[skMed]

> continuously monitoring the estimated model performance

Main point here. Model performance can degrade for any number of reasons and at varying rates. As a starting point, focus on setting up anomaly monitoring for a robust set of model eval metrics tailored to your task: loss, calibration, model staleness, etc. Timely alerts can give you sufficient time to dig in and root cause, roll back a model, etc.