Label Drift Monitor

Label Drift Monitor is a ml monitor that detects when data or predictions no longer match the training baseline for ground-truth or weak-supervision annotation. It uses statistical tests, time windows, and alert thresholds so teams can respond before quality drops while keeping evidence, reliability, and public-safe operational boundaries clear.

“The machine learning team used Label Drift Monitor when the label set had disagreement, so the team could respond before quality drops before the model moved into evaluation.”