Ronen Dar on GPU Orchestration for Building ML Models

Greetings, listeners. Welcome back to the Data Driven Podcast. I'm Bailey, your AI host with the most data, that is, bringing you insights from the ether with my signature wit. In today's episode, we're diving deep into the heart of artificial intelligence's engine room, GPU orchestration. It's the unsung hero of AI research, optimizing the raw power needed to fuel

today's most advanced machine learning models. And who better to guide us through this labyrinth of computational complexity than Ronan Darr, the cofounder and CTO of Run AI, the company that's making GPU resources work smarter, not harder. Now onto the show. Hello, and welcome to Data Driven, the podcast where we For the emergent fields of artificial intelligence, data engineering, and overall data science and analytics. With me as always is my favoritest

Data engineer in the world, Andy Leonard. How's it going, Andy? It's going well, Frank. How are you? I'm doing great. I'm doing great. It's been, we're We're recording this February 1, 2024. And as I said to my kids yesterday, January has been a long year. We're only, like, 1 month into the year, and it was it was a pretty wild ride. But I can tell we're gonna have a blast today, because we're gonna geek out on something that I kinda sort of understand,

but not entirely, and it's GPUs. And in the virtual green room, were chit chatting with some folks, and, but let me do the formal introduction here. Today with us, we have doctor Ronadhar, cofounder and CTO of Run AI, A company at the forefront of GPU orchestration, and he has a distinguished career in technology. His experience includes significant roles at Apple. Yes, That apple. Bell Labs. Yes. That Bell Labs.

And at Run AI, Ronan is instrumental in optimizing GPU usage For AI model training and deployment, leveraging his deep passion for both academia and startups. And, Run AI is a key player in the, and he is a he and Run AI are key player in the AI revolution. Ronan's contribute Contributions are pivotable in shaping and powering the future of artificial intelligence. Now I will add that in my day job at Red Hat, Run AI has come up a couple of times. So this is definitely, definitely

an honor to have you on on on the show, sir. Welcome. Thank you, Frank. Thank you for inviting me. Hey, Andy. Good to be here. I love it. Love Reddit. We're a big fan of Reddit. We're working closely with many people in Reddit, and love that. Right? Love OpenShift, love Reddit, love Linux. Yeah. Cool. Cool. Yeah. So so for those who don't know exactly, I kinda know what, your Run AI does, but can you explain exactly What it is run AI does and why GPU

orchestration is important. Yes. Okay. So run AI is, software, AI infrastructure platform. So we help machine learning teams to get much more out of their GPUs, And we provide those teams with abstraction layers and tools so they can train models And deploy models much easier, much faster. And so We started in 2018, 6 years ago. It's me and my cofounder, Omuri. Omuri is the CEO. He's, he's amazing. I love him. We We know each other for many

years. We we met in the academia, like, more than 10 years ago, and and we started running AI together, and We started running AI because we saw that there are big challenges around, GPU's, around orchestrating GPU's and utilizing GPU's. We saw back then in 2018, the GPUs are going to be very very important. It's like the basic a a component in that any AI company need to train models,

right, and deploy models. So we saw that GPUs are going to be critical, but there are also a lot of challenges with, with utilizing GPUs. I think back then, GPUs were relatively new In the data center, in in the cloud. GPU's were very known in the gaming

industry. Right? We spoke before on gaming. Right? Like, a lot of key things there that GPU's has has has been enabled enabling, But in the data center, they were relatively new and the entire software stack that is that is running the Cloud in data center As was built for traditional microservices applications that are running on commodity CPUs And AI workloads are different, they are much more compute intensive, they they run on on GPUs, maybe on multiple nodes of Meet to point

machines of GPU's, and GPU's are also very different. Right? They are expensive, very scarce in the data center. So The entire software stack was a bit for something else and when it comes to GPUs, it was really hard for many people to to actually manage those GPUs. So we came in And, and we saw those gaps. We've built run AI on top of cloud native technologies like Kubernetes and containers. We're big fans of Of those, technologies, and we added components around scheduling, around

the GPU fractioning. So we enable multiple workloads to run on a on a single GPU and essentially all the provision GPU's. So we build this Engine which we call cluster engine that runs in in in GPU clusters. Right? We help machine learning teach to pull all of their GPU's into 1 cluster, Running that engine, and that engine provides a lot of

performance and lot of capabilities from those GPUs. And on top of that, we built this control plane And and tools and for machine learning, teams to run the Jupyter Notebooks, to run training jobs, batch jobs to deploy their models, right, to just to to have tools for the entire life cycle of AI from Training models in the lab to taking those models into production and running them and serving actual users.

