>>Good morning and welcome back to Detroit, Michigan. My name is Savannah Peterson and I'm here on set of the cube, my co-host John Farer. How you doing this morning, John? >>Doing great. Feeling fresh. Day two of three days of coverage, feeling >>Fresh. That is that for being in the heat of the conference. I love that attitude. It's gonna >>Be a great day today. We'll see you at the end of the day. Yeah, >>Well, we'll hold him to it. All right, everyone hold 'em accountable. Very excited to start the day off with an internet, a legend as well as a cube og. We are joined this morning by Matt Klein. Matt, welcome to the show. >>Thanks for having me. Good to see you. Yep. >>It's so, what's the vibe? Day two, Everyone's buzzing. What's got you excited at the show? You've been here before, but it's been three years you >>Mentioned. I, I was saying it's been three years since I've been to a conference, so it's been interesting for me to see what is, what is the same and what is different pre and post covid. But just really great to see everyone here again and nice to not be sitting in my home by myself. >>You know, Savannah said you're an OG and we were referring before we came on camera that you were your first came on the Cub in 2017, second Cuban event. But you were, I think, on the first wave of what I call the contributor momentum, where CNCF really got the traction. Yeah. You were at Lift, Envoy was contributed and that was really hyped up and I remember that vividly. It was day zero they called it back then. Yeah. And you got so much traction. People are totally into it. Yeah. Now we've got a lot of that going on now. Right. A lot of, lot of day Zero events. They call 'em co, co-located events. You got web assembly, a lot of other hype out there. What do you see out there that you like? How would you look at some of these other Sure. Communities that are developing, What's the landscape look like as you look out? Because Envoy set the table, what is now a standard >>Practice. Yeah. What's been so interesting for me just to come here to the conference is, you know, we open source Envoy in 2016. We donated in 2017. And as you mentioned at that time, Envoy was, you know, everyone wanted to talk about Envoy. And you know, much to my amazement, Envoy is now pervasive. I mean, it's used everywhere around the world. It's like, never in my wildest dreams would I have imagined that it would be so widely used. And it's almost gotten to the point where it's become boring. You know, It's just assumed that Envoy is, is everywhere. And now we're hearing a lot about Eeb p f and Web assembly and GI ops and you know, AI and a bunch of other things. So it's, it's actually great. It's made me very happy that it's become so pervasive, but it's also fun. Yeah. We mention to, to look around all other stuff >>Like congratulate. It's just a huge accomplishment really. I think it's gonna be historic, historical moment for the industry too. But I like how it progressed. I mean, I don't mind hype cycles as long as it's some vetting. Sure. Of course. You know, use cases that are clearly defined, but you gotta get that momentum in the community, but then you start gotta get down to, to business. Yep. So, so to speak and get it deployed, get traction. Yep. What should projects look like? And, and give us the update on Envoy. Cause you guys have a, a great use case of how you got traction. Right. Take us through some of the early days of what made Envoy successful in your opinion. Great question. >>Yeah. You know, I, I think Envoy is fairly unique around this conference in the sense that Envoy was developed by Lyft, which is an end user company. And many of the projects in this ecosystem, you know, no judgment, for better or worse, they are vendor backed. And I think that's a different delivery mechanism when it's coming from an end user where you're solving a, a particular business case. So Envoy was really developed for Lyft in a, you know, very early scaling days and just, you know, trying to help Lyft solve its business problems. So I think when Envoy was developed, we were, you know, scaling, we were falling over and actually many other companies were having similar problems. So I think Envoy became very widely deployed because many companies were having similar issues. So Envoy just became pervasive among lift peer companies. And then we saw a lot of vendor uptake in the service mesh space in the API gateway space among large internet providers. So, I I I, I think it's just, it's an interesting case because I think when you're solving real problems on the ground, in some ways it's easier to actually get adoption than if you're trying to develop it from a commercial backing. >>And that's the class, I mean, almost, It's almost like open source product market fit. It is in its own way. Cause you have a problem. Absolutely. Other people have the same problem finding >>Too. I mean, it's, it's designed thinking from >>A different, When, when I talk to people about open source, I like to tell people that I do not think it's any different than starting a company. I actually think it's all the same problems finding pro product, market fit, hiring, like finding contributors and maintainers, like doing PR and marketing. Yeah. Getting team together, traction, getting, getting funding. I mean, you have to have money to do all these things. Yeah. So I think a lot of people think of open source as I, I don't know, you know, this fantastic collaborative effort and, and it is that, but there's a lot more to it. Yeah. And it is much more akin to starting a >>Company. Let's, let's just look at that for a second. Cause I think that's a good point. And I was having a conversation in the hallway two nights ago on this exact point. If the power dynamics of a startup in the open source, as you point out, is just different, it's community based. So there are things you just gotta be mindful of. It's not top down. >>Exactly. It's not like, >>Right. You know, go take that hill. It's really consensus based, but it is a startup. All those elements are in place. Absolutely. You need leadership, you gotta have debates, alignment, commit, You gotta commit to a vision. Yep. You gotta make adjustments. Build the trajectory. So based on that, I mean, do you see more end user traction? Cause I was, we were talking also about Intuit, they donated some of their tow code R goes out there. Yep. R go see the CDR goes a service. Where's the end user contributions to these days? Do you feel like it's good, still healthy? >>I, I mean, I, I'm, I'm biased. I would like to see more. I think backstage outta Spotify is absolutely fantastic. That's an area just in terms of developer portals and developer efficiency that I think has been very underserved. So seeing Backstage come outta Spotify where they've used it for years, and I think we've already seen they had a huge date, you know, day one event. And I, I think we're gonna see a lot more out of that >>Coming from, I'm an end user, pretend I'm an end user, so pretend I have some code. I want to, Oh man, I'm scared. I don't am I'm gonna lose my competitive edge. What's the, how do you talk to the enterprise out there that might be thinking about putting their project out there for whether it's the benefit of the community, developing talent, developing the product? >>Sure. Yeah. I would say that I, I would ask everyone to think through all of the pros and cons of doing that because it's not for free. I mean, doing open source is costly. It takes developer time, you know, it takes management time, it takes budgeting dollars. But the benefits if successful can be huge, right? I mean, it can be just in terms of, you know, getting people into your company, getting users, getting more features, all of that. So I would always encourage everyone to take a very pragmatic and realistic view of, of what is required to make that happen. >>What was that decision like at Lyft >>When you I I'm gonna be honest, it was very naive. I I think we've, of that we think we need to know. No, just didn't know. Yeah. I think a lot of us, myself included, had very minimal open source experience. And had we known, or had I known what would've happened, I, I still would've done it. But I, I'm gonna be honest, the last seven years have aged me what I feel like is like 70 or a hundred. It's been a >>But you say you look out in the landscape, you gotta take pride, look at what's happened. Oh, it's, I mean, it's like you said, it >>Matured fantastic. I would not trade it for anything, but it has, it has been a journey. What >>Was the biggest surprise? What was the most eye opening thing about the journey for you? >>I, I think actually just the recognition of all of the non-technical things that go into making these things a success. I think at a conference like this, people think a lot about technology. It is a technology conference, but open source is business. It really is. I mean, it, it takes money to keep it going. It takes people to keep >>It going. You gotta sell people on the concepts. >>It takes leadership to keep it going. It takes internal, it takes marketing. Yeah. So for me, what was most eyeopening is over the last five to seven years, I feel like I actually have not developed very many, if any technical skills. But my general leadership skills, you know, that would be applicable again, to running a business have applied so well to, to >>Growing off, Hey, you put it out there, you hear driving the ship. It's good to do that. They need that. It really needs it. And the results speak for itself and congratulations. Yeah. Thank you. What's the update on the project? Give us an update because you're seeing, seeing a lot of infrastructure people having the same problem. Sure. But it's also, the environments are a little bit different. Some people have different architectures. Absolutely different, more cloud, less cloud edges exploding. Yeah. Where does Envoy fit into the landscape they've seen and what's the updates? You've got some new things going on. Give the updates on what's going on with the project Sure. And then how it sits in the ecosystem vis-a-vis what people may use it for. >>Yeah. So I'm, from a core project perspective, honestly, things have matured. Things have stabilized a bit. So a lot of what we focus on now are less Big bang features, but more table stakes. We spend a lot of time on security. We spend a lot of time on software supply chain. A topic that you're probably hearing a lot about at this conference. We have a lot of software supply chain issues. We have shipped Quicken HTB three over the last year. That's generally available. That's a new internet protocol still work happening on web assembly where ha doing a lot of work on our build and release pipeline. Again, you would think that's boring. Yeah. But a lot of people want, you know, packages for their fedora or their ADU or their Docker images. And that takes a lot of effort. So a lot of what we're doing now is more table stakes, just realizing that the project is used around the world very widely. >>Yeah. The thing that I'm most interested in is, we announced in the last six months a project called Envoy Gateway, which is layered on top of Envoy. And the goal of Envoy Gateway is to make it easier for people to run Envoy within Kubernetes. So essentially as an, as an ingress controller. And Envoy is a project historically, it is a very sophisticated piece of software, very complicated piece of software. It's not for everyone. And we want to provide Envoy Gateway as a way of onboarding more users into the Envoy ecosystem and making Envoy the, the default API gateway or edge proxy within Kubernetes. But in terms of use cases, we see Envoy pervasively with service mesh, API gateway, other types of low balancing cases. I mean, honestly, it's, it's all over the place at >>This point. I'm curious because you mentioned it's expanded beyond your wildest dreams. Yeah. And how could you have even imagined what Envoy was gonna do? Is there a use case or an application that really surprised you? >>You know, I've been asked that before and I, it's hard for me to answer that. It's, it's more that, I mean, for example, Envoy is used by basically every major internet company in China. I mean, like, wow. Everyone in China uses Envoy, like TikTok, like Alibaba. I mean like everyone, all >>The large sale, >>Everyone. You know, and it's used, it's used in the, I'm just, it's not just even the us. So I, I think the thing that has surprised me more than individual use cases is just the, the worldwide adoption. You know, that something could be be everywhere. And that I think, you know, when I open my phone and I'm opening all of these apps on my phone, 80 or 90% of them are going through Envoy in some form. Yeah. You know, it's, it's just that pervasive, I blow your mind a little bit sometimes >>That does, that's why you say plumber on your Twitter handle as your title. Cause you're working on all these things that are like really important substrate issues, Right. For scale, stability, growth. >>And, you know, to, I, I guess the only thing that I would add is, my goal for Envoy has always been that it is that boring, transparent piece of technology. Kind of similar to Linux. Linux is everywhere. Right? But no one really knows that they're using Linux. It's, it's justs like Intel inside, we're not paying attention. It's just there, there's >>A core group working on, if they have pride, they understand the mission, the importance of it, and they make their job is to make it invisible. >>Right. Exactly. >>And that's really ease of use. What's some of the ease of use sways and, and simplicity that you're working on, if you can talk about that. Because to be boring, you gotta be simpler and easier. All boring complex is unique is not boring. Complex is stressful. No, >>I I think we approach it in a couple different ways. One of them is that because we view Envoy as a, as a base technology in the ecosystem, we're starting to see, you know, not only vendors, but other open source projects that are being built on top of Envoy. So things like API Gateway, sorry, Envoy Gateway or you know, projects like Istio or all the other projects that are out there. They use Envoy as a component, but in some sense Envoy is a, as a transparent piece of that system. Yeah. So I'm a big believer in the ecosystem that we need to continue to make cloud native easier for, for end users. I still think it's too complicated. And so I think we're there, we're, we're pushing up the stack a bit. >>Yeah. And that brings up a good point. When you start seeing people building on top of things, right? That's enabling. So as you look at the enablement of Envoy, what are some of the things you see out on the horizon if you got the 20 mile stare out as you check these boring boxes, make it more plumbing, Right? Stable. You'll have a disruptive enabling platform. Yeah. What do you see out there? >>I am, you know, I, again, I'm not a big buzzword person, but, so some people call it serverless functions as a service, whatever. I'm a big believer in platforms in the sense that I really believe in the next 10 to 15 years, developers, they want to provide code. You know, they want to call APIs, they want to use pub subsystems, they want to use cas and databases. And honestly, they don't care about container scheduling or networking or load balancing or any of >>These things. It's handled in the os >>They just want it to be part of the operating system. Yeah, exactly. So I, I really believe that whether it's an open source or in cloud provider, you know, package solutions, that we're going to be just moving increasingly towards systems likes Lambda and Fargate and Google Cloud Run and Azure functions and all those kinds of things. And I think that when you do that much of the functionality that has historically powered this conference like Kubernetes and Onvoy, these become critical but transparent components that people don't, they're not really aware of >>At that point. Yeah. And I think that's a great call out because one of the things we're seeing is the market forces of, of this evolution, what you just said is what has to happen Yep. For digital transformation to, to get to its conclusion. Yep. Which means that everything doesn't have to serve the business, it is the business. Right. You know it in the old days. Yep. Engineers, they serve the business. Like what does that even mean? Yep. Now, right. Developers are the business, so they need that coding environment. So for your statement to happen, that simplicity in visibility calling is invisible os has to happen. So it brings up the question in open source, the trend is things always work itself out on the wash, as we say. So when you start having these debates and the alignment has to come at some point, you can't get to those that stay without some sort of defacto or consensus. Yep. And even standards, I'm not a big be around hardcore standards, but we can all agree and have consensus Sure. That will align behind, say Kubernetes, It's Kubernetes a standard. It's not like an i e you know, but this next, what, what's your reaction to this? Because this alignment has to come after debate. So all the process contending for I am the this of that. >>Yeah. I'm a look, I mean, I totally see the value in like i e e standards and, and there's a place for that. At the same time, for me personally as a technologist, as an engineer, I prefer to let the, the market as it were sort out what are the defacto standards. So for example, at least with Envoy, Envoy has an API that we call Xds. Xds is now used beyond Envoy. It's used by gc, it's used by proprietary systems. And I'm a big believer that actually Envoy in its form is probably gonna go away before Xds goes away. So in some ways Xds has become a defacto standard. It's not an i e e standard. Yeah. We, we, we have been asked about whether we should do that. Yeah. But I just, I I think the >>It becomes a component. >>It becomes a component. Yeah. And then I think people gravitate towards these things that become de facto standards. And I guess I would rather let the people on the show floor decide what are the standards than have, you know, 10 people sitting in a room figure out >>The community define standards versus organizational institutional defined standards. >>And they both have places a >>Hundred percent. Yeah, sure. And, and there's social proof in both of them. Yep. >>Frankly, >>And we were saying on the cube that we believe that the developers will decide the standard. Sure. Because that's what you're basically saying. They're deciding what they do with their code. Right. And over time, as people realize the trade of, hey, if everyone's coding this right. And makes my life easier to get to that state of nirvana and enlightenment, as we would say. Yeah. Yeah. >>Starting strong this morning. John, I I love this. I'm curious, you mentioned Backstage by Spotify wonderful example. Do you think that this is a trend we're gonna see with more end users >>Creating open source projects? Like I, you know, I hope so. The flip side of that, and as we all know, we're entering an uncertain economic time and it can be hard to justify the effort that it takes to do it well. And what I typically counsel people when they are about to open source something is don't do it unless you're ready to commit the resources. Because opensourcing something and not supporting it. Yeah. I actually can be think, I think it'd be worse. >>It's an, it's insult that people, you're asking to commit to something. Exactly. Needs of time, need the money investment, you gotta go all in and push. >>So I, so I very much want to see it and, and I want to encourage that here, but it's hard for me to look into the crystal ball and know, you know, whether it's gonna happen more >>Or less at what point there were, are there too many projects? You know, I mean, but I'm not, I mean this in, in a, in a negative way. I mean it more in the way of, you know, you mentioned supply chain. We were riffing on the cube about at some point there's gonna be so much code open source continuing thundering away with, with the value that you're just gluing things. Right. I don't need the code, this code there. Okay. What's in the code? Okay. Maybe automation can help out on supply chain. Yeah. But ultimately composability is the new >>Right? It is. Yeah. And, and I think that's always going to be the case. Case. Good thing. It is good thing. And I, I think that's just, that's just the way of things for sure. >>So no code will be, >>I think, I think we're seeing a lot of no code situations that are working great for people. And, and, but this is actually really no different than my, than my serverless arguing from before. Just as a, as a, a slight digression. I'm building something new right now and you know, we're using cloud native technologies and all this stuff and it's still, >>What are you building? >>Even as a I'm, I'm gonna keep that, I'm gonna keep that secret. I know I'm, but >>We'll find out on Twitter. We're gonna find out now that we know it. Okay. Keep on mystery. You open that door. We're going down see in a couple weeks. >>Front >>Page is still an angle. >>But I, I was just gonna say that, you know, and I consider myself, you know, you're building something, I'm, I see myself an expert in the cloud native space. It's still difficult, It's difficult to, to pull together these technologies and I think that we will continue to make it easier for people. >>What's the biggest difficulties? Can you give us some examples? >>Well, just, I mean, we still live in a big mess of yammel, right? Is a, there's a, there's a lot of yaml out there. And I think just wrangling all of that in these systems, there's still a lot of cobbling together where I think that there can be unified platforms that make it easier for us to focus on our application logic. >>Yeah. I gotta ask you a question cuz I've talked to college kids all the time. My son's a junior in CS and he's, you know, he's coding away. What would you, how does a student or someone who's learning figure out where, who they are? Because there's now, you know, you're either into the infrastructure under the hood Yeah. Or you're, cuz that's coding there option now coding the way your infrastructure people are working on say the boring stuff so everyone else can have ease of use. And then what is just, I wanna just code, there's two types of personas. How does someone know who they are? >>My, when I give people career advice, my biggest piece of advice to them is in the first five to seven to 10 years of their career, I encourage people to do different things like every say one to two to three years. And that doesn't mean like quitting companies and changing companies, it could mean, you know, within a company that they join doing different teams, you know, working on front end versus back end. Because honestly I think people don't know. I think it's actually very, Yeah. Our industry is so broad. Yeah. That I think it's almost impossible to >>Know. You gotta get your hands dirty to jump >>In order to know what you like. And for me, in my career, you know, I've dabbled in different areas, but I've always come back to infrastructure, you know, that that's what I enjoy >>The most. Okay. You gotta, you gotta taste everything. See what you, what >>You like. Exactly. >>Right. Last question for you, Matt. It's been three years since you were here. Yep. What do you hope that we're able to say next year? That we can't say this year? Hmm. Beyond the secrets of your project, which hopefully we will definitely be discussing then. >>You know, I I, I don't have anything in particular. I would just say that I would like to see more movement towards projects that are synthesizing and making it easier to use a lot of the existing projects that we have today. So for example, I'm, I'm very bullish on backstage. Like I, I've, I've always said that we need better developer UIs that are not CLIs. Like I know it's a general perception among many people. Totally agree with you. Frankly, you're not a real systems engineer unless you type on the command line. I, I think better user interfaces are better for humans. Yep. So just for a project like Backstage to be more integrated with the rest of the projects, whether that be Envo or Kubernete or Argo or Flagger. I, I just, I think there's tremendous potential for further integration of some >>Of these projects. It just composability That makes total sense. Yep. Yep. You're, you're op you're operating and composing. >>Yep. And there's no reason that user experience can't be better. And then more people can create and build. So I think it's awesome. Matt, thank you so much. Thank you. Yeah, this has been fantastic. Be sure and check out Matt on Twitter to find out what that next secret project is. John, thank you for joining me this morning. My name is Savannah Peterson and we'll be here all day live from the cube. We hope you'll be joining us throughout the evening until a happy hour today. Thanks for coming. Thanks for coming. Thanks for watching.

(upbeat music) >> Hey everyone, welcome to this Cube Conversion here in the Palo Alto Cube Studios, I'm John Furrier, host of theCUBE. We're here with Vicki Cheung who's the engineering manager at Lyft, and also the co-chair of KubeCon and CloudNativeCon, part of the CNCF, part of the awesome community that's doing all the Cloud Native, been there from the beginning, you guys have been driving it, Vicki welcome, to the Cube Conversation. >> Thank you. >> So I got to ask you this year, more than ever, Cloud Native is the biggest wave, you're starting to everything emerge, hybrid cloud, multicloud right around the corner, all the stuff we were talking about is playing out, it really is pretty exciting. >> Yeah, I'm super excited, 'cause I remember last time we talked about a lot of the enterprise stuff, more complicated use cases and developer productivity, and we're really seeing an uptick in all the talks in those areas so I'm super excited to actually see what everyone else is doing. And yeah, more sophisticated, at-scale use cases, how to run efficiently, how to run in data centers, hybrid situations. >> The industry is growing up right in front of our eyes. >> Vicki: Yeah I know. (both laughing) >> It's exciting. Well I want to just get the news out there, it's in Amsterdam, it's in a couple of weeks, last week of March and going into April, the conference is happening. So in all the anxiety around the Corona virus, you guys are having the policy of no shaking hands, cough in your elbow, be sensitive, what are some of the things you guys are talking about? I'm sure you're fielding a lot of questions. >> Yeah, I think a lot of people are worried, the message is generally just don't panic and be reasonable in how you interact with people and know the context. So, stay a little bit, respect personal space, don't shake hands, which I know will be awkward for a lot of people, 'cause we're trained to do that, but, yeah. >> I was just at the RSA Conference and I tried to go with the fist bump, the elbow bump, or like this, I was still shaking hands, people stuck their hands out, and I'm like, okay, I'll shake their hand and wash it real quick. >> I always feel bad, I always carry hand sanitizer, but I think the conference is going to have hand sanitizing stations, so that's going to help a lot. >> Well I'm looking forward to hearing all the great stories, more importantly, really to me again, the big story we've talked about is the industry's growing up and you're starting to see visibility into the technology trend, where it's really applying it to operations, you seeing the business benefits start to unfold, as well as excitement still for the next journey. So I got to ask you, what are the most exciting talks you're seeing? What are some of the themes that we'll be expecting to hear? >> I continue to be pretty excited about the latest state of security as people start adopting more sensitive or critical applications, developer productivity is very near and dear to my heart 'cause why change the next generation of software if you're not going to be more productive? But I also am very interested in seeing the combination of all these considerations coming together and just people running large-scale clusters, trying to run them efficiently while handling business isolation needs, compliance, how to handle multi-tenant when they have different needs, and you have one ops team that's doing all of that. So I think all the combination is really what I want to see. >> And there's a lot of Day Zero activities happening which has always been popular, so you guys are recommending that people come in if they want to enjoy some of the Day Zero activities, come in a day early. >> Yeah, more and more I have been recommending people to just go to Day Zero, there's a lot of good stuff there, I'm personally super interested in all the Lightning talks, I think people always, especially first timers to the conferences, they miss the Lightning talks because they're like, "Oh, it's Day Zero, I start at one," and yeah. >> So I was going to ask you the next question, what's your advice for the new attendees? Because you guys do have a good in-migration of new first timers, as well as the mature people growing with the industry together, what's your advice for new attendees? >> Yeah, I would say definitely come in for Day Zero, go to all the Lightning talks. There's going to be a new 101 track this time, based on the feedback from last time, a lot of people said it was very overwhelming and so we put together a one day, 101 track, for people to have talks that help them navigate, both the conference itself, and also the community. CNCF projects can also be very intimidating 'cause there's a lot of them now. So I would definitely go and check out the 101 track. Also, the advice that I give to most people is actually take your time, because a lot of the talks are recorded and you can watch them later, so try not to jam pack your schedule all day because otherwise you're not going to last. >> Don't overdose, don't try to hit everything, pace yourself, pick a groove, and then identify talks you want to watch. >> Yeah, exactly. Also I think the value of these conferences is in the people, so the content obviously is great, but the strategy I take is I identify the topics that I am interested in and also the speakers that I want to get to know and I go to their talks and I talk to the speakers afterwards. Start building out your conference network. >> So you guys get a lot of feedback, I know you guys take it seriously and you look at it, what are some of the new things or tracks that are coming this year in Amsterdam that was either part of the learnings or just new interest levels? >> So the 101 track is probably the biggest change we've made, but another thing is because the community is growing up, we've had a lot of feedback about the distribution of beginners versus intermediate versus advanced topics. I think the feedback for last time was people wanted to see more intense, deep technical dives into hard topics for people who've been using Kubernetes for a few years and so we've adjusted that a little bit this time so you should see some more interesting-- >> John: Hardcore track. >> Yes, exactly. >> It's a hardcore track. So even on the board we're saying, it's too lightweight, you can pick your spectrum or where you want to jump in. >> I think it is like the conference is growing, so there's the audience demographics is also diversifying. >> Obviously theCUBE will be there, we'll be broadcasting live. So obviously the keynotes, you'll be streaming live as well, the keynotes, is that going to happen? >> Yeah, I think so. >> So I think you guys are, so I'm going to double check that. In terms of Amsterdam, I know obviously you have the US, North America one, this is Amsterdam, what do you guys plan there, any local flavor there? Is there any twists to the event being in Amsterdam? >> Well, usually-- >> John: More fun? >> Usually all the side social events are themed to whatever region we're hosted in, so yeah, I think we'll see definitely Amsterdam flavors there. I think we also try very hard to make sure to showcase the coolest speakers or technology or projects coming from that area, so definitely, I think a lot of the talks will be from-- >> Definitely the buzz, certainly on Twitter and the scuttlebutt and chatter is, more fun, people love Amsterdam, it's a fun city. >> I know. Yeah, just like generally people when they show up to the conference, they've traveled from elsewhere, so they're just like, the vibe is like. >> There's certainly a good vibe, looking forward to it. So in the 2020 year, a lot's going on with CNCF, there's a lot of different things happening, you're in the machinery, you're in the room talking about all the plans, what's the big picture this year inside the community in terms of figuring out the tracks, the events, I mean obviously there's growth there, how are you guys handling that? What's the conversation like? >> Yeah, I think definitely there's a shift happening, actually the tracks for KubeCon has been pretty stable since the inception of the conference, and this is I think the first year where we've started talking about maybe adjusting the tracks or splitting them because of how use cases have started shifting, for example, application development has always been a really large track, and it's intentionally vague, there's a lot of things that people are building on Kubernetes so we didn't want to be too prescriptive. But because there is an explosion of use cases, we're thinking of potentially splitting that, I'll leave that up to the co-chairs for next conference, but that sort of conversation is happening. >> What are some of the use cases that are exploding, obviously there's a diverse, broad set of new use cases, is there are pattern in what you're seeing? >> Yeah, for example, for HPC, high performance computing, that's always been a topic that we see from time to time, but really for the last couple of conferences, that's been very consistent in quite a few of them, and that ties into how people are using GPUs and even more exotic networking options. We're seeing some of that this time as well, so that's it's own category. I think another thing is application development, sort of, right now, encompasses both the application side, which is your HPC use case, or development, which is developer productivity or developer experience, they're very different and so right now they're lumped together. >> I have a confession for you because one of the reasons why I love KubeCon so much, because it's really the perfect blend of geeking out and nerding out on the tech, so kind of the open-source software. When you say HPC, it's like up and down the stack, a lot of geekiness going on where you can you dig in. Then you've got the entrepreneurial vibe, so you've got open-source devs who are standing up, startups, and I've been there and they are there, and there's a lot more entrepreneurs. And then you've got the big companies who have the big wallets, and they're either buying companies. And so you have that confluence of the down and dirty, getting with the tech, open-source, startups, and big companies, so it just makes for a real fun event. So I have to ask you as you look at trying to balance all the stakeholders, what's it like? You guys see that same kind of dynamic because everyone's playing well right now in the sandbox so to speak. >> Yeah, I think it's been okay trying to balance it just because everyone is still, I think there's still more in common that people are trying to solve than they are different. And so there are a lot of topics that yes, people are solving this problem for their specific use case but actually, there's a lot of things in common for people in small companies as well as large enterprises as well. I think the interesting thing is a lot of cloud providers, they give a lot of talks as well, and you'd think, that might be too vendor specific, but actually what we're seeing is that they have a lot of experience operating many, many clusters and large infrastructure and their experience in scaling that out is helpful to companies like Lyft or other startups that are just trying to scale their deployments. >> Yeah, what's interesting, I looked at Amazon, Azure, and say, Google for instance, each one of those beg tech companies, has a commercial interest and they do have large power, but all of the people that were running these clouds, they've been at the open-source community, they know the contract that they have with the culture and plus it's so early. So I think there's a nice, I mean I see Adrian there, I see all the Google folks there, all awesome people, they're not like the greedy big bad guys, and I think that's what people don't understand about that part of it, although their validation in the business side can really help pull through a lot of these great projects. >> Yeah, for sure, I see them contributing a lot to the community as well, and they definitely are very open to initiating collaboration and I see that a lot in KubeCon, in the vendor booth area, people are talking about like, "Oh, let's start a new project," or like, "That could be open-sourced," that's the type of conversation that's happening in the sponsorship area and not like, "Oh, do you want to buy our stuff?" So, yeah. >> Yeah, I definitely think all three of those clouds I mentioned, all have awesome people over there when it comes to this community, and again, everyone's playing well, but I think that the unification around Kubernetes and what's going with Cloud Native is so powerful because I think everyone agrees that there's a bigger win beyond the short term, there's a bigger defacto standard thing going on here around hybrid and multicloud. And all the conversations we have on theCUBE these days is about hardcore hybrid and then the promise of multicloud, so I got to ask you, is there any multicloud in here, or is that too off the radar on the hype side for you guys? >> I don't think it's too off, yeah there are definitely talks about hybrid and multicloud. And I've come from companies that have done that with Kubernetes and so I don't think it's that far fetched. >> So right now it's happening, there's good conversations happening there? >> I think obviously it depends on the use cause because it does come with its own complexity, but I think the demand on infrastructure teams just keeps growing and so we get to a place where we need to be multicloud because availability reasons or because of regional issues, whatever. So yeah, it's no that far fetched. >> You're hitting the sweet spot there, because you're talking about the scale that's going on, and the operations and everyone's always worried about, change is going to make this happen, the fact of the matter is, one you have large scale and growth, skills gap and skills shortages going on, so the only way to solve that problem, and by the way, and a huge data tsunami, you got cybersecurity, the only way to solve that is automation, I mean software, that's the whole big picture. >> Yeah I think everyone's seeing the benefit of sharing the problems we're solving on the infrastructure layer because that's not our business, we're not selling infrastructure, we just want to get that in a good place, so we can actually do our business. So I think that's what's also fueling all the community and open source efforts. >> Vicki, final question, what's going to be the theme for you guys as you give your talks, and you're in the hallways, talking to folks, what's the posture this year from the group, what do you hope people walk away with from KubeCon and CloudNativeCon this year? >> Oh, that's a good question. Well personally I think a lot of what's on my mind right now, coming into this conference is, a lot of talks from San Diego were about, okay now that we're actually putting this into production, a lot of teams are realizing how much complexity there is, and how we can abstract the complexity away from the rest of engineering team outside of infrastructure and since then I think people have made a lot of contributions and have thought a lot more about that topic, so that's what's on my mind is like, okay that's what we were talking about a few months ago, let's see where we're at now. >> Let's see the proof. >> Yeah. >> Let's see the use cases, let's see the results, it's a prudent world when you start talking about operations. >> Yeah. >> Rubber's hitting the road. Okay, Vicki's here inside theCUBE, giving us a break down of the upcoming KubeCon and CloudNativeCon, she's the co-chair, puts it all together, the great team over there, and also a great community. Again, as it continues to grow, and there's a lot more to go, Cloud Native and Kubernetes is really the center of what I see is a defacto standardization around a whole new cloud operating model that's going to create a lot of benefits and a lot of great stuff for the community. I'm John Furrier with theCUBE, thanks for watching. (upbeat music)

