The fraud example makes delayed labels an important part of the monitoring story. A model can appear healthy or unhealthy simply because recent transactions have not had enough time to receive a confirmed outcome. I would compare quality on cohorts with the same label maturity, while monitoring feature and prediction distributions separately for faster warning signals.
That also changes the retraining trigger. A distribution shift can justify investigation without proving that a replacement model is better. The release gate should compare the current and candidate models on a mature, time-separated cohort using the operating threshold and error costs that the service actually uses.
Ahmet Özel
AI Engineer. Computer Vision, RAG and LLM agents.
The fraud example makes delayed labels an important part of the monitoring story. A model can appear healthy or unhealthy simply because recent transactions have not had enough time to receive a confirmed outcome. I would compare quality on cohorts with the same label maturity, while monitoring feature and prediction distributions separately for faster warning signals.
That also changes the retraining trigger. A distribution shift can justify investigation without proving that a replacement model is better. The release gate should compare the current and candidate models on a mature, time-separated cohort using the operating threshold and error costs that the service actually uses.