And That's the platform that we've built, and we're working with machine learning teams across the globe and on just managing, orchestrating, and letting them Get much more out of their GPUs and essentially run faster, train more than faster and in much easier way and deploy those modules In a much easier and faster and more efficient way. Yeah. The thing that blew me away when I first heard of Run

AI, and this would have been, 2021 ish. No. 20 early 2021, I would say, And, it was the idea of fractional GPU's. Right? So you can have 1, I say 1, but, know, it's realistically, it's gonna be on, but you you can kind of share it out, which I think and we were talking in the virtual green room about how, you know, some of these GPU's, If you can get them because there's a multi month, sometimes multi

year supply chain issue. I mean, these things are expensive bits of hardware, and I think the real value, correct me if I'm wrong, is, like, well, you know, if you I was talking to somebody the other day, and and we're basically talking about how we can, you know, if you get if you get, like, 1 laptop with a killer GPU, right, that GPU is really only useful to that 1 user, Whereas if you can kind of put it in a in a in a

server and use something like RunAI, now everybody in the organization can do that. And these are not trivial expenses. I mean, these are like, You know, you sell a kidney type of costs here. Yeah. Absolutely. So Absolutely. First of all, GPUs are expensive. They cost a lot. Right? And we provide, Technologies like fractional GPUs and other technologies around scheduling that allows teams to share GPUs. Right. So we used book on

GPU fractioning. So that's 1 one day of sharing where you have 1 GPU, which is really expensive. And Not all of the workloads are AI workloads are really compute intensive and require the entire GPU or, you know, maybe multiple GPUs. There are workloads like Jupyter Notebooks where you have researchers that just Debugging their code or cleaning their data or doing some simple stuff, and they need just fractions of GPUs.

In that case, if you have, a lot of data scientists, maybe you wanna host all of their notebooks On a much smaller number of GPUs because, right, each one of them, it's just fractions of GPUs. Another big use case for fractions Of GPUs is inference. So now all of the models are huge and And doesn't fit into, the memory of 1 GPU, and in computer vision, there are a lot of Models that are relatively small, they run on GPU, and you can essentially host multiple of

them on the same GPU. Right. So you can have instead of just 1 computer vision model running on GPU, host 10 of those models on the same GPU and get Factors of 10 x in, in your cost, in your, overall throughput of, of inference. So that's That's one use case for fractional GPU, and we're investing heavily just building that technology. Another layer of sharing GPUs Comes where you have maybe in your organization multiple teams

or multiple projects running in parallel. So for example, may open AI, they now are working on gpt5. It's 1 project. That project needs a lot of GPUs And they have more projects. Right? More research project around alignment or around, reinforcement learning. You know? DALL E. Like, they they they have more than just 1 project. Then DALL E and they have multiple models. Right? Exactly. They have. Right? So each project needs Needs GPUs. Right? Needs a lot of

GPUs. So if you can instead of allocating GPUs Entirely for each project, you could essentially pull all of those GPU's and share them between the those different projects, different teams, And in times where 1 project is idle and not using their GPUs, other projects, other teams can share can get access to those GPUs. Now orchestrating all of that, orchestrating that sharing of resources between projects, between teams can be really complex And requires this advanced scheduling, which

which we're bringing into the game. We're bringing those scheduling capabilities from the high performance computing world known on those schedulers. And so we're bringing Capabilities from that world into the cloud native Kubernetes world. Scheduling around batch batch scheduling fairness, Algorithms, things like that, so teams and projects can just share GPUs in a simple and efficient way. So those are the 2 layers of sharing GPU's. Interesting. And and

I think that I think as As this field matures and it matures in the enterprise, I think you're gonna see organizations kind of be more, more more more I think savvy about, like, okay, like you said, like, data scientists, if they're just doing, like, you know, Traditional statistical modeling really doesn't benefit from GPUs, or they're just doing data cleansing, data engineering.