(upbeat music) >> From our studios, in the heart of Silicon Valley, Palo Alto, California, this is a CUBE Conversation. >> Okay, welcome back everyone. We're here in Palo Alto, California at the CUBE studios. I'm John Furrier, host of theCUBE. For a special CUBE conversation, a preview of the upcoming KubeCon, Cloud Native Con in San Diego. Where theCUBE will be there, as well as a bunch of other folks. The New Stack will be there, a lot of other media producers, as well as the big conference. KubeCon, in it's fourth or fifth year, depending on which year you count. Its a super exciting conference, this is where the Kubernetes and the Cloud Native communities come together to set the agenda to talk about all the great things that are going on in the industry and how it's changing tech for good. We're here with Vicki Cheung, who is the Co-Chair and also Software Engineer Manager at Lyft. Vicki great to see you, thanks for coming in. >> Thanks for having me. >> I'm so proud of KubeCon and the community because when we were there, in the early days, when it was kind of forming and created. There was a big vision that it would play a critical role. A lot of people haven't really seen how big it's become. And it's really become so important that the big companies are now moving towards Open Source, the CNC has been very successful. Both on getting vendors in and end user projects. You're setting the agenda. You're setting the table for this year's KubeCon. >> Yeah. >> Tell us what's going on. >> Yeah, I think we're seeing the maturity of the community coming together. It's sort of continuing on this trend where, as you said, the adoption is growing exponentially. I think, that two years ago if you surveyed the room and asked people, "who is using Kubernetes and Docker in production, you'd maybe get, like, a hand. I think you're seeing this thing where, this trend, where this year, I think, if you surveyed the room, it would be like maybe half the room were raising their hands. >> And the acceleration is interesting. You're seeing in, I mean, huge acceleration of the adoption of Kubernetes and other projects. And I think what's interesting to me, and I think commentary that we've been reporting on is that Kubernetes can be that unifying point. And you're seeing this, de facto standard emerging and a lot of people talking about that de facto. And that has accelerated the Production Use Cases. So, the End User Projects are increasing. Is that going to be a focus or main focus of this year's KubeCon? >> Oh yeah, definitely. I think we're seeing, maybe even last year, we've had a lot of end user talks from, you know, early adopters start ups, like tech giants. But this year we're seeing a lot more enterprise use cases. And that's driving a lot of content as well. So, I think when it comes enterprise use cases, we're seeing a lot of talks around security and governance. We're seeing a lot of developer productivity talks, and we're also seeing a lot more focus on how to scale operations. >> So, take me through the focus this year. Let's get this out on the table, because this is a big event. What can people expect this year, when you guys sat in the room, with the teams, and said, "Okay, here's going to be the Con and agenda, "we have a form of that's not broken, let's not fix, what's not broken, so the format's good." What was the focus, what was this year's focus. What's going to be the focus of this year's KubeCon? >> Yeah, I think Bryan and I, when we sit together, we have all the tracks that we've been using, for the last couple of years. And generally we, sort of stick to them, because they're pretty good. But the way we, I think the interesting thing is, we see over the years how the distribution across the tracks have changed. So, for example, I think this year, operations is a super big track, and it's very competitive to get into. And that's because we're seeing a lot more adoption at scale, and different Use cases, different types of companies and production. So, I think that track have been a main focus. And also, I think customizing Kubernetes is another one, as people's use cases got more sophisticated. And in the serve use case track, I think we see a lot more enterprise, like even banks adopting Kubernetes. >> So, essentially the same game as before, but weighting them differently based on adoption? >> Exactly, I think it's a shift, like earlier it would be maybe more like earlier adopter and serve experimental use cases, and now it's like, people are actually going into production now. So, the shift has been into like, how do we get this running reliably, at scale. So, that's what we're seeing. >> In terms of the industry, if you look back, and again you guys went public at Lyft, and you guys are growing, and you guys have a great open source product with Envoy, I'm sure you guys are going to do the Day Zero thing again this year, last year was a big success. Is there any projects that you see coming out of the woodwork that are going to evolve up? And what can people expect in terms of project growth or emerging projects. Is there any indication, from your standpoint? What's going to come out of the community? >> Yeah, I think there's a lot of projects that are growing, like Helm continues to grow. I think one thing that I'm seeing, from this year's content is there's a lot of focus on, OPA. Like I said, the security is sort of a growing focus. And OPA is certainly one of the things I think people should expect at this year's conference. Another area that I'm personally very interested in, and I see, I'm happy to see it popping up more this year, is developer experience and developer productivity. As we're, even just personally witnessing at Lyft, adopting Cloud Native Architecture, microservices and Kubernetes, comes with a lot of benefits, but also a lot of new challenges into how people should develop in this ecosystem. So, there are projects like Telepresence and Tilt that are coming up more. And there's a few talks around that, in application and development as well. >> How about the developer's side? What's the general sentiment in the community these days? If you had to kind of, put a parameter out there, what's the general vibe in the community, from a developer's stand point around Cloud Native and Kubernetes? >> I think there's, I think it depends on who you ask. Generally, you know, people are very very excited to be sort of moving in this direction. And I think it allows people to be a lot more flexible in how they develop their applications. But I also think that there's a lot of open questions, that we still have to answer. And this is where, I guess some of these new projects come into help fill the gap. >> Well first of all, you guys have, always have a great conference, theCUBE will be there, as well media producer will be a lot on digital. So, folks not going to the event, they should go and see the face-to-face. I want to get the take on some of the submissions. You guys have an interesting dynamic and CNCF and KubeCon and Cloud Native Con, you have a ton of end user projects, A lot of end user focus, obviously it's an end user focused show. But you also have a lot of vendors, suppliers that are also in the community. So, you have an interesting balance going on. Talk about some of the numbers in terms of submissions, because I know, everyone's got submissions, not everyone gets accepted, like the operations you mentioned is a hot track. What's some of the numbers? Can you share any, kind of statistics around number of submissions versus acceptance? >> Yeah, I think typically CNCF will publish some of the numbers, in a blog post. So, I don't know all the numbers off the top of my head. But for example, in operations, I think the acceptance rate was maybe less than 10%. I think, it wasn't that competitive, maybe two years ago, but certainly as everyone moves to deploying Kubernetes on their own, that's sort of a hot topic. >> What's the relationship in the community, with the big vendors? Obviously you see, Amazon, Google, Microsoft, are big players in there, and they're investing heavily in Kubernetes. And VMware, as well, is also investing. Is that good, bad, is it just balancing? What's the communities view on the participation of the big guys? >> Yeah, I think it's actually been really great to the community and I personally would not have expected Microsoft, ADBS to be as active in the community as they are now, if you asked me five years ago. So, I think it's this interesting thing that Kubernetes and CNCF hasn't managed to do, is instead of having the tech giants having to suck out the energy and the technology into their private ecosystem. It's been the other way around. Where Microsoft and ADBS and Google have been contributing a lot of their integrations and other tooling and projects that they've built on top of the projects in CNCF. And just enriching the community. >> So, you're saying that they've been pushing more towards open source, not pulling out of it? >> Yeah. I think that's, obviously I'm super happy to see that. But I think that was not obvious at all from the beginning. >> Yeah, it's super exciting, you know we've been tracking the business model's evolution. And open source is more powerful than ever before now. And it's growing so fast and changing. Let's talk about the Enterprises now, because I think you're seeing adoption on the classic IT Enterprise moving in. We've interviewed many CSO's, CIO's and practitioners, they all have the same kind of reaction, "Oh my God, this is so good for our business, "Kubernetes what Containers are doing, "will allow us to manage the life cycle of our applications. "The same time bringing Cloud Native, "without a lot of disruption." What's your reaction to that, are you guys seeing that same dynamic? And if so, what is some of the use cases of Enterprises, within KubeCon? >> Yeah, I think one thing is, the earlier pitch is the, of course allows you to have that flexibility to move from your data center to Hybrid Cloud, and maybe to different cloud vendors. So, I think that's super appealing. But another thing that we're seeing this year is, as people adopted at scale they're also seeing a lot of cost savings from adopting Kubernetes, just because it allows them to be a lot more flexible in how they deploy things. I think that, in general as you move to serve a community standard, an Open Source Platform, it does help your developers a lot, because now they don't need to build their own in-house thing, which is, for example, what Lyft had before Kubernetes. So, I think it's generally a productivity win. >> So, on Envoy real quick, while I got you here. Lyft has been involved in donating that project and driving it last year, one of the most notable news, at least from out observation was, that the Envoy did that event the day before. And it was really popular. >> Yeah >> Is it going to happen again? What's some of the views on that? >> Yeah, so EnvoyCon is happening again this year, right before Kubernetes. I think it's even more popular than last year. So, there's going to be a lot of talks around, running Envoy at scale, and also on top of Kubernetes. As people sort of integrate the two technologies more. >> Okay, so I got to ask you the personal observations, you can take your Co-Chair hat off and put your KubeCon community hat on. What dark horses are out there, that you think may surprise people this year? What do you think might happen? Because there is always something that goes on, that's just a surprise, a dark horse, if you will, comes out of the woodwork, what do you think might happen? >> Well, I think there's of course going to be a few new Open Source projects that are launched there. And I also think there will be a lot of, maybe more than usual, interesting people that people can meet at the conference. >> I heard there's a rumor that the original gangsters, or the OG's or the original members, the seven original members are going to be there. >> Yeah, I don't-- >> Confirm or deny? >> I don't know if I can confirm or deny, but-- >> Okay, I think that's a yes, possibly. We'll be tracking that, okay, final question for you. What do you think will be the most important story for people to pay attention to this year? What do you think is going to be, evolving out on the stage? Out on the tracks, out on digital? What do you expect to see this year? What is some of the top stories and top notable points that you think is going to happen this year? >> Yeah, I think one thing that maybe, for me, and for a lot of people is this message that Kubernetes is ready. I think it's been sort of building up in this hype for the last few years. And we've seen adoption, but I think this is truly the year that I see a lot of Enterprise end user cases and I can really say that Kubernetes is ready. >> So the new criteria is proof points? Scale, operationally seeing some operations, real proof points, customer adoption, enterprise and hyperscalers? >> Yeah. >> All right, Vicki thanks for coming in and sharing this preview on KubeCon, Cloud Native Con. It's theCUBE covering the KubeCon, Cloud Native Con preview with Vicki Co-Chair, who set the agenda with her fellow Co-Chair Bryan Liles, as well. Great to have her on and share upcoming conversation around KubeCon. I'm John Furrier, thanks for watching. (upbeat music)

>> Live from Seattle, Washinton it's theCUBE, covering KubeCon and CloudNativeCon North America 2018. Brought to you by Red Hat, the Cloud Native Computing Foundation, and it's ecosystem partners. >> Hey, welcome back everyone. We're live here at KubeCon, Cloud Native. This is theCUBE's live coverage of three days of three days of wall to wall coverage. Day two, I'm John Furrier with Stu Miniman. Our next guess is an end user, also a program chair of EnvoyCon, which is sold out. Matt Klein, software engineer with Lyft. Great to have you on again, good to see you. Thanks for spending the time. >> Thank you great to be here. >> I know you've been busy, your voice is getting hoarse. You guys had a successful EnvoyCon, sold out. Was on the front-end of KubeCon and CloudNativeCon. Interesting, right? This is the rising tide. What's going on? How'd that go? Why all the interest? >> It's been I continue to be blown away by the overall reaction. So we had EnvoyCon on Monday. We had, I think almost 350 people come, sold out. I think we could have had a larger room if it was available, but we didn't. Just amazing to walk around this conference and see all the cloud vendors getting behind Envoy, lots of companies building on top of Envoy, all of the end users. It just seems to be everywhere here and to have only been open source for a little over two years, I mean it's just unbelievable. >> Matt you know I think a year ago service mesh was something we were still getting the basic understanding of what it was and it definitely, there's certain interviews we've done this week, you know service mesh, you know Envoy, thing likes Istio are going to be even bigger than Kubernetes. >> Yeah, well you know I've been to the last few KubeCons and every KubeCon, I think that it can't get much bigger or more nuts, and no, no. Everyone seems to be a little bit crazier. But no, just from the community perspective, EnvoyCon was fantastic because we had mostly end user talks so it was really fun to get people together and to see all the different things they're building on top of Envoy. >> One of the things that's impressive and I think is a real notable story, and of course we talked about it a bit last time you were on, is that Lyft as an end user kind of encapsulates and epitomizes kind of the innovation building going on. A lot of people have been building a lot of cool stuff using cloud look and getting down and dirty and rolling their own. And actually creating business value, not in a classic IT by IT, just build IT, build systems >> Yep >> To build business value and then donating it in to scale up with the community is pretty notable so congratulations on that. >> Thanks. >> Now you have startups kind of acting the same way so the line between a vendor and end user is certainly changing. I mean, we were end users. Well they're all kind of end users. This is a dynamic that is, I think notable for this generation and it's real. Talk about that dynamic because I think this is a real success story and also a trend in the industry. >> You know so I think for us what's fun for me about not only building Envoy but seeing how it's evolved is really what you said is that I like solving actual problems for people, right? We can have different opinions on what the different vendors are doing, of course. There's lots of people doing different things, but for me at least working at a company like Lyft it's super fun to be able to build technology that solves specific problems that the business is actually happening. Now if something becomes successful sure we're going to see a lot of vendors come in hopefully build products that can help other folks. The way that I look at it and this has been an interesting evolution for me over the last year is I would say a year ago, people would come to me and say "Hey Matt, I've heard about Envoy I'd like to use to help solve some problems and I went to the website and I don't understand it, like it's too complicated to use. The documentation is not good enough." And I think over the last year my thinking has evolved a little bit in the sense that we've seen so many people or end users or companies build fantastic products on top of Envoy and I think one of the reasons Envoy's become so successful is that it's a building block that other people can come and add vertical value. So whether that's a more sophisticated internet company like Lyft or a vendor or a cloud vendor. I think that's what's made the community so successful is that we can build this base thing and it's amazing but then we can allow people to add vertical value. >> And you know that's an interesting dynamic of both cloud and open source. You look at Amazon, the most successful public cloud Their core building blocks was EC2 and S3 originally. Open source is about building on top of other things. Again the dynamic between open source and cloud scale is really kind of the magic. >> Well and just in terms of how we actually go through and I think fund some of these projects ends up being very interesting. Just in the sense that we have a lot of full time people working on Envoy and they're working on it actually for different reasons. We have people working on it as end users, we have people working on it because they're building vertical products but in the end everyone wins because the base technology stays technology focused. I think that has been what has been successful, is that we allow people to succeed in different ways. >> Alright, so Matt, you're at the forefront of one of the most difficult problems that we're looking at these days. It's scale, distributed systems, and edge and how that ties in. I want to get your kind of macro level viewpoint as to how we're doing in this industry? What are some of those tough challenges we've talked about? We talk about things like IoT and Edge and vehicles of course have a lot of them. >> Yeah so I mean, I think when you say scale there's two things that comes to mind. There's physical scale, and I do agree actually that we are continuing to push more compute out to the edge and in fact, I talked about this a little at EnvoyCon, but I have some very exciting projects or plans to bring Envoy actually to mobile phones and to Edge devices starting next year. I'll have more to say about that in the spring. I'm very excited about that. I do think there's a lot of opportunity to better evolve how we ingress data from the edge, how we do compute out at the edge, a bunch of other things. And I think Envoy will be at the forefront of that but when you talk about scale I still think that there's a lot of human scale involved of how we scale the number of developers that are working on all of these architectures. And I do think that Service Mesh and Kubernetes and a bunch of other stuff ultimately if we're successful it helps us grow the number of product developers that can successfully work on these systems. I still think we have a long way to go but I think that's one of those areas where I think some of these technologies help people both at physical internet scale but also at human scale. >> Well I really appreciate your work you're doing. Your contributions to the community, both on solving the problems with Envoy and also being the program chair of EnvoyCon I think is going to be great for the community. I got to ask you as you get pulled into a lot of these, I won't say political, or media kind of conversations you got to kind of be a helicopter and get above and get high level and talk to people who are discovering and learning for the first time which is part of what communities do. How do you talk about those other end users that say "Hey Matt, I'm going to reshape our company, I'm going to reshape their IT investments all based on open source and I really want to learn more about Envoy and just the benefits of Cloud Native in general. I got to go, and I'm a believer, I got to go talk to some wanna-believers or non-believers in my company and I got to make my point home?" How do they be successful? What's your advice to that? Because that's a challenge a lot of people are having. >> I totally agree My advice, first and foremost, is to start by understanding what problems are trying to be solved. And I actually think that sounds very obvious but I think that people don't do it enough because I think sometimes we come to conferences like this and we see all the amazing technology that people are building and it seems fantastic but if one tries to adopt everything that they see here without understanding the incremental steps and the things that are the problems that are being solved that can be very problematic. >> It's a new kind of technical depth. It's kind of a new way >> My advice is to start with what are the actual problems, right? And whether that be observability issues, or authentication issues, or security issues, or whatever, is to start with the problems and then work backwards and my advice is always incremental, no big bang. And try to figure out the right incremental path of adopting the smallest piece of technology that solves a particular problem and go from there. >> And build economies of scale to the mission. >> Right, and whether that means working with a vendor or working with the raw open source technology that's a personal decision of each company to figure out what their comfort level is. But that really is my advice, is start with the problem statement and then figure out the easiest and the quickest incremental path forward. >> The trends that we're seeing Stu was talking earlier, a lot of hyper-scalers here, a lot of diversity coming into the community just what's the hallway conversation amongst the people in the community around as the community grows larger? I mean open source community core persona or constituency, then you got the down-stream impact of that is IT is changing, developers are coming in. So it's not so much changing personas and target audiences of the environment. Open source is still core. That's kind of the down-stream impacts. So you're seeing a lot of people come in, IT people, new developers. How does the community look at that? What's your view on how to engage but also not alienate new people? >> Well I think ultimately we are attempting to build systems help people be successful and be more productive, right? I think the natural evolution of that is bringing some of this technology into the enterprise. We have to recognize that as the community scales the base line level of knowledge is different. I mean we all come at it with different understanding of whether it be networking or orchestration or security. And I think what I would say is that we're never going to build one technology that makes everyone happy. It is impossible. It's impossible to build a technology that satisfies both the expert user and the entry level user. So I believe that we need to build layered technologies, layered abstraction that allow people to plug in at different levels and some of them are more opinionated than others. And I think it is recognizing and supporting a community that has base level technology, has vendors adding value at different layers to help people, and really just respecting the fact that people come at it with different levels. >> I mean application assembly is really where it's going. >> Exactly, I agree >> Matt, I'm wondering if you could reflect back for us. You're the creator of Envoy, I saw you up on stage yesterday, the supportive team and the community that helped this grow. And you've reached graduation. What does that mean to you, for the team? It's different than a school graduation, this is not the end of something, you don't get a diploma out of it. >> Is there a party? >> I don't know if there was. I don't think they invited me. >> Get pictures? >> Cloud Foundation picking up the bar tab? >> I don't know, maybe. So like from a project perspective, in terms of how we go about our day to day I don't think that much changes. I think we have been operating as a mature graduated level project probably for quite some time, in terms of adoption and methodology and stuff like that. I think what graduation means for the project is it's a vote of respect from the larger industry and the community that Envoy isn't going to disappear, it's not going to become an abandoned project on GitHub if for example if Lyft stops investing in it. I think we've reached a critical mass of project success and I think what that means is that it allows folks that may be at more conservative organizations who may be a little later to adopt newer technologies to give them the confidence that says Envoy is not going disappear, that we can potentially bet some of our future on Envoy. So I think it's a vote of confidence, I don't think it changes a lot about how we operate on a day to day basis. >> Matt, thanks for coming on theCUBE. Again, congratulations. Seminal work, you guys are doing great. Lyft is really, I think, a great example of the new dynamic in open source where they're building and they're working with the community to continue to extend that. And this is what we want, that's what open source is all about. >> It is. >> Congratulations. And we got to have a graduation party for Envoy. We'll figure it out, get photos and pictures and everything else. Thanks for coming on theCUBE. >> Cool, thank you very much. >> theCUBE coverage here live, I'm John Furrier with Stu Miniman. More coverage after this short break, stay with us. (upbeat music)

