Exploring the Relationship between Post-deployments Monitoring and Model Explainability for Responsible AI Models

This chapter explores the importance of tools like drift detection, data quality checks, production checks, and fairness checks in understanding how a model is performing and reacting to new data. It emphasizes the need for observability and highlights the long-term goal of building responsible AI models.