A drift alert is a question, not a verdict. Here is how to build the monitor, and how to tell which alerts actually mean your model is getting worse.

Most teams meet drift monitoring the same way. The dashboard lights up red in its first week. Two features have "drifted". Someone spends a day digging, nothing gets retrained, and when the labels finally arrive, accuracy has not moved at all.

The opposite failure is quieter and costs more. Every feature looks stable, the dashboard stays green, and the model is mispricing an entire customer segment.

Both failures come from the same confusion: treating statistical drift (the inputs look different) as if it were model degradation (the predictions got worse).…