model-monitoring

name	model-monitoring
description	Keep production models honest: monitor input/data drift, prediction/concept drift, and performance decay (when labels arrive); define the retraining trigger up front (schedule/threshold/drop); alert on model health; and close the loop to retraining.

Model Monitoring

Three signals

• Input/data drift — the world changed (early warning, no labels needed).
• Prediction/concept drift — the model's outputs / the input->output relation shifted.
• Performance — the truth, but only when labels arrive (often delayed).

Retraining trigger (decide up front)

Schedule, drift threshold, or performance drop. Not after accuracy quietly fell for a quarter.

Operate it

Thresholds + alerts (with observability-sre); a drift alert must trigger a documented response + the retraining loop. Significance of a drop -> applied-statistics.