Awesome. I'm gonna go ahead and welcome our panelists up. So we have our moderator, Tobias. We have Colleen and Daniel. I'll let the three of you take it away, and I'll drop into the background. But welcome. Super excited to have you.

All right, well, hello, everybody, and thanks for joining. I'm happy to be able to be here and host a conversation with Colleen and Daniel. So just to get us started, why don't you go ahead and give us a brief introduction. Colleen?

Hi. Thanks, Tobias. I am Colleen Tartow. I am field CTO and head of strategy at vast data. We are an AI data platform, and I've been doing data for a long time. I was a data engineer way back in the day and kind of have focused on data and really the lifecycle of data, the supply chain of data, really what it means to engineer data and get value out of it. So that's been my focus for a long time, and I'm excited to be here with you and Daniel.

All right, and Daniel, how about yourself?

Hey, everybody, I'm Daniel, one of the co founders of superlinked. We are working on helping people turn data into vector embeddings and then do interesting things with the data. You know, I guess people heard about drag over the last couple, maybe months or years, but there is a lot more that you can do with vectors. And we try to advocate for the broader use cases and we have open source framework for building some of those. And then in terms of my background, I was ML tech lead at YouTube before this, working on the ad systems and yeah, trying to kind of marry the data initiatives to the world of modeling and creating value to the end users. Yeah, happy to. Excited to chat.

And for myself, most folks probably know me from running the data engineering podcast, and I've also launched a little while ago the AI engineering podcast. So focusing on this space in particular, how do we actually design and build AI applications and infrastructure to support them so that we don't all go crazy trying to figure out this crazy new landscape. And so I guess with that, I'm just wondering if we can kick off having a conversation about, as we all start to get our hands on and understand the complic, yeah. The complexities and the requirements around these AI applications. What new requirements does that actually bring for people who are responsible for the underlying data systems infrastructure? What is it that is actually net new and what is just business as usual? We actually need to make sure that we have good data so that we don't get the garbage in, garbage out problem.

Yeah, I mean, I'm sure we all have lots of thoughts about this. It's a really good question, and it's one I hear a lot. I think that we've been building these data platforms many, many years, right? Like 40 something years, 50 something years, maybe. And the focus has always been on sort of that trifecta of performance, scale, and cost and balancing those three in a way that makes sense. And you can get the ROI out of your data that you need, but still makes it flexible enough that you can address new use cases and new technologies, et cetera. And, you know, over time, you know, that's turned into the modern data stack, and then it's kind of come away from the modern data stack. I think we're seeing a resurgence of folks repatriating and thinking about new ways to do things. I think the balance getting back to the performance, cost and scale, the scale is now changing a lot.

Right. We're talking about unstructured data, which is a lot bigger and a lot more complex than structured data. Things in tables, SQL. We've got that. We can do that. And the question is now how do we get value out of unstructured data? And that's, I think, incredible. And I think there's so much we can do with that. But it means that the platforms we're building and that we've built in the past won't necessarily work for these new use cases, for this new scale.