>> From Sand Hill Road in the heart of Silicon Valley, it's theCUBE. Presenting, the People First Network; insights from entrepreneurs and tech leaders. >> Hello everyone, we are here for CUBE conversation in San Francisco. I'm John Furrier with siliconANGLE media theCUBE. We are in San Francisco with John Zimmer, who is the co-founder of president of Lyft, the famous ride sharing company that's dominating the world and changing the game in transportation. We all use Lyft, we love it. John, great to see you here for this People First Network special conversation. Thanks for spending the time. >> Thanks for having me. >> I know you're super busy, you guys are growing, billions of dollars in raised capital. You guys are growing like a weed on a rocket ship. A lot of things happening. But, you know, it's interesting, you guys are not that old of a company and the growth has just been fantastic. So, as you continue to ride the wave here, there's a lot of lessons that you've learned. So, tell the story about how you guys got started. You and your co-founder have a great relationship, and this has been a part of the culture at Lyft. How did it all get started? >> Yeah, so I'll start with Logan, my co-founder. He grew up in L.A. surrounded by traffic and he hated that. And he wanted to find a better way to get around. So when he went to college, he went to UC Santa Barbara, he did not take his car. He rode the bus, he car pooled, he had friends with cars. And then he went to start a car sharing program before Zipcar was around on college campuses. He got the attention of the local transit board, he got elected as the youngest member ever on the transit board. And he fell in love with the promise of public transportation. Affortable, accessible transportation for everyone. But frustrated by the reality that it was dependent on tax money. So, he wanted to create a better solution and he started coding his own website, named Zimride, named after a trip he took to Zimbabwe, for long distance car pooling. My own journey was I was on the east coast. I did not know Logan, was in love with hospitality, making people happy through great service. So I went to Cornell Hotel School, I took a city planning course, and I saw that the most important hospitality experience we have in society today is the city itself, and yet unfortunately we've designed cities for cars, and not people. What I mean by that is most of our cities are paved over. There's roads, there's parking lots, and if you design a city instead for people, pedestrians, safe places to bike, and don't need people to own cars in order to get around, then you could have a much more durable place to live. So we came together in 2007 to work on Zimride. And then a few years later, in 2012, we launched Lyft. >> So this is a transportation problem, ultimately, to solve. But the itch you guys were scratching was just the need for transportation. You saw it as more of a convenience thing as well. The hospitality thing kind of comes together, boom, Lyft is born. Then you guys enter the market, and the transportation problems are still there, and then you have the growth of mobile, so sort of a perfect storm coming together. What is the biggest challenge and exciting things that you guys see in this transportation scheme? Is it it's antiquated and inadequate? Is it a technical thing? What are some of the challenges that you guys are exited about? >> Well I think the biggest thing is this fact that the American dream has almost become, or been, historically, synonymous with a car in every garage. And that everyone should own a car. And that was your sense of freedom. But the reality is not quite that. American families spend more on their car than they do on food. It's the second highest household expense. A new car costs, on average, an American family $9,000 per year to own and operate. And so, there's a lot of ingrained behaviors, and designs of cities so that it does cater to needing to own a car. So we're trying to break that down piece by piece and making progress. But we're about 1% of the way there. >> Yeah, it's a cultural change too. But I also want to get to that in a second about culture, both with Lyft and and into your audience, which is the cities and the environments you guys deploy in, but also the users. But the founding and the story of you guys growing is interesting, because startups are all about execution and culture. You've had an interesting relationship with your co-founder. And this is the secret sauce of startups. It's documented somewhat, but it's a people first mindset, where you get a good team early on, you kind of feel your way through those first couple of years. Talk about that relationship with the founders, because this is something that's important. It's not just a number on a cap table, it's a little more than that. Talk about the relationship. >> I mean Logan has become my best friend. We actually carpool to work, still. Almost every day. And we weren't friends prior. So, a lot of times you have friends that start a company together. We were two people that were incredibly passionate about our mission, which is to improve people's lives with the best transportation. So we shared this passion, we share this vision, and we're two completely different people. So our approaches were different. His approach is often product-oriented and my approach is often hospitality-oriented. And the fact is, for transportation, you need to combine those two pieces. So it worked out really well for us. So I think having a co-founder is a massive advantage, because you can have two different people and then you want to find the thing in common, which is the thing you're fighting for, within our case the mission. >> How did you guys work together to play off each other, to get that innovation spark. Because when you get into the ride sharing, certainly it's a brand new category, huge demand, and there's a lot of build up, a lot of things you've got to stand up for the business. At the same time, you also want to differentiate and be innovative. You're kind of a first mover, with Uber, these guys are out there too. You guys are building a business, and growing really fast. So, how do you guys nurture that innovation? How do you put a twist on it? How do you keep it alive, versus the blocking and tackling and standing up the basic business activities? >> Well I think because we, you know at the beginning, we created a new category. We're the first to do peer-to-peer ride sharing. Uber existed, but they were doing cabs and limos. And we said, that may work for 1% of the population, but we wanted to use this under-utilized asset, which is the car that's sitting in everyone's parking spot or garage. And so that DNA of innovation, that DNA of being the underdog, the challenger, has always been true to us, but also the people that we we've brought on and hired. People and the hiring is something that, over the last ten years, is probably the one activity we've spent the most time on. Because that's the best way to keep those values, keep that focus on vision. >> And certainly these days, people want to work for a company that has a purpose. And that has a mission. When you hear the word people first, what pops into your head? >> Obvious. It just feels, in everything I've tried to do as a person, whether that was studying- like hospitality is the business of people first. How do you give people a great service and a great experience. And so I think often times, when people think about technology, they think about the what, which is I made this phone, I made this device, or I made this app, when way more important to that, is the why. Why did you do that? Who are you doing that for? And so we try to start everything we do with the person we're trying to- you know our mission is to improve people's lives with the world's best transportation. It's not to build the worlds best transportation. >> So that's your why. I was talking about how you guys scaled to a world-class organization. You guys have build a world-class team, certainly got great investors, Floodgate, Mayfield and then the rest is all on the web. You guys raised a lot of money, but you can't just throw money at the problem, you have to have that foundation and culture. How do you scale up a world-class organization? What's the learnings, can you share your perspective? >> Yeah, so first having clarity on the mission, which we've talked about, but also having clarity on core values. So we have three core values that have been true for a very long time. So, one is to be yourself. It also sounds very simple, like people first, but a lot of corporate environments have made spaces where people aren't comfortable being themselves, where there's group think, where people don't feel comfortable bringing their full self, and therefore their most productive self, to work. So be yourself, respecting the diversity of our team, has been critical from the beginning. The second is uplift others. So we use that both internally and externally. Life's short, we spend a lot of our time working. We might as well enjoy what we're doing. Again, all these values are both the right thing to do, make for a better place to work, and lead to better productivity and business success. And the last is make it happen. That's pretty self explanatory. Be an owner, go out and take action and get stuff done. And so with those three simple core values, looking for amazing, talented people, who also care about our mision. People are mission oriented, people want to care about what they're working on. And if you're fortunate to have a choice where you work, what we've seen is that people will follow a mission. >> Yeah, it's totally true. I can see that in culture here. And I've also seen you guys got kind of a cool factor too in the way I've seen some of your activations out in the marketplace. You kind of got a cool factor going on as well. But I think what's interesting, and I want to get your reaction to this, I think this points to some of the cultural discussions, just recently during the elections I saw you guys really wanted to make an effort to help people to get to the polls. Here in California, the disasters of wildfires are really tragic. You guys are doing some work there. This speaks to the culture. You say, hey, Lyft's available, and you're helping people out. Talk about what that means to you and the team here, and the culture at Lyft. >> Yeah, at the end of the day, when we look back on the work we've done, we want to make sure it has improved people's lives. And when we see opportunities to take our ability to provide transportation that will benefit people in a meaningful way, whether it was, you know, in the last- not this most recent election, but in the last election, in the last presidential election, I believe it was about 15 million people listed transportation as a reason why they couldn't vote. >> They've got a way, hey! >> Yeah, let's solve that. We can. When you think about unfortunate natural disasters, if we can help people get to safety, or help a horrible situation, then we should do that. I think that's just a moral and civic responsibility. It allows us to be aware and proud of the solution we've created, and I think it keeps our team extremely motivated. >> And I think it's one of those intangibles in terms of the mission, changing the transportation industry sounds academic and corporate. But here, you're changing lives by one, the voting, and two, saving lives potentially, with the disasters. So, great job. Okay, so what I thought, let's talk about the growth okay. I had a great conversation with the CEO of Amazon Web Services, Andy Jassy, a few years ago, talking about the early days of AWS. You have to be misunderstood for a while, and get through that early on, if you're going to be successful, because most big things are misunderstood. He also made a point about the key learnings during the early days. When you're trying to do stuff, things going so fast, that there's learnings that come out of it. And if you can persevere through it, that sets the culture. Share a story around something that you guys have been through at Lyft, where you persevered through it. It might have been some scar tissue. It might have been you got a little bloody, a little dirty. But you got through it and you learned from it. You applied it, and changed the culture. >> Well I think there's two main ones that come to mind. So, you know, people may think Lyft, in the last five years, has really come out of nowhere, but Logan and I have been working together for eleven years. And the first idea was Zimride, was long distance car pooling. And we built a team of 20, 25 people, we got this to break even. That's actually the company that Mayfield invested in, or the product. But it didn't have product-market fit in a massive way. It wasn't a massive success. And then so we tried to reinvent ourselves five years later, and that was Lyft. And at this point, that was a crazy idea. To have people riding in what everyone thought of as a stranger's other vehicle. And so that was a reinvention, an acknowledgement that the first solution we created did not fully work in the way that we wanted it to. The second was about four to five years ago, we wake up and Uber raises three billion dollars. And we have a hundred million dollars in the bank and about five months left. And everyone said Lyft is done. There is no way that they can survive this, it's a winner take all market, Uber is way more aggressive. And we proved that wrong. By focusing and staying true to our values and to our mission. By having an incredible team. An amazing community of drivers providing great service to our customers, we've gone from the early days of single digit market share to nearly 40% market share, amidst that pressure and belief that we couldn't survive. >> Game's on. Either rally or fold, right? It's a cultural test really. What's your mindset around the capital market. I know, I've done a lot of startups myself, I know a lot of fellow entrepreneurs, and when you raise that money, and you guys had that product-market fit, post the first venture, where you got through that. Then you get lightning in a bottle, whoa, let's double down on this. I want to go back to the early stages when you were thinking about investment. Was there any cautions around VC, cause a lot of startups have that conversation. What was the narrative for you guys at that time? Hey, let's go to Mayfield, should we raise money, should we bootstrap and make it cashflow positive. What was your mindset as founders, at that time when you were doing the venture round? >> Well, I think we knew that we needed a certain amount of capital to get to a scale that was interesting to us. So, not every business needs as much capital. But for they type of transportation infrastructure that we wanted to change, the type of scale we wanted to get to, we knew that it was important to raise VC money. So, money that was substantial and also understood the level of risk we were taking. So, at that point, we were fortunate to have a firm like Mayfield believe in us. And what we were looking for was people that care about who we were, cared about our mission, and understood what it was like to be an entrepreneur and an operator, not just an investor. >> What's the rallying call now for the team as you guys look out a6nd continue to have this growth? Obviously you guys cleared the runway in a big way. And there's still a lot more work to do, the market's still early. You know, you think about transportation and the regulatory environment and how technology and policy are coming together. A lot of forces out there, you got some tailwinds and some headwinds. How do you guys look at the future? What's the next mountain you're going to climb? >> Yeah, so, we've now done a billion rides. Since inception. And we're focused on providing a full alternative to car ownership. So I don't think people grasp that. The idea is not to provide an alternative to a taxi, or a late ride home. It's to completely replace car ownership. And so, we are 1% of the way there. Those that are joining our team and our mission get to be there for the 99% rest of that. And at the same time, as we go towards the next billion rides, we want to stay focused and rally around the individual stories behind each ride. So, every single week, we have over ten million rides happening, where two people are coming together. They could be two people that helped each other have a better day. They could be a Democrat and a Republican sitting next to each other and finding common ground. And so to us, yes we have big milestones and big opportunities ahead, but also care about each ride that's happening on the platform. >> And the other thing I love about your background in hospitality is you're bringing an experience as well. Not just math, in terms of the bottom line numbers. There's a lot of people doing the math and saying hmm, should I have a car? But I got to ask you a question. So what you learned at school, Cornell great school, great Lacrosse team, great Ivy League school, they teach you the textbook, the old hospitality. This is a new era we're living in. What is happening in your world that they don't teach you in the textbook from a hospitality standpoint? As you look at the experience of ride sharing and transportation for users, what is different, what's the twist in hospitality that has not yet been written in the textbooks, that you're exploring or thinking about? >> I actually think the old basics are more important than ever. There's all this flashy technology and opportunity to do it at larger scale, and to use data, that's new. To use data in ways that help inform providing great service. But, the basics of human interaction, communication, and treating people with respect, can get you pretty far. >> And happy customers, right? Final question, I know you got to go, I appreciate your time. Share a story or something about Lyft that people might not know about. First of all, everyone knows about your brass, you guys are doing a great job out there with the market share. But tell a story about Lyft, or something a datapoint, anecdotal piece of information, that they might not know about, that they should know about. Share an inside story or factoid about Lyft, that people should know about that they might not know about. >> I think it's really deep, deep in the mission. That people may not understand what gets us out of bed in the morning. You know, every time I have a new hire orientation, I try to talk to every new hire that comes to the company and really emphasize the importance of every driver, every passenger. And I read a story about a driver and passenger that really helped each other. And don't really want to provide the details because they're private to those individuals, but it's incredibly powerful to hear about. And so, I would just, we may look like a big company or brand at this point, but we care deeply about each individual that's on the platform. >> The fabric of society is being changed by you guys, really appreciate the work you've done, and congratulations, and a lot more work to do. Thanks for the conversation. >> Yeah, thanks. >> I'm John Furrier, here in San Francisco at Lyft's headquarters, talking with John Zimmer, who's the co-founder and President of Lyft, sharing his stories and successes, and a lot more work to do here at the People First conversations. With theCUBE, and Mayfield, I'm John Furrier, thanks for watching. (outro music)

>> Narrator: Live from San Francisco, it's theCUBE covering Flink Forward brought to you by Data Artisans. >> This is George Gilbert. We are at Data Artisan's conference Flink Forward. It is for the Apache Flink commmunity, sponsored by Data Artisans, and all the work they're doing to move Flink Forward, and to surround it with additional value that makes building stream-processing applications accessible to mainstream companies. Right now though, we are not talking to a mainstream company, we're talking to Greg Fee from Lyft. Not Uber. (laughs) And Greg tell us a little bit about what you're doing with Flink. What's the first-use case, that comes to mind that really exercises its capabilities? >> Sure, yeah, so the process of adopting Flink at Lyft has really started with a use case, which was, we're trying to make machine learning more accessible across all of Lyft. So we already use machine learning in quite a few applications, but we want to make sure that we use machine learning as much as possible, we really think that's the path forward. And one of the fundamental difficulties with that is having consistent feature generation between these offline batch-y training scenarios and the online real-time streaming scenarios. And the unified processing engine of Flink really helps us bridge that gap, so. >> When you say unified processing engine, are you saying that the fact that you can manage code and data, as sort of an application version, and some of the, either code or data, is part of the model, and so your versioning? >> That's even a step beyond what I'm talking about. >> Okay. >> Just the basic fundamental ability to have one piece of business logic that you can apply at the batch bulk layer, and in the real-time layer. >> George: Yeah. >> So that's sort of like the core of what Flink gives you. >> Are you running both batch and streaming on Flink? >> Yes, that's right. >> And using the, so, you're using the windows? Or just periodic execution on a stream to simulate batch? >> That's right. So we have, so feature generation crosses a broad spectrum of possible use cases in Flink. >> George: Yeah. >> And this is where we sort of transition more into what dA platform could give for us. So, we're looking to have thousands of different features across all of our machine learning models. So having a platform that can help us host many of these little programs running, help with the application life-cycle of each of these features, as we version them over time. So, we're very excited about what dA platform can do for us. >> Can you tell us a little more about how the stream processing helps you with the feature selection engineering, and is it that you're using streaming, or simulated batch, or batch using the same programming model to train these models, and you're using, you're picking up different derived data, is that how it's working? >> So, typical life-cycle is, it's going to be a feature engineering stage, so the data scientist is looking at their data, they're trying figure out patterns in the data, and they're going to, how you apply Flink there, is as you come up with potential algorithms for how you generate your feature, can run that through Flink, generate some data, apply machine learning model on top of it, and sort of play around with that data, prototype things. >> So, what you're doing is offline, or out of the platform, you're doing the feature selection and the engineering. >> Man: Right. >> Then you attach a stream to it that has just the relevant, perhaps, the relevant features. >> Man: Right. >> And then that model gets sort of, well maybe not yet, but eventually versioned as part of the application, which includes the application, the rest of the application logic and the data. >> Right. So, like some of the stuff that was touched on this morning at the keynotes, the versioning and maintaining machine learning applications, is a much, is a very complex ecosystem there. So being able to say, okay, going from the prototype stage, doing stuff in batch, to doing stuff in production, and real-time, then being able to version those over time, to move to better and better versions of the future generation, is very important to us. >> I don't know if this is the most politically correct thing, but you just explained it better than everyone else we have talked to. >> Great. (laughs) >> About how it all fits together with the machine learning. So, once you've got that in place, it sounds like you're using the dA platform, as well as, you know, perhaps some extensions for machine learning, to sort of add that as a separate life-cycle, besides the application code. Then, is that going to be the enterprise-wide platform for deploying, developing and deploying, machine learning applications? >> Yes, certainly we think there's probably a broad ecosystem to do machine learning. It's a very, sort of, wide open area. Certainly my agenda is to push it across the company and get as many things running in this system as possible. I think the real-time aspects of it, a unifying aspect, of what Flink can give us, and the platform can give us, in terms of the life-cycles. >> So, are you set up essentially like where you're the, a shared resource, a shared service, which is the platform group? >> Man: Right. >> And then, all the business units, adopt that platform and build their apps on it. >> Right. So my initiative is part of a greater data science platform at Lyft, so, my goal is to have, we have hundreds of data scientists who are going to be looking at this data, giving me little features that they want to do, and we're probably going to end up numbering in the thousands of features, being able to generate all those, maintain all those little programs. >> And when you say generate all those little programs, that's the application logic, and the models specific to that application? >> That's right, well. >> Or is it this? >> There's features that are typically shared across many models. >> Okay. >> So there's like two layers of things happening. >> So you're managing features separately from the models. >> That's right. >> Interesting. Okay, haven't heard that. And is the application manager tooling going to help address that, or is that custom stuff that you have to do? >> So, I think there's, I think there's a potential that that's the way we're going to manage the model stuff as well, but it's still little new over there. >> That you put it on the application platform? >> Right. >> Then that's sort of at the boundary of what you're doing right now, or what you will be doing shortly. >> Right. It's all, it's a matter of use-case, whether it's online or offline, and how it fits best in with the rest of the Lyft engineering system. >> When you're talking about your application landscape, do you have lots of streaming applications that feed other streaming applications, going through a hub. Or, are they sort of more discrete, you know, artifacts, discrete programs, and then when do you keep, stay within the streaming processors, and when do you have it in a shared database? >> That's a, that's a lot of questions, kind of a deep question. So, the goal is to have a central hub, where sort of all of our event data passes through it, and that allows us to decouple. >> So that's to be careful, that's not a database central hub, that's a, like a? >> An event hub. >> Event hub. >> Right. >> Yeah, okay. >> So, an event hub in the middle allows us to decompose the different, sort of smaller programs, which again are probably going to number in the thousands, so that being able to have different parts of the company maintain their own part of the overall system is very important to us. I think we'll probably see Flink as a major player, in terms of how those programs run, but we'll probably be shooting things off to other systems like Druid, like Hive, like Presto, like Elasticsearch. >> As derived data? >> As all derived data, from these Flink jobs. And then also, pushing data directly out into some of our production systems to feed into machine learning decisions. >> Okay, this is quite, sounds like the most ambitious infrastructure that we've heard, in that it sounds like pretty ubiquitous. >> We want to be a machine-learning first company. So, it's everywhere. >> So, now help me clarify for me, when? Because this is, you know, for mainstream companies who've programmed with, you know, DBMS, as a shared state manager for decades, help explain to them when you would still use a DBMS for shared state, and when you would start using the distributed state that's embedded in Flink, and the derived data, you know, at the endpoints, at the syncs. >> So I mean, I guess this kind of gets into your exact, your use cases and, you know, your opinions and thoughts about how to use these things best, but. >> George: Your opinion is what we're interested in. >> Right. From where I'm coming, I see basically databases as potential one sync for this data. They do things very well, right? They do structured queries very well. You can have indices built off that, aggregates, really feed into a lot of visualization stuff. >> George: Yeah. >> But, from where I am sitting, like we're really moving away from databases as something that feeds production data. We've got other stores to do that, that are sort of more tailored towards those scenarios. >> When you say to feed production data, this is transaction capture, or data capture. >> Right. So we don't have a lot of atomic transactions, outside the payments at Lyft, most of the stuff is eventually consistent. So we have stores, more like Dynamo or Cassandra HBase that feed a lot of our production data. >> And those databases, are they for like ambient information like influencing an interaction, it doesn't sound like automating a transaction. It would be, it sounds like, context that helps with analytics, but very separate from the OLTP apps. >> That's right. So we have, you can kind of bifurcate the company into the data that's used in production to make decisions that are like facing the user, and then our analytics back end, that really helps business analysts and like the executives make decisions about how we proceed. >> And so that second part, that backend, is more like operational efficiency. >> Man: Right. >> And coding new business processes to support new ways of doing business, but the customer-facing stuff specifically like with payments, that still needs a traditional OLTP. >> Man: Right. >> But there not, those use cases aren't growing that much. >> That's right. So, basically we have very specific use-cases for like a traditional database, but in terms of capturing the types of scale, and the type of growth, we're looking for at Lyft, we think some of the other storage engines suit those better. >> So in that use-case, would the OLTP DBMS be at the front end, would it be a source, or a sync? It sounds like it's a source. >> So we actually do it both ways. Right, so, it's great to get our transactional data flowing through our streaming system, it's a lot of value in that, but also then pushing it out, back to some of the aggregate results to DBMS, helps with our analytics pipeline. >> Okay, okay. Well this is actually really interesting. So, where do you see the dA platform helping, you know, going forward; is it something you don't really need because you've built all that scaffolding to help with sort of application life-cycle management, or or do you see it as something that'll help sort of push Flink sort of enterprise-wide? >> I think the dA platform really helps people sort of adopt Flink at an enterprise level. Maintaining the applications is a core part of what it means to run it as a business. And so we're looking at dA platform as a way of managing our applications, and I think, like I'm just talking about one, I'm mostly talking about one application we have for Flink at Lyft. >> Yeah. >> We have many other Flink programs actually running, that are sort of unrelated to my project. >> What about managing non-Flink applications? Do you need an application manager? Is it okay that it's associated with one service, or platform like Flink, or is there a desire you know among bleeding edge customers to have an overall, sort of infrastructure management, application management kind of suite. >> Yes, for sure. You're touching on something I have started to push inside of Lyft, which is the need for an overall application life-cycle management product that's not technology specific. >> Would these sort of plug into the dA platform and whatever the confluent, you know, equivalent is, or is it going to to directly tie to the, you know, operational capabilities, or the functional capabilities, not the management capabilities. In other words would it plug into like core Flink, core Kafka, core Spark, that sort of stuff? >> I think that's sort of largely to be determined. If you go back to sort of how distributed design system works, typically. We have a user plane, which is going to be our data users. Then you end up with the thing we're probably most familiar with, which is our data plane, technologies like Flink and Kafka and Hive, all those guys. What's missing in the middle right now is a control plane. It's a map from the user desire, from the user intention, to what we do with all of that data plane stuff. So launch a new program, maybe you need a new Kafka topic, maybe you need to provision in Kafka. Higher, you need to get some Flink programs running, and whether that talks directly talks to Flink, and goes against Kubernetes, or something like that, or whether it talks to a higher level, like more application-specific platform. >> Man: Yeah. >> I think, you know it's certainly a lot easier, if we have some of these platforms in the way. >> Because they give you better abstractions. >> That's right. >> To talk to the platforms. >> That's right. >> That's interesting. Okay, geesh, we learn something really, really interesting with each interview. I'm curious though, if you look out a couple years, how much of your application landscape will be continuous processing, and is that something you can see mainstream enterprises adopting, or has decades of work with, you know, batch and interactive sort of made people too difficult to learn something so radically new? >> I think it's all going to be driven by the business needs, and whether the value is there for people to make that transition 'cause it is quite expensive to invest in new infrastructure. For companies like Lyft, where we're trying to make decisions very quickly, you know, users get down to two seconds makes a difference for the customer, so we're trying to be as, you know, real-time as possible. I used to work at Salesforce. Salespeople are a little less sensitive to these things, and you know it's very, very traditional world. >> That's interesting. (background applauding) >> But even Salesforce is moving towards that style. >> Even Salesforce is moving? >> Is moving toward streaming processing. >> Really? >> George: So like, I think we're going to see it slowly be adopted across the big enterprises. >> George: I imagine that's probably for their analytics. >> That's where they're starting, of course, yeah. >> Okay. So, this was, a little more affirmation on to how we're going to see the control plane evolve, and the interesting use-cases that you're up to. I hope we can see you back next year. And you can tell us how far you've proceeded. >> I certainly hope so, yeah. >> This was really interesting. So, Greg Fee from Lyft. We will hopefully see you again. And this is George Gilbert. We're at the Data Artisans Flink Forward conference in San Francisco. We'll be back after this break. (techno music)

>> Narrator: Live from Austin Texas. It's theCUBE, covering KubeKon and CloudNativeCon 2017. Brought to you by Red Hat, the Linux Foundation, and theCUBE's ecosystem partners. >> Welcome back everyone, live here in Austin Texas, theCUBE's exclusive coverage of CloudNativeConference and KubeKon, for Kubernetes' Conference. I'm John Furrier, co-founder of SiliconANGLE and my co-host Stu Miniman, our analyst. And next is Matt Klein, a software engineer at Lyft, ride-hailing service, car sharing, social network, great company, everyone knows that everyone loves Lyft. Thanks for coming on. >> Thanks very much for having me. >> All right so you're a customer of all this technology. You guys built, and I think this is like the shiny use cases of our generation, entrepreneurs and techies build their own stuff because they can't get product from the general market. You guys had a large-scale demand for the service, you had to go out and build your own with open source and all those tools, you had a problem you had to solve, you build it, used some open source and then give it back to open source and be part of the community, and everybody wins, you donated it back. This is, this is the future, this is what it's going to be like, great community work. What problem were you solving? Obviously Lyft, everyone knows it's hard, they see their car, lot of real time going on, lot of stuff happening >> Matt: Yeah, sure. >> magic's happening behind the scenes, you had to build that. Talk about the problem you solved. >> Well, I think, you know, when people look at Lyft, like you were saying, they look at the app and the car, and I think many people think that it's a relative simple thing. Like how hard could it be to bring up your app and say, I want a ride, and you know, get that car from here to there, but it turns out that it's really complicated. There's a lot of real-time systems involved in actually finding what are all the cars that are near you, and what's the fastest route, all of that stuff. So, I think what people don't realize is that Lyft is a very large, real-time system that, at current scale, operates at millions of requests per second, and has a lot of different use cases around databases, and caching, you know, all those technologies. So, Lyft was built on open source, as you say, and, you know Lyft grew from what I think most companies do, which is a very simple, monolithic stack, you know, it starts with a PHP application, we're a big user of MongoDB, and some load balancer, and then, you know-- >> John: That breaks (laughs) >> Well, well no but but people do that because that's what's very quick to do. And I think what happened, like most companies, is, or that most companies that become very successful, is Lyft grew a lot, and like the few companies that can become very successful, they start to outgrow some of that basic software, or the basic pieces that they're actually using. So, as Lyft started to grow a lot, things just didn't actually start working, so then we had to start fixing and building different things. >> Yeah, Matt, scale is one of those things that gets talked about a lot. But, I mean Lyft, you know, really does operate at a significant scale. >> Matt: Yeah, sure. >> Maybe you can talk a little bit about, you know, what kind of things were breaking, >> Matt: Absolutely, yeah, and then what led to Envoy and why that happened. >> Yeah, sure. I mean, I think there's two different types of scale, and I think this is something that people don't talk about enough. There's scale in terms of things that people talk about, in terms of data throughput or requests per second, or stuff like that. But there's also people scale, right. So, as organizations grow, we go from 10 developers to 50 developers to 100, where Lyft is now many hundreds of developers and we're continuing to grow, and what I think people don't talk about enough is the human scale, so you know, so we have a lot of people that are trying to edit code, and at a certain size, that number of people, you can't all be editing on that same code base. So that's I think the biggest move where people start moving towards this microservice or service-oriented architecture, so you start splitting that apart to get people-scale. People-scale probably usually comes with requests per second scale and data scale and that kind of stuff. But these problems come hand in hand, where as you grow the number of people, you start going into microservices, and then suddenly you have actual scale problems. The database is not working, or the network is not actually reliable. So from Envoy perspective, so Envoy is an open source proxy we built at Lyft, it's now part of CNCF, it's having tremendous uptake across the industry, which is fantastic, and the reason that we built Envoy is what we're seeing now in the industry is people are moving towards polyglot architectures, so they're moving towards architectures with many different applications, or many different languages. And it used to be that you could use Java and you could have one particular library that would do all of your networking and service discovery and load balancing, and now you might have six different languages. So how as an organization do you actually deal with that? And what we decided to do was build an out-of-process proxy, which allows people to build a lot of functionality into one place, around load balancing, and service discovery, and rate limiting, and buffering, and all those kinds of things, and also most importantly, observability. So things like tracing and stats and logging. And that allowed us to actually understand what was going on in the network, so that when problems were happening, we could actually debug what was going on. And what we saw at Lyft, about three years ago, is we had started our microservices journey, but it was actually almost, it was almost stopped, because what people found is they had started to build services because supposedly it was faster than the monolith, but then we would start having problems with tail latency and other things, and they didn't know hot to debug it. So they didn't trust those services, and then at that point they say, not surprisingly, we're just going to go back and we're going to build it back into the monolith. So, we're almost in that situation where things are kind of in that split. >> So Matt I have to think that's the natural, where you led to service mesh, and Istio specifically and Lyft, Google, IBM all working on that. Talk a little bit about, more about what Istio, it was really the buzz coming in with service mesh, there's also there's some competing offerings out there, Conduit, new one announced this week, maybe give us the landscape, kind of where we are, and what you're seeing. >> So I think service mesh is, it's incredible to look around this conference, I think there's 15 or more talks on service mesh between all of the Buoyant talks on Linker D and Conduit and Istio and Envoy, it's super fantastic. I think the reason that service mesh is so compelling to people is that we have these problems where people want to build in five or six languages, they have some common problems around load balancing and other types of things, and this is a great solution for offloading some of those problems into a common place. So, the confusion that I see right now around the industry is service mesh is really split into two pieces. It's split into the data plane, so the proxy, and the control plane. So the proxy's the thing that actually moves the bytes, moves the requests, and the control plane is the thing that actually tells all the proxies what to do, tells it the topology, tells it all the configurations, all the settings. So the landscape right now is essentially that Envoy is a proxy, it's a data plane. Envoy has been built into a bunch of control planes, so Istio is a control plane, it's reference proxy is Envoy, though other companies have shown that they can integrate with Istio. Linker D has shown that, NGINX has shown that. Buoyant just came out with a new combined control plane data plane service mesh called Conduit, that was brand new a couple days ago, and I think we're going to see other companies get in there, because this is a very popular paradigm, so having the competition is good. I think it's going to push everyone to be better. >> How do companies make sense of this, I mean, if I'm just a boring enterprise with complexity, legacy, you know I have a lot of stuff, maybe not the kind of scale in terms of transactions per second, because they're not Lyft, but they still have a lot of stuff. They got servers, they got data center, they got stuff in the cloud, they're trying to put this cloud native package in because the developer movement is clearly pushing the legacy guy, old guard, into cloud. So how does your stuff translate into the mainstream, how would you categorize it? >> Well, what I counsel people is, and I think that's actually a problem that we have within the industry, is that I think sometimes we push people towards complexity that they don't necessarily need yet. And I'm not saying that all of these cloud native technologies aren't great, right, I mean people here are doing fantastic things. >> You know how to drive a car, so to speak, you don't know how to use the tech. >> Right, and I advise companies and organizations to use the technology and the complexity that they need. So I think that service mesh and microservices and tracing and a lot of the stuff that's being talked about at this conference are very important if you have the scale to have a service-oriented microservice architecture. And, you know, some enterprises they're segmented enough where they may not actually need a full microservice real-time architecture. So I think that the thing to actually decide is, number one, do you need a microservice architecture, and it's okay if you don't, that's just fine, take the complexity that you need. If you do need a microservice architecture, then I think you're going to have a set of common problems around things like networking, and databases, and those types of things, and then yes, you are probably going to need to build in more complicated technologies to actually deal with that. But the key takeaway is that as you bring on, as you bring on more complexity, the complexity is a snowballing effect. More complexity yields more complexity. >> So Matt, might be a little bit out of bounds for what we're talking about, but when I think about autonomous vehicles, that's just going to put even more strain on the kind of the distributed natured systems, you know, things that have to have the edge, you know. Are we laying the groundwork at a conference like this? How's Lyft looking at this? >> For sure, and I mean, we're obviously starting to look into autonomous a lot, obviously Uber's doing that a fair amount, and if you actually start looking at the sheer amount of data that is generated by these cars when they're actually moving around, it's terabytes and terabytes of data, you start thinking through the complexity of ingesting that data from the cars into a cloud and actually analyzing it and doing things with it either offline or in real-time, it's pretty incredible. So, yes, I think that these are just more massive scale real-time systems that require more data, more hard drives, more networks, and as you manage more things with more people, it becomes more complicated for sure. >> What are you doing inside Lyft, your job. I mean obviously, you're involved in open source. Like, what are you coding specifically these days, what's the current assignment? >> Yeah, so I'm a software engineer at Lyft, I lead our networking team. Our networking team owns obviously all the stuff that we do with Envoy, we own our edge system, so basically how internet traffic comes into Lyft, all of our service discovery systems, rate limiting, auth between services. We're increasingly owning our GRPC communications, so how people define their APIs, moving from a more polling-based API to a more push-based API. So our team essentially owns the end-to-end pipe from all of our back-end services to the client, so that's APIs, analytics, stats, logging, >> So to the app >> Yeah, right, right, to the app, so, on the phone. So that's my job. I also help a lot with general kind of infrastructure architecture, so we're increasingly moving towards Kubernetes, so that's a big thing that we're doing at Lyft. Like many companies of Lyft's kind of age range, we started on VMs and AWS and we used SaltStack and you know, it's the standard story from companies that were probably six or eight years old. >> Classic dev ops. >> Right, and >> Gen One devops. >> And now we're trying to move into the, as you say, Gen Two world, which is pretty fantastic. So this is becoming, probably, the most applicable conference for us, because we're obviously doing a lot with service mesh, and we're leading the way with Envoy. But as we integrate with technologies like Istio and increasingly use Kubernetes, and all of the different related technologies, we are trying to kind of get rid of all of our bespoke stuff that many companies like Lyft had, and we're trying to get on that general train. >> I mean you guys, I mean this is going to be written in the history books, you look at this time in a generation, I mean this is going to define open source for a long, long time, because, I say Gen one kind of sounds pejorative but it's not. It's really, you need to build your own, you couldn't just buy Oracle database, because, you probably have some maybe Oracle in there, but like, you build your own. Facebook did it, you guys are doing it. Why, because you're badass, you had to. Otherwise you don't build customers. >> Right and I absolutely agree about that. I think we are in a very unique time right now, and I actually think that if you look out 10 years, and you look at some of the services that are coming online, and like Amazon just did Fargate, that whole container scheduling system, and Azure has one, and I think Google has one, but the idea there is that in 10 years' time, people are really going to be writing business logic, they're going to insert that business logic >> They may do a powerpoint slides. >> That would be nice. >> I mean it's easy to me, like powerpoint, it's so easy, that's, I'm not going to say that's coding, but that's the way it should be. >> I absolutely agree, and we'll keep moving towards that, but the way that's going to happen is, more and more plumbing if you will, will get built into these clouds, so that people don't have to worry about all this stuff. But we're in this intermediate time, where people are building these massive scale systems, and the pieces that they need is not necessarily there. >> I've been saying in theCUBE now for multiple events, all through this last year, kind of crystallized and we were talking about with Kelsey about this, Hightower, yesterday, craft is coming back to programming. So you've got software engineering, and you've got craftsmanship. And so, there's real software engineering being done, it's engineering. Application development is going to go back to the old school of real craft. I mean, Agile, all it did was create a treadmill of de-risking rapid build scale, by listening to data and constantly iterating, but it kind of took the craft out of it. >> I agree. >> But that turned into engineering. Now you have developers working on say business logic or just solving, building a healthcare app. That's just awesome software. Do you agree with this craft? >> I absolutely agree, and actually what we say about Envoy, so kind of the catchword buzz phrase of Envoy is to make the network transparent to applications. And I think most of what's happening in infrastructure right now is to get back to a time where application developers can focus on business logic, and not have to worry about how some of this plumbing actually works. And what you see around the industry right now, is it is just too painful for people to operate some of these large systems. And I think we're heading in the right direction, all of the trends are there, but it's going to take a lot more time to actually make that happen. >> I remember when I was graduating college in the 80s, sound old but, not to date myself, but the jobs were for software engineering. I mean that is what they called it, and now we're back to this devops brought it, cloud, the systems kind of engineering, really at a large scale, because you got to think about these things. >> Yeah, and I think what's also kind of interesting is that companies have moved toward this devops culture, or expecting developers to operate their systems, to be on call for them and I think that's fantastic, but what we're not doing as an industry is we're not actually teaching and helping people how to do this. So like we have this expectation that people know how to be on-call and know how to make dashboards, and know how to do all this work, but they don't learn it in school, and actually we come into organizations where we may not help them learn these skills. >> Every company has different cultures, that complicates things. >> So I think we're also, as an industry, we are figuring out how to train people and how to help them actually do this in a way that makes sense. >> Well, fascinating conversation Matt. Congratulations on all your success. Obviously a big fan of Lyft, one of the board members gave a keynote, she's from Palo Alto, from Floodgate. Great investors, great fans of the company. Congratulations, great success story, and again open source, this is the new playbook, community scale contribution, innovation. TheCUBE's doing it's share here live in Austin, Texas, for KubeKon, for Kubernetes conference and CloudNativeCon. I'm John Furrrier, for Stu Miniman, we'll be back with more after this short break. (futuristic music)

