Q The demo that actually worked in the, especially through the, World-renowned, uh, Bloomberg firewall. Very, uh, very impressive, among other things. Um, what else the, I mean, you mentioned a broad surface area. What else does a product do that you can talk about?
A Yeah, so the way we think about the, um, I like to describe the analytical life cycle, which I view as going from sort of early exploration and ideation, and so we support a number of interactive workspaces, like you saw briefly, Jupyter Notebooks, RStudio, Zeppelin. So spinning those up on remote More powerful hardware through our reproducibility engine that I showed you. The next phase I, I think of is sort of experimentation, and so that's mainly what I showed here. How can you run a lot of experiments, keep them tracked? And then the final phase is productionization or operationalization. How do you take what you built and get it exposed out of the business? So we support that in a few ways. You can deploy models as APIs for integration into production automated systems. You can wrap models you built in lightweight web forms, um, so that human consumers can interact with them without bothering a, a quant. Uh, we support kind of app hosting, shiny, Flask apps, things like that. And, um, and so that's sort of the life cycle of a particular piece of research. Then, you know, sitting underneath that is all this stuff around collaboration, preserving organizational knowledge, and so, um, I think I briefly touched on that. Commenting, discussion, search, knowledge management.
AI assessment note: “we support a number of interactive workspaces, like you saw briefly, Jupyter Notebooks”