I was the main data science engineer at one of my previous companies. We used tools like airflow for running python scripts to import data, clean/transform it, train models, and even test various models against datasets. We also used Azure for similar things.

It's easy to do "dev ops" for machine learning. Basically, just automate everything and implement gatekeeping mechanisms along with active monitoring.

It's true, though. I had to cobble together a lot of custom things at the time, but it wasn't that hard to do.