Model Drift Detection Failed Silently: Evidently AI Fix

The Silent Failure

Evidently AI's drift detection returned None instead of raising an error. The dashboard showed green checkmarks. The model was degrading in production.

This one hits different because the monitoring tool itself gave false confidence. When Report.run() completes without exceptions but the drift metrics are missing, you assume everything's fine. It's not. The feature schema mismatch between training data and production inference was silently ignored, and I only caught it when manual accuracy checks showed a 12% drop over three weeks.

Here's what actually happens when Evidently's column mapping breaks.

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What Evidently Expects (and Doesn't Tell You)

Evidently AI builds drift reports by comparing reference data (training set) against current data (production inference). The core assumption: both datasets share the same feature schema.

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