stuff. So, we've got Azure going up there. We're kicking the tires on Google. And I got this whole IoT Edge project. SignalFx, instrument that for me. (laughs) Is that what you do? Or how do you deal with that? How would you deal with that kind of conversation? >> Well, I think most enterprises, the larger companies we see looking at multiple clouds. And they have different workloads running in different clouds, depending on the needs and what they're looking to do. So, the nice thing about a solution like SignalFx is we span all of these different architectures. And what we find is that most of the larger companies want to separate their business process solutions from their runtime architectures. Because they want to have a solution like SignalFx that it doesn't matter who you're using. If you choose to have your analytics intensive workloads in Google Cloud and your eCommerce workloads in Amazon, but you only want one system that will page someone in the middle of the night if there's a problem, then you have SignalFx to do that. And then you have your choice of runtime environments depending on what your developers need or what the business demands. We provide a lot of that glue across the different environments. >> Do you see that as the preferred architecture with most customers? Cause that makes a lot of sense. I mean, whether you're doing other data services, it kind of makes sense to separate out. Is that consistent? >> To have different applications >> Yeah. >> In different clouds? It depends. I mean, I think we see some people who are more comfortable running on a single cloud vendor and they make the decision based on what a portfolio of platforms and service features that are available. And they really like those, and they say it's easy to just go with one. But more often, we find people wanting to at least have some percentage running in a different cloud vendor. >> John: All right, final question. What's the secret sauce for the company? Tell us about the secret sauce. >> Arijit: I think-- >> We got the patents. I heard patents. You don't have to show all this exactly. But what is the secret DNA of the tech? What's the magic? >> I think it's our very unique architecture. It's entirely different from what you have. It's streaming and it focuses on scale, on timeliness, as well as on analytics capability. I think that unique combination is very special for us. And that, in a way, sort of allows us to address very, very different use cases, including this hybrid environments and what not, in a very effective way. So, it's a very, very powerful platform that can be used for many use cases. >> All right, so that was John's final question. Karthik, I've got one last one for you. What's it like being a CEO of a software company in the cloud era today compared to what it's been earlier in our career? >> Well, it's moving very, very quickly, right? I mean, technology always move very quickly. But I think compared to when I was at VMware in the mid 2000s, it just feels like every 18 months there's a new technology wave. You know, when we started our company five years ago, that was the first year that AWS eclipsed a billion dollars in sales and Dagra hadn't even launched. It launched a month after we started the company. And then serverless came. And now function architecture is all there. So, there's just so much change happening, and it's happening so quickly, it forces vendors like us to really be on the cutting edge and forward looking and making sure that you're keeping an eye out for what's coming cause the markets are moving way faster, I think, then they were 15 years ago. >> John: Well, Karthik, thanks so much. We appreciate you guys coming on, SignalFx. I'll give you the final word on the interview. Take a minute to share something with the audience that they might not know about SignalFx that they should know about. >> Well, I think what people may not realize is how realtime we can actually get. I think most people are used to doing all their monitoring and observation, and they think of realtime in the order of minutes, or if you can get stuff every 30 seconds. We really are the only realtime solution. That's why we say real realtime. We're on the order of seconds. You can build really, really sophisticated analytics and get visibility like you can't anywhere else. So, it's real, realtime. >> And that's soon to be table stakes. TheCUBE is realtime. We're live right here, on theCUBE here, in San Francisco at Amazon Web Services, AWS Summit 2018. We've been covering all the Amazon re:Invents since it started, of course. I'm John Furrier with Stu Miniman. Back with more live coverage after this short break. (upbeat techno music) (gentle instrumental music)

>> Announcer: Live from the Moscone Center, it's The Cube covering AWS Summit San Francisco 2018. Brought to you by Amazon Web Services. >> Welcome back to our exclusive Cube coverage here at AWS, Amazon Web Services Summit 2018 in San Francisco. I'm John Furrier with my cohost, Stu Miniman. We have a special guest. We have an influencer, authority figure on AWS, Corey Quinn, editor of Last Week in AWS, also has got a podcast called Screaming, >> Corey: In the Cloud. >> Screaminginthecloud.com just launched. Corey, great to have you on. Thanks for joining us. >> No, thank you for letting me indulge my ongoing love affair with the sound of my own voice. (laughing) >> Well we love to have you on and again, love the commentary on the keynote on Twitter. Lot of action, we were in the front row, kind of getting all the scene. Okay, if you're going to