Google Cloud Next '24 | Chen Goldberg | Google

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Chen Goldberg

SVP, Engineering

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Fusing Kubernetes and AI: Google brings new AI capabilities to the cloud

As Kubernetes celebrates its 10th anniversary, the future of storage management is looking bright, even as generative artificial intelligence complicates that vision.“The most important thing that we are seeing people do right now, they are experimenting,” said Chen Goldberg (pictured), general manager and vice president of engineering, Kubernetes and serverless at Google LLC. “There are a lot of areas that are experimenting. They are experimenting in frameworks, they are experimenting in use cases. We see a lot of AI startups that are building their own models, and they are solving problems in very creative ways

play_circle_outline Interview with Chen Goldberg, Vice President and General Manager of Kubernetes and serverless at Google

play_circle_outline The Significant Role of Chen's Team in Modern Run Times and Managing Workloads at Scale with Introduction to Cloud Run and GK

play_circle_outline The growth of the cloud-native ecosystem and its impact on Kubernetes

play_circle_outline Addressing Challenges and Maximizing Opportunities in Securely Scaling Containerized Workloads for Enhanced Innovation with AI

play_circle_outline Transforming Business Objectives: Enabling Innovation, Simplifying Integration, and Enhancing Customer Interoperability through Managed Services

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Chen Goldberg | Google

Chen Goldberg

SVP, Engineering CoreWeave

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(bright music)

>> Welcome back everyone. Google Next here, live theCUBE coverage. I'm John Furrier, host of theCUBE with Rob Strechay Savannah Peterson and Rebecca Knight is all here. We're getting all the action. Day two of three days of wall to wall coverage. It's great to see Google Next level. We have a CUBE alumni here, Chen Goldberg, Vice President and General Manager of Kubernetes and serverless at Google. She's working on all the cool stuff that's sitting right above all the CPUs. Chen, great to see you.

>> Thanks for coming on theCUBE.

>> Thank you so much for having me.

>> It's always a pleasure.

>> John: It's been quite a journey. We were talking before we came on camera real quick that Kubernetes is celebrating its 10th anniversary. We are reminiscing looking at old footage from 2017 CUBE interviews. So much has changed, but there's a lot of similarities between Kubernetes and AI. But first, congratulations. Well congrats to all of us.

>> Chen: For all of us, definitely you know, it's an amazing testimony to the community 'cause I think it's not easy to sustain a project for so long. And I really remember those early days thinking about will there be Kubernetes 2.0 and what will migration look like? And it was really like our North Star was to avoid those kind of migration project

>> for our customers and users. So I think the community has done an amazing job.

>> Yeah, a lot of debates, but a lot of solidarity, a lot of great community came together. Now AI's got a similar kind of view in the sense of you can see the future, you get it, it's coming together, it's unfolding right in front of us. And you have a role and you have a team building out on top of Kubernetes, which is everything from containers to server. Take a minute to explain what your current role is now. How do you fitting into this, I won't say new Google, but let's say Google Cloud. I mean you've got a CEO of public sector with board of directors. You have now all this horsepower starting to see the layers of the stack and the workspaces user experience. What's your team do? How do you fit in to the Google Cloud equation?

>> So my team is responsible for what we call modern run times. Meaning when you come bring to the cloud your workloads, no matter if it's a traditional workload, AI workload, any modern workload, we are helping our customers to manage and operate that at scale. It's probably sounds familiar with Kubernetes and we have two main offerings that we do it through. One is Cloud Run, think about it as a container as a service. The easiest way to build applications on Google Cloud. And the other one is when you're looking for something more customizable, flexible, you want to build your own platform on Kubernetes. GK got you covered. And within Google Cloud, that's exactly the role that we are playing. We are helping our customers run digital transformation, innovate, create new experience. And now with AI it's just amazing to see what's possible.

>> Things are coming together. What's the keynote highlights that you see that you got excited about that your team's working on where you see opportunities

>> for a more innovation?

>> So the first thing that I will highlight is the opportunity with AI to improve technology team's velocity. So we made one announcement Gemini Code Assist a few months back and I'm sure we're going to talk about it with a Gabe, but yesterday we also announced Gemini Cloud Assist, which is talking about how do you take Gemini and create Gemini powered insights and recommendation within the console and just help me be more productive, be more effective. So that's something that I'm really excited about. I'm asking my team to become more effective and productive and really leverage AI. And we are doing it in the context of testing and coding. So I think that's one area which is super exciting.

