Q very cool. Do you, do you want to talk a little more, uh, about the specifics to the extent you can? Actually, I don't know if you, if you can, but like, how does that work? And does that work? I mean, AI means lots of different things, like from, you know, deep learning and LP. Like, does it work? Is, is a model a model for your purposes or?
A Yes. We, I mean, we primarily see, uh, like our focus is on tabular business data, computer vision, NLP. Um, those are the ones that we see the most regularly. Uh, there's, there's plenty of other examples, but that covers a huge part of the market. And, uh, Um, and, you know, we are kind of agnostic. Like, people can build their models. Like, what we see in large organizations, they have this really heterogeneous mix of modeling environments. They're using DataRobot, and H-to-O, and open source, and cloud providers like AWS SageMaker, and things like that. And, ah, that's great. Like, I think it's important to allow this kind of highly sought after data science talent to be free to use whatever tools they want. Um, but you, you don't want to have to check 87 different places just to understand kind of what the overall Uh, health of your, of the AI across your organization is.
AI assessment note: “Yes. We, I mean, we primarily see, uh, like our focus is on”