write the newsletter next week for what happened this week, if this week was last week, next week, what's your take on this? Because again, Amazon keeps pounding the freight train that's just the cadence of AWF announcements. But they're laying it out clear. They're putting up the numbers. They're putting out the architecture. They're putting out machine learning. It's more than developers right now. What's your analysis, what's your take of what's happening this week? >> I think that certain trends are continuing to evolve that we've seen before where it used to be that if you're picking an entire technology that you're going to bet your business on, what you're going to build on next. It used to be which vendor do I pick, which software do I pick? Now even staying purely within the AWS ecosystem, that question still continues to grow. Oh so I want to use a database, great. I have 12 of them that I can choose between. And whatever I pick, the consensus is unanimous, I'm wrong. So there needs to be, I still think there needs to be some thoughtful analysis done as far as are these services solving different problems. If so, what are the differentiating points? Right now, I think the consensus emerges that when you look into a product or service offering from AWS, the first reaction all of us feel is to some extent confusion. I'm lost, I'm scared. I don't really know what's going on. And whatever I'm about to do, I feel like I'm about to do it badly. >> Yes, scale is the big point. I want to get your reaction. Matt Wood, Dr. Matt Wood, Cube alum, been on many times, he nailed it I thought when he said, look it, machine learning and data analysis was on megabytes and gigabytes, they're offering petaflop level compute, high performance, and then Werner Vogels has also said something around the services where, you can open things up in parallel scale. So, what's your reaction to that, as you look at that and say whoa, I've got a set of services I can launch in parallel, and the scale of leveraging that petaflops. I mean, this is kind of like the new, you know, compute model. Your reaction is it real? Are customers ready for it? Where are we in that evolutionary customer journey? Are they still cavemen trying to figure out how to make fire and make the wheel? I mean where are we with this? >> I think that we see the same thing continuing to emerge as far as patterns go, where they talk about, yes there's this service. Just start using it and it scales forever. And that's great in theory, but in practice, all of the demos, all of the quick starts, all of the examples, paint by numbers examples that they'll give you, tend to be at very small scale. And yes, it works really well when you have effectively five instances all playing together. When you have 5,000 of those instances, a lot of sharp edges start to emerge. Scale becomes a problem. Fail overs take far longer. And let's not even talk about what the bill does at that point. Additionally once you're at that point, it's very difficult to change course. If I write a silly blog, and effectively baby seals get more hits than this thing does, it's not that difficult for me to migrate that. Whereas if I'm dealing with large scale production traffic that's earning me money on a permanent basis, moving that is no longer trivial or in some cases feasible at all. >> Yeah Corey, how does anybody reasonably make a decision as to how they're going to build something because tomorrow, everything might change. You said oh okay great, I had my environment and I kind of you know, built my architecture a certain way, oh wait there's a new container service. Oh, and start building a, oh wait now there's the orchestrated version of that that I need to change to. Oh wait, now there's a serverless built way that kind of does it in a similar way. So you know, it seems like it used to be the best time to do things would've been two months ago, but now I should do it now. Now the answer is, the best time for me to do things would be if I could wait another quarter, but really I have to get started now. >> I tend to put as much on future Corey as I possibly can. The problem is that at one time I could've sat here and said the same thing to you about, oh virtualization is the way to go. You should migrate your existing bare metal servers there. And then from virtualization to Cloud and Cloud to containers. Then containers to serverless. And this narrative doesn't ever change. It's oh what you're doing is terrible and broken. The lords of thought have decried that now it's time to do this differently, and that's great, but what's the business use case for doing it? Well, we did this thing that effectively people get on stage at keynotes and make fun of us for now, so we should really change it. Okay maybe, but why? Is there a business value driving that decision? And I think that gets lost in the weeds of the new shiny conference ware that gets trotted out. >> Well I mean Amazon's not, I mean they're being pretty forthright. I mean, you can't deny what Intuit put out there today. The Intuit head of machine learning and data science laid out old way, new way. Classic case of old way, new way. Eight months, six to eight months, ton of cluster, you-know-what going on as things changed it. They're just data scientists. They're not back-end developers. They went to one week. Nine months to one week. That's undeniable right? I mean how do you, I mean that's a big company but, that seems to be the big enchilada that Amazon's going for, not the pockets of digital disruption. You know what I'm saying? So