Right? They're probably gonna say, like, well, Let's run it on this cluster, and then we'll break it apart into discrete parts where, you know, then we will need a GPU. And I also like the idea that, you know, you're you're basically doing What what I learned in college, which was time slicing. Right? Sounds like this is kind of, like, everything old is

new again. Right? I mean, this is, Obviously, you know, when you're when you're taking kind of that old mainframe concept and applying it to something like Kubernetes, orchestration is gonna be a big deal, because these are not systems that were Not built from the ground up to have time slicing. Is that a is that a good kind of explanation? Yeah. Absolutely. Absolutely. I like I like that analogy. Yeah. Exactly. Time slicing it's, it's 1 so

1 implementation, Yeah. And that we enable around fractionalizing GPU's, and I agree when you have resources, It can be different kind of resources. Right? It can be CPU resources and networking were also, You know, as people created that technology to share the networking and communication going through those networking, but just the bandwidth of the networking. We're doing it for GPU's. Right. Sharing those

resources. And I think now it interestingly, LLMs I also becoming a kind of, resources as well, right, that people need access to. Right? You have those models, you have GPT, JGPT. A lot of people are trying to get access to that resource, essentially. And I think it's interesting, because you kinda pointed this out, but it it it's something that I think that if you're in the gen AI space, you kinda don't it's so it's obvious

like error. You don't think about it. Right? But when when you get inference on traditional, I somebody once referred to it as legacy AI. Right. But where the infrared side of the equation, you don't really need a lot of compute power. Right? Like, it's not really a heavy lift. Right? But with generative AI, you do need a lot of compute on I I guess it's not really inference, but on the other side of the use

while it's actually in use, not just the training. Right. So traditionally, GPU heavy use in training, and then inference, not so much. Now we need heavy use before, after, and during, which I imagine your technology would help because, I mean, look, I love chat I love chat g p t. I'm one of the 1st people to sign up for a subscription, But even, you know, they had trouble keeping up, and they have a lot of money, a lot of power, a lot of

influence. So I mean, this is something that if you're just a regular old enterprise, this is probably something they struggle with. Right? Right. Yeah. I absolutely agree. It's like amazing point, Frank. So 1 year ago, the inference use case on GPU's. Wasn't that big. Totally agree. That's also what we saw in the market.

Deep learning Convolution neural networks were running on GPUs, mostly for computer vision applications, But they could also run on CPUs and you could get, like, relatively okay performance. If you needed maybe, like, a very low latency, then you might use GPUs because they're much faster and you get much

lower latency. But it was, it was all, and it's still very difficult to deploy more than it's on GPU's Compared to just deploying those models on CPUs, because deploying more than deploying applications on CPUs, you know, people are doing for so many years. So many times it was much easier for people to just deploy their models on CPU's And not on GPUs, so that was, like, the

fallback to CPUs. But then came, and as you said, chair GPT was introduced, A little bit more than a year ago, and that generative AI use case just blown. It was blown. Right? And it's it's inference essentially. And those models are so big that they can't really run on CPU. They, they LLMs are running in production on

GPU's and now the inference use case on GPU's is just exploding In the market right now, it's really big. Is a lot of demand for GPU's for inference And if for open AI, they need to support this huge scale that I guess, just Just them are seeing such scale, maybe a little, a few more companies, but that's like huge, huge scale. But I think that we will see more and more companies building products based on AI, on LLMs, And we'll see more and more applications using AI, which

then that AI runs on on GPU. So That is going to go and that's the that's an amazing new market for us around AI and for me as a CTO, it was so fun to Get into that market because it now comes with new problems, new challenges, new use cases Compared to deep learning on on GPS. New new pains because the models are so big. Right? Right. And challenges around cold start problems, about auto scaling, about, About just, giving access to LLMs. So a lot of

challenges, new challenges there. We at Tron AI will studying those problems and we're Now building solutions for those problems, and I'm really, really excited about the Inference use case. That is very cool. So just, going back a little bit. I was trying to keep up. I promise. But Run AI is I I get Run AI Run AI's platform Support fractional, GPU usage.

