Production, Monitoring, and Next Steps

This capstone unit focuses on operationalizing machine learning systems: establishing model monitoring, building maintainable deployment and maintenance workflows, and communicating results to stakeholders. It also situates work within emerging LLM trends and outlines concrete next steps for continued learning and professional practice.

Learning Objectives

• Analyze model performance, data drift, and integrity issues using Evidently AI and other monitoring metrics to identify when models require investigation or retraining
• Apply best practices to design and implement a model monitoring system—incorporating logging, alerting, retraining triggers, and scalable infrastructure—for a deployed model
• Demonstrate end-to-end deployment and maintenance workflows including model versioning, CI/CD integration, rollback strategies, and routine operational checks
• Evaluate collaboration and communication strategies with stakeholders, and formulate a professional learning plan that incorporates emerging LLM trends and responsible adoption practices