If I understand you correctly, I think there is the same purpose for any FS design choices. For example, I saw both approaches data store and declarative feature computation on demand.

An important part for product is basically support of 2 features:
feature retrieval for a batch of entities (useful for generating training sets together with ability to do time travel; batch prediction (offline) ); feature serving in near real-time (online).
This is minimum functionality I would expect a feat...

Got it. Thank you!

btw, what would be an extension point for clear ml if somebody would want to integrate custom feature store like feast?

Would be helpful to have pointers to front-end part as well, like if we would like to maintain single UI for feature governance and use our own feature store back-end that would make it easier. There must be already some logging possibility within experiment tracking, so it should not be too hard to log features and probably dataset that is used + tags for models metadata can hold required schema, so the only questionable part is monitoring of feature skew.

Anyway, thank you for confirming t...

My favourite is the one that https://www.youtube.com/watch?v=E8839ENL-WY , because it touches the higher level picture of how feature store helps Data Scientists to progress further towards deploying model to production and continuously monitor its performance rather then too deep tech dive into challenges of having one place to avoid feature training serving skew.