(upbeat music) >> Hello everyone. Welcome to theCube's presentation of the "AWS Startup Showcase." The topic this episode is AI and machine learning, top startups building foundational model infrastructure. This is season three, episode one of the ongoing series covering exciting startups from the AWS ecosystem. And this time we're talking about AI and machine learning. I'm your host, John Furrier. I'm excited I'm joined today by Robert Nishihara, who's the co-founder and CEO of a hot startup called Anyscale. He's here to talk about Ray, the open source project, Anyscale's infrastructure for foundation as well. Robert, thank you for joining us today. >> Yeah, thanks so much as well. >> I've been following your company since the founding pre pandemic and you guys really had a great vision scaled up and in a perfect position for this big wave that we all see with ChatGPT and OpenAI that's gone mainstream. Finally, AI has broken out through the ropes and now gone mainstream, so I think you guys are really well positioned. I'm looking forward to to talking with you today. But before we get into it, introduce the core mission for Anyscale. Why do you guys exist? What is the North Star for Anyscale? >> Yeah, like you mentioned, there's a tremendous amount of excitement about AI right now. You know, I think a lot of us believe that AI can transform just every different industry. So one of the things that was clear to us when we started this company was that the amount of compute needed to do AI was just exploding. Like to actually succeed with AI, companies like OpenAI or Google or you know, these companies getting a lot of value from AI, were not just running these machine learning models on their laptops or on a single machine. They were scaling these applications across hundreds or thousands or more machines and GPUs and other resources in the Cloud. And so to actually succeed with AI, and this has been one of the biggest trends in computing, maybe the biggest trend in computing in, you know, in recent history, the amount of compute has been exploding. And so to actually succeed with that AI, to actually build these scalable applications and scale the AI applications, there's a tremendous software engineering lift to build the infrastructure to actually run these scalable applications. And that's very hard to do. So one of the reasons many AI projects and initiatives fail is that, or don't make it to production, is the need for this scale, the infrastructure lift, to actually make it happen. So our goal here with Anyscale and Ray, is to make that easy, is to make scalable computing easy. So that as a developer or as a business, if you want to do AI, if you want to get value out of AI, all you need to know is how to program on your laptop. Like, all you need to know is how to program in Python. And if you can do that, then you're good to go. Then you can do what companies like OpenAI or Google do and get value out of machine learning. >> That programming example of how easy it is with Python reminds me of the early days of Cloud, when infrastructure as code was talked about was, it was just code the infrastructure programmable. That's super important. That's what AI people wanted, first program AI. That's the new trend. And I want to understand, if you don't mind explaining, the relationship that Anyscale has to these foundational models and particular the large language models, also called LLMs, was seen with like OpenAI and ChatGPT. Before you get into the relationship that you have with them, can you explain why the hype around foundational models? Why are people going crazy over foundational models? What is it and why is it so important? >> Yeah, so foundational models and foundation models are incredibly important because they enable businesses and developers to get value out of machine learning, to use machine learning off the shelf with these large models that have been trained on tons of data and that are useful out of the box. And then, of course, you know, as a business or as a developer, you can take those foundational models and repurpose them or fine tune them or adapt them to your specific use case and what you want to achieve. But it's much easier to do that than to train them from scratch. And I think there are three, for people to actually use foundation models, there are three main types of workloads or problems that need to be solved. One is training these foundation models in the first place, like actually creating them. The second is fine tuning them and adapting them to your use case. And the third is serving them and actually deploying them. Okay, so Ray and Anyscale are used for all of these three different workloads. Companies like OpenAI or Cohere that train large language models. Or open source versions like GPTJ are done on top of Ray. There are many startups and other businesses that fine tune, that, you know, don't want to train the large underlying foundation models, but that do want to fine tune them, do want to adapt them to their purposes, and build products around them and serve them, those are also using Ray and Anyscale for that fine tuning and that serving. And so the reason that Ray and Anyscale are important here is that, you know, building and using foundation models requires a huge scale. It requires a lot of data. It requires a lot of compute, GPUs, TPUs, other resources. And to actually take advantage of that and actually build these scalable applications, there's a lot of infrastructure that needs to happen under the hood. And so you can either use Ray and Anyscale to take care of that and manage the infrastructure and solve those infrastructure problems. Or you can build the infrastructure and manage the infrastructure yourself, which you can do, but it's going to slow your team down. It's going to, you know, many of the businesses we work with simply don't want to be in the business of managing infrastructure and building infrastructure. They want to focus on product development and move faster. >> I know you got a keynote presentation we're going to go to in a second, but I think you hit on something I think is the real tipping point, doing it yourself, hard to do. These are things where opportunities are and the Cloud did that with data centers. Turned a data center and made it an API. The heavy lifting went away and went to the Cloud so people could be more creative and build their product. In this case, build their creativity. Is that kind of what's the big deal? Is that kind of a big deal happening that you guys are taking the learnings and making that available so people don't have to do that? >> That's exactly right. So today, if you want to succeed with AI, if you want to use AI in your business, infrastructure work is on the critical path for doing that. To do AI, you have to build infrastructure. You have to figure out how to scale your applications. That's going to change. We're going to get to the point, and you know, with Ray and Anyscale, we're going to remove the infrastructure from the critical path so that as a developer or as a business, all you need to focus on is your application logic, what you want the the program to do, what you want your application to do, how you want the AI to actually interface with the rest of your product. Now the way that will happen is that Ray and Anyscale will still, the infrastructure work will still happen. It'll just be under the hood and taken care of by Ray in Anyscale. And so I think something like this is really necessary for AI to reach its potential, for AI to have the impact and the reach that we think it will, you have to make it easier to do. >> And just for clarification to point out, if you don't mind explaining the relationship of Ray and Anyscale real quick just before we get into the presentation. >> So Ray is an open source project. We created it. We were at Berkeley doing machine learning. We started Ray so that, in order to provide an easy, a simple open source tool for building and running scalable applications. And Anyscale is the managed version of Ray, basically we will run Ray for you in the Cloud, provide a lot of tools around the developer experience and managing the infrastructure and providing more performance and superior infrastructure. >> Awesome. I know you got a presentation on Ray and Anyscale and you guys are positioning as the infrastructure for foundational models. So I'll let you take it away and then when you're done presenting, we'll come back, I'll probably grill you with a few questions and then we'll close it out so take it away. >> Robert: Sounds great. So I'll say a little bit about how companies are using Ray and Anyscale for foundation models. The first thing I want to mention is just why we're doing this in the first place. And the underlying observation, the underlying trend here, and this is a plot from OpenAI, is that the amount of compute needed to do machine learning has been exploding. It's been growing at something like 35 times every 18 months. This is absolutely enormous. And other people have written papers measuring this trend and you get different numbers. But the point is, no matter how you slice and dice it, it' a astronomical rate. Now if you compare that to something we're all familiar with, like Moore's Law, which says that, you know, the processor performance doubles every roughly 18 months, you can see that there's just a tremendous gap between the needs, the compute needs of machine learning applications, and what you can do with a single chip, right. So even if Moore's Law were continuing strong and you know, doing what it used to be doing, even if that were the case, there would still be a tremendous gap between what you can do with the chip and what you need in order to do machine learning. And so given this graph, what we've seen, and what has been clear to us since we started this company, is that doing AI requires scaling. There's no way around it. It's not a nice to have, it's really a requirement. And so that led us to start Ray, which is the open source project that we started to make it easy to build these scalable Python applications and scalable machine learning applications. And since we started the project, it's been adopted by a tremendous number of companies. Companies like OpenAI, which use Ray to train their large models like ChatGPT, companies like Uber, which run all of their deep learning and classical machine learning on top of Ray, companies like Shopify or Spotify or Instacart or Lyft or Netflix, ByteDance, which use Ray for their machine learning infrastructure. Companies like Ant Group, which makes Alipay, you know, they use Ray across the board for fraud detection, for online learning, for detecting money laundering, you know, for graph processing, stream processing. Companies like Amazon, you know, run Ray at a tremendous scale and just petabytes of data every single day. And so the project has seen just enormous adoption since, over the past few years. And one of the most exciting use cases is really providing the infrastructure for building training, fine tuning, and serving foundation models. So I'll say a little bit about, you know, here are some examples of companies using Ray for foundation models. Cohere trains large language models. OpenAI also trains large language models. You can think about the workloads required there are things like supervised pre-training, also reinforcement learning from human feedback. So this is not only the regular supervised learning, but actually more complex reinforcement learning workloads that take human input about what response to a particular question, you know is better than a certain other response. And incorporating that into the learning. There's open source versions as well, like GPTJ also built on top of Ray as well as projects like Alpa coming out of UC Berkeley. So these are some of the examples of exciting projects in organizations, training and creating these large language models and serving them using Ray. Okay, so what actually is Ray? Well, there are two layers to Ray. At the lowest level, there's the core Ray system. This is essentially low level primitives for building scalable Python applications. Things like taking a Python function or a Python class and executing them in the cluster setting. So Ray core is extremely flexible and you can build arbitrary scalable applications on top of Ray. So on top of Ray, on top of the core system, what really gives Ray a lot of its power is this ecosystem of scalable libraries. So on top of the core system you have libraries, scalable libraries for ingesting and pre-processing data, for training your models, for fine tuning those models, for hyper parameter tuning, for doing batch processing and batch inference, for doing model serving and deployment, right. And a lot of the Ray users, the reason they like Ray is that they want to run multiple workloads. They want to train and serve their models, right. They want to load their data and feed that into training. And Ray provides common infrastructure for all of these different workloads. So this is a little overview of what Ray, the different components of Ray. So why do people choose to go with Ray? I think there are three main reasons. The first is the unified nature. The fact that it is common infrastructure for scaling arbitrary workloads, from data ingest to pre-processing to training to inference and serving, right. This also includes the fact that it's future proof. AI is incredibly fast moving. And so many people, many companies that have built their own machine learning infrastructure and standardized on particular workflows for doing machine learning have found that their workflows are too rigid to enable new capabilities. If they want to do reinforcement learning, if they want to use graph neural networks, they don't have a way of doing that with their standard tooling. And so Ray, being future proof and being flexible and general gives them that ability. Another reason people choose Ray in Anyscale is the scalability. This is really our bread and butter. This is the reason, the whole point of Ray, you know, making it easy to go from your laptop to running on thousands of GPUs, making it easy to scale your development workloads and run them in production, making it easy to scale, you know, training to scale data ingest, pre-processing and so on. So scalability and performance, you know, are critical for doing machine learning and that is something that Ray provides out of the box. And lastly, Ray is an open ecosystem. You can run it anywhere. You can run it on any Cloud provider. Google, you know, Google Cloud, AWS, Asure. You can run it on your Kubernetes cluster. You can run it on your laptop. It's extremely portable. And not only that, it's framework agnostic. You can use Ray to scale arbitrary Python workloads. You can use it to scale and it integrates with libraries like TensorFlow or PyTorch or JAX or XG Boost or Hugging Face or PyTorch Lightning, right, or Scikit-learn or just your own arbitrary Python code. It's open source. And in addition to integrating with the rest of the machine learning ecosystem and these machine learning frameworks, you can use Ray along with all of the other tooling in the machine learning ecosystem. That's things like weights and biases or ML flow, right. Or you know, different data platforms like Databricks, you know, Delta Lake or Snowflake or tools for model monitoring for feature stores, all of these integrate with Ray. And that's, you know, Ray provides that kind of flexibility so that you can integrate it into the rest of your workflow. And then Anyscale is the scalable compute platform that's built on top, you know, that provides Ray. So Anyscale is a managed Ray service that runs in the Cloud. And what Anyscale does is it offers the best way to run Ray. And if you think about what you get with Anyscale, there are fundamentally two things. One is about moving faster, accelerating the time to market. And you get that by having the managed service so that as a developer you don't have to worry about managing infrastructure, you don't have to worry about configuring infrastructure. You also, it provides, you know, optimized developer workflows. Things like easily moving from development to production, things like having the observability tooling, the debug ability to actually easily diagnose what's going wrong in a distributed application. So things like the dashboards and the other other kinds of tooling for collaboration, for monitoring and so on. And then on top of that, so that's the first bucket, developer productivity, moving faster, faster experimentation and iteration. The second reason that people choose Anyscale is superior infrastructure. So this is things like, you know, cost deficiency, being able to easily take advantage of spot instances, being able to get higher GPU utilization, things like faster cluster startup times and auto scaling. Things like just overall better performance and faster scheduling. And so these are the kinds of things that Anyscale provides on top of Ray. It's the managed infrastructure. It's fast, it's like the developer productivity and velocity as well as performance. So this is what I wanted to share about Ray in Anyscale. >> John: Awesome. >> Provide that context. But John, I'm curious what you think. >> I love it. I love the, so first of all, it's a platform because that's the platform architecture right there. So just to clarify, this is an Anyscale platform, not- >> That's right. >> Tools. So you got tools in the platform. Okay, that's key. Love that managed service. Just curious, you mentioned Python multiple times, is that because of PyTorch and TensorFlow or Python's the most friendly with machine learning or it's because it's very common amongst all developers? >> That's a great question. Python is the language that people are using to do machine learning. So it's the natural starting point. Now, of course, Ray is actually designed in a language agnostic way and there are companies out there that use Ray to build scalable Java applications. But for the most part right now we're focused on Python and being the best way to build these scalable Python and machine learning applications. But, of course, down the road there always is that potential. >> So if you're slinging Python code out there and you're watching that, you're watching this video, get on Anyscale bus quickly. Also, I just, while you were giving the presentation, I couldn't help, since you mentioned OpenAI, which by the way, congratulations 'cause they've had great scale, I've noticed in their rapid growth 'cause they were the fastest company to the number of users than anyone in the history of the computer industry, so major successor, OpenAI and ChatGPT, huge fan. I'm not a skeptic at all. I think it's just the beginning, so congratulations. But I actually typed into ChatGPT, what are the top three benefits of Anyscale and came up with scalability, flexibility, and ease of use. Obviously, scalability is what you guys are called. >> That's pretty good. >> So that's what they came up with. So they nailed it. Did you have an inside prompt training, buy it there? Only kidding. (Robert laughs) >> Yeah, we hard coded that one. >> But that's the kind of thing that came up really, really quickly if I asked it to write a sales document, it probably will, but this is the future interface. This is why people are getting excited about the foundational models and the large language models because it's allowing the interface with the user, the consumer, to be more human, more natural. And this is clearly will be in every application in the future. >> Absolutely. This is how people are going to interface with software, how they're going to interface with products in the future. It's not just something, you know, not just a chat bot that you talk to. This is going to be how you get things done, right. How you use your web browser or how you use, you know, how you use Photoshop or how you use other products. Like you're not going to spend hours learning all the APIs and how to use them. You're going to talk to it and tell it what you want it to do. And of course, you know, if it doesn't understand it, it's going to ask clarifying questions. You're going to have a conversation and then it'll figure it out. >> This is going to be one of those things, we're going to look back at this time Robert and saying, "Yeah, from that company, that was the beginning of that wave." And just like AWS and Cloud Computing, the folks who got in early really were in position when say the pandemic came. So getting in early is a good thing and that's what everyone's talking about is getting in early and playing around, maybe replatforming or even picking one or few apps to refactor with some staff and managed services. So people are definitely jumping in. So I have to ask you the ROI cost question. You mentioned some of those, Moore's Law versus what's going on in the industry. When you look at that kind of scale, the first thing that jumps out at people is, "Okay, I love it. Let's go play around." But what's it going to cost me? Am I going to be tied to certain GPUs? What's the landscape look like from an operational standpoint, from the customer? Are they locked in and the benefit was flexibility, are you flexible to handle any Cloud? What is the customers, what are they looking at? Basically, that's my question. What's the customer looking at? >> Cost is super important here and many of the companies, I mean, companies are spending a huge amount on their Cloud computing, on AWS, and on doing AI, right. And I think a lot of the advantage of Anyscale, what we can provide here is not only better performance, but cost efficiency. Because if we can run something faster and more efficiently, it can also use less resources and you can lower your Cloud spending, right. We've seen companies go from, you know, 20% GPU utilization with their current setup and the current tools they're using to running on Anyscale and getting more like 95, you know, 100% GPU utilization. That's something like a five x improvement right there. So depending on the kind of application you're running, you know, it's a significant cost savings. We've seen companies that have, you know, processing petabytes of data every single day with Ray going from, you know, getting order of magnitude cost savings by switching from what they were previously doing to running their application on Ray. And when you have applications that are spending, you know, potentially $100 million a year and getting a 10 X cost savings is just absolutely enormous. So these are some of the kinds of- >> Data infrastructure is super important. Again, if the customer, if you're a prospect to this and thinking about going in here, just like the Cloud, you got infrastructure, you got the platform, you got SaaS, same kind of thing's going to go on in AI. So I want to get into that, you know, ROI discussion and some of the impact with your customers that are leveraging the platform. But first I hear you got a demo. >> Robert: Yeah, so let me show you, let me give you a quick run through here. So what I have open here is the Anyscale UI. I've started a little Anyscale Workspace. So Workspaces are the Anyscale concept for interactive developments, right. So here, imagine I'm just, you want to have a familiar experience like you're developing on your laptop. And here I have a terminal. It's not on my laptop. It's actually in the cloud running on Anyscale. And I'm just going to kick this off. This is going to train a large language model, so OPT. And it's doing this on 32 GPUs. We've got a cluster here with a bunch of CPU cores, bunch of memory. And as that's running, and by the way, if I wanted to run this on instead of 32 GPUs, 64, 128, this is just a one line change when I launch the Workspace. And what I can do is I can pull up VS code, right. Remember this is the interactive development experience. I can look at the actual code. Here it's using Ray train to train the torch model. We've got the training loop and we're saying that each worker gets access to one GPU and four CPU cores. And, of course, as I make the model larger, this is using deep speed, as I make the model larger, I could increase the number of GPUs that each worker gets access to, right. And how that is distributed across the cluster. And if I wanted to run on CPUs instead of GPUs or a different, you know, accelerator type, again, this is just a one line change. And here we're using Ray train to train the models, just taking my vanilla PyTorch model using Hugging Face and then scaling that across a bunch of GPUs. And, of course, if I want to look at the dashboard, I can go to the Ray dashboard. There are a bunch of different visualizations I can look at. I can look at the GPU utilization. I can look at, you know, the CPU utilization here where I think we're currently loading the model and running that actual application to start the training. And some of the things that are really convenient here about Anyscale, both I can get that interactive development experience with VS code. You know, I can look at the dashboards. I can monitor what's going on. It feels, I have a terminal, it feels like my laptop, but it's actually running on a large cluster. And I can, with however many GPUs or other resources that I want. And so it's really trying to combine the best of having the familiar experience of programming on your laptop, but with the benefits, you know, being able to take advantage of all the resources in the Cloud to scale. And it's like when, you know, you're talking about cost efficiency. One of the biggest reasons that people waste money, one of the silly reasons for wasting money is just forgetting to turn off your GPUs. And what you can do here is, of course, things will auto terminate if they're idle. But imagine you go to sleep, I have this big cluster. You can turn it off, shut off the cluster, come back tomorrow, restart the Workspace, and you know, your big cluster is back up and all of your code changes are still there. All of your local file edits. It's like you just closed your laptop and came back and opened it up again. And so this is the kind of experience we want to provide for our users. So that's what I wanted to share with you. >> Well, I think that whole, couple of things, lines of code change, single line of code change, that's game changing. And then the cost thing, I mean human error is a big deal. People pass out at their computer. They've been coding all night or they just forget about it. I mean, and then it's just like leaving the lights on or your water running in your house. It's just, at the scale that it is, the numbers will add up. That's a huge deal. So I think, you know, compute back in the old days, there's no compute. Okay, it's just compute sitting there idle. But you know, data cranking the models is doing, that's a big point. >> Another thing I want to add there about cost efficiency is that we make it really easy to use, if you're running on Anyscale, to use spot instances and these preemptable instances that can just be significantly cheaper than the on-demand instances. And so when we see our customers go from what they're doing before to using Anyscale and they go from not using these spot instances 'cause they don't have the infrastructure around it, the fault tolerance to handle the preemption and things like that, to being able to just check a box and use spot instances and save a bunch of money. >> You know, this was my whole, my feature article at Reinvent last year when I met with Adam Selipsky, this next gen Cloud is here. I mean, it's not auto scale, it's infrastructure scale. It's agility. It's flexibility. I think this is where the world needs to go. Almost what DevOps did for Cloud and what you were showing me that demo had this whole SRE vibe. And remember Google had site reliability engines to manage all those servers. This is kind of like an SRE vibe for data at scale. I mean, a similar kind of order of magnitude. I mean, I might be a little bit off base there, but how would you explain it? >> It's a nice analogy. I mean, what we are trying to do here is get to the point where developers don't think about infrastructure. Where developers only think about their application logic. And where businesses can do AI, can succeed with AI, and build these scalable applications, but they don't have to build, you know, an infrastructure team. They don't have to develop that expertise. They don't have to invest years in building their internal machine learning infrastructure. They can just focus on the Python code, on their application logic, and run the stuff out of the box. >> Awesome. Well, I appreciate the time. Before we wrap up here, give a plug for the company. I know you got a couple websites. Again, go, Ray's got its own website. You got Anyscale. You got an event coming up. Give a plug for the company looking to hire. Put a plug in for the company. >> Yeah, absolutely. Thank you. So first of all, you know, we think AI is really going to transform every industry and the opportunity is there, right. We can be the infrastructure that enables all of that to happen, that makes it easy for companies to succeed with AI, and get value out of AI. Now we have, if you're interested in learning more about Ray, Ray has been emerging as the standard way to build scalable applications. Our adoption has been exploding. I mentioned companies like OpenAI using Ray to train their models. But really across the board companies like Netflix and Cruise and Instacart and Lyft and Uber, you know, just among tech companies. It's across every industry. You know, gaming companies, agriculture, you know, farming, robotics, drug discovery, you know, FinTech, we see it across the board. And all of these companies can get value out of AI, can really use AI to improve their businesses. So if you're interested in learning more about Ray and Anyscale, we have our Ray Summit coming up in September. This is going to highlight a lot of the most impressive use cases and stories across the industry. And if your business, if you want to use LLMs, you want to train these LLMs, these large language models, you want to fine tune them with your data, you want to deploy them, serve them, and build applications and products around them, give us a call, talk to us. You know, we can really take the infrastructure piece, you know, off the critical path and make that easy for you. So that's what I would say. And, you know, like you mentioned, we're hiring across the board, you know, engineering, product, go-to-market, and it's an exciting time. >> Robert Nishihara, co-founder and CEO of Anyscale, congratulations on a great company you've built and continuing to iterate on and you got growth ahead of you, you got a tailwind. I mean, the AI wave is here. I think OpenAI and ChatGPT, a customer of yours, have really opened up the mainstream visibility into this new generation of applications, user interface, roll of data, large scale, how to make that programmable so we're going to need that infrastructure. So thanks for coming on this season three, episode one of the ongoing series of the hot startups. In this case, this episode is the top startups building foundational model infrastructure for AI and ML. I'm John Furrier, your host. Thanks for watching. (upbeat music)