>> It would seem that it's also with Cloud Run and with Kubernetes and getting up and running, a lot of people don't necessarily want to run their own kit right now. And is that where you're seeing the biggest uptick from a Cloud Run perspective?

>> I think that's what's happening is that if five years ago, I think many wanted to build their own platform, but the world is changing. There are more things we want to do, there are more investments we want to make. It's actually harder to hire great talent. You know, and I talk with the different customers, I'll give some examples and you know, those VPs, the CIOs that I talk to they are saying, I want my team to invest where it matters, where we want to innovate with our unique IP. And just give two examples.

>> So we had the founder of LangChain,

>> which is a very successful opensource.

>> John: Harrison Chase. >> Yes.

>> John: Great guy.

>> So they did a benchmarking on what platform will be the best. And they decided we are going to go with Cloud Run. Why? Because it gives us the velocity, it's the best for us. And we were like, yay. But you know what, on the flip side of that, I'll give two other examples. Today I had a session, my spotlight when we were talking about the new things we are working on. And Farhan Thawar, he's a VP at Shopify and we've been working very closely together. And about a year ago I was asking him, hey, what about Cloud Run? And you know, he's like, yeah, you know, we always do our stuff, we build our own. I'm like, what about trying it? And he's amazing leader and his team is amazing and they try it out

>> and now they're like, yeah, we're all in.

>> John: Really? They went from tire kicking, do you a favor?

>> A give me a taste, taste test.

>> It's not for me, right? I think it's for them of where they invest their time and we see enterprises that they just feel that value. So I think that we will see more and more use of managed services. We can see it with Vertex AI. Okay, as well. And I love it because we like to build platforms and build services and I love to see

>> what it enables others. Why is Google Run working so well for folks? What's the reason, simplicity?

>> Chen: Simplicity.

>> Oh my god, you see me smile, right? You know, every time I think that we made it the simplest ever, then the team comes with another way to make it even simpler. So this week we announced a Cloud Run application canvas, which you can use natural language, say what you want. And behind the scene with powered with Gemini, we are creating the architecture. All the APIs enabled, everything you need.

>> You just click deploy and voila.

>> John: Magic.

>> It's not magic, it's technology,

>> but it works and it makes things simple.

>> Well AI gives a feeling of magic. I mean that's what ChatGPT did to the whole world when it said, wow, it's just getting a response to a query. But it was generating a response. That's what people want. They want ease of use coding. How's that going to impact new application developers? Either is it a low-code/no-code you see it much more about creativity. What's that going to do for the developer?

>> Obviously it's going to create some value, time.

>> I think it's - >> John: Headaches.

>> I Have lots of ideas around that topic. But maybe, you know, going back to Kubernetes early days, I think that the most important thing that we are seeing people do right now, they are experimenting. And there are a lot of areas that are experimenting. They are experimenting in frameworks, they are experimenting in use cases. There are experiment like, which model should I use? Okay, we see a lot of options. How do I decide? And I think that in the next, I don't know how long it'll take, probably not much because everything is moving very fast. We are going to see a lot of learnings and experiments, which will then tell us what will be that developer experience, what will people expect. And from an innovation perspective, I will say that we see a lot of AI startups that are building their own models

>> and they are solving problems in very creative ways. The back of my head, I'm thinking about for this to really change the world, we need to make sure that the technology is accessible. And now maybe it'll be the last thing I will tie to Kubernetes. When we started that journey, one of the things that excited me is that through open source, we made this technology accessible for people. So no matter where I'm from, where in the world, what kind of education I can try and scale. And we enabled a lot of experiments and learnings. We didn't try to solve all of the problems at once. You know, slowly.

>> I think that will be key for such a technology.

>> Yeah, I mean I think one of the things, and we were over at, I was over at KubeCon in Paris and one of the really interesting things was that it seemed like it, it's called KubeCon Cloud Native Con. But it looked like the cloud native part was really becoming more front and center. And it was the ecosystem around that. How does that really impact what you're building and how your team's work with all of the different ecosystems that's around Kubernetes in general.

