Fiddler: drift metrics break when production features do not match training names and types

TL;DR: Ensure feature names match exactly between training and production, verify data types are consistent, and check for missing features in the production data. [Official Fiddler docs (ML platforms integration)]: Schema mismatches are the quiet killer of model monitoring. A renamed column or a type that changed upstream shows up as drift or broken explainability, not as an error, so compare the production schema against the training schema directly when metrics look wrong.

[Official Fiddler docs (ML platforms integration)]: Schema mismatches are the quiet killer of model monitoring. Ensure feature names match exactly between training and production, verify data types are consistent, and check for missing features in the production data. A renamed column or a type that changed upstream shows up as drift or broken explainability, not as an error, so compare the production schema against the training schema directly when metrics look wrong.

Context: Official docs (Fiddler ML Platforms integration guide): documents the schema-mismatch gotcha that trips agents wiring model monitoring. Drift analysis breaks when feature names or data types differ between training and production, or when production rows are missing features the model was trained on.

When to use

You hit exactly this: Fiddler: drift metrics break when production features do not match training names and types in Fiddler.

When not to use

A different error, or the same symptom in a different tool. This page only covers the failure above.

Compatibility

Fiddler.

Edge cases

• Context: Official docs (Fiddler ML Platforms integration guide): documents the schema-mismatch gotcha that trips agents wiring model monitoring.

Official docs (Fiddler ML Platforms integration guide): documents the schema-mismatch gotcha that trips agents wiring model monitoring. Drift analysis breaks when feature names or data types differ between training and production, or when production rows are missing features the model was trained on. Resolved: [Official Fiddler docs (ML platforms integration)]: Schema mismatches are the quiet killer of model monitoring. Ensure feature names match exactly between training and production, verify data types are consistent, and check for missing features in the production data. A renamed column or a type that changed upstream shows up as drift or broken explainability, not as an error, so compare the production schema against the training schema directly when metrics look wrong.