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Model Interpretability

What is model interpretability (Explainable AI)?

Model interpretability (also known as explainable AI) is the process by which a ML model's predictions can be explained and understood by humans. In MLOps, this typically requires logging inference data and predictions together, so that a library (such as Alibi) or framework (such as LIME or SHAP) can later process and produce explanations for the predictions. 

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