(upbeat instrumental) >> Hello and welcome to this CUBE conversation. I'm John Furrier, host of theCUBE, here in Palo Alto, California. Got a great conversation with Robert Nishihara who's the co-founder and CEO of Anyscale. Robert, great to have you on this CUBE conversation. It's great to see you. We did your first Ray Summit a couple years ago and congratulations on your venture. Great to have you on. >> Thank you. Thanks for inviting me. >> So you're first time CEO out of Berkeley in Data. You got the Databricks is coming out of there. You got a bunch of activity coming from Berkeley. It's like a, it really is kind of like where a lot of innovations going on data. Anyscale has been one of those startups that has risen out of that scene. Right? You look at the success of what the Data lakes are now. Now you've got the generative AI. This has been a really interesting innovation market. This new wave is coming. Tell us what's going on with Anyscale right now, as you guys are gearing up and getting some growth. What's happening with the company? >> Yeah, well one of the most exciting things that's been happening in computing recently, is the rise of AI and the excitement about AI, and the potential for AI to really transform every industry. Now of course, one of the of the biggest challenges to actually making that happen is that doing AI, that AI is incredibly computationally intensive, right? To actually succeed with AI to actually get value out of AI. You're typically not just running it on your laptop, you're often running it and scaling it across thousands of machines, or hundreds of machines or GPUs, and to, so organizations and companies and businesses that do AI often end up building a large infrastructure team to manage the distributed systems, the computing to actually scale these applications. And that's a, that's a, a huge software engineering lift, right? And so, one of the goals for Anyscale is really to make that easy. To get to the point where, developers and teams and companies can succeed with AI. Can build these scalable AI applications, without really you know, without a huge investment in infrastructure with a lot of, without a lot of expertise in infrastructure, where really all they need to know is how to program on their laptop, how to program in Python. And if you have that, then that's really all you need to succeed with AI. So that's what we've been focused on. We're building Ray, which is an open source project that's been starting to get adopted by tons of companies, to actually train these models, to deploy these models, to do inference with these models, you know, to ingest and pre-process their data. And our goals, you know, here with the company are really to make Ray successful. To grow the Ray community, and then to build a great product around it and simplify the development and deployment, and productionization of machine learning for, for all these businesses. >> It's a great trend. Everyone wants developer productivity seeing that, clearly right now. And plus, developers are voting literally on what standards become. As you look at how the market is open source driven, a lot of that I love the model, love the Ray project love the, love the Anyscale value proposition. How big are you guys now, and how is that value proposition of Ray and Anyscale and foundational models coming together? Because it seems like you guys are in a perfect storm situation where you guys could get a real tailwind and draft off the the mega trend that everyone's getting excited. The new toy is ChatGPT. So you got to look at that and say, hey, I mean, come on, you guys did all the heavy lifting. >> Absolutely. >> You know how many people you are, and what's the what's the proposition for you guys these days? >> You know our company's about a hundred people, that a bit larger than that. Ray's been going really quickly. It's been, you know, companies using, like OpenAI uses Ray to train their models, like ChatGPT. Companies like Uber run all their deep learning you know, and classical machine learning on top of Ray. Companies like Shopify, Spotify, Netflix, Cruise, Lyft, Instacart, you know, Bike Dance. A lot of these companies are investing heavily in Ray for their machine learning infrastructure. And I think it's gotten to the point where, if you're one of these, you know type of businesses, and you're looking to revamp your machine learning infrastructure. If you're looking to enable new capabilities, you know make your teams more productive, increase, speed up the experimentation cycle, you know make it more performance, like build, you know, run applications that are more scalable, run them faster, run them in a more cost efficient way. All of these types of companies are at least evaluating Ray and Ray is an increasingly common choice there. I think if they're not using Ray, if many of these companies that end up not using Ray, they often end up building their own infrastructure. So Ray has been, the growth there has been incredibly exciting over the, you know we had our first in-person Ray Summit just back in August, and planning the next one for, for coming September. And so when you asked about the value proposition, I think there's there's really two main things, when people choose to go with Ray and Anyscale. One reason is about moving faster, right? It's about developer productivity, it's about speeding up the experimentation cycle, easily getting their models in production. You know, we hear many companies say that they, you know they, once they prototype a model, once they develop a model, it's another eight weeks, or 12 weeks to actually get that model in production. And that's a reason they talk to us. We hear companies say that, you know they've been training their models and, and doing inference on a single machine, and they've been sort of scaling vertically, like using bigger and bigger machines. But they, you know, you can only do that for so long, and at some point you need to go beyond a single machine and that's when they start talking to us. Right? So one of the main value propositions is around moving faster. I think probably the phrase I hear the most is, companies saying that they don't want their machine learning people to have to spend all their time configuring infrastructure. All this is about productivity. >> Yeah. >> The other. >> It's the big brains in the company. That are being used to do remedial tasks that should be automated right? I mean that's. >> Yeah, and I mean, it's hard stuff, right? It's also not these people's area of expertise, and or where they're adding the most value. So all of this is around developer productivity, moving faster, getting to market faster. The other big value prop and the reason people choose Ray and choose Anyscale, is around just providing superior infrastructure. This is really, can we scale more? You know, can we run it faster, right? Can we run it in a more cost effective way? We hear people saying that they're not getting good GPU utilization with the existing tools they're using, or they can't scale beyond a certain point, or you know they don't have a way to efficiently use spot instances to save costs, right? Or their clusters, you know can't auto scale up and down fast enough, right? These are all the kinds of things that Ray and Anyscale, where Ray and Anyscale add value and solve these kinds of problems. >> You know, you bring up great points. Auto scaling concept, early days, it was easy getting more compute. Now it's complicated. They're built into more integrated apps in the cloud. And you mentioned those companies that you're working with, that's impressive. Those are like the big hardcore, I call them hardcore. They have a good technical teams. And as the wave starts to move from these companies that were hyper scaling up all the time, the mainstream are just developers, right? So you need an interface in, so I see the dots connecting with you guys and I want to get your reaction. Is that how you see it? That you got the alphas out there kind of kicking butt, building their own stuff, alpha developers and infrastructure. But mainstream just wants programmability. They want that heavy lifting taken care of for them. Is that kind of how you guys see it? I mean, take us through that. Because to get crossover to be democratized, the automation's got to be there. And for developer productivity to be in, it's got to be coding and programmability. >> That's right. Ultimately for AI to really be successful, and really you know, transform every industry in the way we think it has the potential to. It has to be easier to use, right? And that is, and being easier to use, there's many dimensions to that. But an important one is that as a developer to do AI, you shouldn't have to be an expert in distributed systems. You shouldn't have to be an expert in infrastructure. If you do have to be, that's going to really limit the number of people who can do this, right? And I think there are so many, all of the companies we talk to, they don't want to be in the business of building and managing infrastructure. It's not that they can't do it. But it's going to slow them down, right? They want to allocate their time and their energy toward building their product, right? To building a better product, getting their product to market faster. And if we can take the infrastructure work off of the critical path for them, that's going to speed them up, it's going to simplify their lives. And I think that is critical for really enabling all of these companies to succeed with AI. >> Talk about the customers you guys are talking to right now, and how that translates over. Because I think you hit a good thread there. Data infrastructure is critical. Managed services are coming online, open sources continuing to grow. You have these people building their own, and then if they abandon it or don't scale it properly, there's kind of consequences. 'Cause it's a system you mentioned, it's a distributed system architecture. It's not as easy as standing up a monolithic app these days. So when you guys go to the marketplace and talk to customers, put the customers in buckets. So you got the ones that are kind of leaning in, that are pretty peaked, probably working with you now, open source. And then what's the customer profile look like as you go mainstream? Are they looking to manage service, looking for more architectural system, architecture approach? What's the, Anyscale progression? How do you engage with your customers? What are they telling you? >> Yeah, so many of these companies, yes, they're looking for managed infrastructure 'cause they want to move faster, right? Now the kind of these profiles of these different customers, they're three main workloads that companies run on Anyscale, run with Ray. It's training related workloads, and it is serving and deployment related workloads, like actually deploying your models, and it's batch processing, batch inference related workloads. Like imagine you want to do computer vision on tons and tons of, of images or videos, or you want to do natural language processing on millions of documents or audio, or speech or things like that, right? So the, I would say the, there's a pretty large variety of use cases, but the most common you know, we see tons of people working with computer vision data, you know, computer vision problems, natural language processing problems. And it's across many different industries. We work with companies doing drug discovery, companies doing you know, gaming or e-commerce, right? Companies doing robotics or agriculture. So there's a huge variety of the types of industries that can benefit from AI, and can really get a lot of value out of AI. And, but the, but the problems are the same problems that they all want to solve. It's like how do you make your team move faster, you know succeed with AI, be more productive, speed up the experimentation, and also how do you do this in a more performant way, in a faster, cheaper, in a more cost efficient, more scalable way. >> It's almost like the cloud game is coming back to AI and these foundational models, because I was just on a podcast, we recorded our weekly podcast, and I was just riffing with Dave Vellante, my co-host on this, were like, hey, in the early days of Amazon, if you want to build an app, you just, you have to build a data center, and then you go to now you go to the cloud, cloud's easier, pay a little money, penny's on the dollar, you get your app up and running. Cloud computing is born. With foundation models in generative AI. The old model was hard, heavy lifting, expensive, build out, before you get to do anything, as you mentioned time. So I got to think that you're pretty much in a good position with this foundational model trend in generative AI because I just looked at the foundation map, foundation models, map of the ecosystem. You're starting to see layers of, you got the tooling, you got platform, you got cloud. It's filling out really quickly. So why is Anyscale important to this new trend? How do you talk to people when they ask you, you know what does ChatGPT mean for Anyscale? And how does the financial foundational model growth, fit into your plan? >> Well, foundational models are hugely important for the industry broadly. Because you're going to have these really powerful models that are trained that you know, have been trained on tremendous amounts of data. tremendous amounts of computes, and that are useful out of the box, right? That people can start to use, and query, and get value out of, without necessarily training these huge models themselves. Now Ray fits in and Anyscale fit in, in a number of places. First of all, they're useful for creating these foundation models. Companies like OpenAI, you know, use Ray for this purpose. Companies like Cohere use Ray for these purposes. You know, IBM. If you look at, there's of course also open source versions like GPTJ, you know, created using Ray. So a lot of these large language models, large foundation models benefit from training on top of Ray. And, but of course for every company training and creating these huge foundation models, you're going to have many more that are fine tuning these models with their own data. That are deploying and serving these models for their own applications, that are building other application and business logic around these models. And that's where Ray also really shines, because Ray you know, is, can provide common infrastructure for all of these workloads. The training, the fine tuning, the serving, the data ingest and pre-processing, right? The hyper parameter tuning, the and and so on. And so where the reason Ray and Anyscale are important here, is that, again, foundation models are large, foundation models are compute intensive, doing you know, using both creating and using these foundation models requires tremendous amounts of compute. And there there's a big infrastructure lift to make that happen. So either you are using Ray and Anyscale to do this, or you are building the infrastructure and managing the infrastructure yourself. Which you can do, but it's, it's hard. >> Good luck with that. I always say good luck with that. I mean, I think if you really need to do, build that hardened foundation, you got to go all the way. And I think this, this idea of composability is interesting. How is Ray working with OpenAI for instance? Take, take us through that. Because I think you're going to see a lot of people talking about, okay I got trained models, but I'm going to have not one, I'm going to have many. There's big debate that OpenAI is going to be the mother of all LLMs, but now, but really people are also saying that to be many more, either purpose-built or specific. The fusion and these things come together there's like a blending of data, and that seems to be a value proposition. How does Ray help these guys get their models up? Can you take, take us through what Ray's doing for say OpenAI and others, and how do you see the models interacting with each other? >> Yeah, great question. So where, where OpenAI uses Ray right now, is for the training workloads. Training both to create ChatGPT and models like that. There's both a supervised learning component, where you're pre-training this model on doing supervised pre-training with example data. There's also a reinforcement learning component, where you are fine-tuning the model and continuing to train the model, but based on human feedback, based on input from humans saying that, you know this response to this question is better than this other response to this question, right? And so Ray provides the infrastructure for scaling the training across many, many GPUs, many many machines, and really running that in an efficient you know, performance fault tolerant way, right? And so, you know, open, this is not the first version of OpenAI's infrastructure, right? They've gone through iterations where they did start with building the infrastructure themselves. They were using tools like MPI. But at some point, you know, given the complexity, given the scale of what they're trying to do, you hit a wall with MPI and that's going to happen with a lot of other companies in this space. And at that point you don't have many other options other than to use Ray or to build your own infrastructure. >> That's awesome. And then your vision on this data interaction, because the old days monolithic models were very rigid. You couldn't really interface with them. But we're kind of seeing this future of data fusion, data interaction, data blending at large scale. What's your vision? How do you, what's your vision of where this goes? Because if this goes the way people think. You can have this data chemistry kind of thing going on where people are integrating all kinds of data with each other at large scale. So you need infrastructure, intelligence, reasoning, a lot of code. Is this something that you see? What's your vision in all this? Take us through. >> AI is going to be used everywhere right? It's, we see this as a technology that's going to be ubiquitous, and is going to transform every business. I mean, imagine you make a product, maybe you were making a tool like Photoshop or, or whatever the, you know, tool is. The way that people are going to use your tool, is not by investing, you know, hundreds of hours into learning all of the different, you know specific buttons they need to press and workflows they need to go through it. They're going to talk to it, right? They're going to say, ask it to do the thing they want it to do right? And it's going to do it. And if it, if it doesn't know what it's want, what it's, what's being asked of it. It's going to ask clarifying questions, right? And then you're going to clarify, and you're going to have a conversation. And this is going to make many many many kinds of tools and technology and products easier to use, and lower the barrier to entry. And so, and this, you know, many companies fit into this category of trying to build products that, and trying to make them easier to use, this is just one kind of way it can, one kind of way that AI will will be used. But I think it's, it's something that's pretty ubiquitous. >> Yeah. It'll be efficient, it'll be efficiency up and down the stack, and will change the productivity equation completely. You just highlighted one, I don't want to fill out forms, just stand up my environment for me. And then start coding away. Okay well this is great stuff. Final word for the folks out there watching, obviously new kind of skill set for hiring. You guys got engineers, give a plug for the company, for Anyscale. What are you looking for? What are you guys working on? Give a, take the last minute to put a plug in for the company. >> Yeah well if you're interested in AI and if you think AI is really going to be transformative, and really be useful for all these different industries. We are trying to provide the infrastructure to enable that to happen, right? So I think there's the potential here, to really solve an important problem, to get to the point where developers don't need to think about infrastructure, don't need to think about distributed systems. All they think about is their application logic, and what they want their application to do. And I think if we can achieve that, you know we can be the foundation or the platform that enables all of these other companies to succeed with AI. So that's where we're going. I think something like this has to happen if AI is going to achieve its potential, we're looking for, we're hiring across the board, you know, great engineers, on the go-to-market side, product managers, you know people who want to really, you know, make this happen. >> Awesome well congratulations. I know you got some good funding behind you. You're in a good spot. I think this is happening. I think generative AI and foundation models is going to be the next big inflection point, as big as the pc inter-networking, internet and smartphones. This is a whole nother application framework, a whole nother set of things. So this is the ground floor. Robert, you're, you and your team are right there. Well done. >> Thank you so much. >> All right. Thanks for coming on this CUBE conversation. I'm John Furrier with theCUBE. Breaking down a conversation around AI and scaling up in this new next major inflection point. This next wave is foundational models, generative AI. And thanks to ChatGPT, the whole world's now knowing about it. So it really is changing the game and Anyscale is right there, one of the hot startups, that is in good position to ride this next wave. Thanks for watching. (upbeat instrumental)

(soft music) >> Good morning from Detroit, Michigan. theCUBE is live on our second day of coverage of KubeCon + CloudNativeCon North America 2022. Lisa Martin here with John Furrier. John, great to be back with you. The buzz is here, no doubt. We've been talking a lot about the developers. And one of the biggest bottlenecks that they face in software delivery, is when they're stuck waiting for access to environments. >> Yeah, this next segment's going to be very interesting. It's a company that's making DevOps more productive, but recognizing the reality of how people are working remotely, but also company to company developers. People are collaborating in all kinds of forms, so this is really going to be a great segment. >> Exactly. Two new guests to theCUBE who know theCUBE, but are first time on theCUBE from Release Hub, Tommy McClung, it's CEO and Matt Carter, it's CMO. Guys, great to have you on the program. >> Thank you. >> Thanks for having us here. >> So we want to dig into Release Hub, so the audience really gets an understanding. But Tommy, I want to get an understanding of your background. >> Sure. >> You've been at Release Hub for what, three years? >> Yep, I'm the co-founder. >> Before that you were at TrueCar? >> I was, yeah, I was the CTO at TrueCar. And prior to that, I've been a software engineer my entire career. I've started a couple of companies before this. Software engineer at heart. I've been working on systems management and making developers productive since 2000, long time. So it's fun to be working on developer productivity stuff. And this is our home and this is where I feel the most comfortable. >> Lisa: Yeah. And Matt, you're brand new to the company as it's chief marketing officer. >> Matt: Yeah, so I just joined earlier this month, so really excited to be here. I came over from Docker, so it's great to be able to keep working with developers and helping them, not only get their jobs done better and faster, but just get more delight out of what they do every day, that's a super important privilege to me and it's exciting to go and work on this at Release here. >> Well, they're lucky to have you. And we work together, Matt, at Docker, in the past. Developer productivity's always been a key, but communities are now more important. We've been seeing on theCUBE that developers are going to decide the standards, they're going to vote with their axes and their code. And what they decide to work on, it has to be the best. And that's going to be the new defacto standard. You guys have a great solution that I like. And I love the roots from the software engineering background because that's the hardest thing right now, is how do you scale the software, making things simpler and easier. And when things happen, you don't want to disrupt the tool chains, you want to make sure the code is right, you guys have a unique solution. Can you take a minute to explain what it is and why it's so important? >> Tommy: Yeah, I'll use a little bit of my experience to explain it. I was the CTO of a company that had 300 engineers, and sharing a handful of environments, really slowed everybody down, you bottleneck there. So in order to unlock the productivity of that team, developers need environments for development, they need it for testing, they need it for staging, you run your environments in production. So the environment is the key building block in every software development process. And like my last company, there were very few of them, one or two, everybody sharing them. And so the idea at Release is to make environments available on demand, so if a developer needs one for anything, they can spin one up. So if they want to write their code in a environment based in the cloud, they can do that, if they want to test on a poll request, an environment will automatically spin up. And the environments are full stack, include all the services, data, settings, configuration that runs the app. So developers literally get an isolated copy of the application, so they can develop knowing they're not stepping on other developers' toes. >> John: Can you give an example of what that looks like? Do they have to pre-configure the environment, or how does that work? Can you give an example? >> Yeah, sure. You have to, just like infrastructure is code, we call this environments is code. So you need to define your environment, which we have a lot of tools that help you do that. Analyze your repositories, help you define that environment. Now that you have the template for that, you can easily use that template to derive multiple environments out of it. A key part of this is everybody wants to make sure their development and data is secure. It runs within the AWS account of our customer. So we're the control plane that orchestrates it and the data and applications run within the context of their AWS account, so it's- >> John: What's the benefit? >> Tommy: Well, bottlenecking, increased developer productivity, developer happiness is a big one. Matt talks about this all the time, keeping developers in flow, so that they're focused on the job and not being distracted with, "Hey DevOps team, I need you to go spin up an environment." And a lot of times in larger organizations, not just the environments, but the process to get access to resources is a big issue. And so DevOps was designed to let developers take control of their own development process, but were still bottlenecking, waiting for environments, waiting for resources from the DevOps team, so this allows that self-service capability to really be there for the developer. >> Lisa: Matt, talk about... Target audience is the developer, talk about though... Distill that down into the business value. What am I, if I'm a financial services organization, or a hospital, or a retailer in e-commerce, what is my business value going to be with using technology like this and delighting those developers? >> Matt: I think there's three things that really matter to the developers and to the financial leader in the organization, A, developers are super expensive and they have a lot of opportunities. So if a developer's not happy and finding joy and productivity in what they're doing, they're going to look elsewhere. So that's the first thing, the second thing is that when you're running a business, productivity is one measure, but also, are you shipping something confidently the first time, or do you have to go back and fix things? And by having the environment spun up with all of your name space established, your tendencies are managed, all of your data being brought in, you're testing against a very high fidelity version of your application when you check in code. And so by doing that, you're testing things more quickly, and they talk a lot about shifting left, but it's making that environment as fully functional and featured as possible. So you're looking at something as it will appear in production, not a subset of that. And then the last thing, and this is one where the value of Figma is very important, a lot of times, you'll spin up an environment on AWS and you may forget about it and might just keep running and chewing up resources. Knowing that when you're done it goes away, means that you're not spending money on things just sitting there on your AWS instance, which is very important for competitors. >> Lisa: So I hear retention of developers, you're learning that developers, obviously business impact their speed to value as well. >> Tommy: Yep. >> And trust, you're enabling your customers to instill trust in their developers with them. >> Tommy: That's right, yeah. >> Matt: And trust and delight, they can be across purposes, a developer wants to move fast and they're rewarded for being creative, whereas your IT team, they're rewarded for predictability and consistency, and those can be opposing forces. And by giving developers a way to move quickly and the artifact that they're creating is something that the IT team understands and works within their processes, allows you to let both teams do what they care about and not create a friction there. >> John: What about the environment as a service? I love that 'cause it makes it sound like it's scaling in the cloud, which you have mentioned you do that. Is it for companies that are working together? So I don't want to spin up an environment, say we're a businesses, "Hey, let's do a deal. "I'm going to integrate my solution into yours. "I got to get my developers to maybe test it out, "so I'm spinning up an environment with you guys," then what do I do? >> Tommy: Well as far as if you're a customer of ours, is that the way you're asking? Well, a lot of times, it's being used a lot in internal development. So that's the first use case, is I'm a developer, you have cross collaboration amongst teams, so a developer tools. And what you're talking about is more, I'm using an environment for a demo environment, or I'm creating a new feature that I want to share with a customer, That's also possible. So if I'm a developer and I'm building a feature and it's for a specific customer of mine, I can build that feature and preview it with the customer before it actually goes into production. So it's a sandbox product development area for the developers to be actually integrating with their customers very, very quickly before it actually makes its way to all of the end users. >> A demo? >> It could be a demo. >> It's like a collaboration feature? >> Sandbox environment. We have customers- >> Kind of like we're seeing more of this collaboration with developers. This becomes a well- >> Tommy: And it's not even just collaboration with internal teams, it's now you're collaborating with your customer while you're building your software, which is actually really difficult to do if you only have one environment, you can't have- >> John: Yeah, I think that's a killer right there, that's the killer app right there. >> Matt: Instead of sending a Figma to a customer, this is what's going to look like, it's two dimensions, this is the app. That is a massive, powerful difference. >> Absolutely. In terms of customer delay, customer retention, employee engagement, those are all inextricably linked. Can you share, Matt, the voice of the customer? I just saw the release with TripActions, I've been a TripActions user myself, but give us this sense, I know that you're brand new, but the voice of the customer, what is it? What is it reflecting? How is it reinforcing your value prop? >> Matt: I think the voice that comes through consistently is instead of spending time building the system that is hard to do and complicated and takes our engineering cycles, our engineers can focus on whether it's platform engineering, new features and whatnot, it's more valuable to the company to build features, it's more exciting for a developer to build features and to not have to keep going back and doing things manually, which you're doing a... This is what we do all day long. To do it as a sideline is hard. And the customers are excited 'cause they get to move onto higher value activities with their time. >> Lisa: And everybody wants that, everybody wants to be able to contribute high value projects, programs for their organization rather than doing the boring stuff. >> Tommy: Yeah. I think with TripActions specifically, a lot of platform engineering teams are trying to build something like this in house, and it's a lot of toil, it's work that isn't value added, it enables developers to get their job done, but it's not really helping the business deliver a feature to the user. And so this whole movement of platform engineering, this is what those groups are doing and we're a big enabler to those teams, to get that to market faster. >> John: You're targeting businesses, enterprises, developers. >> That's right. >> Mainly, right, developers? >> Yeah. >> What's the business model? How are you guys making money? What's the strategy there? >> Yeah, I mean we really like to align with the value that we deliver. So if a user creates an environment, we get paid when that happens. So it's an on-demand, if you use the environment, you pay us, if you don't, you don't. >> John: Typical cloud-based pricing. >> Yeah. >> Pay as you go. >> Tommy: Usage based pricing. >> Is there a trigger on certain of how it gets cost? Is it more of the environment size, or what's the- >> Yeah, I mean there's a different tier for if you have really large, complicated environments. And that's the trend, that distributed applications aren't simple anymore, so if you have a small little rails app, it's going to be cheaper than if you have a massive distributed system. But manageable, the idea here is that this should help you save money over investing deeply into a deep platform engineering team. So it's got to be cost effective and we're really cognizant of that. >> So you got a simple approach, which is great. Talk about the alternative. What does it look like for a customer that you want to target? What's their environment? What does it look like, so that if I'm a customer, I would know I need to call you guys at Relief Hub. Is it sprawl? Is it multiple tool chains? Chaos, mayhem? What does it look like? >> Tommy: Yeah, let's have Matty, Matt could do this one. >> When you look at the systems right now, I think complexity is the word that keeps coming up, which is that, whether you're talking about multi-cloud or actually doing it, that's a huge thing. Microservices proliferation are happening over and over again, different languages. What I'm excited about with Release, is not dissimilar from what we saw in the Docker movement, which is that there's all this great stuff out there, but there's that common interface there, so you can actually run it locally on your machine, do your dev and test, and know that it's going to operate with, am I using Couchbase or Postgres or whatever, I don't care, it's going to work this way. Similar with Release, people are having to build a lot of these bespoke solutions that are purpose built for one thing and they're not designed to the platform. And the platform for platform engineering gives us a way to take that complexity out the equation, so you're not limited to what you can do, or, "Oh crud, I want to move to something else, "I have to start over again," that process is going to be consistent no matter what you're doing. So you're not worried about evolution and success and growth, you know that you've got a foundation that's going to grow. Doing it on your own, you have to build things in that very bespoke, specific manner, and that just creates a lot more toil than you'd want to get if you were using a platform and focusing on the value after your company. >> Matt Klein was just on here. He was with Lyft, he was the one who open source Envoy, which became very popular. We asked him what he thought about the future and he's like, it's too hard to work with all this stuff. He was mentioning Yamo code, but he got triggered a little bit, but his point was there's a lot to pull together. And it sounds like you guys have this solution, back in the old days, spin up some EC2, compute, similar way, right? "Hey, I don't want to person a server, I person a server, rack and stack, top of rack switch, I'm going to go to the cloud, use EC2. >> Tommy: Yeah, I mean just think about if- >> You're an environment version of that. Why wait for it to be built? >> Yeah. >> Is that what I'm getting- >> Yeah, I mean, and an application today isn't just the EC2 instances, it's all of your data, it's your configuration. Building it one time is actually complicated to get your app to work it, doing it lots of times to make your developers productive with copies of that, is incredibly difficult. >> John: So you saw the problem of developers waiting around for someone to provision an environment. >> Tommy: That's right. >> So they can do whatever they want to do. >> Tommy: That's right. >> Test, ship, do, play around, test the customer. Whatever that project scope is, they're waiting around versus spinning up an environment. >> Yeah, absolutely, 100%. >> And that's the service. >> That's what it is. >> Take time, reduce the steps it takes, make it more productive. >> And build an amazing developer experience that you know your developers are going to love. If you're at Facebook or Google, they have thousands of DevOps people building platforms. If you're a company that doesn't have that resource, you have a choice of go build this yourself, which is a distraction, or invest in something like us and focus on your core. >> John: You got Matt on board, got a new CMO, you got enterprise class features and I saw the press release. Talk about the origination story, why you developed it, and then take a minute to give a plug for the company, on what you're looking for, I'm sure you're hiring, what's going on? >> Tommy: Yeah, I've been an entrepreneur for 20 years. My last experience at TrueCar, I saw this problem firsthand. And as the CTO of that company, I looked into the market for a solution to this, 'cause we had this problem of 300 developers, environments needed for everything. So we ended up building it ourselves and it costs multiple millions of dollars to build it. And so as the buyer at the time, I was like, man, I would've spent to solve this, and I just couldn't. So as a software engineer at heart, having seen this problem my entire career, it was just a natural thing to go work on. So yeah, I mean, for anybody that wants to create unlimited environments for their team, just go to releasehub.com. It's pretty self-explanatory, how to give it a shot and try it out. >> Environments is a service, from someone who had the problem, fixed it, built it- >> That's right. >> For other people. What are you guys hiring, looking for some people? >> Yeah, we have engineering hires, sales hires, Matt's got a few marketing hires coming, >> Matt: I was going to say, got some marketing coming. >> Selfishly he has that. (John laughs) The team's growing and it's a really great place to work. We're 100% remote. Part of this helps that, we build this product and we use it every day, so you get to work on what you build and dog food, it's pretty cool. >> Great solution. >> We love remote development environments. Being here and watching that process where building a product and a feature for the team to work better, wow, we should share this with customers. And the agility to deliver that was really impressive, and definitely reinforced how excited I am to be here 'cause we're building stuff for ourselves, which is- >> Matt: Well we're psyched that you're here in theCUBE. Matt, what's your vision for marketing? You got a hiring plan, you got a vision, I'm sure you got some things to do. What's your goals? What's your objective? >> My goal is... The statement people say, you can't market to developers. And I don't want to market to developers, I want to make sure developers are made aware of how they can learn new things in a really efficient way, so their capabilities grow. If we get people more and more successful with what they're doing, give them joy, reduce their toil and create that flow, we help them do things that make you excited, more creative. And that's to me, the reward of this. You teach people how to do that. And wow, these customers, they're building the greatest innovations in the world, I get to be part of that, which is awesome. >> Lisa: Yeah. Delighted developers has so many positive business outcomes that I'm sure organizations in any industry are going to be able to achieve. So exciting stuff, guys. Thank you so much for joining John and me on the program. Good luck with the growth and congrats on what you've enabled so far in just a few short years. >> Thank you, appreciate it. >> Thanks you so much. >> Thank you for having us on. >> Appreciate it. >> Pleasure. >> Thank you. >> For our guests and for John Furrier, I'm Lisa Martin. You're watching theCUBE, live in Detroit, at KubeCon + CloudNativeCon '22. We're back after a short break. (soft music)

