This text discusses the importance of monitoring and evaluating the performance of machine learning (ML) models in production, as their functionality can degrade over time due to various factors such as changes in the production environment, data drift, and training-serving skew. The article highlights key metrics and strategies for monitoring ML model performance, including tracking evaluation metrics, monitoring proxy metrics like data and prediction drift, detecting data processing pipeline issues, and using drift detection tools. It also emphasizes the importance of continuous evaluation and retraining to maintain the accuracy and effectiveness of ML models. The text concludes by recommending Datadog as a tool to help centralize ML observability data and form more powerful insights for full-stack ML platform observability.