>> I think that before everything happened with AI, our goal was to keep Kubernetes boring. Okay, I think that's one of the critical success factors in order to get to the 10 years anniversary. And we've done that by creating those abstraction and really enabling that ecosystem that you're talking about that I think is crucial for any innovation. What I think is now happening though, specifically with AI and you know, I'm sure you also heard that like in Paris and we'll hear about it in the next one as well is that Kubernetes and containers have some key attributes that are a great fit for what's happening. Containers are great for innovation and moving quickly. We have an obstruction to the infrastructure. So it's a good way to manage GPUs and TPUs. And of course I have orchestration, which helps me to optimize. But together with that, there is some new things that are needed. The scale is different, okay. Like we always take pride in our 15,000 nodes cluster. But then, you know, we have customers like Character as an example that they need a much bigger cluster. Then what, how do we do that? How do we work with them?

>> So I think this will be interesting moving forward.

>> And you mentioned accessibility, you know, obviously you've been dealing with all this and developed all the time. You got performance availability, scalability, maintainability, those are all the usual conversations. But accessibility is good, but it also brings up two other factors that are part of the AI, I call it the glue layer that's the merging really really fast. That's security and compliance. So governance has become a big topic because if you get the governance right from day one, a lot of the data can be scaling and be fast, and be into AI much better, more safety, more security. So talk about the security compliance that comes along with accessibility. How do you see that unfolding? Or is that too early for the conversation or?

>> I think it's a great point. That's by the way definitely an area that we invest a lot in at Google, at every layer, okay. So my team of course is focusing more on the containers and the runtime from that perspective and the data team. But of course we care about data in transit. So there's a lot of factors there. Who has access? So that's one piece. Another piece, especially when we talk about sovereignty for example, a lot of people are worried like what of access we will have as cloud provider for things running on our cloud. So that's another angle that we're working on,

>> but that we continue with your data is your data.

>> And container security I mean the big concern that's been always there is supply chain. SBOMs have been a solution. Any update there? I mean I'm just curious 'cause since you brought it up,

>> I might as well ask.

>> So we are working on that for sure. And just trying to think about couple of things from a container security, a lot of things are staying the same, but what is becoming interesting that with MLOps

>> or tool chain is changing. Okay, so working through that for sure.

>> So those are the kind of things

>> that we are investing in right now.

>> And it seems like as the ecosystem grows and there are new personas coming in, like the persona of a platform engineer and we were kind of talking about that earlier and how do you look at addressing that the skills are changing and platform engineers sometimes have a much broader view and they're more than just, you know, Kubernetes. How do you see that as you build out the services

>> like Cloud Run and GKE?

>> So first of all, I think this is a super exciting time for a platform engineering team because I think, you know, at the heart of it, if you're a platform engineer, you want to enable innovation and innovation is happening. We see all of our customers are thinking how can they take AI? How can they empower their developers to build game changing experiences? So I think that's very exciting for everyone in the field. Our goal also with GKE, by the way, is to make things as simple as possible. So if there is a problem or a challenge that we've already solved for you and we can automate, we'll do it. You know, I'll give you a different example. Array framework, which is being used by many of AI developers for their workloads. It's actually pretty complicated to have it available on a GKE cluster, on a Kubernetes cluster.

>> Now for us, it's a checkbox. Okay, we automated that. Why would you invest the time if it's a problem that we can solve for you. You want to enable TPUs, it's a checkbox. Other problems that we are solving specifically for AI workloads, which I think is, again, interesting. One of the challenges, especially in inference, is that the container images are much bigger. Sometimes they have the model and if you want to scale out quickly, you need to take into consideration the cold start time. So what do you do if you don't have a solution, then you overprovision, but GPUs are very expensive. You don't want to overprovision. So we are creating a lot of new mechanism in the platform to do that. And I think what, for example, we're doing the image preloading, which Vertex AI, which is also running on GKE, we like to drink our own champagne, have seen 29 times improvement because of that. So we are making an effort to solve everything we know at scale. And I think from a platform team, my role hasn't changed. My role is to enable tens and hundreds and thousands of engineers to build new AI applications. And I shouldn't forget about all the other workloads that are running. Okay, so my job maybe from that perspective is becoming more complicated.

>> Now tell me about your team. How big is it? How are you guys organized? Do you organize by technology? Do you organize by groups?

>> It might be confidential information but.

>> We don't talk about size.

>> It's a large team.

>> John: That's okay.

>> But you got a lot going on. You got the container, it's a big part of the tool chain. You mentioned MLOps. This is going to be the hottest area on the planet because you're going to have models coming in.

