Yes, Fiddler documents three levers for this. First, switch from full SHAP computation to SHAP sampling for large models, which gives you representative explanations at a fraction of the cost. 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. 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.

Context: 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?