[joshvm]

I would agree on one aspect though - deploying a model at scale is much closer to SWE than it is to foundational ML research. At a high level, you're deploying a function which has some known compute requirements. It requires setting up infrastructure, monitoring/logging, API setup etc. This is the sort of thing that a good devops engineer could probably make a horizontal move to, because a lot of the practical experience is similar. I don't think you need a particularly deep knowledge of ML unless you're also expected to be involved in trying to track model performance that might require re-training. Leaving aside the really distributed systems that require multi-node, multi-GPU (but again, if you have HPC experience, that should transfer).

The problem is a lot of tutorials just show you how to make a Flask/Gradio website (maybe FastAPI) and call it a day. A lot of the experience here is the sort of in the trenches practical stuff that you can't cover in a MOOC (and it's expensive to experiment with GPU clusters). I suspect there are better non-ML courses people could take though.