Drift monitoring that someone will actually read

An alert nobody acts on is worse than none

It creates the impression of coverage while the model quietly degrades. Before adding monitoring, decide who will receive each alert and what they are expected to do about it. If there is no answer, do not send it.

Three things worth measuring

Input drift. The distribution of a feature has moved. Useful as an early signal, noisy on its own, and rarely worth paging anyone.

Prediction drift. The shape of the model’s output has changed. Closer to something that matters, because it is visible downstream.

Outcome drift. Accuracy against the actual result, once it is known. The one that genuinely matters, and the one that arrives late — sometimes months late for credit or churn.

Match the cadence to the decision

A fraud model scoring continuously needs daily checks. A quarterly planning forecast does not. Monitoring more often than you could act is how alert fatigue starts.

Give every alert an owner and a runbook

Alert: churn_propensity — outcome accuracy below 0.72
Owner: Retention analytics
Action: compare against challenger, check feature freshness,
        decide retrain or roll back within 5 working days

Without the action line, the alert becomes a notification.

Review monthly with the business owner

Fifteen minutes, with the person whose decision the model supports. They often explain a drift before the data does — a pricing change, a new region, a campaign. That context is not in any dashboard.