How do you ensure model interpretability in production?

Ensuring model interpretability in production involves several strategies. First, you can choose inherently interpretable models, like decision trees or linear models, when feasible. For black-box models, such as neural networks, you can use techniques like SHAP (SHapley Additive exPlanations) or LIME (Local Interpretable Model-agnostic Explanations) to provide insights into model predictions. Additionally, you can implement logging mechanisms that track feature importance and changes over time. Providing documentation and visuals to stakeholders will also help contextualize model decisions, enhancing trust and transparency.