Model Performance Analysis and Root Cause Analysis Questions

Techniques for diagnosing and troubleshooting production ML models, including monitoring metrics such as accuracy, precision, recall, ROC-AUC, latency and throughput; detecting data drift, feature drift, data quality issues, and model drift. Covers root-cause analysis across data, features, model behavior, and infrastructure, instrumentation and profiling, error analysis, ablation studies, and reproducibility. Includes remediation strategies to improve model reliability, performance, and governance in production systems.