It it also sounds to me, maybe I misunderstood, That in order to achieve that, you first had to or or maybe along with that, you made it possible to use multiple GPUs. You've you've created Something like an API that allows, companies to take advantage of multiple GPUs or fractions of GPUs. Did I Did I miss that? No, that's right. That's right, Andy. And Okay. So we've built this, way of, For people to scale their workloads from fractions of GPUs to multiple GPUs within 1 machine,

Okay. To multiple, machines. Right? You have big workloads running on on multiple nodes of GPUs. So Think about it when you have multiple users each running their own workload. Some are running on fractions of GPUs. Some are running batch jobs on on a lot of GPUs. Some Deploying models and running them on in inference, and some just launching their Jupyter Notebooks. All of that is happening on the same

pool of GPU's, same cluster. So you need this lay of orchestration of scheduling just to Manage everything and make sure that everything getting there right, access the right, and and and g p u's And everything is scheduled according to priorities. Yeah. Well, being just, you know, a mere data engineer, Here talking about all of that analytics workload. That that sounds very

complex. So and as you mentioned earlier, you know, you were talking about how traditional coding is targeting CPUs, and that's my background. You know, I've written applications and and done data work targeted for traditional work. I can't imagine, just how complex that is, because GPUs came into AI as a unique solution, designed to solve problems That they weren't really built for. You know, GPUs were built for graphics, and you didn't manage that. But the fact that They have to be

so parallel, internally. I think just added this dimension to it. And I don't know who came up with that idea, you know, who thought of, well, goodness, we could we could use all of this, you know, massive parallel processing to To to run these other class of problems. So pretty cool pretty cool idea, but I just I yeah. I'm amazed at even

cooler than that. Because Yeah. Yeah. A wise man once told me, he goes, GPU's are really good at solving linear algebra problems, And if you're clever enough, you can turn anything into a linear algebra problem. And even simulating quantum computers when I was kind of, like, going through that, I was like Mhmm. You know, like, gee, looks like looks like this will be useful there too. Right? Like so it's an it's an interesting,

It's an interesting thing. So, like, you know, everyone is, you know, everyone's talking about how this is, you know, we're in the hype cycle, but I think if you're in the GPU space, you have Pretty good run because one, these things are gonna these things are gonna be important. Right? Whether or not, you know, hype cycle will will kinda crash, and how what that'll look like. Think they're gonna be important anyway. Right? Because they're gonna be just the cost of

doing business, table stakes, as the cool kids like to say. But also, over the next horizon, Simulating quantum computers is going to be the next big hype cycle. Right? Or one of them. Right? So like it's it's it's a It's a foundational technology. I think that we didn't think would be a foundational technology even like 6 7 years ago. Right? Yeah. I go with a few things that you said.

Regarding the Parallel computation, right? And just running linear algebra calculations on GPU's and accelerating such workloads. In Nvidia, I love Nvidia, Nvidia has this big vision, and they had big vision Around GPU's already in 26 when they built CUDA. Yep. Right. So They've been good at just for that. Right? The GPU's were used for graphics processing, For gaming.

Right? Great use case. Great market. But they had this vision of bringing more Applications to GPU is just accelerating more applications and mainly applications with a lot of Linear algebra calculations. And they created that, they created CUDA To simplify that. Right? To allow more developers to use GPUs because just using GPUs directly, that's so complex. That's so hub.

So we've built CUDA to bring more developers, to bring more applications and they started in 20 2006, but think about the big breakthrough in AI, it happened just in 2012, 2013 with AlexNet and the Toronto researchers who used G2 GPU's actually, because they trained Alex Net on 2 GPU's and they had CUDA, so for them it was feasible To train their model on a GPU. And that was the new thing that they did.

They were able to Train much bigger model with more parameters than ever before because they use GPU's because the training Process ran much faster. And, and, and that triggered the entire revolution, the Die hyper on the AI that we're seeing now. So from 26, when Nvidia started to build CUDA until 2013, right, 7 years, Then we started to see those big breakthrough. And in the last decade, it's just exploding, and we're Seeing more and more applications.

The entire AI ecosystem is running on on an on GPUs. So that's amazing to see. It's impressive. And, like, People don't realize, like, the the revolution we're seeing today really started in 2006, like you said. I didn't even put the 2 and 2 together until I was listening to a podcast. I think it's called Acquired, And really good podcast. Right? Like, I they don't pay me to say that or whatever, but they did a 3 hour deep dive on the history of NVIDIA. 3 hours. I couldn't stop listening.