>> So the way we are usually structured is that every team thinks about what's their role in Google Cloud. And I think you asked that in the beginning. Our expertise is in that runtime piece and orchestration and integration to other systems. So we are not building new MLOPs but we will be enabling, creating new tool chains as an example. And I believe that we are doing a good job enabling other platform teams for AI workloads because just last year we've seen growth of 900% in GPUs and TPUs on GKE. 900%, one year.

>> So I guess we're helping that innovation.

>> And your North Star goal, like you at Kubernetes to make things simpler or what's your overall goal that you're trying to achieve?

>> Your vision is it to have the most cohesive integration layer?

>> Fastest runtime?

>> In essence, I believe that I would love to see customers use more managed services as much as possible because you don't need to reinvent the wheel. And I want to make sure that if I need more complexity, if I need more flexibility, we are not creating barriers for that customer. So really allowing innovation to move on. So we are really investing in that, what we call interoperability. So we started, you know, talking about GKE and Cloud Run, but now we are also talking about GKE and Vertex. You know, maybe I will start my work with Vertex and maybe there will be one use case that I will need for inference and I will be really specific about what kind of utilization I want to get. You know what, maybe GKE is great, can I use them together? Can I use my Model Garden with it? Yes I can. Can I use Colab Enterprise notebooks? Yes I can.

>> So thinking about that, I think from my perspective is how we enable innovation.

>> I mean, distributed computing is the paradigm. I mean, making things work, it's going to be a big deal. Final question from me and Rob might have a couple more questions. What's your business plan for the year? Obviously we just came back from KubeCon EU in Paris, we got North America coming up in the fall. In your job, what is your business objectives, goals for the year? Can you share your plans and what you're trying to do and what you hope to accomplish this year?

>> So I think the good thing that our mission or our vision hasn't changed, meaning for the past few years and you know, I can also maybe go back or beginning of Kubernetes. It is about enabling innovation and maybe there are different challenges and maybe in different workloads, but in essence it's about making things easy, okay? Comprehensive. Okay, you don't want to stitch things for no reason. And that's something we take a lot of pride in GCP. Can we integrate the entire stack? Okay, I don't want to build like point solutions. It has to work together and it has to be reliable. We are a cloud provider, you should trust us with the most important workloads. So across that we continue on that mission, you should expect us to continue in investing security. It's a high priority for us. The second thing is supporting AI workloads. And the last thing is, how can I scale it within the organization? I would love to see more enterprise customers, more traditional workloads benefiting

>> from all those new technologies.

>> And the commercial opportunities are great. Even the public sector.

>> We had the new CEO Karen Dahut on earlier. She's going to be, I mean, all this AI's going to,

>> I mean think about the government, Rob.

>> All those procurements, paperwork

>> and inadequate processes.

>> Well, I was going to say, I mean, and part of what you have to be concerned with is that Google's not just running in Google Cloud now. You have the distributed cloud and you have sovereign clouds and you've got all these certifications now with the government, with the agencies and stuff. Do you see that as just another set of requirements that are coming in

>> and how do you keep that for being simple as well, I guess.

>> So first of all, we have a special team that is focused on that and their role is doing exactly that. So how can we take all that power, all that amazing things that are coming from GCP, but make them accessible with those set of constraints.

>> Rob: That makes total sense. My last question would be what do you want us be able to be on stage with us next year to be able to say? What do you hope to be able to say next year that you can't say today?

>> I would love to see more and more customers running AI workloads at scale. Okay, we see a lot of the startups now innovating, but I expect this will change how we live our lives. So I think that will be amazing for sure. And from a technology perspective, I think it'll be interesting to think about what is that AI platform will look like. But you know, the painful truth,

>> we're not excellent at predicting the future.

>> Well you've done an amazing job. We love you on theCUBE. And again, 10 years ago Kubernetes started and you know, the CNCF wasn't around then, but then when they picked up the project a few years later, you were a big part of that success and the community's glad to have you, we're glad to have you on theCUBE as a CUBE alumni. You're like a contributor, you're like an analyst for us.

>> You're running all the good stuff at Google.

>> Yeah, the key part there, from my perspective was focus and optimizing for learning. And really working with customers. I mean that was from my perspective so something actually pretty new that we've done in an open source project, both creating focus and theme, like think about like a product in a sense. And that's my philosophy for innovation.

>> Well, and I think you're on the right track. I've been saying on theCUBE I've been like a broken record. Generative AI is just distributed computing with the notion of runtime assembling things. And this has like been around computer science principles. So it's a kind of another shot in the arm for computer science, engineering, and with low-code/no-code tools the creativity barrier can be right in the front line. So you don't need to be a super coder, you just got to have computer science systems thinking and you can make things happen. So I think the revolution's legit. It's very hype bubbly right now, but it's a good bubble. It's not a bad bubble, but it's a good bubble.