it's like, how do you square that out? I mean how do you think about that? >> Cloudability had a great survey that they released the results of somewhat recently where they were discussing that something like four or five of the, or I'm sorry 85% of the global spend on AWS went to four or five services that all have been around for a long time. RDS, EC2, S3, PBS, Data Transfer. And so as much as people talk about this and you're seeing pockets of this, it's not the common gaze by a wide margin. People don't get up on stage and talk about, well I have these bunch of EC2 instances behind a low balancer, storing data on S3 and that's good enough for me, because that's not interesting anymore. People know how to do that. Instead, they're talking about these far future things that definitely add capability, but do come at a cost-- >> I mean it's the classic head room. It's like here's some head room, but at the end of the day it's EC2, S3, Kinesis, Redshift, bunch of services that's U.S that seem to dominate. The question I want to ask you is that they always flaunt out the, every year it changes, Kinesis was at one point the fastest growing service in the history of AWS. Now it's Aurora. We made a, I made a prediction on the opening that a SageMaker will be the fastest growing service, because there just seemed to be so much interest in turn-key machine learning. It's hard as you-know-what to do it. >> I agree. >> Your thoughts on SageMaker? >> In one of my issues a few weeks back, I wound up asking, so who's using SageMaker and for what? And the response was ridiculous. What astounded me was that no two answers were alike as far as what the use case was. But they all started the same way. I'm not a data scientist, but. So this is something that's becoming-- >> John: What does that mean to you? What does that tell you? >> It tells me that everyone thinks they're unqualified to be playing around in the data science world, but they're still seeing results. >> But Corey I wonder because you know, think back a few years ago. That's what part of the promise of big data, is we have all this data and we're going to be able to have the business analysts rather than you know, some PhD sort this out. And machine learning is more right. We want to have these tools and we want to democratize data, you know. Data is the new bacon. It's the new oil. Data's the new everything. So you know, machine learning, you think this is all vapor and promise, or do you think it's real? >> I think big data is very real and very important. Ask anyone who sells storage by the gigabyte. And they will agree with me. In practice I think it's one of those areas where the allure is fascinating but the implementation is challenging. Okay we have history going back 20 years of every purchase someone has ever made in our book store. That's great, why do I still wind up getting recommendations? >> Well yeah and I guess, I want to talk that it was the, I see it more as, everything that was big data is now kind of moving to the ML and AI stage. Because big data didn't deliver on it, will this new wave deliver on the promise of really extracting value from my data? And it's things like this, live data. It's doing things now with my data, not the historical, lots of different types of data that we were trying to do with like the Hadoops of the world. >> Got ya. I think it's a great move because either yes it will or no it won't, but if it doesn't, you're going to see emergent behaviors of so why didn't it work? Well we don't understand the model that this system has constructed, so we can't even tell you why it's replacing the character I with some weird character that's unprintable, so let alone why we decide to target a segment of customers who never buys anything. So it does become defensible from that perspective. Whether there's something serious there that's going to wind up driving a revolution in the world of technology, I think it's too soon to say and I wouldn't dare to predict. But I will be sarcastic about it either way. >> Okay well let's get sarcastic for a second. I wan to talk to you about some moves other people are making. We'll get to the competition in a minute but Salesforce required MuleSoft. That got a lot of news and we were speculating on our studio session this week or last week with the CEO of Rubric that it's great for Salesforce. It can bring structured data in, on PRIM and the Cloud. Salesforce is one big SaaS platform. Amazon is trying to SaaS-ify business through the Cloud. So, but one of the things that's missing from MuleSoft is the unstructured data. So the question for you is, how are you seeing and how is your community looking at the role of the data as a strategic asset in a modern stack, one, both structured and unstructured data, is that becoming, even happening or is it more like, well we don't even know what it means. Your thoughts? >> I think that there's been a long history of people having data in a variety of formats and being able to work with that does require some structure. That's why we're seeing things emerging around S3's, increasing capabilities, being able to manipulate data at rest. We're seeing that with S3 and Glacier Select. We're seeing it with Athena which is named after the goddess of spending money on Cloud services, and there's a number of different tooling options that are, okay we're not going to move three x-abytes of data in so we have to do something with where it is. As far as doing any form of analysis on it, there needs to be some structure to it in order for that to make sense. From that