[montanalow]

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.

[streetcat1]

Well, you would also need new labels to retrain.

[montanalow]

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.