>> Chen: It's fun.

>> It's fun. It's a lot of fun.

>> Hey, thank you so much for your time.

>> Thank you so much for having me.

>> John: Chen Goldberg here on theCUBE, Vice President, General Manager at Google. Covering serverless containers at Kubernetes here. I'm John Furrier with Rob Strechay, Savannah Peterson and Rebecca Knight, dream team is here. Team coverage here at Google Next. We'll be right back after this short break. (bright music)

Chen Goldberg | Google

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(bright music)

>> Welcome back everyone. Google Next here, live theCUBE coverage. I'm John Furrier, host of theCUBE with Rob Strechay Savannah Peterson and Rebecca Knight is all here. We're getting all the action. Day two of three days of wall to wall coverage. It's great to see Google Next level. We have a CUBE alumni here, Chen Goldberg, Vice President and General Manager of Kubernetes and serverless at Google. She's working on all the cool stuff that's sitting right above all the CPUs. Chen, great to see you.

>> Thanks for coming on theCUBE.

>> Thank you so much for having me.

>> It's always a pleasure.

>> John: It's been quite a journey. We were talking before we came on camera real quick that Kubernetes is celebrating its 10th anniversary. We are reminiscing looking at old footage from 2017 CUBE interviews. So much has changed, but there's a lot of similarities between Kubernetes and AI. But first, congratulations. Well congrats to all of us.

>> Chen: For all of us, definitely you know, it's an amazing testimony to the community 'cause I think it's not easy to sustain a project for so long. And I really remember those early days thinking about will there be Kubernetes 2.0 and what will migration look like? And it was really like our North Star was to avoid those kind of migration project

>> for our customers and users. So I think the community has done an amazing job.

>> Yeah, a lot of debates, but a lot of solidarity, a lot of great community came together. Now AI's got a similar kind of view in the sense of you can see the future, you get it, it's coming together, it's unfolding right in front of us. And you have a role and you have a team building out on top of Kubernetes, which is everything from containers to server. Take a minute to explain what your current role is now. How do you fitting into this, I won't say new Google, but let's say Google Cloud. I mean you've got a CEO of public sector with board of directors. You have now all this horsepower starting to see the layers of the stack and the workspaces user experience. What's your team do? How do you fit in to the Google Cloud equation?

>> So my team is responsible for what we call modern run times. Meaning when you come bring to the cloud your workloads, no matter if it's a traditional workload, AI workload, any modern workload, we are helping our customers to manage and operate that at scale. It's probably sounds familiar with Kubernetes and we have two main offerings that we do it through. One is Cloud Run, think about it as a container as a service. The easiest way to build applications on Google Cloud. And the other one is when you're looking for something more customizable, flexible, you want to build your own platform on Kubernetes. GK got you covered. And within Google Cloud, that's exactly the role that we are playing. We are helping our customers run digital transformation, innovate, create new experience. And now with AI it's just amazing to see what's possible.

>> Things are coming together. What's the keynote highlights that you see that you got excited about that your team's working on where you see opportunities

>> for a more innovation?

>> So the first thing that I will highlight is the opportunity with AI to improve technology team's velocity. So we made one announcement Gemini Code Assist a few months back and I'm sure we're going to talk about it with a Gabe, but yesterday we also announced Gemini Cloud Assist, which is talking about how do you take Gemini and create Gemini powered insights and recommendation within the console and just help me be more productive, be more effective. So that's something that I'm really excited about. I'm asking my team to become more effective and productive and really leverage AI. And we are doing it in the context of testing and coding. So I think that's one area which is super exciting.

>> It would seem that it's also with Cloud Run and with Kubernetes and getting up and running, a lot of people don't necessarily want to run their own kit right now. And is that where you're seeing the biggest uptick from a Cloud Run perspective?

>> I think that's what's happening is that if five years ago, I think many wanted to build their own platform, but the world is changing. There are more things we want to do, there are more investments we want to make. It's actually harder to hire great talent. You know, and I talk with the different customers, I'll give some examples and you know, those VPs, the CIOs that I talk to they are saying, I want my team to invest where it matters, where we want to innovate with our unique IP. And just give two examples.

>> So we had the founder of LangChain,

>> which is a very successful opensource.