perspective, MuleSoft was a brilliant acquisition. The question is, is what is SalesForce going to do with that? They have a history of acquisition, some of which have gone extremely well. Others of which we prefer not to talk about in polite company. >> It comes back down to the IDE thing. How many IDE's does Salesforce have now? I mean it's a huge number. >> I'm sure there's three more since we've started talking. (laughing) >> Yeah so Corey, you brought up, you know, money. So you know, the trillion dollar, what feedback are you getting from the community? You know there's always, well I get on Amazon and then my bills continue to grow and continue to grow. Same thing at Salesforce by the way if you use them. So you know, there's always as you gain power, people will push back against it. We saw with with Mike Hichwa with Oracle. I hear it some but it's not an overriding thing from when I talk to customers about Amazon. But I'm curious what you're hearing. Where are the customers feeling they're getting squeezed? Where is it you know, phenomenal? What are you seeing kind of on the monetary side of Cloud? >> In my day job, I solve one problem. I fix the horrifying AWS bill, both in terms of dollars and cents as well as analysis and allocation. And what astonishes me, and I'm still not sure how they did it. It's that AWS has somehow put the onus onto the customer. If you or I go out and we buy a $150,000 Ferrari, we wake up with a little bit of buyer's remorse of dear lord, that was an awful lot of money. When you do the equivalent in AWS, you look at that, and instead of blaming the vendor for overcharging, instead we feel wow, I'm not smart enough. I haven't managed that appropriately. Somehow it's my fault that I'm writing what looks like a phone number of a check every month over to AWS. >> John: It creeps up on you. >> It does. It's the boiling a frog problem. And by the time people start to take it seriously, there's a lot of ill will. There's a sense of, our team is terrible, and wasn't caring about this. But you don't ever cost-optimize your way to success. That's something you do once you have something that's up and working and viable. You don't start to build a product day one for the least possible amount of money and expect to attain any success. >> Well let's talk about that real quick to end the segment because I think that's a really important thing. Success is a double-edged sword. The benefit of the Cloud is to buy what you need, get proof of concept going, get some fly wheels going or whatever, virtuous circle of the application. But at some point, you hit a tipping point of oh shit this is working. And then the bill is huge. Better than over-provisioning and having a failed product. So where's that point with you guys or with your customers? Is there like analytics you do? Is that more of a subjective qualitative thing? You say, okay are you successful? Now let's look at it. So how do you deal with customers? 'Cause I can imagine that success is, it becomes the opportunity but also the problem. >> I think it's one of those, you know it when you see it type of moments, where if a company is spending $80,000 a month on their Cloud environment and could be spending 40, that's more interesting to a company that's three people than it is to an engineering team of 50. At that point, sorry they're embezzling more than that in office supplies every month. So that's not the best opportunity to start doing an optimization pass. More important than both of those scales to me has always been about understanding the drivers of it. So what is it that's costing that? Is it a bunch of steady state things that aren't doing work most of the time? Well, maybe there's an auto-scaling story in there. Maybe there's a serverless opportunity. Maybe nobody's using that product and it's time to start looking at rolling it in to something. >> They've left the lights on right? So to speak. >> Exactly. >> The server's are still up. Wait a minute, take them down. So, writing code, analytics, is that the answer? >> All of the above. In a vacuum, if you spin up an instance today, and don't touch it again, you will retire before that instance does. And it will continue to charge you every hour of every day. Understanding and being able to attribute who spun that up, when was it done, why was it done, and what project is it tied to? Is it some failed experiment someone did who hasn't worked here in six months? Or is that now our master database? We kind of need to know in either direction what that looks like. >> Alright before we wrap, you got to tell us, what do we expect to hear from your podcast? >> Good question. My podcast generally focuses on one-on-one conversations with people doing interesting things in the world of Cloud, which is vague enough for me to get away with almost anything as far as it goes. It's less sarcastic and snarky than some of my other work, and more at the why instead of the how. I'm not going to sit here and explain how to use an ABI. There are people far better at that than I am. But I will talk about why you might use a service, and what problem it reports to solve. >> Alright Corey, great to have you on. Uh the Screaming Pod, Screaming Cloud, >> Corey: ScreamingInTheCloud.com >> ScreamingInTheCloud.com, it's a podcast. Corey thanks for coming on and sharing the commentary, the insight on AWS, the how and the why, the Cube breaking down. All the action here in Moscone Western San Francisco, AWS 2018 Summit, back after more, after this short break. (spacey music)

