deployment-explainability

Use when turning a trained model into something usable (an app, an inference function, a demo) or adding explainability. Selects the RIGHT interpretability method per problem: Grad-CAM for CNN image classification, predicted boxes for detection, mask overlays for segmentation, embedding neighbors for face recognition, SHAP for tabular, attributions or attention for text, feature and temporal importance for time-series. Also covers preprocessing parity between training and inference, calibrated scores, out-of-distribution guardrails, and decision-support framing for high-stakes domains. Triggers on 'deploy', 'inference', 'Grad-CAM', 'SHAP', 'saliency', 'explainability', 'feature importance', 'build an app', 'gradio', 'streamlit'.