>> John: Harrison Chase. >> Yes.

>> John: Great guy.

>> So they did a benchmarking on what platform will be the best. And they decided we are going to go with Cloud Run. Why? Because it gives us the velocity, it's the best for us. And we were like, yay. But you know what, on the flip side of that, I'll give two other examples. Today I had a session, my spotlight when we were talking about the new things we are working on. And Farhan Thawar, he's a VP at Shopify and we've been working very closely together. And about a year ago I was asking him, hey, what about Cloud Run? And you know, he's like, yeah, you know, we always do our stuff, we build our own. I'm like, what about trying it? And he's amazing leader and his team is amazing and they try it out

>> and now they're like, yeah, we're all in.

>> John: Really? They went from tire kicking, do you a favor?

>> A give me a taste, taste test.

>> It's not for me, right? I think it's for them of where they invest their time and we see enterprises that they just feel that value. So I think that we will see more and more use of managed services. We can see it with Vertex AI. Okay, as well. And I love it because we like to build platforms and build services and I love to see

>> what it enables others. Why is Google Run working so well for folks? What's the reason, simplicity?

>> Chen: Simplicity.

>> Oh my god, you see me smile, right? You know, every time I think that we made it the simplest ever, then the team comes with another way to make it even simpler. So this week we announced a Cloud Run application canvas, which you can use natural language, say what you want. And behind the scene with powered with Gemini, we are creating the architecture. All the APIs enabled, everything you need.

>> You just click deploy and voila.

>> John: Magic.

>> It's not magic, it's technology,

>> but it works and it makes things simple.

>> Well AI gives a feeling of magic. I mean that's what ChatGPT did to the whole world when it said, wow, it's just getting a response to a query. But it was generating a response. That's what people want. They want ease of use coding. How's that going to impact new application developers? Either is it a low-code/no-code you see it much more about creativity. What's that going to do for the developer?

>> Obviously it's going to create some value, time.

>> I think it's - >> John: Headaches.

>> I Have lots of ideas around that topic. But maybe, you know, going back to Kubernetes early days, I think that the most important thing that we are seeing people do right now, they are experimenting. And there are a lot of areas that are experimenting. They are experimenting in frameworks, they are experimenting in use cases. There are experiment like, which model should I use? Okay, we see a lot of options. How do I decide? And I think that in the next, I don't know how long it'll take, probably not much because everything is moving very fast. We are going to see a lot of learnings and experiments, which will then tell us what will be that developer experience, what will people expect. And from an innovation perspective, I will say that we see a lot of AI startups that are building their own models

>> and they are solving problems in very creative ways. The back of my head, I'm thinking about for this to really change the world, we need to make sure that the technology is accessible. And now maybe it'll be the last thing I will tie to Kubernetes. When we started that journey, one of the things that excited me is that through open source, we made this technology accessible for people. So no matter where I'm from, where in the world, what kind of education I can try and scale. And we enabled a lot of experiments and learnings. We didn't try to solve all of the problems at once. You know, slowly.

>> I think that will be key for such a technology.

>> Yeah, I mean I think one of the things, and we were over at, I was over at KubeCon in Paris and one of the really interesting things was that it seemed like it, it's called KubeCon Cloud Native Con. But it looked like the cloud native part was really becoming more front and center. And it was the ecosystem around that. How does that really impact what you're building and how your team's work with all of the different ecosystems that's around Kubernetes in general.

>> I think that before everything happened with AI, our goal was to keep Kubernetes boring. Okay, I think that's one of the critical success factors in order to get to the 10 years anniversary. And we've done that by creating those abstraction and really enabling that ecosystem that you're talking about that I think is crucial for any innovation. What I think is now happening though, specifically with AI and you know, I'm sure you also heard that like in Paris and we'll hear about it in the next one as well is that Kubernetes and containers have some key attributes that are a great fit for what's happening. Containers are great for innovation and moving quickly. We have an obstruction to the infrastructure. So it's a good way to manage GPUs and TPUs. And of course I have orchestration, which helps me to optimize. But together with that, there is some new things that are needed. The scale is different, okay. Like we always take pride in our 15,000 nodes cluster. But then, you know, we have customers like Character as an example that they need a much bigger cluster. Then what, how do we do that? How do we work with them?

>> So I think this will be interesting moving forward.

