- In short
- Anomaly detection sets threshold alerts on the metrics that matter, such as a cost spike exceeding a percentage of the trailing average or a p95 latency crossing the SLA line. Model drift, a gradual change in output distribution on stable inputs over time, is harder to catch with simple threshold alerts and benefits from periodic distribution comparison instead. Change-attribution instrumentation should distinguish model drift, data drift, and model update effects when a monitored metric moves. Threshold alerts work well for sudden shifts; gradual drift needs a different detection method.
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