That's exactly the target use case. Models make online predictions as part of Postgres queries, and can be periodically retrained in a cadence that makes sense for the particular data set. In my experience the real value of retraining at a fixed cadence is so that you can learn when your data set changes, and have fewer changes to work through when there is some data bug/anomaly introduced into the eco system. Models that aren't routinely retrained tend to die in a catastrophic manner when business logic changes, and their ingestion pipeline hasn't been updated since they were originally created.
Yep! Part of the power of being inside the OLTP is that you can just create a VIEW of your training data, which could be anything from customer purchases, search results, whatever, and that VIEW can be re-scanned every time you do the training run to pickup the latest data.