Best practices for monitoring ML models in production

There are several key issues that can affect ML models' functional performance in production, including training-serving skew, data and concept drift, and data processing pipeline issues. Monitoring model performance in production requires setting up a system that can ingest data and prediction logs to calculate metrics for analysis. Evaluating prediction accuracy using backtest metrics and monitoring data and prediction drift can help identify issues affecting model performance and inform retraining decisions.