Right? Like Nice. You know Yeah. We tried a long form, like, multi hour podcast. We Weren't that entertaining, apparently. But the way they go through the history of this where it was basically Jensen Huang. Hopefully, I said his name right. He was, like, we wanna be a player, not just in gaming, but also in scientific computing. This is 2005, 2006, which at the time seemed kind of, like, Little out there, little kooky.

But what you're seeing today is, like, the the fruits and the tree the the seeds that he planted, I, you know, almost 20 years ago, like, 19, 20 years ago. So, you know, it's you know, when people look at

NVIDIA and say it's overnight Success. I'm like, well, I don't know about that, but, you know, but no. I mean, you're right. Like, you know and it's probably not a coincidence that once they made it easy to take these Multi parallel processor. Say that 10 times fast on a Thursday morning. But also make it so it's a lot easier for developers to use. Right? And I'll quote the great Steve Ballmer, developers, developers, developers. Right?

So, it's it's, it's just fascinating, like and and I think that, you know, we've really on Leafy a gate of creativity in terms of researchers and applied, research, and, I mean and I think that what's really cool about your Product is that you're you're kind of making this what is now a sparks resource, maybe in some fashion of time, GPU's won't Cost an arm and a leg.

But, like, for now, I think I think the one thing that I've seen that I think is, not obvious For the casual observer is if you can if an organization, like a large enterprise, can pull their resources, they have a lot more money to buy better GPUs, And you offer a platform where everybody can get a stake in it. Right? As opposed to, you know you know, that department is gonna hog everything. Right? You know, you and and and and,

here's a question. Do you do you have, like, an audit trail where you could kinda, you know, figure out, like, you know, Andy's department's really hogging the GPUs. No. No. No. It's Frank. Frank is like mining Bitcoin or whatever. Like, do you do you have some kind of, audit trail like that? Yeah. I I love that you mentioned hugging, We GPU hugging. We Mhmm. We use that term as well. Right? Because it it's so difficult sometimes to get

access to GPUs. So when you get access to GPU as a researcher, as a member practitioner, you don't wanna Let it go. Right. Cause if you let it go, someone else would take it and hug it. Right.

So you're getting this GPU hugging problem. What we do to solve that is that we do provide monitoring and visibility tools into who is using what, and who is actually utilizing their GPU's, and so on, but more than that We allow the researchers just to give up their GPS and not hardware GPS because we provide this, Concept of

guaranteed quotas. So each researcher or each project or each team has their own guaranteed quotas of GPU's That are always available for them whenever they will get access to the the cluster, they will get like, you know, the the 2 GPUs or 4 All the quarter of GPU's it's guaranteed. So they can just let go their GPU's and not hug them. That's one thing. The second thing is that they

can also go above their quota. They can use the GPUs of Other teams or other users, if they are idle, and they can run this preemptible jobs in an opportunistic way, utilize those GPUs. And so in that way, they are not limited to fixed quotas, to help limit

quotas. They can just take as many GPUs as they want from their clusters if those GPUs are available in idle right but if someone will need those gpus because those gpus are guaranteed to them we will make sure our scheduler The Run AI schedule that the Run AI platform will make sure to preempt workload and give those Guarantee GPUs to the right users. Oh, that's cool. Alright. So 1 last question before we switch over to the the stock questions, cause I could geek

out and look at this for hours. Yep. This could be a long form. Sure. This could be. Yeah. And that's and I I wanna be respectful of your time because you're an important guy, and it's also late where you are. So who deals with this? Like, who would set up these quotas? Is it the is it the is it the data scientist? Is it IT ops? Like, who do you obviously, the data scientists, Researchers, they all

benefit from this product. But who's actually administering it? Right? Like, who is it you know, do I have to talk to, you know, Say pretend Andy's in ops. Do I have to say, hey, Andy. I really need a boost in my quota. You know, like, I mean, who does it? Or do or my this sounds like you as I say it, I'm like, yeah, that wouldn't work. Like, I'm the researcher. I'm gonna turn the dial up on my own. Like

like, who's who's who's the primary? Obviously, we know who the prime primary beneficiary is, but who's the primary user?

