Model Retraining Strategies

Treat retraining as a release process for a new model built from new data.

Retrain too slowly and the model serves stale assumptions. Retrain too often and you burn compute, add operational risk, and promote models before you understand whether they are better.

The design problem is choosing when to retrain, what data to use, how to train, how to validate, and how to roll out safely.