(upbeat techno music) >> Hello, everyone. Welcome to theCUBE here live in Detroit for KubeCon + CloudNativeCon 2022. I'm John Furrier, host of theCUBE. This is our seventh consecutive KubeCon + CloudNativeCon. Since inception, theCube's been there every year. And of course, theCUBE continues to grow. So does the community as well as our host roster. I'm here with my co-host, Lisa Martin. Lisa, great to see you. And our new theCube host, Savannah Peterson. Savannah, welcome to theCUBE. >> Thanks, John. >> Welcome. >> Welcome to the team. >> Thanks, team. It's so wonderful to be here. I met you all last KubeCon and to be sitting on this stage in your company is honestly an honor. >> Well, great to have you. Lisa and I have done a lot of shows together and it's great to have more cadence around. You know, more fluid around the content, and also the people. And I would like you to take a minute to tell people your background. You know the community here. What's the roots? You know the Cloud Native world pretty well. >> I know it as well as someone my age can. As we know, the tools and the tech is always changing. So hello, everyone. I'm Savannah Peterson. You can find me on the internet @SavIsSavvy. Would love to hear from you during the show. Big fan of this space and very passionate about DevOps. I've been working in the Silicon Valley and the Silicon Alley for a long time, helping companies scale internationally as a community builder as well as a international public speaker. And honestly, this is just such a fun evolution for my career and I'm grateful to be here with you both. >> We're looking forward to having you on theCUBE. Appreciate it. Lisa? >> Yes. >> KubeCon. Amazing again this year. Just keeps growing bigger and bigger. >> Yes. >> Keynote review, you were in there. >> Yup. >> I had a chance to peek in a little bit, but you were there and got most of the news. What was the action? >> You know, the action was really a big focus around the maintainers, what they're doing, giving them the props and the kudos and the support that they deserve. Not just physically, but mentally as well. That was a really big focus. It was also a big focus on mentoring and really encouraging more people- >> Love that. >> I did, too. I thought that was fantastic to get involved to help others. And then they showed some folks that had great experiences, really kind of growing up within the community. Probably half of the keynote focus this morning was on that. And then looking at some of the other projects that have graduated from CNCF, some of these successful projects, what they're doing, what folks are doing. Cruise, one of the ones that was featured. You've probably seen their driverless cars around San Francisco. So it was great to see that, the successes that they've had and where that's going. >> Yeah. Lisa, we've done how many shows? Hundreds of shows together. When you see a show like this grow and continue to mature, what's your observation? You've seen many shows we've hosted together. What jumps out this year? Is it just that level of maturization? What's your take on this? >> The maturization of the community and the collaboration of the community. I think those two things jumped out at me even more than last year. Last year, obviously a little bit smaller event in North America. It was Los Angeles. This year you got a much stronger sense of the community, the support that they have for each other. There were a lot of standing ovations particularly when the community came out and talked about what they were doing in Ukraine to support fellow community members in Ukraine and also to support other Ukrainians in terms of getting in to tech. Lot of standing ovations. Lot of- >> Savannah: Love that, yeah. >> Real authenticity around the community. >> Yeah, Savannah, we talked on our intro prior to the event about how inclusive this community is. They are really all in on inclusivity. And the Ukraine highlight, this community is together and they're open. They're open to everybody. >> Absolutely. >> And they're also focused on growing the educational knowledge. >> Yeah, I think there's a real celebration of curiosity within this community that we don't find in certain other sectors. And we saw it at dinner last night. I mean, I was struck just like you Lisa walking in today. The energy in that room is palpably different from last year. I saw on Twitter this morning, people are very excited. Many people, their first KubeCon. And I'm sure we're going to be feeding off of that, that kind of energy and that... Just a general enthusiasm and excitement to be here in Detroit all week. It's a treat. >> Yeah, I even saw Stu Miniman earlier, former theCube host. He's at Red Hat. We were talking on the way in and he made an observation I thought was interesting I'll bring up because this show, it's a lot "What is this show? What isn't this show?" And I think this show is about developers. What it isn't is not a business show. It's not about business. It's not about industry kind of posturing or marketing. All the heavy hitters on the dev side are here and you don't see the big execs. I mean, you got the CEOs of startups here but not the CEOs of the big public companies. We see the doers. So, I mean, I think my take is this show's about creating products for builders and creating products that people can consume. And I think that is the Cloud Native lanes that are starting to form. You're either creating something for builders to build stuff with or you're creating stuff that could be consumed. And that seems for applications. So the whole app side and services seem to be huge. >> They also did a great job this morning of showcasing some of the big companies that we all know and love. Spotify. Obviously, I don't think a day goes by where I don't turn on Spotify. And what it's done- >> Me neither. >> What it's done for the community... Same with Intuit, I'm a user of both. Intuit was given an End User Award this morning during the keynote for their contributions, what they're doing. But it was nice to see some just everyday companies, Cloud Native companies that we all know and love, and to understand their contributions to the community and how those contributions are affecting all of us as end users. >> Yeah, and I think those companies like Intuit... Argo's been popular, Arlo now new, seeing those services, and even enterprises are contributing. You know, Lyft is always here, popular with Envoy. The community isn't just vendors and that's the interesting thing. >> I think that's why it works. To me, this event is really about the celebration of developer relations. I mean, every DevRel from every single one of these companies is here. Like you said, in lieu of the executive, that's essentially who we're attracting. And if you look out over the show floor here, I mean, we've probably got, I don't know, three to four extra vendors that we had last year. It totally is a different tone. This community doesn't like to be sold to. This community likes to be collaborative. They like to learn and they like to help. And I think we see that within the ecosystem inside the room today. >> It's not a top down sales pitch. It's really consensus. >> No. >> Do it out in the open transparency. Don't sell me stuff. And I think the other thing I like about this community is that we're starting to see that... And then we've said this in theCube before. We'll say it again. Maybe be more controversial. Digital transformation is about the developer, right? And I think the power is going to shift in every company to the developer because if you take digital transformation to completion, everything happens the way it's happening, the company is the application. It's not IT who serves the organization- >> I love thinking about it like that. That's a great point, John. >> The old phase was IT was a department that served the business. Well, the business is IT now. So that means developer community is going to grow like crazy and they're going to be in the front lines driving all the change. In my opinion, you going to see this developer community grow like crazy and then the business side on industry will match up with that. I think that's what's going to happen. >> So, the developers are becoming the influencers? >> Developers are the power source for all companies. They're in charge. They're going to dictate terms to how businesses will run because that's going to be natural 'cause digital transformation's about the app and the business is the app. So that mean it has to be coded. So I think you're going to see a lot of innovation around app server-like experiences where the the apps are just being developed faster than the infrastructures enabling that completely invisible. And I think you're going to see this kind of architecture-less, I'll put it out there that term architecture-less, environment where you don't need an architecture. It's just you code away. >> Yeah, yeah. We saw GitHub's mentioned in the keynote this morning. And I mean, low code, no code. I think your fingers right on the pulse there. >> Yeah. What did you guys see? Anything else you see? >> I think just the overall... To your point, Savannah, the energy. Definitely higher than last year. When I saw those standing ovations, people really come in together around the sense of community and what they've accomplished especially in the last two plus years of being remote. They did a great job of involving a lot of folks, some of whom are going to be on the program with us this week that did remote parts of the keynote. One of our guests on today from Vitess was talking about the successes and the graduation of their program so that the sense of community, but also not just the sense of it, the actual demonstration of it was also quite palpable this morning, and I think that's something that I'm excited for us to hear about with our guests on the program this week. >> Yeah, and I think the big story coming out so far as the show starts is the developers are in charge. They're going to set the pace for all the ops, data ops, security ops, all operations. And then the co-located events that were held Monday and Tuesday prior to kickoff today. You saw WebAssembly's come out of the woodwork as it got a lot of attention. Two startups got funded heavily on Series A. You're starting to see that project really work well. That's going to be an additional to the container market. So, interesting to see how Docker reacts to that. Red Hat's doing great. ServiceMeshCon was phenomenal. I saw Solo.iOS got massive traction with those guys. So like Service Mesh, WebAssembly, you can start to see the dots connecting. You're starting to see this layer below Kubernetes and then a layer above Kubernetes developing. So I think it's going to be great for applications and great for the infrastructure. I think we'll see how it comes out and all these companies we have on here are all about faster, more integrated, some very, very interesting to see. So far, so good. >> You guys talked about in your highlight session last week or so. Excited to hear about the end users, the customer stories. That's what I'm interested in understanding as well. It's why it resonates with me when I see brands that I recognize. Well, I use it every day. How are they using containers and Kubernetes? How are they actually not just using it to deploy their app, their technologies, that we all expect are going to be up 24/7, but how are they also contributing to the development of it? So I'm really excited to hear those end users. >> We're going to have Lockheed Martin. And we wrote a story on SiliconANGLE, the Red Hat, Lockheed Martin, real innovation on the edge. You're starting to see educate with the edge. It's really the industrial edge coming to be big. It'd be very interesting to see. >> Absolutely, we got Ford Motor Company coming on as well. I always loved stories, Savannah, that are history of companies. Ford's been around since 1903. How is a company that- >> Well, we're in the home of Ford- as well here. >> We are. How they evolved digitally? What are they doing to enable the developers to be those influencers that John says? It's going to be them. >> They're a great example of a company that's always been on the forefront, too. I mean, they had a head of VRs 25 years ago when most people didn't even know what VR was going to stand for. So, I can't wait for that one. You tease the Docker interview coming up very well, John. I'm excited for that one. One last thing I want to bring up that I think is really refreshing and it's reflected right here on this stage is you talked about the inclusion. I think there's a real commitment to diversity here. You can see the diversity stats on CNCF's website. It's right there on KubeCon. At the bottom, there's a link in every email I've gotten highlighting that. We've got two women on this stage all week which is very exciting. And the opening keynote was a woman. So quite frankly, I am happy as a female in this industry to see a bit more representation. And I do appreciate just on the note of being inclusive, it's not just about gender or age, it's also about the way that CNCF thinks about your experience since we're in this kind of pandemic transitional period. They've got little pins. Last year, we had bracelets depending on your level of comfort. Equivocally like a stoplight which is... I just think it's really nice and sensitive and that attention to detail makes people feel comfortable. Which is why we have the community energy that we have. >> Yeah, and being 12 years in the business... With theCUBE, we've been 12 years in the business, seven years with KubeCon and Cloud Native, I really appreciate the Linux Foundation including me as I get older. (Lisa and Savannah laugh) >> Savannah: That's a good point. >> Ageism were, "Hey!" Thank you. >> There was a lot of representation. You talked about females and so often we go to shows and there's very few females. Some companies are excellent at it. But from an optics perspective, to me it stands out. There was great representation across. There was disabled people on stage, people of color, women, men of all ages. It was very well-orchestrated. >> On the demographic- >> And sincere. >> Yeah, yeah. >> And the demographics, too. On the age side, it's lower too. You're starting to see younger... I mean, high school, college representation. I saw a lot of college students last night. I saw on the agenda sessions targeting universities. I mean, I'm telling you this is reaching down. Open source now is so great. It's growing so fast. It's continuing to thunder away. And with success, it's just getting better and better. In fact, we were talking last night about at some point we might not have to write code. Just glue it together. And that's why I think the supply chain and security thing is an issue. But this is why it's so great. Anyone can code and I think there's a lot of learning to have. So, I think we'll continue to do our job to extract the signal from the noise. So, thanks for the kickoff. Good commentary. Thank you. All right. >> Of course. >> Let's get started. Day one of three days of live coverage here at KubeCon + CloudNativeCon. I'm John Furrier with Lisa Martin, and Savannah Peterson. Be back with more coverage starting right now. (gentle upbeat music)

(intro music) >> Hi, everybody, we're back. Dave Vellante and Dave Nicholson. We're covering Fal.Con 22. This is CrowdStrike's big user conference. CrowdStrike is a very hot company, as you probably know started on endpoint security, expanding into another, a number of other areas trying to build the next great generational company in cybersecurity. Michael Sherwood is here. He's the chief innovation and technology officer for the city of Las Vegas. >> Got to love that. >> Thanks so much for coming to theCUBE. >> Welcome! >> Yeah, we got to love that. I mean, if it weren't for Las Vegas, I'm not sure where we would have our CUBE events, but so thank you for hosting us. >> Thank you for being here. This is awesome. It's a great day and a lot of people, and it's exciting to see everything that's going on here. >> Yeah, the city is booming. Obviously the convention, the conference business is booming. Tech is a big part of that but there's so many other industries that come to Las Vegas. Talk about your role, really interesting, chief innovation, technology officer, CTO. Tell us about what you do day to day. >> Kind of all over the place. But a lot of it has to do with day to day technology within the organization. So managing all the different technology components. When you start looking at any city, it's a lot of different companies inside of it. Think of fire service as a different company. They all have different missions. And so our technology needs are expansive. So while we have operational IT, we also have our innovation unit. Innovation unit works on next generation technology. So Las Vegas was one of the first cities in the United States to have a autonomous vehicle drive in mix-flow traffic, meaning it was out there with, driving along cars. We're also the first city to have an accident in a autonomous vehicle. That happened on day two. (Vellante laughing) So, there's always a lot of firsts in Las Vegas, but. >> Despite the grid. >> Despite the grid, you know. But even today, so that was in 2017, when we first started working with autonomous vehicles. Up until today, where you have the ability, anybody in Las Vegas, including yourselves right after the show can go ahead and use Lyft, go outside and hail an autonomous taxi to come pick you up and drive you up and down the strip. Those vehicles actually communicate with our infrastructure. So the innovation is, how do cities work with private companies to start building next generation amenities, next generation technologies? And so that happens a lot of times. People don't realize. They come to Las Vegas for entertainment, and now we're known for sports but we do have a lot of technology here that permeates through the entire community. >> So I'm from Boston. We're trying to get the smart traffic lights, we're not quite there yet. But I was at a session, Dave you'll appreciate it, it was John Rose, who was the CTO. He was the CTO of, he's a CTO of Dell Technologies now. And the mayor of Boston, we were talking about the vision for a smart city. But Boston and I mean talk about, a challenge for building a smart city. So when I come out here, it's like amazing to me to see the technology that's there. So as a CTO and innovation officer, you've got a playground where... Now, of course you have legacy infrastructure, you've got technical debt, but you also have, in certain cases, an opportunity and more latitude to get creative. So what are some of the cool things that you're working on that you're really excited about? >> There's a lot of things I'm excited about. It's just great being in this city. But a lot of the things that we're excited about here in the next year to two years, we have an innovation district. So not a lot of cities have this but Downtown around the Fremont Street Experience, there's a corridor there that covers government, covers entertainment, medical. And so this innovation district is where we test out new technologies. So some of the things we're testing out, computer vision. So we're, our smart parks program is how do we provide better security and enjoyment of those amenities without providing physical labor to constantly patrol. And so we're using cameras and vision and different types of AI algorithms to kind of manage the park. And while we're doing that, we're also getting data back on how often is the park used? Are the facilities, are the sprinklers going on during the day? Water's a big deal here. And so those type of projects. Again, autonomy is still huge, vehicle autonomy, still working on driving those next generation changes where you'll actually have a driverless vehicle. Right now, there's a safety driver in a lot of the autonomous vehicles. Even the one I talked about earlier, you have the, while the vehicles driving itself, for safety reasons, there's still a human driver in the seat. But as we go forward in the next year to two, that >> That's soon. >> is getting ready to change. I believe that's soon. You can quote it here, you heard it here first. >> Wow. >> But that would be coming up. You got drones as well. We've already started looking at a few types of drone delivery systems. It may not be too far away. Your pizza or maybe some other item that you want is delivered in the general area. Probably not in the hotel corridor but in the outside areas of the city. I just think there's a lot of, again, we're building amenities for the future. We really want people to understand that Las Vegas is not just a place to come visit, but it's a place to live and have fun and be part of a community. >> So from an academic perspective, what you just described is a highly ambidextrous organization, right? >> Yes. >> Because you're not just worried about keeping the lights on, but you're also looking at innovation. How did your organization get to this place? What you're describing is sort of the gold standard that any organization public or private would seek to implement. How did you get there? >> Baby steps, small steps. It all started back when there was the Smart Cities Challenge. So we were not selected as the finalist. We were in the, I think top 15 at the time but we didn't give up on it. And we continued to move forward. The pandemic helped us do things. When you ask, what do I do? Well, my normal job is running the day to day infrastructure. I also see my role as economic development to help bring companies here and bring new ideas. We have a great community, diverse and ready to do things. But when you take, talk about the innovation and the technology and what we're doing. Like I said, during a pandemic, we came up with the idea of, Hey, we don't want to send our building inspectors or our inspectors in the people's homes, one for the inspector's health and one for the citizen's health. So we used normal tools. We took an iPhone and made it a virtual inspector. So now if you get a new water heater, you can actually do your inspection via like a FaceTime. And you hold your phone up around the water heater. We can view it, we record the video, save it, and boom give you an inspection remotely. And so you build on it. So how do you get, I wouldn't quite say we're the gold. I appreciate, we're moving there, that's the bar. You've laid out the bar for us, but we're moving in that direction. But it's building on one win and not all of our things that we've deployed. We can talk about those as well. Some of the things like trash can sensors, we looked at doing, which would monitor when the trash can was full or empty, just didn't pan out. So a lot of the times I talk about the wins a lot not as much about the things that didn't pan out. >> So what're the big challenges, generally of building out a smart city and then specifically around cyber? >> So there's, community acceptance number one. Las Vegas, I'm very lucky cameras are everywhere. So there's not as much resistance to using video technology. But a lot of times it's just getting the constituents, getting people to understand the value of what we're trying to do. Not everybody is interested in autonomous vehicles or believes they're ready for that. But when you start looking at the increments, more than any other city I know, the community here is so robust and so supportive of bringing on these technologies. Look, what other city do you know that builds new buildings and knocks them down five years later to build something new again? Or, who has a volcano in the middle of their downtown? So different things like that. But when you start looking at all the advancements we're making, you brought up one of the biggest concerns. When people ask me, what keeps you up at night? It's not the autonomous vehicle not performing, its the cyber, it's the cyber issues that go along with becoming more advanced. And as you bring innovation in, you start bleeding the lines of what's government, what's private. And then how do you continue to have the data transmission between these multiple entities? How do you keep the endpoint secure? And that is something that you learn as you go, but it's always out there. And endpoint security and security in general is a huge, huge area. >> And how about the data? You were talking before about you can get actually approval for an inspection. That's data, it's video data. How have you changed the way in which you're using data? What are you doing with that data? How do you leverage it? How do you secure it? >> It's all great questions. One of the things we've undertaken is called an open data initiative. So we have an open data portal. It's opendata.lasvegasnevada.gov, where we publish a lot of the data sets that we collect. If it's air quality, if it's ambulance runs, and we make that data available. A lot of that is, one for the public for transparency, two though, it's, we hope enables the private sector to build apps off of the data that we have. A lot of times, you either you have the data but you don't have the app or you have the app, but no data. So in our way, it's trying to help the community build up new ideas. Our push has been moving to the cloud a lot. So we're pushing a lot more data into the cloud where before I think a lot of governments keep a lot of that internal, but obviously look, the cloud's here to stay and it's not going anywhere. And so now it's more about as we migrate, using our partners, our relationship with CrowdStrike, to start securing not only our endpoints but start looking at the cloud space as well. And then we have this new technology. It's not really new, but edge compute. You've heard a lot of, there's different people talking about it. When you start talking about autonomous vehicles, autonomous delivery, drones. We own a large private wireless network. A lot of data now is computed at the edge and we're only taking the metadata and sending it up to the cloud. So it becomes rather complicated with security being at the forefront. >> Yeah, so that very small portion of the actual amount of data that's created goes back but it's such a massive amount of data. It's not to trivialize it, it's still a lot. And some of it is probably ephemeral. Do you persist at all? Or probably not. >> Not always, I mean. A lot of it, what we're learning is, it's a learning process as you go through this smart city or what we call just basically emerging into, 'cause I believe all cities are smart. Not one city smarter than another necessarily. So I'm not really a fan of the term smart city. It's more in line with me as we're building amenities for the future and building amenities for people. And a lot of that is built upon data and then built upon providing things that citizens want. And we all know, we all live somewhere and we live there because it's safe community, it has good education, good infrastructure whatever it might be. And so we're trying to build out that smart community to be as many things as we can to as many people. >> Yeah, that's fair. And there's automation, there's certainly machine intelligence that's heavily involved. Of course, you talking autonomous. Now I understand your work transcends the city of Las Vegas into the broader state of Nevada helping make Nevada a safer state. What's that all about? >> So we have a great partnership. One of the great things, I come from California, so a rather large state. Here in Nevada, it's a very close knit state. So we have a lot of communications with the state. We get to work with them very closely. One of the initiatives we've been working on is how do we, a lot of organizations spend a lot of time doing cybersecurity for just their organization. So it's focused internal on the employees that might work in that organization. We're kind of now looking outwards and saying, how do we not only do that for our internal government employees but how do we involve the entire community? One of the things is, is Las Vegas over 40,000 conventions per year. You're here a lot. What happens in Vegas stays in Vegas and a lot of people bring malware with them and it stays here. We're trying to educate people. We do a lot in government to help people with police and fire and services. What is local government doing to help the community prepare for the next generation of cyber threats and issues? So our initiative is really working with the community, bringing in CrowdStrike and other partners to help us not only work with small business, but work with those entrepreneurs as well as the midsize businesses. >> So what do you do with Crowd? You got the cool little CrowdStrike, not CrowdStrike, but you got the red splash in your lapel. Very cool cuff links, I noticed that you have there. I love the red. >> Little poker chips there. >> They're Very nice, very nice. >> They're very cool. So what do you do with CrowdStrike? >> So CrowdStrike is one of our major components in our security posture. We use them as endpoint protection. I can tell you a quick story. I know my CISO's listening probably was going to cringe now when I tell this story, but our journey with CrowdStrike has been amazing. We deployed the product and when that first week of deployment, we had a malicious actor and CrowdStrike was able to catch it. I would probably would not be here today with you two gentlemen if it wasn't for CrowdStrike. That's not an endorsement it's just a, that's a fact of how things rolled out. But we depend on CrowdStrike and their capabilities to ensure the safety of our digital assets. >> You wouldn't be here 'cause we, it used to be failure means fire. Is that what you mean? >> That's what I mean. I'm not going to, I don't like to use that word in my terminology, but basically failure is not an option in my job. It's just not there. >> Well, it's funny, we had Kevin Mandy on early, he was like, look I started my company in 2004 with the assumption that breaches will happen, you are going to get breached. >> Yes >> So that's why I say, I think there was a day when, if you got breached, oh, you're fired. Well that, then everybody got breached. So I think that that sentiment changing 'cause CrowdStrike saying that the unstoppable breach is a myth. Well, we're not there yet, but. >> I'd say damage control now. At least we have a little bit more control but, again, look, government is about trust. And so when you have that trust level, from my perspective, I keep a high standard and try to prevent any loss of data or any type of malicious activity from happening. I hope the mayor's listening and she doesn't fire me if anything would happen, but you know. >> You got a fun job. How'd you get into this? >> It was a great opportunity. I worked in law enforcement prior to here. I was a Deputy Police Chief in city of Irvine. I oversaw technology as part of that role. I've always loved Las Vegas, always liked the energy of the city and I had a great opportunity to apply and I applied and was lucky enough to be selected. I have a great team that supports me. >> Deputy Police Chief, it sounds like, what you just described, the technology role. You had an operations role essentially, is that right? >> Correct. And so kind of gave me a lot of insights and really helped me, as you progress in government, having different roles in your portfolio makes you a little bit more adaptive and it's kind of, it helps in, especially now with so much video and cameras prevalent in cities, having that law enforcement role, understanding a little of the legal aspects and understanding some of the, what law enforcement wants kind of makes that bridge from technology to the actual end user. >> A really interesting story, Michael. Thanks so much for sharing on theCUBE, appreciate it. >> Thank you for having me here. >> You're very welcome. All right, keep it right there. Dave Nicholson and Dave Vellante will be back from Las Vegas at the Aria from Fal.Con 22. You're watching theCUBE. (outro music)

