What have you seen in the past five years that's made progress in this area? There's a lot of the "Modern Data Stack" tools that have certainly helped, but those generally feel separate from ML/AI workloads (usually).
I'm not aware of a recent "killer app" in that area.
Practically, people who succeed at this get it working end-to-end with whatever compromises it entailed, they might tell anyone that they never would have done it that way had they known how it would turn out, yet, they have a bird in the hand.
to make reliable scripts that can rebuild a model when the data changes. The version built into scikit-learn has the features I need, other ones don't. scikit-learn is great for problems of a certain size that take, say, 10 minutes to run.
That scale turns out to be appropriate for very fast prototyping of systems need about a human-week of judgements to light up, that can be updated daily, etc.
Someone is going to insist on using slower models that take two hours to train (wrapped up in a model selection process), where you worry the machine might crash, and have to take a "distributed systems" approach that adds a terrible overhead for jobs that don't need it. If I liked the model selection story I could probably live with that but so far I don't.