Fiddler slow on big models: use SHAP sampling and incremental registry sync

TL;DR: [Official Fiddler docs (ML platforms integration)]: For large models, use SHAP sampling instead of full SHAP computation, enable lazy loading for model artifacts, and use incremental sync for the model registry instead of syncing all historical versions. These three settings are the documented fix for slow or timing-out monitoring pipelines on big models.

Fix

1. [Official Fiddler docs (ML platforms integration)]: For large models, use SHAP sampling instead of full SHAP computation, enable lazy loading for model artifacts, and use incremental sync for the model registry instead of syncing all historical versions. Expected: this specific failure stops.

Details

These three settings are the documented fix for slow or timing-out monitoring pipelines on big models. Context: Official docs (Fiddler ML Platforms integration guide): documents the performance fixes for slow monitoring on large models and registries. Full SHAP computation and full-registry syncs are the expensive paths; sampling, lazy loading, and incremental sync are the documented relief.

When to use

You hit exactly this: Fiddler slow on big models: use SHAP sampling and incremental registry sync in Fiddler slow on big models.

When not to use

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

Compatibility

Fiddler slow on big models.

Official docs (Fiddler ML Platforms integration guide): documents the performance fixes for slow monitoring on large models and registries. Full SHAP computation and full-registry syncs are the expensive paths; sampling, lazy loading, and incremental sync are the documented relief. Resolved: [Official Fiddler docs (ML platforms integration)]: For large models, use SHAP sampling instead of full SHAP computation, enable lazy loading for model artifacts, and use incremental sync for the model registry instead of syncing all historical versions. These three settings are the documented fix for slow or timing-out monitoring pipelines on big models.