Question

You operate a Vertex AI online endpoint serving a demand-forecasting model for a retailer. You enabled feature drift monitoring with default settings, but the on-call team is now flooded with drift alerts—most triggered during predictable weekend traffic patterns and seasonal promotions that do not actually degrade model accuracy. The team is beginning to ignore alerts (alert fatigue), risking a missed real incident. What is the most effective way to reduce false alarms while still catching genuine drift?

Accepted Answer

Raise per-feature drift alert thresholds and align the monitoring baseline/comparison window to reflect known seasonal and weekly cyclical patterns.

Answer

Disable feature drift monitoring entirely and rely only on prediction-latency SLO alerts.

Answer

Reduce the prediction request sampling rate to zero so fewer requests are analyzed for drift.

Answer

Switch the endpoint to batch prediction so drift monitoring no longer runs.