Detecting operational issues like data drift is only part of the story. Understanding the precise impact on your model's effectiveness and identifying why performance changes requires a more detailed view. This chapter concentrates on techniques for granular performance monitoring and diagnostics.

You will learn to select metrics appropriate for your specific task, moving beyond simple accuracy ($A$) to metrics like precision ($P$) and recall ($R$). We will cover methods for assessing performance not just overall, but across specific segments or slices of your data to find hidden weaknesses. Additionally, we'll examine approaches for monitoring model fairness, analyzing the influence of outliers, performing systematic root cause analysis when performance degrades, and applying model explainability methods (like SHAP or LIME) as diagnostic tools in a production setting.