>> And you mentioned accessibility, you know, obviously you've been dealing with all this and developed all the time. You got performance availability, scalability, maintainability, those are all the usual conversations. But accessibility is good, but it also brings up two other factors that are part of the AI, I call it the glue layer that's the merging really really fast. That's security and compliance. So governance has become a big topic because if you get the governance right from day one, a lot of the data can be scaling and be fast, and be into AI much better, more safety, more security. So talk about the security compliance that comes along with accessibility. How do you see that unfolding? Or is that too early for the conversation or?

>> I think it's a great point. That's by the way definitely an area that we invest a lot in at Google, at every layer, okay. So my team of course is focusing more on the containers and the runtime from that perspective and the data team. But of course we care about data in transit. So there's a lot of factors there. Who has access? So that's one piece. Another piece, especially when we talk about sovereignty for example, a lot of people are worried like what of access we will have as cloud provider for things running on our cloud. So that's another angle that we're working on,

>> but that we continue with your data is your data.

>> And container security I mean the big concern that's been always there is supply chain. SBOMs have been a solution. Any update there? I mean I'm just curious 'cause since you brought it up,

>> I might as well ask.

>> So we are working on that for sure. And just trying to think about couple of things from a container security, a lot of things are staying the same, but what is becoming interesting that with MLOps

>> or tool chain is changing. Okay, so working through that for sure.

>> So those are the kind of things

>> that we are investing in right now.

>> And it seems like as the ecosystem grows and there are new personas coming in, like the persona of a platform engineer and we were kind of talking about that earlier and how do you look at addressing that the skills are changing and platform engineers sometimes have a much broader view and they're more than just, you know, Kubernetes. How do you see that as you build out the services

>> like Cloud Run and GKE?

>> So first of all, I think this is a super exciting time for a platform engineering team because I think, you know, at the heart of it, if you're a platform engineer, you want to enable innovation and innovation is happening. We see all of our customers are thinking how can they take AI? How can they empower their developers to build game changing experiences? So I think that's very exciting for everyone in the field. Our goal also with GKE, by the way, is to make things as simple as possible. So if there is a problem or a challenge that we've already solved for you and we can automate, we'll do it. You know, I'll give you a different example. Array framework, which is being used by many of AI developers for their workloads. It's actually pretty complicated to have it available on a GKE cluster, on a Kubernetes cluster.

>> Now for us, it's a checkbox. Okay, we automated that. Why would you invest the time if it's a problem that we can solve for you. You want to enable TPUs, it's a checkbox. Other problems that we are solving specifically for AI workloads, which I think is, again, interesting. One of the challenges, especially in inference, is that the container images are much bigger. Sometimes they have the model and if you want to scale out quickly, you need to take into consideration the cold start time. So what do you do if you don't have a solution, then you overprovision, but GPUs are very expensive. You don't want to overprovision. So we are creating a lot of new mechanism in the platform to do that. And I think what, for example, we're doing the image preloading, which Vertex AI, which is also running on GKE, we like to drink our own champagne, have seen 29 times improvement because of that. So we are making an effort to solve everything we know at scale. And I think from a platform team, my role hasn't changed. My role is to enable tens and hundreds and thousands of engineers to build new AI applications. And I shouldn't forget about all the other workloads that are running. Okay, so my job maybe from that perspective is becoming more complicated.

>> Now tell me about your team. How big is it? How are you guys organized? Do you organize by technology? Do you organize by groups?

>> It might be confidential information but.

>> We don't talk about size.

>> It's a large team.

>> John: That's okay.

>> But you got a lot going on. You got the container, it's a big part of the tool chain. You mentioned MLOps. This is going to be the hottest area on the planet because you're going to have models coming in.

>> So the way we are usually structured is that every team thinks about what's their role in Google Cloud. And I think you asked that in the beginning. Our expertise is in that runtime piece and orchestration and integration to other systems. So we are not building new MLOPs but we will be enabling, creating new tool chains as an example. And I believe that we are doing a good job enabling other platform teams for AI workloads because just last year we've seen growth of 900% in GPUs and TPUs on GKE. 900%, one year.

>> So I guess we're helping that innovation.

>> And your North Star goal, like you at Kubernetes to make things simpler or what's your overall goal that you're trying to achieve?

>> Your vision is it to have the most cohesive integration layer?

>> Fastest runtime?

