How do I keep my machine learning model working after it goes live?

Shipping the model was the easy part, and now you know it. A model does not stay right just because it was right once. The world keeps moving, the data keeps changing, and the thing you built quietly goes stale. Feeling overwhelmed by that is not a sign you lack skill. It is a sign you finally see the real size of the job. You cannot watch everything at once, so stop trying. Pick the one or two numbers that would tell you the model is drifting off course, and set a simple alert on them. Let the rest wait. A steady watch on a few key signals beats an anxious watch on all of them. Think of this less as a burden you carry and more as a garden you tend. It needs regular, small attention, not one heroic effort. Set a recurring time to check in, decide in advance what would trigger a retrain, and let that plan do some of the worrying for you.