## TL;DR

Second, enable lazy loading for model artifacts so you are not paying the load cost up front. Third, use incremental sync for the model registry instead of syncing all historical versions, since pulling every old version slows everything down. Yes, Fiddler documents three levers for this.

## Fix

1. Second, enable lazy loading for model artifacts so you are not paying the load cost up front.
   Expected: You get the expected result; the problem is gone.
2. Third, use incremental sync for the model registry instead of syncing all historical versions, since pulling every old version slows everything down.
   Expected: You get the expected result; the problem is gone.
3. For tree models (XGBoost, LightGBM, CatBoost) Fiddler also has native fast SHAP implementations, so make sure the model is registered with its real framework type rather than a generic wrapper.
   Expected: You get the expected result; the problem is gone.

## When to use

- You are dealing with this situation.
- The symptom matches: Fiddler AI slow SHAP explanations on large models.

## When NOT to use

- Unrelated issues with a different cause.
- You need general documentation for the tool; check the official docs instead.

## Compatibility

Reported per the linked source. Source: https://docs.fiddler.ai/integrations/ml-platforms-and-tools/ml-platforms#performance-issues.

## Variant phrasings

### Fiddler AI slow SHAP explanations on large models

## Why it happens

Fiddler explanations (SHAP) on our large model are painfully slow and the UI times out. Is there a way to make explainability cheaper without losing monitoring?

## Edge cases

- If your error message differs even slightly, this is probably a different issue; search the exact text.
- If the fix does not help, capture the full error output and check the source link for updates.

## Source

https://docs.fiddler.ai/integrations/ml-platforms-and-tools/ml-platforms#performance-issues