How do I handle model drift and retraining?

You can deploy and monitor now, which is already more than most manage. What still trips you is that a model does not stay good on its own. The world shifts under it, the data changes, and performance quietly slides. Drift is not a sign you built it wrong. It is the nature of a model meeting a world that never stops moving. The key is to stop treating a deployed model as finished and start treating it as something alive that needs tending. That means watching not just whether it runs but whether it is still right, and deciding in advance what signal tells you it has drifted far enough to retrain. Without that line drawn ahead of time, you are always reacting late. With it, retraining becomes routine instead of a scramble. Pick one model and define, in plain terms, the one metric that tells you it is degrading and the threshold that triggers a retrain. Set up the alert, then let it tell you when to act instead of guessing. Do this per model and drift stops being a lurking fear and becomes a managed cycle. The model was never going to stay still. Your job is to keep noticing and keep tending.