(upbeat music) >> Hey everyone and welcome back to The Cube. We are covering VMware Explore live in San Francisco. This is our third day of wall to wall coverage. And John Furrier is here with me, Lisa Martin. We are excited to welcome two guests from Kasten by Veeam, please welcome Tom Laden, VP of marketing and Matt LeBlanc, not Joey from friends, Matt LeBlanc, the systems engineer from North America at Kasten by Veeam. Welcome guys, great to have you. >> Thank you. >> Thank you for having us. >> Tom-- >> Great, go ahead. >> Oh, I was going to say, Tom, talk to us about some of the key challenges customers are coming to you with. >> Key challenges that they have at this point is getting up to speed with Kubernetes. So everybody has it on their list. We want to do Kubernetes, but where are they going to start? Back when VMware came on the market, I was switching from Windows to Mac and I needed to run a Windows application on my Mac and someone told me, "Run a VM." Went to the internet, I downloaded it. And in a half hour I was done. That's not how it works with Kubernetes. So that's a bit of a challenge. >> I mean, Kubernetes, Lisa, remember the early days of The Cube Open Stack was kind of transitioning, Cloud was booming and then Kubernetes was the paper that became the thing that pulled everybody together. It's now de facto in my mind. So that's clear, but there's a lot of different versions of it and you hear VMware, they call it the dial tone. Usually, remember, Pat Gelter, it's a dial tone. Turns out that came from Kit Colbert or no, I think AJ kind of coined the term here, but it's since been there, it's been adopted by everyone. There's different versions. It's open source. AWS is involved. How do you guys look at the relationship with Kubernetes here and VMware Explore with Kubernetes and the customers because they have choices. They can go do it on their own. They can add a little bit with Lambda, Serverless. They can do more here. It's not easy. It's not as easy as people think it is. And then this is a skill gaps problem too. We're seeing a lot of these problems out there. What's your take? >> I'll let Matt talk to that. But what I want to say first is this is also the power of the cloud native ecosystem. The days are gone where companies were selecting one enterprise application and they were building their stack with that. Today they're building applications using dozens, if not hundreds of different components from different vendors or open source platforms. And that is really what creates opportunities for those cloud native developers. So maybe you want to... >> Yeah, we're seeing a lot of hybrid solutions out there. So it's not just choosing one vendor, AKS, EKS, or Tanzu. We're seeing all the above. I had a call this morning with a large healthcare provider and they have a hundred clusters and that's spread across AKS, EKS and GKE. So it is covering everything. Plus the need to have a on-prem solution manage it all. >> I got a stat, I got to share that I want to get your reactions and you can laugh or comment, whatever you want to say. Talk to big CSO, CXO, executive, big company, I won't say the name. We got a thousand developers, a hundred of them have heard of Kubernetes, okay. 10 have touched it and used it and one's good at it. And so his point is that there's a lot of Kubernetes need that people are getting aware. So it shows that there's more and more adoption around. You see a lot of managed services out there. So it's clear it's happening and I'm over exaggerating the ratio probably. But the point is the numbers kind of make sense as a thousand developers. You start to see people getting adoption to it. They're aware of the value, but being good at it is what we're hearing is one of those things. Can you guys share your reaction to that? Is that, I mean, it's hyperbole at some level, but it does point to the fact of adoption trends. You got to get good at it, you got to know how to use it. >> It's very accurate, actually. It's what we're seeing in the market. We've been doing some research of our own, and we have some interesting numbers that we're going to be sharing soon. Analysts don't have a whole lot of numbers these days. So where we're trying to run our own surveys to get a grasp of the market. One simple survey or research element that I've done myself is I used Google trends. And in Google trends, if you go back to 2004 and you compare VMware against Kubernetes, you get a very interesting graph. What you're going to see is that VMware, the adoption curve is practically complete and Kubernetes is clearly taking off. And the volume of searches for Kubernetes today is almost as big as VMware. So that's a big sign that this is starting to happen. But in this process, we have to get those companies to have all of their engineers to be up to speed on Kubernetes. And that's one of the community efforts that we're helping with. We built a website called learning.kasten.io We're going to rebrand it soon at CubeCon, so stay tuned, but we're offering hands on labs there for people to actually come learn Kubernetes with us. Because for us, the faster the adoption goes, the better for our business. >> I was just going to ask you about the learning. So there's a big focus here on educating customers to help dial down the complexity and really get them, these numbers up as John was mentioning. >> And we're really breaking it down to the very beginning. So at this point we have almost 10 labs as we call them up and they start really from install a Kubernetes Cluster and people really hands on are going to install a Kubernetes Cluster. They learn to build an application. They learn obviously to back up the application in the safest way. And then there is how to tune storage, how to implement security, and we're really building it up so that people can step by step in a hands on way learn Kubernetes. >> It's interesting, this VMware Explore, their first new name change, but VMWorld prior, big community, a lot of customers, loyal customers, but they're classic and they're foundational in enterprises and let's face it. Some of 'em aren't going to rip out VMware anytime soon because the workloads are running on it. So in Broadcom we'll have some good action to maybe increase prices or whatnot. So we'll see how that goes. But the personas here are definitely going cloud native. They did with Tanzu, was a great thing. Some stuff was coming off, the fruit's coming off the tree now, you're starting to see it. CNCF has been on this for a long, long time, CubeCon's coming up in Detroit. And so that's just always been great, 'cause you had the day zero event and you got all kinds of community activity, tons of developer action. So here they're talking, let's connect to the developer. There the developers are at CubeCon. So the personas are kind of connecting or overlapping. I'd love to get your thoughts, Matt on? >> So from the personnel that we're talking to, there really is a split between the traditional IT ops and a lot of the people that are here today at VMWare Explore, but we're also talking with the SREs and the dev ops folks. What really needs to happen is we need to get a little bit more experience, some more training and we need to get these two groups to really start to coordinate and work together 'cause you're basically moving from that traditional on-prem environment to a lot of these traditional workloads and the only way to get that experience is to get your hands dirty. >> Right. >> So how would you describe the persona specifically here versus say CubeCon? IT ops? >> Very, very different, well-- >> They still go ahead. Explain. >> Well, I mean, from this perspective, this is all about VMware and everything that they have to offer. So we're dealing with a lot of administrators from that regard. On the Kubernetes side, we have site reliability engineers and their goal is exactly as their title describes. They want to architect arch applications that are very resilient and reliable and it is a different way of working. >> I was on a Twitter spaces about SREs and dev ops and there was people saying their title's called dev ops. Like, no, no, you do dev ops, you don't really, you're not the dev ops person-- >> Right, right. >> But they become the dev ops person because you're the developer running operations. So it's been weird how dev ops been co-opted as a position. >> And that is really interesting. One person told me earlier when I started Kasten, we have this new persona. It's the dev ops person. That is the person that we're going after. But then talking to a few other people who were like, "They're not falling from space." It's people who used to do other jobs who now have a more dev ops approach to what they're doing. It's not a new-- >> And then the SRE conversation was in site, reliable engineer comes from Google, from one person managing multiple clusters to how that's evolved into being the dev ops. So it's been interesting and this is really the growth of scale, the 10X developer going to more of the cloud native, which is okay, you got to run ops and make the developer go faster. If you look at the stuff we've been covering on The Cube, the trends have been cloud native developers, which I call dev ops like developers. They want to go faster. They want self-service and they don't want to slow down. They don't want to deal with BS, which is go checking security code, wait for the ops team to do something. So data and security seem to be the new ops. Not so much IT ops 'cause that's now cloud. So how do you guys see that in, because Kubernetes is rationalizing this, certainly on the compute side, not so much on storage yet but it seems to be making things better in that grinding area between dev and these complicated ops areas like security data, where it's constantly changing. What do you think about that? >> Well there are still a lot of specialty folks in that area in regards to security operations. The whole idea is be able to script and automate as much as possible and not have to create a ticket to request a VM to be billed or an operating system or an application deployed. They're really empowered to automatically deploy those applications and keep them up. >> And that was the old dev ops role or person. That was what dev ops was called. So again, that is standard. I think at CubeCon, that is something that's expected. >> Yes. >> You would agree with that. >> Yeah. >> Okay. So now translating VM World, VMware Explore to CubeCon, what do you guys see as happening between now and then? Obviously got re:Invent right at the end in that first week of December coming. So that's going to be two major shows coming in now back to back that're going to be super interesting for this ecosystem. >> Quite frankly, if you compare the persona, maybe you have to step away from comparing the personas, but really compare the conversations that we're having. The conversations that you're having at a CubeCon are really deep dives. We will have people coming into our booth and taking 45 minutes, one hour of the time of the people who are supposed to do 10 minute demos because they're asking more and more questions 'cause they want to know every little detail, how things work. The conversations here are more like, why should I learn Kubernetes? Why should I start using Kubernetes? So it's really early day. Now, I'm not saying that in a bad way. This is really exciting 'cause when you hear CNCF say that 97% of enterprises are using Kubernetes, that's obviously that small part of their world. Those are their members. We now want to see that grow to the entire ecosystem, the larger ecosystem. >> Well, it's actually a great thing, actually. It's not a bad thing, but I will counter that by saying I am hearing the conversation here, you guys'll like this on the Veeam side, the other side of the Veeam, there's deep dives on ransomware and air gap and configuration errors on backup and recovery and it's all about Veeam on the other side. Those are the guys here talking deep dive on, making sure that they don't get screwed up on ransomware, not Kubernete, but they're going to Kub, but they're now leaning into Kubernetes. They're crossing into the new era because that's the apps'll end up writing the code for that. >> So the funny part is all of those concepts, ransomware and recovery, they're all, there are similar concepts in the world of Kubernetes and both on the Veeam side as well as the Kasten side, we are supporting a lot of those air gap solutions and providing a ransomware recovery solution and from a air gap perspective, there are a many use cases where you do need to live. It's not just the government entity, but we have customers that are cruise lines in Europe, for example, and they're disconnected. So they need to live in that disconnected world or military as well. >> Well, let's talk about the adoption of customers. I mean this is the customer side. What's accelerating their, what's the conversation with the customer at base, not just here but in the industry with Kubernetes, how would you guys categorize that? And how does that get accelerated? What's the customer situation? >> A big drive to Kubernetes is really about the automation, self-service and reliability. We're seeing the drive to and reduction of resources, being able to do more with less, right? This is ongoing the way it's always been. But I was talking to a large university in Western Canada and they're a huge Veeam customer worth 7000 VMs and three months ago, they said, "Over the next few years, we plan on moving all those workloads to Kubernetes." And the reason for it is really to reduce their workload, both from administration side, cost perspective as well as on-prem resources as well. So there's a lot of good business reasons to do that in addition to the technical reliability concerns. >> So what is those specific reasons? This is where now you start to see the rubber hit the road on acceleration. >> So I would say scale and flexibility that ecosystem, that opportunity to choose any application from that or any tool from that cloud native ecosystem is a big driver. I wanted to add to the adoption. Another area where I see a lot of interest is everything AI, machine learning. One example is also a customer coming from Veeam. We're seeing a lot of that and that's a great thing. It's an AI company that is doing software for automated driving. They decided that VMs alone were not going to be good enough for all of their workloads. And then for select workloads, the more scalable one where scalability was more of a topic, would move to Kubernetes. I think at this point they have like 20% of their workloads on Kubernetes and they're not planning to do away with VMs. VMs are always going to be there just like mainframes still exist. >> Yeah, oh yeah. They're accelerating actually. >> We're projecting over the next few years that we're going to go to a 50/50 and eventually lean towards more Kubernetes than VMs, but it was going to be a mix. >> Do you have a favorite customer example, Tom, that you think really articulates the value of what Kubernetes can deliver to customers where you guys are really coming in and help to demystify it? >> I would think SuperStereo is a really great example and you know the details about it. >> I love the SuperStereo story. They were a AWS customer and they're running OpenShift version three and they need to move to OpenShift version four. There is no upgrade in place. You have to migrate all your apps. Now SuperStereo is a large French IT firm. They have over 700 developers in their environment and it was by their estimation that this was going to take a few months to get that migration done. We're able to go in there and help them with the automation of that migration and Kasten was able to help them architect that migration and we did it in the course of a weekend with two people. >> A weekend? >> A weekend. >> That's a hackathon. I mean, that's not real come on. >> Compared to thousands of man hours and a few months not to mention since they were able to retire that old OpenShift cluster, the OpenShift three, they were able to stop paying Jeff Bezos for a couple of those months, which is tens of thousands of dollars per month. >> Don't tell anyone, keep that down low. You're going to get shot when you leave this place. No, seriously. This is why I think the multi-cloud hybrid is interesting because these kinds of examples are going to be more than less coming down the road. You're going to see, you're going to hear more of these stories than not hear them because what containerization now Kubernetes doing, what Dockers doing now and the role of containers not being such a land grab is allowing Kubernetes to be more versatile in its approach. So I got to ask you, you can almost apply that concept to agility, to other scenarios like spanning data across clouds. >> Yes, and that is what we're seeing. So the call I had this morning with a large insurance provider, you may have that insurance provider, healthcare provider, they're across three of the major hyperscalers clouds and they do that for reliability. Last year, AWS went down, I think three times in Q4 and to have a plan of being able to recover somewhere else, you can actually plan your, it's DR, it's a planned migration. You can do that in a few hours. >> It's interesting, just the sidebar here for a second. We had a couple chats earlier today. We had the influences on and all the super cloud conversations and trying to get more data to share with the audience across multiple areas. One of them was Amazon and that super, the hyper clouds like Amazon, as your Google and the rest are out there, Oracle, IBM and everyone else. There's almost a consensus that maybe there's time for some peace amongst the cloud vendors. Like, "Hey, you've already won." (Tom laughs) Everyone's won, now let's just like, we know where everyone is. Let's go peace time and everyone, then 'cause the relationship's not going to change between public cloud and the new world. So there's a consensus, like what does peace look like? I mean, first of all, the pie's getting bigger. You're seeing ecosystems forming around all the big new areas and that's good thing. That's the tides rise and the pie's getting bigger, there's bigger market out there now so people can share and share. >> I've never worked for any of these big players. So I would have to agree with you, but peace would not drive innovation. And in my heart is with tech innovation. I love it when vendors come up with new solutions that will make things better for customers and if that means that we're moving from on-prem to cloud and back to on-prem, I'm fine with that. >> What excites me is really having the flexibility of being able to choose any provider you want because you do have open standards, being cloud native in the world of Kubernetes. I've recently discovered that the Canadian federal government had mandated to their financial institutions that, "Yes, you may have started all of your on cloud presence in Azure, you need to have an option to be elsewhere." So it's not like-- >> Well, the sovereign cloud is one of those big initiatives, but also going back to Java, we heard another guest earlier, we were thinking about Java, right once ran anywhere, right? So you can't do that today in a cloud, but now with containers-- >> You can. >> Again, this is, again, this is the point that's happening. Explain. >> So when you have, Kubernetes is a strict standard and all of the applications are written to that. So whether you are deploying MongoDB or Postgres or Cassandra or any of the other cloud native apps, you can deploy them pretty much the same, whether they're in AKS, EKS or on Tanzu and it makes it much easier. The world became just a lot less for proprietary. >> So that's the story that everybody wants to hear. How does that happen in a way that is, doesn't stall the innovation and the developer growth 'cause the developers are driving a lot of change. I mean, for all the talk in the industry, the developers are doing pretty good right now. They've got a lot of open source, plentiful, open source growing like crazy. You got shifting left in the CICD pipeline. You got tools coming out with Kubernetes. Infrastructure has code is almost a 100% reality right now. So there's a lot of good things going on for developers. That's not an issue. The issue is just underneath. >> It's a skillset and that is really one of the biggest challenges I see in our deployments is a lack of experience. And it's not everyone. There are some folks that have been playing around for the last couple of years with it and they do have that experience, but there are many people that are still young at this. >> Okay, let's do, as we wrap up, let's do a lead into CubeCon, it's coming up and obviously re:Invent's right behind it. Lisa, we're going to have a lot of pre CubeCon interviews. We'll interview all the committee chairs, program chairs. We'll get the scoop on that, we do that every year. But while we got you guys here, let's do a little pre-pre-preview of CubeCon. What can we expect? What do you guys think is going to happen this year? What does CubeCon look? You guys our big sponsor of CubeCon. You guys do a great job there. Thanks for doing that. The community really recognizes that. But as Kubernetes comes in now for this year, you're looking at probably the what third year now that I would say Kubernetes has been on the front burner, where do you see it on the hockey stick growth? Have we kicked the curve yet? What's going to be the level of intensity for Kubernetes this year? How's that going to impact CubeCon in a way that people may or may not think it will? >> So I think first of all, CubeCon is going to be back at the level where it was before the pandemic, because the show, as many other shows, has been suffering from, I mean, virtual events are not like the in-person events. CubeCon LA was super exciting for all the vendors last year, but the attendees were not really there yet. Valencia was a huge bump already and I think Detroit, it's a very exciting city I heard. So it's going to be a blast and it's going to be a huge attendance, that's what I'm expecting. Second I can, so this is going to be my third personally, in-person CubeCon, comparing how vendors evolved between the previous two. There's going to be a lot of interesting stories from vendors, a lot of new innovation coming onto the market. And I think the conversations that we're going to be having will yet, again, be much more about live applications and people using Kubernetes in production rather than those at the first in-person CubeCon for me in LA where it was a lot about learning still, we're going to continue to help people learn 'cause it's really important for us but the exciting part about CubeCon is you're talking to people who are using Kubernetes in production and that's really cool. >> And users contributing projects too. >> Also. >> I mean Lyft is a poster child there and you've got a lot more. Of course you got the stealth recruiting going on there, Apple, all the big guys are there. They have a booth and no one's attending you like, "Oh come on." Matt, what's your take on CubeCon? Going in, what do you see? And obviously a lot of dynamic new projects. >> I'm going to see much, much deeper tech conversations. As experience increases, the more you learn, the more you realize you have to learn more. >> And the sharing's going to increase too. >> And the sharing, yeah. So I see a lot of deep conversations. It's no longer the, "Why do I need Kubernetes?" It's more, "How do I architect this for my solution or for my environment?" And yeah, I think there's a lot more depth involved and the size of CubeCon is going to be much larger than we've seen in the past. >> And to finish off what I think from the vendor's point of view, what we're going to see is a lot of applications that will be a lot more enterprise-ready because that is the part that was missing so far. It was a lot about the what's new and enabling Kubernetes. But now that adoption is going up, a lot of features for different components still need to be added to have them enterprise-ready. >> And what can the audience expect from you guys at CubeCon? Any teasers you can give us from a marketing perspective? >> Yes. We have a rebranding sitting ready for learning website. It's going to be bigger and better. So we're not no longer going to call it, learning.kasten.io but I'll be happy to come back with you guys and present a new name at CubeCon. >> All right. >> All right. That sounds like a deal. Guys, thank you so much for joining John and me breaking down all things Kubernetes, talking about customer adoption, the challenges, but also what you're doing to demystify it. We appreciate your insights and your time. >> Thank you so much. >> Thank you very much. >> Our pleasure. >> Thanks Matt. >> For our guests and John Furrier, I'm Lisa Martin. You've been watching The Cube's live coverage of VMware Explore 2022. Thanks for joining us. Stay safe. (gentle music)

>>Welcome everyone to the cubes coverage of women in data science, worldwide conference 2022. I'm Lisa Martin, coming to you live from Stanford university at the Arriaga alumni center. It's great to be back at widths in person, and I'm pleased to welcome fresh from the main stage Tiara Bill's assistant professor at UCLA Tierra. Welcome to the program. >>I'm glad to be here. Thank you for having me. Tell >>Me a little bit about your background. You're a civil engineer and I was telling you, so it was my dad. So I'm, I'm partial to civil engineers, but give our audience an overview of your background, what you studied and all that. Good. >>Yeah. So I'm a civil engineer, um, specifically transportation engineer, um, at UCLA. I also have an appointment in the public policy department. And so, um, I'm split between the two, my work focuses on travel demand modeling and how to use these tools to better inform, uh, and learn more about transportation equity and how to advance transportation equity. Um, and what that means is that we are prioritizing the needs of vulnerable communities, um, in terms of the data that we're using, the models that we're using to guide decision-making, um, in terms of the very projects that we evaluate and ultimately the decisions that we make to invest in certain transportation improvements. How >>Did you get interested in transportation equity? >>Yeah, so I think it, it stems from growing up, uh, in Detroit, some or Detroit born and raised native, and it stems from growing up in an environment where it was very clear that space matters that where you live the most, that you have access to, uh, whether you have a car or not. Um, whether you have flexibility in your, in your travel, it all matters. And it all governs the opportunities that you have access to. So it was very clear to me, um, when I would realize that certain certain kids didn't really leave their neighborhood, you know, they didn't travel about the city, let alone outside of the city and abroad. And so, um, and there are also other, you know, examples of, um, there are examples and cases after case where it's clear that communities are, um, being exposed to a high level of emissions, for example, um, that might result from transportation, but they're not positioned to benefit, um, in the same ways that the people who own the infrastructure on the freight or what have you. So, um, these are all very real experiences that have motivated my interest in transportation equity. >>Interesting. It's something I actually had never thought about, but you bring up a great point. How are talk to me about the travel demand models, how they're relevant and, and where some of the biases are in travel data, >>Right? So travel demand models, they are they're computational tools. They're empirically estimated meaning that their estimated from raw data, um, everything about them is driven by the data that you have access to. And how they're used is in largely in regional transportation planning, when it is necessary for regions to assess 10, maybe 15, 20 years into the future. Um, how is transportation going to change as a result of changes in travel patterns, growth in the population, um, changes and how firms are distributed across the landscape. Um, environmental changes, all sorts of changes that, um, that guide and direct our transportation decisions at an individual level. So regions are assessing these things over time and they need these powerful travel demand models in order to perform those assessments. And then they also, once they have an understanding of what the need is, because for example, they expect traffic congestion to improve, or sorry to increase over time. Um, there needs to be a means of assessing alternatives for mitigating those issues. And so they use the same types of models to understand if we expand highway capacity, if we, uh, build a new form of transit, is that going to mitigate, uh, the challenges that we're going to face in the future >>And travel demand, modeling and equity? What's the connection there? I imagine there's a pretty good >>Deep connection, right? So the connection is that. So we're using these tools to decide on the future of transportation investments and because of a history of understanding that we have around how ignoring the conditions for vulnerable communities, ignoring how, um, uh, transportation decisions might differentially impact different, different groups, different segments. Um, if we ignore that, then it can lead to devastating outcomes. And so I'm citing, um, examples of the construction of the Eisenhower interstate system back in the fifties and sixties, where, uh, we know today that there were millions of black and minority communities that were, uh, displace. Um, they weren't fairly compensated all because of lack of consideration for, for outcomes to these communities and the planning process. And so we are aware that these kinds of things can happen. Um, and because of that, we now have federal regulations that require, uh, equity analysis to occur for any project that's going to leverage federal funding. And so it's, it's tied to our understanding of what can happen when we don't focus on equity is also tied to what the current regulations are, but challenge is that we need better guidance on how to do this, how to perform the equity analysis. What types of improvements are actually going to move the needle and advance us toward a state where we can prioritize the needs of the vulnerable travelers and residents? What >>Excites you about the work that you're doing? >>You know, I, I have a vested interest in seeing conditions improve for, um, for the underdog, if you will, for folks who, um, they, they work hard, but they still struggle, um, for folks who experience discrimination in different forms. Um, and so I have a vested interest in seeing conditions improve for them. And so I'm really excited about, uh, the time that we're in, I'm excited that equity is now at the height of many discussions, um, because it's opening up resources, right? To have, uh, more folks paying attention, more folks, researching more folks, developing methods and processes that will actually help to advance equity, >>Advancing equity. We definitely need that. And you're right. There's, there's good V visibility on it right now. And let's take advantage of that for the good things that can come out of it. Talk to me a little bit about what you talked about in your talk earlier today here at widths. >>Right? So today I got a chance to elaborate on how travel demand models can end up, um, uh, with, with issues of bias and under-representation, and it's tied to a number of things, but one of them is the data that reusing, because these are, uh, empirically estimated tools. They take their form, they take their, uh, significance. Everything about them is shaped by the data that we use. Um, and at the same time, we are aware that vulnerable communities are more prone to issues that contribute to data bias. And under-representation so issues, for example, like non-response, um, issues like coverage bias with means that, um, certain groups are for whatever reason, not in the sample link frame. Um, and so, because we know that these types of errors are more prevalent for vulnerable communities, it brings, uh, it raises questions about, um, the quality of the decisions that come out of these models that we estimate based on these data. >>And so I'm interested in weaving these parts together. Um, and part of it has to do with understanding the conditions that, um, that underlie the data. So what do I mean by conditions? I gave an example of, uh, cases where there is discrimination and as evidenced by the data that we have available as evidenced, uh, for example, by examining, um, the quality of service across racial groups, um, using Uber and Lyft, right? So we have information that, that, that presents this to us, but that information is still outside of what we typically use to estimate travel, demand models. That information is not being used to understand the context under which people are making decisions. It's not being used to better understand the constraints that people are facing when they're making, uh, decisions. And so what is the connection that means that we are using data, um, that does not will capture the target group. >>People who are low income, elderly, um, transit dependent, uh, we're not capturing these groups very well because of the prevalence of, of various types of survey bias. Um, and it is shaping our models in unknown ways. And so my group is really trying to make that connection between, okay, how do we collect Bader, better data, first of all, but second, what does that mean? What are the ramifications for prediction, accuracy for VR, for various groups, and then beyond that, what are the policy implications? Right. Um, I think that the risk is that we might be making wrong decisions, right? We might be assuming that, uh, certain types of improvements are actually going to improve quality of service for vulnerable communities when they actually don't. Right. Um, and so that's the worry and that's part of the unknown, and that's why I'm working in this >>Part of the anonymity. Also, I'm sure part of your passion and your interest international women's day is tomorrow. And the theme this year is break the bias of breaking the bias with >>Mercy back >>To travel equity. Where do you think we are on, on being able to start mitigating some of the biases that you've talked about? >>I think that it's all about phasing. I think that there are things that we can do now, right? And so, um, at the point of making decisions, um, we can view the results that we have through this lens, that it might be an incomplete picture. We can view it through a historical lens. We can also view it, um, using emerging data that allows for us to explore some of these constraints that, you know, might be exogenous to the models or X, not in, not included in how we estimate the models. Um, and so that's one thing that we can do in practice is okay. We already know that there are some challenges let's view this from a different lens, as opposed to assuming that it's giving us the complete picture. Right. Um, and that's kind of been my theme, uh, today is that, you know, as decision-makers, as analysts, as data scientists, as researchers, we do have tendency of assuming that the data that we have, the results that we have is giving us the complete picture when we know, but it's not, we know that we act as if it is, but we know that it's not right. >>So, you know, we need to, there's a lot of learning and changing of behaviors, um, that that has to happen. >>Changing behaviors is challenging. >>It is behavior changes is tough, but it's necessary, but it's necessary. It's necessary. And it's urgent. And it's critical, especially if you're going to, uh, improve conditions for vulnerable community. >>What are some of the things that excite you, that looking at where we are now, we've got a nice visibility on equity. There, there's the conscious understanding of the bias and data and the work to help to mitigate that. What are some of the things that excite you about what you're doing and maybe even some of the policies that you think should be enacted as a result of more encompassing datasets? >>It's a good question. Um, one thing I will say is what excites me is it's also tied to the emerging data that we have available. So I'm trying to go back to an example that I gave about measuring constraints. Think that we can now do that in interesting ways, because we're collecting data about everything we're collecting data about, um, not just about where we travel, but how we travel, why we travel. Um, you know, we, we collect information on who we're traveling with, you know, so there's a lot more information that we can make use of, um, in particular to understand constraints. So it's, it's really exciting to me. And when I say that again, um, talking about, um, how would we make a choice to take a certain mode of transportation or to leave our house at a certain time in the morning to, to get to work. >>Um, we're making that under some conditions, right? Right. And those conditions aren't always observed and traditional data sets. I think now we're at a time where emerging data sources can start to capture some of that. And so we can ask questions that we weren't able to, or answer questions that we weren't able to answer before. And the reason why it's important in the modeling is because in the models, you have this sort of choice driven side and you have the alternatives. So you're making a choice amongst some set of alternatives. We model the choices and we spend a lot of time and pay a lot of attention to the decision process. And what factors goes into making the choice, assuming that everyone really has the same set of universal choices. Right. I think that we need to take a little, pay a little more intention, um, to understanding the constraints that people have, um, and how that guides the overall outcomes. Right? So, so that's what I'm excited about. I mean, it's basically leveraging the new data in new ways that we weren't able to before >>Leveraging the data in new ways. Love it. Tierra, thank you for joining me, talking about transportation equity, what you're doing there, the opportunities and kind of where we are on that road. If you will. Thank you so much for having me, my pleasure. I'm Lisa Martin. You're watching the cubes coverage of women in data science conference, 2022. We'll be right back with our next guest.

