SHAP in Production: Scaling Model Explainability Without Killing Latency
Every team that ships SHAP to production learns the same lesson the hard way: the explainability demo that looked great in a notebook falls over the moment it has to run inside a real-time inference path. A model that serves predictions in 40ms suddenly takes 800ms once you bolt SHAP onto it. Product wants explanations on every request. Compliance wants them logged. Latency budgets don't care about either.
This piece is about closing that gap, the concrete techniques for making SHAP fast enough to actually ship, not just accurate enough to demo.
Why SHAP Is Slow in the First Place
SHAP values are expensive because, in the general case, computing an exact Shapley value for a feature requires evaluating the model across every possible subset of features it could be combined with. That's combinatorial by definition. The library gives you several estimators that trade off exactness for speed, and picking...
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