Detect Data Drift Before It Hurts

The silent failure

A model trained on last year's data slowly gets worse as inputs shift. This is drift, and it often goes unnoticed until metrics tank.

What to monitor

• Input drift — feature distributions change vs. training.
• Prediction drift — the mix of outputs shifts.
• Performance — accuracy on freshly labeled data, where available.

Practical setup

Log production inputs, compare their distributions to a training baseline on a schedule, and alert on big shifts. Tools like Evidently make this straightforward. Catching drift early is the difference between a tweak and an outage.