Production ML Monitoring and Deployment

This unit synthesizes model lifecycle practices by focusing on monitoring, deployment, maintenance, collaboration, and forward-looking trends in large language models. Students will learn to build and operate monitoring systems, communicate with stakeholders, and plan next steps for responsible model stewardship and continued learning.

Learning Objectives

• Analyze model performance, data drift, and bias using monitoring outputs (including Evidently AI reports) to identify degradation and recommend remediation actions.
• Apply engineering principles to design, implement, and test a model monitoring pipeline that captures data and predictions, computes key metrics, triggers alerts, and supports retraining or rollback workflows.
• Demonstrate deployment and maintenance competencies by producing a deployment checklist, maintenance schedule, and incident response plan for a deployed machine learning model.
• Evaluate communication strategies, governance considerations, and emerging LLM trends to propose ethical, technical, and professional next steps for stakeholder collaboration and continued learning.