drift detection.

Where drift detection fits on a long engagement.

Models degrade without any error occurring. The service stays up, latency is fine, and predictions get steadily worse because the world has moved away from the training data. Nothing in conventional monitoring detects that.

Data drift and concept drift are different problems. Data drift means the inputs have changed distribution and is detectable immediately by comparing against a training baseline. Concept drift means the relationship between inputs and outcomes has changed, and detecting it requires outcome labels — which often arrive weeks later, if at all.

What an assigned team does with drift detection.

Where labels are delayed, proxy signals carry the load: prediction distribution shifts, confidence changes, and downstream business metrics. They are indirect and they are usually the only early warning available.

Designing that monitoring for a specific model and its label latency is part of what AI capacity is responsible for under managed ai services.

What we use drift detection for.

• Silent degradation made visible Alerts on distribution change, where conventional monitoring reports nothing wrong.
• Proxy signals where labels lag Prediction and confidence shifts watched when outcomes arrive weeks later.
• Retraining triggered by evidence A defined threshold, rather than a calendar schedule or a complaint.

How drift detection capacity is assigned.

Model monitoring is assigned inside AI capacity, with the signal design matched to how quickly outcome labels actually arrive.