>> Announcer: Live from Manhattan it's the Cube. Covering AWS Summit New York City 2017. Brought to you buy Amazon Web Services. >> Hello and welcome to the Big Apple. AWS Summit kicking off here at the Javits Convention Center New York, New York. Along with Stu Miniman, I'm John Walls, welcome to the Cube as we continue our coverage here. Really I feel like this is ongoing, Stu, as far as what we're doing with AWS (mumbles) public sector summit. AWS from the outside in for a very long time. So tell me what you make of this. I mean regional show, we probably have four or 5,000 folks here, good turnout. What's the vibe you got, what's the feeling? >> It's really interesting 'cause we've covered a few of the regional summits but it's the first one that I've attended. I'm actually already have been starting to plan for AWS reinvent, which is the big show in November. Expecting probably around 50,000 people at that show, but I think four years ago, four and a half years ago when I went to the first (mumbles) summit in Las Vegas, it was about the size of what this show is. So Adrian Cockcroft got up on stage, said there were about 20,000 people registered. Of course registered doesn't mean that they're all here. A lot of people I know watching the live stream as well as it's free to attend so if I'm in New York City, there's just a few people in New York that care about tech probably. So maybe they'll pop in sometime for today, but in the keynote there's definitely a few thousand people. It's a good sized expo hall here. This could be a five or 6,000 person event for the size of the expo hall that they have here, and the Javits center can really hold some big activity here. Impressive at scope because Amazon and the cloud is still in early days. As Jeff (mumbles) says, there is no day two, we're always day one and what's going on. Went through a lot of announcements, a lot of momentum, a lot of revenue in this big cloud thing. >> You talk about Adrian too, we'll get to his keynote comments in a little bit. Talking about revenue growth still in the uptick year to year 42%. So still going there, but then on the other side you do se some writing going on that maybe upticks slowing down just a hair as far as cloud deployment goes. >> Yeah that's a great thing, 'cause we're all staring at the numbers and it's no longer, Amazon right now is not growing 75, 80% as opposed to the companies trying to catch up to them, like Microsoft, is growing at more of that 75 (talking over each other) >> But Amazon if you look at infrastructured service, is the largest out there. What was it, it was a 16 billion dollar run rate looking at the last 12 months looking back. Still over 40% growth rate. So yes is the growth slowing down a little bit, but that's just because they're not at a big number so it's a little tougher, but they keep adding services, they keep adding users. Some big users up on stage, some new services getting announced because the way Andy Jassy puts it, I mean everyday when you wake up, there's another three services from Amazon. So it's not like they had to say, oh geeze, can we hold something off? I go to the typical enterprise show and it's like, oh we're going to have this bundle announcements that we do. Amazon could have one of these every week somewhere and everyday could be like, here's three new services and they're kind of interesting because everyday that's kind of what they have. >> Yeah and I don't mean to paint it like the wolf is at the door, by any means, but the competitors are at the door. So how much of that factors into this space (mumbles) you pointed everybody else has this huge market share. They're not even (mumbles) they're like the elephant and the gorilla in the room, but at the same time, you do, as you're coming on, Google's still out there looking. There's another player as well. >> Well if you talk to the Amazon people, they don't care about the competitors, they care about their customers. So they focus very much on what their customers are doing. They work on really small teams. If we want to talk about a couple of the announcements today, one of the ones that, at least the community I was watching, it's AWS glue, which really helps to get ETL, which is the extract, transform, and load really a lot of the heavy lifting and undifferentiated heavy lifting that data scientists are doing. Matt Wood, who was up on the keynote said 75% of their time is done on this kind of stuff, and here's something that can greatly reduce it. Few people in the Twitter stream were talking about they've used the beta of it. They're really excited. It was one that didn't sound all that exciting, but once you get into it it's like, oh wow, game changer. This is going to free up so much time. Really accelerate that speed of what I'm doing. Adrian Cockcroft talked about speed and flight freeing me from some of the early constraints. I'm an infrastructure guy by background and everything was like, and I've got that boat anchor stuff that I need to move along and the refresh cycles, and what do I have budget for today? And now I can spin things up so much faster. They give an example of, oh I'm going to do this on Hive and it's going to take me five years to do it as opposed to if I do it in the nice AWS service it takes 155 seconds. We've had lots of examples like this. One of the earliest customers I remember talking to over four years ago, Cycle Computing was like, we would build the super computer and it would have taken us two years and millions of dollars to build, and instead we did the entire project in two months and it cost us $10,000. So