So okay. Great. So if you have a team, right, if if you're a team of researchers, all all of you Need access to GPU, so maybe the team lead is the one who's managing the quotas for the different team members. And if you have multiple teams, then you might have a department manager or an admin of the cluster or platform owner that will Allocate the quotas for each team, right? And then those teams would manage their own quotas within That's what

they they they were giving. Right? So it's like a a hierarchical thing in a hierarchy manner. People can manage their own quota, their own, priorities, their own access to the GPUs within their teams. Okay. So it's kind of like a hybrid of, like, you know, it's like a budget almost. Right? Like, you know, you get this much, Figure it out about yourselves. Exactly. So we're trying to decentralize the how the quotas are being managed and how the GPUs are being accessed.

So, you know, I'm giving as much power, as much control to the end users as possible. Sure. That's It sounds like a great administrative question, very important. And I imagine, because a little bird told me that you're not the only, you know, your your provisioning provisioning of these GPU resources is not the only thing that, enterprises have to deal with. So it's an it's an interesting just GPUs.

It's compute. Like, it's not a Sure. It's not it's not limited. Although, because of what you said, you know, Managing GPUs is an order of magnitude harder because they were never really built for this. Right? Like, this kind of Right. You know, we're talking about technology that wasn't really in the server room until Few years ago. Right? This isn't a tried and true kind of this is how it works, you know? Right. But we hit that point in the show where we'll, switch the preform questions.

These are not complicated. I mean, you know, we're not we're not Mike Wallace or, like, you know, 60 minutes or whatever. We're not trying to trap you or anything. But since I've been gabbing on most of the show, I figured I'll get Andy kick this off. Well, thanks, Frank. And I don't think you were gabbing on. You know more about this So now I do. So I'm just a lowly data engineer. I'll plug No. You if you

will. Data engineers are the heroes we need. Well well, I'm gonna plug Frank's Roadies versus Rockstar's, writing on LinkedIn. It's it's good articles about this. But, let's see. How did you, how did you find your way in into this field? And, did did this feel fine you or did you find it? This feel totally fine found me. Awesome. Yeah. I I've I did my post doc, and I've been in Bailabs. And Jan Hakon came to Bell Labs and

gave a presentation about AI. It was around 2017, And Jan Hakun spent a lot of years in Bell Labs, and his presentation was amazing. And When I heard him talking about AI, I I said, okay, that's the space where I wanna be. It's going to change the world. There is this New amazing technology here that is going to change everything. And I knew that I want to start a company In the AI space for sure. Cool. That's a good answer. So cool.

Yeah. That's cool. I was at Bell Labs, doing a presentation a while ago, and somebody I didn't realize that he worked at Bell Labs because, like, you know, the guy was like, no. No. He used to work here, like, in this building. I was like, no way. Because I knew him as the guy from NYU. Right? Like, that's who I thought. Right. For the guy from from Meta. Yeah. And now the guy from Meta. Right? Like

so it's interesting how that how that you know? They have this amazing pictures from the nineties where they run like deep learning models on very old pieces and, And recognizing like, numbers on the computer. Maybe you saw those pictures like amazing Emmis. It's the Emmis problem. Is that Yep. Right. Exactly. Exactly. Cool. So second question is, what's your favorite part of your current job?

That everything is changing so fast. Things are moving so fast right away in this business for 6 years, and the entire space is moving and advancing. And so many people are working in this field A new innovation, new tools, new new advancements are are getting out every day. You know, just 6 years ago, it was about deep learning and computer

vision. And now it's about language models And generative AI, and we're gonna just at the start, right, there are so many amazing things that are going to happen in this space, and I love it. Absolutely. So we have 3 fill in the blank of sentences here. The first Is complete this sentence when I'm not working, I enjoy blank. You'll get a you'll get a very boring And so this is just spending time with friends and family, because I think That I'm always working. It's like, if you ask my wife,

she'll tell you that I'm working 24 hours. And Yeah. So I don't have much time that I'm not working in. So when I I do I'm not when I'm not working then I'm trying Trying to be with my kids and my wife and friends. Cool. Cool. The 2nd complete the sentence. I think the coolest thing about technology today is blank. And this, I really wanna hear your perspective on that. Yeah. I think everyone will say AI, right? Or something in

