Monitoring: the model drifts while nobody watches

The model is trained, deployed, and shows 0.87 AUC. Three months later it is 0.71—with no code changes. What happened? Production data shifted: users behave differently, new patterns appear, seasonality kicks in. That is drift, and without monitoring you only notice after business metrics tank. ML monitoring is the first line of defense.

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