I don't know if I'd say this is reinventing themselves, or at least not from the ground up, they've been somewhat active in the open source LLM scene for some time now. Not that releasing dolly alone caused some major foundational shift, but it was a solid contribution. Additionally, acquiring Mosaic fills a pretty gaping hole in their existing product offering. The whole process of pretraining, fine-tuning, and evaluating open source LLMs in production is a total nightmare currently. Mosaic's stack at least attempts to make that better (on top of releasing solid foundational models themselves).
> “corporate leaders under pressure to get their data ready for AI”. That has nothing to do with LLMs
I agree that its buzzwordy and a little abstract. But also in my experience, getting "data ready for AI" is actually the primary constraint many orgs have with respect to using LLMs in an enterprise context. Their data is not stored in a way thats easy to tokenize/label/embed for effective training or fine-tuning. And you could argue the preprocessing actually should the easy part for non-ML devs to tackle, as its primarily a software engineering problem. Yet, its still the thing that keeps many folks from getting started (before you even get to attempt to tackle the inference problem).
> “corporate leaders under pressure to get their data ready for AI”. That has nothing to do with LLMs
I agree that its buzzwordy and a little abstract. But also in my experience, getting "data ready for AI" is actually the primary constraint many orgs have with respect to using LLMs in an enterprise context. Their data is not stored in a way thats easy to tokenize/label/embed for effective training or fine-tuning. And you could argue the preprocessing actually should the easy part for non-ML devs to tackle, as its primarily a software engineering problem. Yet, its still the thing that keeps many folks from getting started (before you even get to attempt to tackle the inference problem).