AI. Yeah. I think there are so many new innovations that are coming around LLMs. I think everything relating to searches, right? Searching in data, in getting insights From data, it's all going to change. We're going to have a new interface. Right? Just getting insights from data from And natural with natural language, oh, you know, no SQL and, you know, needing to programming and stuff like that. Just With natural inter language, you could

do amazing stuff with data. I think, We're seeing this, advancement in, And like digit digital twins right now. You can, you can, Fake my voice and your voice and fake my image and your image. And, and, and, you know, In in the future, we'll have digital twins of us, right, doing this stuff. That would be amazing. So a lot of amazing stuff are going to happen in the next few years for sure. Very cool. Our last complete sentence. I look forward to the day when I can use technology to blank.

To have a robot in my house. Yeah. Yeah. You're swapping the flow in instead of me doing that, right, cleaning dishes and things like that. If that would happen, that would be amazing. Right? That's a that's a good answer. Yeah. I I agree. I have I have 3 boys, 4 dogs. So, like, cleaning is safe. Yeah. Yeah. I'm a heavy cleaning. Ranging from, like, 1 to, like,

a teenager. So it's it's, and and and fighting with them to, Like, empty the dishwasher is takes a lot more mental energy than it should, but that's probably a subject for another type of show. The next question is share something different about yourself, and we always like to Joke like, well, let's just make sure that we keep our clean Itunes rating. So Yeah. Yeah. What what yeah. Well, I I This is a hard question, I needed to think about it.

So, I found 2 answers that I can say. So one is about my professional life, right, I think that it's somewhat different that I'm coming this With back from the academia and the industry. So I love academia. I love to research problems. I love to understand problems in in a deep

way And combining it with startups in the industry. And, and in my past, I worked for cheap companies, for hardware companies. I work for Intel, for startup, and for Apple. I did cheap stuff, and now 1 AI is a software company, so really like a diverse background of Academia, hardware, software, so I love that, and, like, I love to do with few things, and so that I think is different.

And the 2nd answer that I could find is, that I have a nickname that goes with me since my high school days, Which is, the Duke. The Duke. All of them all of them are calling me the Duke. It's like, they don't call me Ronan, the the Duke. So That's funny. Yeah. That's awesome. Automotive is a sponsor of, Data Driven, And you can go to the datadrivenbook.com. And if you, if you do that, you can sign up for a free

month Of Audible. And if you decide later to then join Audible, use one of their their sign up plans, then Frank and I get to Split a cup of coffee, I think, out of that. And, every little bit helps. So we really appreciate that when you do. What we'd like to ask Yes. Do you listen to audiobooks? And if you do okay. Good. I see you nodding. So do you have a recommendation? Do you have a favorite book or two you'd like To share. Yeah.

So I'm a heavy user of, audible. I'll give them the, a classical book with Classical for entrepreneurs, on their how the hard things about how things from by Ben Horowitz, it's Classic book, love it, really did a lot of impact on me, I read it when we started run AI And I recommend it for every entrepreneur, to read it and for everyone to read it. It's like a Cool. Amazing book. Yep. Awesome. I have a flight to Vegas this next week, so I'll definitely be listening to

it then. And finally, where can people learn more about you and run AI? And best place will be on our website, Run dot a I. Yeah. And on social. LinkedIn, Twitter, we'll we'll do. Awesome any parting thoughts I really enjoyed this episode love to speak about gpu's love the ai Based on it, I had a lot of fun. Thank you for having me here. Awesome. It it was an honor to have you, and every once in a while, Andy

and I will do deep dive kinda shows. We love to invite you back if you wanna do 1 just on GPUs, because I know where my knowledge drops off, you probably could pick up on that. And with that, I'll let the nice AI British lady end the show. And just like

that, dear listeners, We've come to the end of another enlightening episode of the data driven podcast. It's always a bittersweet moment like finishing the last biscuit in the tin, satisfying, yet leaving you wanting just a bit more. A colossal thank you to each and every one of you tuning in from across the digital sphere. Without you, we're just a bunch of ones and zeros floating in the ether. Your support is what keeps this digital ship afloat, and believe me, It's much appreciated.

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