(upbeat music) >> Hello, and welcome back to AWS Startup Showcase, I'm John Furrier, your host. This is the Hero panel, the AWS Heroes. These are folks that have a lot of experience in Open Source, having fun building great projects and commercializing the value and best practices of Open Source innovation. We've got some great guests here. Liz Rice, Chief Open Source Officer, Isovalent. CUBE alumni, great to see you. Brian LeRoux, who is the Co-founder and CTO of begin.com. Erica Windisch who's an Architect for Developer Experience. AWS Hero, also CUBE alumni. Casey Lee, CTO Gaggle. Doing some great stuff in ed tech. Great collection of experts and experienced folks doing some fun stuff, welcome to this conversation this CUBE panel. >> Hi. >> Thanks for having us. >> Hello. >> Let's go down the line. >> I don't normally do this, but since we're remote and we have such great guests, go down the line and talk about why Open Source is important to you guys. What projects are you currently working on? And what's the coolest thing going on there? Liz we'll start with you. >> Okay, so I am very involved in the world of Cloud Native. I'm the chair of the technical oversight committee for the Cloud Native Computing Foundation. So that means I get to see a lot of what's going on across a very broad range of Cloud Native projects. More specifically, Isovalent. I focus on Cilium, which is it's based on a technology called EBPF. That is to me, probably the most exciting technology right now. And then finally, I'm also involved in an organization called OpenUK, which is really pushing for more use of open technologies here in the United Kingdom. So spread around lots of different projects. And I'm in a really fortunate position, I think, to see what's happening with lots of projects and also the commercialization of lots of projects. >> Awesome, Brian what project are you working on? >> Working project these days called Architect. It's a Open Source project built on top of AWSM. It adds a lot of sugar and terseness to the SM experience and just makes it a lot easier to work with and get started. AWS can be a little bit intimidating to people at times. And the Open Source community is stepping up to make some of that bond ramp a little bit easier. And I'm also an Apache member. And so I keep a hairy eyeball on what's going on in that reality all the time. And I've been doing this open-source thing for quite a while, and yeah, I love it. It's a great thing. It's real science. We get to verify each other's work and we get to expand and build on human knowledge. So that's a huge honor to just even be able to do that and I feel stoked to be here so thanks for having me. >> Awesome, yeah, and totally great. Erica, what's your current situation going on here? What's happening? >> Sure, so I am currently working on developer experience of a number of Open Source STKS and CLI components from my current employer. And previously, recently I left New Relic where I was working on integrating with OpenTelemetry, as well as a number of other things. Before that I was a maintainer of Docker and of OpenStack. So I've been in this game for a while as well. And I tend to just put my fingers in a lot of little pies anywhere from DVD players 20 years ago to a lot of this open telemetry and monitoring and various STKs and developer tools is where like Docker and OpenStack and the STKs that I work on now, all very much focusing on developer as the user. >> Yeah, you're always on the wave, Erica great stuff. Casey, what's going on? Do you got some great ed techs happening? What's happening with you? >> Yeah, sure. The primary Open Source project that I'm contributing to right now is ACT. This is a tool I created a couple of years back when GitHub Actions first came out, and my motivation there was I'm just impatient. And that whole commit, push, wait time where you're testing out your pipelines is painful. And so I wanted to build a tool that allowed developers to test out their GitHub Actions workflows locally. And so this tool uses Docker containers to emulate, to get up action environment and gives you fast feedback on those workflows that you're building. Lot of innovation happening at GitHub. And so we're just trying to keep up and continue to replicate those new features functionalities in the local runner. And the biggest challenge I've had with this project is just keeping up with the community. We just passed 20,000 stars, and it'd be it's a normal week to get like 10 PRs. So super excited to announce just yesterday, actually I invited four of the most active contributors to help me with maintaining the project. And so this is like a big deal for me, letting the project go and bringing other people in to help lead it. So, yeah, huge shout out to those folks that have been helping with driving that project. So looking forward to what's next for it. >> Great, we'll make sure the SiliconANGLE riders catch that quote there. Great call out. Let's start, Brian, you made me realize when you mentioned Apache and then you've been watching all the stuff going on, it brings up the question of the evolution of Open Source, and the commercialization trends have been very interesting these days. You're seeing CloudScale really impact also with the growth of code. And Liz, if you remember, the Linux Foundation keeps making projections and they keep blowing past them every year on more and more code and more and more entrance coming in, not just individuals, corporations. So you starting to see Netflix donates something, you got Lyft donate some stuff, becomes a project company forms around it. There's a lot of entrepreneurial activity that's creating this new abstraction layers, new platforms, not just tools. So you start to see a new kickup trajectory with Open Source. You guys want to comment on this because this is going to impact how fast the enterprise will see value here. >> I think a really great example of that is a project called Backstage that's just come out of Spotify. And it's going through the incubation process at the CNCF. And that's why it's front of mind for me right now, 'cause I've been working on the due diligence for that. And the reason why I thought it was interesting in relation to your question is it's spun out of Spotify. It's fully Open Source. They have a ton of different enterprises using it as this developer portal, but they're starting to see some startups emerging offering like a hosted managed version of Backstage or offering services around Backstage or offering commercial plugins into Backstage. And I think it's really fascinating to see those ecosystems building up around a project and different ways that people can. I'm a big believer. You cannot sell the Open Source code, but you can sell other things that create value around Open Source projects. So that's really exciting to see. >> Great point. Anyone else want to weigh in and react to that? Because it's the new model. It's not the old way. I mean, I remember when I was in college, we had the Pirate software. Open Source wasn't around. So you had to deal under the table. Now it's free. But I mean the old way was you had to convince the enterprise, like you've got a hard knit, it builds the community and the community manage the quality of the code. And then you had to build the company to make sure they could support it. Now the companies are actually involved in it, right? And then new startups are forming faster. And the proof points are shorter and highly accelerated for that. I mean, it's a whole new- >> It's a Cambrian explosion, and it's great. It's one of those things that it's challenging for the new developers because they come in and they're like, "Whoa, what is all this stuff that I'm supposed to figure out?" And there's no right answer and there's no wrong answer. There's just tons of it. And I think that there's a desire for us to have one sort of well-known trot and happy path, that audience we're a lot better with a more diverse community, with lots of options, with lots of ways to approach these problems. And I think it's just great. A challenge that we have with all these options and all these Cambrian explosion of projects and all these competing ideas, right now, the sustainability, it's a bit of a tricky question to answer. We know that there's a commercialization aspect that helps us fund these projects, but how we compose the open versus the commercial source is still a bit of a tricky question and a tough one for a lot of folks. >> Erica, would you chime in on that for a second. I want to get your angle on that, this experience and all this code, and I'm a new person, I'm an existing person. Do I get like a blue check mark and verify? I mean, these are questions like, well, how do you navigate? >> Yeah, I think this has been something happening for a while. I mean, back in the early OpenStack days, 2010, for instance, Rackspace Open Sourcing, OpenStack and ANSU Labs and so forth, and then trying, having all these companies forming in creating startups around this. I started at a company called Cloudccaling back in late 2010, and we had some competitors such as Piston and so forth where a lot of the ANSUL Labs people went. But then, the real winners, I think from OpenStack ended up being the enterprises that jumped in. We had Red Hat in particular, as well as HP and IBM jumping in and investing in OpenStack, and really proving out a lot of... not that it was the first time, but this is when we started seeing billions of dollars pouring into Open Source projects and Open Source Foundations, such as the OpenStack Foundation, which proceeded a lot of the things that we now see with the Linux Foundation, which was then created a little bit later. And at the same time, I'm also reflecting a little bit what Brian said because there are projects that don't get funded, that don't get the same attention, but they're also getting used quite significantly. Things like Log4j really bringing this to the spotlight in terms of projects that are used everywhere by everything with significant outsized impacts on the industry that are not getting funded, that aren't flashy enough, that aren't exciting enough because it's just logging, but a vulnerability in it brings every everything and everybody down and has possibly billions of dollars of impact to our industry because nobody wanted to fund this project. >> I think that brings up the commercialization point about maybe bringing a venture capital model in saying, "Hey, that boring little logging thing could be a key ingredient for say solving some observability problems so I think let's put some cash." Again then we'd never seen that before. Now you're starting to see that kind of a real smart investment thesis going into Open Source projects. I mean, Promethease, Crafter, these are projects that turned off companies. This is turning up companies. >> A decade ago, there was no money in Dev tools that I think that's been fully debunked now. They used to be a concept that the venture community believed, but there's just too much evidence to the contrary, the companies like Cash Court, Datadog, the list goes on and on. I think the challenge for the Open Source (indistinct) comes back to foundations and working (indistinct) these developers make this code safe and secure. >> Casey, what's your reaction to all of this? You've got, so a project has gained some traction, got some momentum. There's a lot of mission critical. I won't say white spaces, but the opportunities in the big cloud game happening. And there's a lot of, I won't say too many entrepreneurial, but there's a lot of community action happening that's precommercialization that's getting traction. How does this all develop naturally and then vector in quickly when it hits? >> Yeah, I want to go back to the Log4j topic real quick. I think that it's a great example of an area that we need to do better at. And there was a cool article that Rob Pike wrote describing how to quantify the criticality. I think that's sort of quantifying criticality was the article he wrote on how to use metrics, to determine how valuable, how important a piece of Open Source is to the community. And we really need to highlight that more. We need a way to make it more clear how important this software is, how many people depend on it and how many people are contributing to it. And because right now we all do that. Like if I'm going to evaluate an Open Source software, sure, I'll look at how many stars it has and how many contributors it has. But I got to go through and do all that work myself and come up with. It would be really great if we had an agreed upon method for ranking the criticality of software, but then also the risk, hey, that this is used by a ton of people, but nobody's contributing to it anymore. That's a concern. And that would be great to potential users of that to signal whether or not it makes sense. The Open Source Security Foundation, just getting off the ground, they're doing some work in this space, and I'm really excited to see where they go with that looking at ways to stop score critically. >> Well, this brings up a good point while we've got everyone here, let's take a plug and plug a project you think that's not getting the visibility it needs. Let's go through each of you, point out a project that you think people should be looking at and talking about that might get some free visibility here. Anyone want to highlight projects they think should be focused more on, or that needs a little bit of love? >> I think, I mean, particularly if we're talking about these sort of vulnerability issues, there's a ton of work going on, like in the Secure Software Foundation, other foundations, I think there's work going on in Apache somewhere as well around the bill of material, the software bill of materials, the Secure Software supply chain security, even enumerating your dependencies is not trivial today. So I think there's going to be a ton of people doing really good work on that, as well as the criticality aspect. It's all like that. There's a really great xkcd cartoon with your software project and some really big monolithic lumps. And then, this tiny little piece in a very important point that's maintained by somebody in his bedroom in Montana or something and if you called it out. >> Yeah, you just opened where the next lightening and a bottle comes from. And this is I think the beauty of Open Source is that you get a little collaboration, you get three feet in a cloud of dust going and you get some momentum, and if it's relevant, it rises to the top. I think that's the collective intelligence of Open Source. The question I want to ask that the panel here is when you go into an enterprise, and now that the game is changing with a much more collaborative and involved, what's the story if they say, hey, what's in it for me, how do I manage the Open Source? What's the current best practice? Because there's no doubt I can't ignore it. It's in everything we do. How do I organize around it? How do I build around it to be more efficient and more productive and reduce the risk on vulnerabilities to managing staff, making sure the right teams in place, the right agility and all those things? >> You called it, they got to get skin in the game. They need to be active and involved and donating to a sustainable Open Source project is a great way to start. But if you really want to be active, then you should be committing. You should have a goal for your organization to be contributing back to that project. Maybe not committing code, it could be committing resources into the darks or in the tests, or even tweeting about an Open Source project is contributing to it. And I think a lot of these enterprises could benefit a lot from getting more active with the Open Source Foundations that are out there. >> Liz, you've been actively involved. I know we've talked personally when the CNCF started, which had a great commercial uptake from companies. What do you think the current state-of-the-art kind of equation is has it changed a little bit? Or is it the game still the same? >> Yeah, and in the early days of the CNCF, it was very much dominated by vendors behind the project. And now we're seeing more and more membership from end-user companies, the kind of enterprises that are building their businesses on Cloud Native, but their business is not in itself. That's not there. The infrastructure is not their business. And I think seeing those companies, putting money in, putting time in, as Brian says contributing resources quite often, there's enough money, but finding the talent to do the work and finding people who are prepared to actually chop the wood and carry the water, >> Exactly. >> that it's hard. >> And if enterprises can find peoples to spend time on Open Source projects, help with those chores, it's hugely valuable. And it's one of those the rising tide floats all the boats. We can raise security, we can reduce the amount of dependency on maintain projects collectively. >> I think the business models there, I think one of the things I'll react to and then get your guys' comments is remember which CubeCon it was, it was one of the early ones. And I remember seeing Apple having a booth, but nobody was manning. It was just an Apple booth. They weren't doing anything, but they were recruiting. And I think you saw the transition of a business model where the worry about a big vendor taking over a project and having undue influence over it goes away because I think this idea of participation is also talent, but also committing that talent back into the communities as a model, as a business model, like, okay, hire some great people, but listen, don't screw up the Open Source piece of it 'cause that's a critical. >> Also hire a channel, right? They can use those contributions to source that talent and build the reputation in the communities that they depend on. And so there's really a lot of benefit to the larger organizations that can do this. They'll have a huge pipeline of really qualified engineers right out the gate without having to resort to cheesy whiteboard interviews, which is pretty great. >> Yeah, I agree with a lot of this. One of my concerns is that a lot of these corporations tend to focus very narrowly on certain projects, which they feel that they depend greatly, they'll invest in OpenStack, they'll invest in Docker, they'll invest in some of the CNCF projects. And then these other projects get ignored. Something that I've been a proponent of for a little bit for a while is observability of your dependencies. And I don't think there's quite enough projects and solutions to this. And it sounds maybe from lists, there are some projects that I don't know about, but I also know that there's some startups like Snyk and so forth that help with a little bit of this problem, but I think we need more focus on some of these edges. And I think companies need to do better, both in providing, having some sort of solution for observability of the dependencies, as well as understanding those dependencies and managing them. I've seen companies for instance, depending on software that they actively don't want to use based on a certain criteria that they already set projects, like they'll set a requirement that any project that they use has a code of conduct, but they'll then use projects that don't have codes of conduct. And if they don't have a code of conduct, then employees are prohibited from working on those projects. So you've locked yourself into a place where you're depending on software that you have instructed, your employees are not allowed to contribute to, for certain legal and other reasons. So you need to draw a line in the sand and then recognize that those projects are ones that you don't want to consume, and then not use them, and have observability around these things. >> That's a great point. I think we have 10 minutes left. I want to just shift to a topic that I think is relevant. And that is as Open Source software, software, people develop software, you see under the hood kind of software, SREs developing very quickly in the CloudScale, but also you've got your classic software developers who were writing code. So you have supply chain, software supply chain challenges. You mentioned developer experience around how to code. You have now automation in place. So you've got the development of all these things that are happening. Like I just want to write software. Some people want to get and do infrastructure as code so DevSecOps is here. So how does that look like going forward? How has the future of Open Source going to make the developers just want to code quickly? And the folks who want to tweak the infrastructure a bit more efficient, any views on that? >> At Gaggle, we're using AWS' CDK, exclusively for our infrastructure as code. And it's a great transition for developers instead of writing Yammel or Jason, or even HCL for their infrastructure code, now they're writing code in the language that they're used to Python or JavaScript, and what that's providing is an easier transition for developers into that Infrastructure as code at Gaggle here, but it's also providing an opportunity to provide reusable constructs that some Devs can build on. So if we've got a very opinionated way to deploy a serverless app in a database and do auto-scaling behind and all stuff, we can present that to a developer as a library, and they can just consume it as it is. Maybe that's as deep as they want to go and they're happy with that. But then they want to go deeper into it, they can either use some of the lower level constructs or create PRs to the platform team to have those constructs changed to fit their needs. So it provides a nice on-ramp developers to use the tools and languages they're used to, and then also go deeper as they need. >> That's awesome. Does that mean they're not full stack developers anymore that they're half stack developers they're taking care of for them? >> I don't know either. >> We'll in. >> No, only kidding. Anyway, any other reactions to this whole? I just want to code, make it easy for me, and some people want to get down and dirty under the hood. >> So I think that for me, Docker was always a key part of this. I don't know when DevSecOps was coined exactly, but I was talking with people about it back in 2012. And when I joined Docker, it was a part of that vision for me, was that Docker was applying these security principles by default for your application. It wasn't, I mean, yes, everybody adopted because of the portability and the acceleration of development, but it was for me, the fact that it was limiting what you could do from a security angle by default, and then giving you these tuna balls that you can control it further. You asked about a project that may not get enough recognition is something called DockerSlim, which is designed to optimize your containers and will make them smaller, but it also constraints the security footprint, and we'll remove capabilities from the container. It will help you build security profiles for app armor and the Red Hat one. SELinux. >> SELinux. >> Yeah, and this is something that I think a lot of developers, it's kind of outside of the realm of things that they're really thinking about. So the more that we can automate those processes and make it easier out of the box for users or for... when I say users, I mean, developers, so that it's straightforward and automatic and also giving them the capability of refining it and tuning it as needed, or simply choosing platforms like serverless offerings, which have these security constraints built in out of the box and sometimes maybe less tuneable, but very strong by default. And I think that's a good place for us to be is where we just enforced these things and make you do things in a secure way. >> Yeah, I'm a huge fan of Kubernetes, but it's not the right hammer for every nail. And there are absolutely tons of applications that are better served by something like Lambda where a lot more of that security surface is taken care of for the developer. And I think we will see better tooling around security profiling and making it easier to shrink wrap your applications that there are plenty of products out there that can help you with this in a cloud native environment. But I think for the smaller developer let's say, or an earlier stage company, yeah, it needs to be so much more straightforward. Really does. >> Really an interesting time, 10 years ago, when I was working at Adobe, we used to requisition all these analysts to tell us how many developers there were for the market. And we thought there was about 20 million developers. If GitHub's to be believed, we think there is now around 80 million developers. So both these groups are probably wrong in their numbers, but the takeaway here for me is that we've got a lot of new developers and a lot of these new developers are really struck by a paradox of choice. And they're typically starting on the front end. And so there's a lot of movement in the stack moved towards the front end. We saw that at re:Invent when Amazon was really pushing Amplify 'cause they're seeing this too. It's interesting because this is where folks start. And so a lot of the obstructions are moving in that direction, but maybe not always necessarily totally appropriate. And so finding the right balance for folks is still a work in progress. Like Lambda is a great example. It lets me focus totally on just business logic. I don't have to think about infrastructure pretty much at all. And if I'm newer to the industry, that makes a lot of sense to me. As use cases expand, all of a sudden, reality intervenes, and it might not be appropriate for everything. And so figuring out what those edges are, is still the challenge, I think. >> All right, thank you very much for coming on the CUBE here panel. AWS Heroes, thanks everyone for coming. I really appreciate it, thank you. >> Thank you. >> Thank you. >> Okay. >> Thanks for having me. >> Okay, that's a wrap here back to the program and the awesome startups. Thanks for watching. (upbeat music)

(bright music) >> Hi, everyone. Welcome to the CUBE's presentation of the AWS Startup Showcase around open cloud innovations. It's the season two episode one of the ongoing series covering exciting startups from the AWS ecosystem and talking about open source and innovation. I'm John Furrier, your host. Today, we're joined by two great guests. Dan Garfield, chief open source officer and co-founder of Codefresh IO, and Raziel Tabib, CEO and co-founder. Two co-founders in the middle of all the innovation. Gentlemen thanks for coming on. >> Thank you. >> So you guys have a great platform and as cloud native goes mainstream in the enterprise and for developers, the big topic is unification, end-to-end, horizontally scalable, leveraging data. All these things around agile that I call agile cloud next level. This is kind of what we're seeing. The CNCF is growing. You've seen KubeCon every year is more about these kinds of things. Words like orchestration, Kubernetes, container, security. All of those complexities are now at the center of making things easier for developers. This is a key value proposition and you guys at Codefresh are offering really the first enterprise delivery solution powered by Argo, which is an open source project. Again, open source driving really big changes. So let's get into it. And first of all, congratulations, and thanks for working on this project. What's so special about- >> Thank you for that. >> Argo the project, and why have you guys decided to build a platform on it, and where is this coming together? Take us through why this is so important. >> I think Argo has been a very fast growing open source project for multiple reasons. A, it has been built for the new way of building and deploying an application. It's cloud native. You mentioned Kubernetes becoming kind of the de facto way of running application. It's the de facto way to run automation and pipeline. But also Argo has been built from the ground up to the latest practices of how we deploy software. We deploy software now differently. We deploy it using a GitOps practice. We're deploying it using canary blue-green progressive deployment. And Argo has been built around these practices, around these technologies, and has been very much widely adopted by the community. In the past, the KubeCon you've mentioned, Argo was all over the place. And we were very glad to be working with the community to talk about what the next steps with Argo. >> Yeah, it's a really good point. I would like to just follow up on that because you see this being talked about. It always comes up, where is open source really outside of a pure contributors matter? And when you have corporations contributing, you seeing this has been the trend. You saw it with Lyft, with Envoy, companies doing more and more open source. This is part of a big collaboration. And again, this comes back down to this whole why it's relevant and why it's so special with Argo. Continue to talk about relationship because it's not just you guys, it's now community. >> Yeah, I can speak to that. The Argo project is something that we maintain in partnership with several other companies and really our relationship with it is that this is something that we're actively contributing to. This is something that we're helping build the roadmap on and planning the events around and all those kinds of things. And we're doing that because we really believe in this technology and we've built our platform on it. So when you deploy Codefresh, you're deploying technology that's built directly on Argo and is designed specifically to solve that problem that you spoke to at the top of the hour. We all want to deliver software faster. We all want to have fewer regressions. We want to have fewer breaking changes. We want software to be super reliable. We want to be comfortable with what we're doing. That's really why we picked Argo because that technology that we have it is to Raziel's point delivered in this new way. It's delivered using GitOps. And that's a whole revolution and change in the way that people build and deploy software. And bringing cohesion into that experience is so critical to building the confidence that lets you actually deploy often and frequently and more. >> Dan, if you don't mind just expanding on that one point about the problem you solve, because to me, this has been kind of that evolution. It's almost like, yeah, there's been problems, plural, and opportunities that you saw with those in growing markets like this with DevOps and DevSecOps and now cloud native. What is the catalyst behind all of this? What was the epiphany behind it? How did it get so much momentum? What was it really doing under the covers? >> Well, it's a very simple and easy to use set of tools. And that's one of the big things is that if you look at the ideas of GitOps and there's actually a foundation around this that were part of called open GitOps to GitOps working group under the CNCF. And those principles of, I want to, yes, do my software defined as code. I want to do my infrastructure defined as code and I need something monitoring by production run times and making sure that the declared desired state is always matching the actual state. Those principles have actually been around for a number of years. And with Kubernetes, we really unlocked an API that allowed us to start doing GitOps and this is why we bring in Argo and you see the rise of Argo CD and other workflows and what we've been doing is really because that technology has been unlocked now. So the ability to define how your software is supposed to run and now your entire software delivery stack should run, all defined and then monitored and then kept in check using the GitOps operator. That critical unlock is what's really driving the massive adoption. And like Raziel said, Argo is the fastest growing and most popular open source project for delivering software. And it's not even close. >> Yeah, this is really great point. And I want to get into that 'cause I want to know why, what you guys do on your platform versus the open source and get that relationship settled? Before we get there, though, I want to get your reaction to some of the commentary in the industry 'cause GitOps trend has been exploding into new directions. I mean, it used to be a term about 10 years ago called big data. And at the beginning where data was all big data. Now it was DevOps revolution around data as well. But now you're hearing people talk about big code. Like, I mean, the code bases are becoming so huge. So as a developer, you're leveraging large open source code. This idea of the software delivery with existing code and new code just adds to more code. There's more code being developed every day. >> There is more code delivered every day. And I think that organization realize today, almost in every industry that they have to pace up how fast and how frequent they update their software delivery. We're living in a world in which every aspect of our life has been disrupted by software and organization realize that they have to keep up and figure out how to deploy software more frequent and more lively. And I think, you mentioned that really Kubernetes, the cloud native became the de facto way of running application. I think most of organization has made that decision to move into cloud native. The second question is after, is okay, now we have all applications running, how fast and how more frequent we can deploy applications to the cloud native? And that's the stage in which we're super excited about Argo and our up platform because that's basically streamline the building application for these cloud native, deploying applications for the cloud native, and so on. >> Yeah, and I think that highlights the business value. You getting a lot of the conversations with businesses that say they want the modern application on the cloud scale. And at the end of the day, it comes down to speed and security. So how fast can I get the app out? How well does it work? Does it run performance? And does it have security? And I don't want a slow. >> Exactly. Exactly. It kind of oversimplifies it, but that's kind of the net net. So when you look at Argo open source, what's that's done and kind of where you guys are taking it. Can you talk about the differences between your enterprise version and the open source version and the interplay there, the relationship, the business model health customers can play on both sides or understand the difference? >> Sure. >> Go ahead. >> Go ahead, Raziel. Okay, so I think Argo, as you mentioned, is probably the most advanced technology today to both run pipelines. They're like events to trigger pipelines and Argo work for the one that pipelines, the Argo CD for GitOps and Rollout, for Canary blue-green strategies. And the adoption is really exploding. Just as an Advocate that we had in December, we have worked with the community and organized ArgoCon events in which we had initially kind of thought about 500 attendees. And so we have more than 4,000 registrants and majority of them are coming from enterprise. Now as we have talked to the community during this conference and figure out, okay, so what are the things that you're still missing? And that will help you take the benefit that you get from Argo to the next level. The few things that came up. One is Argo is a great technology. However, Argo now is fragmented into four projects. There is an advance. There is workflow. There is Argo CD. And there is Argo Rollout. And there is a need to bring them all together into a solid platform, solid one run time that can be easily installed, monitor all of these in a single UI, in a single control plane. That's one aspect. The second is the scalability. Really being able to manage it centrally across multiple clusters, not in one cluster. And what we bring in with the new one, we're so excited about this platform, is we're bringing that big. The first to get all of these four projects in one runtime, and one control plane, but also allow the community to run it across multiple cluster from one place getting into the solution, not just as a technology. >> If I may add to that, the value of bringing these projects together, it provides so many insights. So when you're trying to figure out, there's some breaking change that has been made, but you don't necessarily know where it is because you have a lot of microservices that are out there. You have a lot of teams working on it. By bringing all of these things together, we're able to look at all of the commits, all of the deployments, all of the Jira issues. All of these components combined together, so you really get a single view where you can see everything that's going on. And this is another element where when you're trying to deploy software at scale, you're trying to deliver it faster. People are getting a little bit overwhelmed because there are so many updates and so many different services and so many teams working that they're starting to miss that visibility. So this is what we want to bring into the ecosystem is we really want them that visibility to be super clear. And by bringing all of the Argo components, the Argo tools together, we're able to do that in a single dashboard. >> Yeah, so if I get this right, let me just double click on that because it sounds like, yeah, Argo's great. It's been organically growing, a lot of different components to it, but when you start getting into pushing code in an organization, you have, I call the old-school version control kind of vibe going on where it's like you don't know what's out there and how that affects the system as it's a distributed system, which cloud is. There are consequences when stuff breaks. So we all know that. Is that kind of where you guys are getting at? The challenge is actually the opportunity at the same time where it's all goodness, but then when you start looking at scale and the system impact, is that kind of where the open source and you guys pick up, is that right? >> This is one aspect. I think the second one is that again, when you look at each individual component of Argo, each provide a lot of value by itself. But when you sum it, the value of the sum is greater than the value of the individual. So when you're taking, really the events and workflow, Argo CD and Argo Rollout, and you bring them all together into single runtime. The value of its time is really automation all the way from code to cloud. It's not breaking into, there is like an automation for CI, there's an automation for CD, there's information for progressive delivery. It's actually automated all the way from the Git commit through the GitOps through the deployment strategy, and so on. And being able to monitor it and scale it in the enterprise scale. So, of course, it's helping enterprise and make Argo to some level more crucial for enterprise, if I may say, but second is really bringing all of these components together and get the outcome be greater than the individual parts. >> Yeah, that's a good point. Yeah, make it make a commercial grade, if you will, for enterprise who wants to have support and consistency and whatnot. What other problems are you solving? Dan, can you chime in on the whole, how you guys resolve some of these challenges for the enterprise? Because, again, some stability is key as well, but also the business benefit has got to be there for the development teams. >> Yeah. So there's several. One aspect is that the way that most people operate today is they essentially do a bunch of commands and engage with systems. And then hopefully at the end, they write those things to Git. And this is a little bit backwards if you think about it because there's a situation where you can end up with things in production that were never checked in, or maybe somebody is operating and they're making a change. If we look at most of the downtime that's occurred over the last two years, it's because people have flubbed a key when they were typing in a command or something like that. The way that this system works is that we provide an interface, both the CLI and the GUI, where those operations interactions actually end with a Git commit. So rather than doing an operation and then hopefully committing to Git, most of the operations are actually done first in Git, or if there is something that can't be done first in Git, it's maybe bootstrapped and then committed to Git as part of a single command. So this means you have end-to-end traceability. It also means your auditability is way better. And then the second, the other component that we're adding is that security and scale layer. So we are securing these things, we're building in single sign-on, and all those robust security things you would expect to have across all these instances. So many organizations, when they're building their software delivery tools, they have to deploy instances in many locations. And so this is how you end up with companies that have 5,000 instances that are all out of date and insecure. Well with Codefresh, if you need to deploy a component onto this end cluster or something like that, you may have thousands of them. All of those are monitored and taken care of in a centralized way, so I can do all of my updates at once. I can make sure they're all up to date. I'm not running with a bunch of known CVEs or something like that and it's clear. The components are also designed in an architectural way. So that only the information that is needed is ever passed out. So I can have a cluster that is remotely managed, that checks out code, that the control plane never has access to. So this hybrid model has been really popular with our customers. We have customers in healthcare, we have customers in defense and in financial services, all these regulated industries. The flow of information is really critical. So this hybrid model allows you to deploy something that has the ease of a SaaS solution, but has the security of an on-prem solution while being centrally managed and easy to take care of. >> Yeah, it's a platform. It's what it is. It's not a tool. It's not a tool anymore. It's a platform. >> Exactly. >> I think the foundational aspect of this is critical. And you mentioned automation before. If you're going to go end-to-end automation, you have some stuff in the system that whether it hasn't been checked in yet. I mean, we know what this leads to. Disaster or a lot of troubleshooting and disruption. That's what it seems to solve. Am I getting that right? Is that right? >> Yeah. >> Go ahead. >> Yeah, it helps automate the whole process. But as you say, it's really like identify what needs not to be going all the way to production and really kind of avoid vulnerabilities or any flaws in the software. So it automates everything, but in a way that the automation can identify issues and avoid them from coming into the production. >> Well, great stuff here. I've got to ask you guys now that you've got that settled. It's really, I see the value there, how you guys are letting it grow organically and with Argo and then building that platform for businesses and developers. It's really cool. And I see the foundational value there. It just only gets better. How you guys contributing back to open source and helping the wider GitOps and Argo communities? Because this is, again, the rising tide that's bringing all the boats into the harbor, so to speak. So this is a good trend and people will acknowledge that. So how's this going to work as you guys work back into the open source community? >> So we work closely with both myself and the other maintainers worked closely with the community on the roadmap and making sure that we're addressing issues. I think if you look in the last quarter, we probably have upwards of 40 or 50 different issues that we've solved in terms of fixing a bug or adding features or things like that. So making sure that these tools, which are really the undergirding components of our platform, they have to be really robust. They have to be really strong. And so we're contributing those things back. And then when it comes to the scalability side, these are things that we can build into the platform. So the value should be really clear. I can deploy this, I can manage it myself, I can build tools on top of it. And if I want to start doing it at scale, maybe I want support. That's when I really am going to go to Codefresh and start saying, let's get the enterprise little platform. >> Awesome. GitOps, a lot of people like some naysayers may say, Hey, it's the latest fad. Is it here to stay? We were talking about big code earlier. GitOps, obviously seeing open source. Just every year, just get better and better and growth. I mean, I remember when I was breaking into the business, you have to sell under the table. Now it's all free and open and getting better every year. Just the growth of code. Is GitOps a fad? How do you talk to people who say that? I mean, besides slapping around saying wake up. I mean, how do you guys address that when people say it's just the latest fad? >> So if I may comment here and Dan feel free to chime in, I think that the GitOps is a continuation of a trend that everything is a source code. As a developer, many years ago myself and still writing code, always both code and code was the source of tool that's where we write the code. But now code actually is also describing how our application is running in production. And we've already seen kind of where it's get next. We also hear about infrastructure as a code. So now actually we storing the code the way the infrastructure should be. And I think that the benefit of storing all this configuration in a source code, which has been built to track changes, to be enabled to roll back, that is just going to be here to stay. And I think that's the new way of doing things. >> All right, gentlemen, great. Closing statements. Please share an update on the company. What it's all about? What event you got coming? I know you got a big launch. Can you take us through? Take us home. >> Join on February 1st, we're going to be launching the Codefresh software delivery platform. Raziel and I will be hosting the event. We've got a number of customers, a number of members of the community who are going to be joining us to show off that platform. So you're going to be able to see it in action, see how the features work, and understand the value of it. And you'll see how it works with GitOps. You'll see how it helps you deliver software at scale. That's February 1st. You can get information at codefresh.io. >> Raziel, Dan, thanks for coming on. >> Thank you. >> Pretty good showcase. Thanks for sharing. Congratulations. Great venture. Loved the approach. Love the growth in cloud native and you guys sure on the cutting edge. Fresh code, people love fresh code, codefresh.io. Thanks for coming on. >> Thank you. Thank you. >> Okay, this is the AWS Startup Showcase Open Cloud Innovations. Cloud scale, software, data. That's the future of modern applications being developed, changing the game to the next level. This is the CUBE's coverage season two episode one of the ongoing AWS Startup series here in theCUBE.