>> In essence, I believe that I would love to see customers use more managed services as much as possible because you don't need to reinvent the wheel. And I want to make sure that if I need more complexity, if I need more flexibility, we are not creating barriers for that customer. So really allowing innovation to move on. So we are really investing in that, what we call interoperability. So we started, you know, talking about GKE and Cloud Run, but now we are also talking about GKE and Vertex. You know, maybe I will start my work with Vertex and maybe there will be one use case that I will need for inference and I will be really specific about what kind of utilization I want to get. You know what, maybe GKE is great, can I use them together? Can I use my Model Garden with it? Yes I can. Can I use Colab Enterprise notebooks? Yes I can.

>> So thinking about that, I think from my perspective is how we enable innovation.

>> I mean, distributed computing is the paradigm. I mean, making things work, it's going to be a big deal. Final question from me and Rob might have a couple more questions. What's your business plan for the year? Obviously we just came back from KubeCon EU in Paris, we got North America coming up in the fall. In your job, what is your business objectives, goals for the year? Can you share your plans and what you're trying to do and what you hope to accomplish this year?

>> So I think the good thing that our mission or our vision hasn't changed, meaning for the past few years and you know, I can also maybe go back or beginning of Kubernetes. It is about enabling innovation and maybe there are different challenges and maybe in different workloads, but in essence it's about making things easy, okay? Comprehensive. Okay, you don't want to stitch things for no reason. And that's something we take a lot of pride in GCP. Can we integrate the entire stack? Okay, I don't want to build like point solutions. It has to work together and it has to be reliable. We are a cloud provider, you should trust us with the most important workloads. So across that we continue on that mission, you should expect us to continue in investing security. It's a high priority for us. The second thing is supporting AI workloads. And the last thing is, how can I scale it within the organization? I would love to see more enterprise customers, more traditional workloads benefiting

>> from all those new technologies.

>> And the commercial opportunities are great. Even the public sector.

>> We had the new CEO Karen Dahut on earlier. She's going to be, I mean, all this AI's going to,

>> I mean think about the government, Rob.

>> All those procurements, paperwork

>> and inadequate processes.

>> Well, I was going to say, I mean, and part of what you have to be concerned with is that Google's not just running in Google Cloud now. You have the distributed cloud and you have sovereign clouds and you've got all these certifications now with the government, with the agencies and stuff. Do you see that as just another set of requirements that are coming in

>> and how do you keep that for being simple as well, I guess.

>> So first of all, we have a special team that is focused on that and their role is doing exactly that. So how can we take all that power, all that amazing things that are coming from GCP, but make them accessible with those set of constraints.

>> Rob: That makes total sense. My last question would be what do you want us be able to be on stage with us next year to be able to say? What do you hope to be able to say next year that you can't say today?

>> I would love to see more and more customers running AI workloads at scale. Okay, we see a lot of the startups now innovating, but I expect this will change how we live our lives. So I think that will be amazing for sure. And from a technology perspective, I think it'll be interesting to think about what is that AI platform will look like. But you know, the painful truth,

>> we're not excellent at predicting the future.

>> Well you've done an amazing job. We love you on theCUBE. And again, 10 years ago Kubernetes started and you know, the CNCF wasn't around then, but then when they picked up the project a few years later, you were a big part of that success and the community's glad to have you, we're glad to have you on theCUBE as a CUBE alumni. You're like a contributor, you're like an analyst for us.

>> You're running all the good stuff at Google.

>> Yeah, the key part there, from my perspective was focus and optimizing for learning. And really working with customers. I mean that was from my perspective so something actually pretty new that we've done in an open source project, both creating focus and theme, like think about like a product in a sense. And that's my philosophy for innovation.

>> Well, and I think you're on the right track. I've been saying on theCUBE I've been like a broken record. Generative AI is just distributed computing with the notion of runtime assembling things. And this has like been around computer science principles. So it's a kind of another shot in the arm for computer science, engineering, and with low-code/no-code tools the creativity barrier can be right in the front line. So you don't need to be a super coder, you just got to have computer science systems thinking and you can make things happen. So I think the revolution's legit. It's very hype bubbly right now, but it's a good bubble. It's not a bad bubble, but it's a good bubble.

>> Chen: It's fun.

>> It's fun. It's a lot of fun.

>> Hey, thank you so much for your time.

>> Thank you so much for having me.

>> John: Chen Goldberg here on theCUBE, Vice President, General Manager at Google. Covering serverless containers at Kubernetes here. I'm John Furrier with Rob Strechay, Savannah Peterson and Rebecca Knight, dream team is here. Team coverage here at Google Next. We'll be right back after this short break. (bright music)