those are the kind of transformational things that we expect to hear from Amazon. Lots of customers, but getting into the nuance of it's a lot of building new service. Hulu got on stage and it wasn't that, they didn't say we've killed all of our data centers and everything that you do under Hulu is now under AWS. They said, we wanted to do live TV and live TV is very different from what we had built for in our infrastructure, and the streaming services that Amazon had, and the reach, and the CDN, and everything that they can do there makes it so that we could do this much faster and integrate what we were doing before with the live TV. Put those things together, transformational, expand their business model, and helps move forward Hulu so as they're not just a media company, they're a technology company and Amazon and Amazon support as a partner helps them with that transformation. >> So they're changing their mission obviously, and then technologically they have the help to do that. Part of the migration of AWS migration, we talked about that as well, one of those new services that they rolled out today. I think the quote was migration is a journey and we're going to make it a little simpler right now. >> Yeah we've been hearing for the last couple of years the database. So you know whether I've got Oracle databases, whether it was running SQL before. I want to migrate them, and with Amazon now, I have so many different migration tools that this migration hub now is going to allow me to track all of my migrations across AWS. So this is not for the company that's saying, oh yeah I'm tinkering with some stuff and I'm doing some test dev, but the enterprise that has thousands of applications or lots of locations and lots of people, they now need managers of managers to watch this and some partners involved to help with a lot of these services, but really sprawling all of the services that Amazon have every time they put up one of those eye charts with just all of these different boxes. Every one of them, when you tend to dig in it's like, oh machine learning was a category before and now there's dozens of things inside it. You keep drilling down, I feel like it's that Christopher Nolan movie, Inception. We keep going levels deep as to kind of figure it out. We need to move at cloud time, which is really fast as opposed to kind of the old enterprise time. >> We hit on machine learning. We saw a lot of examples that cut across a pretty diverse set of brands and sectors, and really the democratization of machine learning more or less. At least that was the takeaway I got from it. >> And absolutely. When you mention the competition, this is where Google has a strong position in machine learning. Amazon and Microsoft also pushing there. So it is still early days in machine learning and while Amazon has an undisputed lead in overall cloud, machine learning is one of those areas where everybody's starting from kind of the starting point and Amazon's brought in a lot of really good people. They've got a lot of people working on teams and building out new services. The one that was announced at the end of the keynote is Amazon Macie, which is really around my sensitive data in a global context using machine learning to understand when something's being used when it shouldn't and things like that. I was buying my family some subway tickets and you could only buy two metro cards with one credit card because even if I put in all the data, it was like, no we're only going to let you buy two because if somebody got your credit card they could probably get that and do that. So that's the kind of thing that you're trying to act fast with data no matter where you are because malicious people and hackers, data is the new oil, as we said. It's something that we need to watch and be able to manage even better. So Amazon keeps adding tools and services to allow us to use our data, protect our data, and harness the value of data. I've really said, data is the new flywheel for technology going forward. Amazon for years talked about the flywheels of customers. They add new services, more customers come on board that drives new services and now data is really that next flywheel that's going to drive that next bunch of years of innovation to come. >> You've talked a lot about announcements that we just heard about in the keynote. Big announcement fairly recently about the cloud data computing foundation. So all of the sudden they, I'd say not giving the Heisman, if you will, the Kubernetes, but maybe not embracing it, right? Fair enough to say. Different story now. All of the sudden they're platinum level on the board. They have a voice on how Kubernetes is going to be rolled out going forward, or I guess maybe how Kubernetes is going to be working with AWS going forward. >> And my comment, I gave a quote to SiliconANGLE. I'm on the analyst side of the media. This side had written an article and I said, it's a good step. I saw a great headline that was like, Amazon gives $350,000. They're at least contributing with the financial piece, but when you dig in and read, there was a medium blog post written by Adrian Cockcroft. He didn't touch on it at all in the keynote this morning. Which I was a little surprised about, but what he said is, we're contributing, we're greatly involved, and there's all of these things that are happening in the CNCF, but Amazon has not said, and here is our service to enable Kubernetes as a first class citizen in there. They have the AWS container service, which is ACS which doesn't use Kubernetes. Until this recent news, I could layer Kubernetes on top and there are a lot of offerings to do that. What I'd like to be able to hear is, what service is really Amazon going to offer with that. My expectation not knowing any concrete details is by the time we get to the big show in November, they will have that baked out war, probably have some announcements there. Hoping at this show to be able to talk to some people to really find out what's happening inside really that Kubernetes piece, 'cause that helps not only with really migrations. If I'm built with Kubernetes, it's built with containers. Containers are also the underlying component when I'm doing things like serverless, AWS Lambda. So if I can use Kubernetes, I can build one way and use multiple environments. Whether that be public cloud or private clouds. So how much will Amazon embrace that, how much will they use this. as well we're enabling Kubernetes so if you've got a Kubernetes solution, you can now get into another migration service to Amazon or will they open up a little bit more? We've really been watching to see as Amazon builds out their hybrid cloud offering. Which is how do they get into the customer's data center because we've seen that maturation of public cloud only, everything into the public cloud to now Lambda starts to reach out a little bit with the green grass, they've got their snow balls, they've got the partnership with VMware, which we expect to hear lots more about at VMworld at the end of this month. They've got partnerships with Redhat and a whole lot of other companies that they're working at to really expanding how they get all of these wonderful Amazon services that are in the public cloud. How do they reach into the customer's data centers themselves and start leveraging those services? All of those free services of data that are getting added. Lots of companies would want to get access to them. >> Well full lineup of guests, as always. Great lineup of guests, but before we head out, you said you're with Wikibon, you do great analyst work there and you've got that inquiring mind. You're a curious guy. What are you curious about today? What do you kind of want to walk away from here tonight learning a little bit more about? >> So as I mentioned, the whole Kubernetes story absolutely is one that we want to hear about. Going to talk to a lot of the partners. So we've seen a lot of the analytics machine learning type solutions really getting to the public (mumbles) so it's good to get a pulse of really this ecosystem because while Amazon is, we've said it's not only the elephant in the room, Dave Alante, the chief analyst at Wikibon said, they're the cheetah, they move rally fast, they're really nimble. Amazon, not the easiest always to partner with. How's the room feel, how are the customers, how are the partners, how much are they really in on AWS, how many of them are multi cloud and I'm using Google for some of the data solutions and Microsoft apps really have me involved. So Amazon loves to say people that are all in. We had one of the speakers that talked, Zocdoc, which one that allows me to set appointments with doctors much faster using technology. Analytics say rather than 24 days you could do 24 hours. They went from no AWS to fully 100% in on AWS in less than 12 months. So those are really impressive ones. Obviously it's a technology center company but you see large companies. FICO was the other one up on stage. Actually hopping to have FICO on the program today. They are, what was it, over a 60 year old company so obviously they have a lot of legacy, and how AWS fits into their environment. I actually interviewed someone from FICO a couple of years ago at an OpenStack show talking about their embrace of containers and containers allows them to get into public cloud a little bit easier. So I'd love to kind of dig into those pieces. What's the post of the customers, what's the post of the partner ecosystem, and are there chinks in the armor? You mentioned the competitive piece there. Usually when you come to an Amazon show, it's all Amazon all the time. The number one gripe usually is it's kind of pricing, and Amazon's made some moves. We did a bunch of interviews the week of the Google Next event talking about Google cloud and there was a lot of kind of small medium business that said Google was priced better, Google has a clear advantage (mumbles) I'm going away from Amazon. The week after the show, Amazon changed their pricing, talked to some of the same people and they're like, yeah Amazon leveled the playing field. So Amazon listens and moves very fast. So if they're not the first to create an offering, they will spin something up very fast. They can readjust their security, their pricing to make sure that they are listening to their customers and meeting them not necessarily in response to competitors, but getting what the customers need and therefore if the customers are griping a little bit about something that they see that's interesting, or a pain point that they've had. Like we've talked about the AWS Glue wasn't something that a competitor had. It was that this is a pain point that they saw a lot of time is on it, and they are looking to take that pain out. One of the line that always gets poked about Amazon is they say your margin is our opportunity and your pain as a customer is our opportunity too. So Amazon always listening. >> All right, a lot on the plate here this day we have for you at AWS Summit. We'll be back with much more as we continue here on the Cube and AWS Summit 2017 from New York City. (upbeat techno music)