ML Deployment and Monitoring

This unit teaches students how to move machine learning models into production and keep them reliable through monitoring, maintenance, and stakeholder communication. It also covers collaboration best practices, emerging trends in large language models (LLMs), and planning next steps for continued learning and ethical operation.

Learning Objectives

• Analyze model performance, data drift, and concept drift using monitoring tools (e.g., Evidently AI) and define measurable alerting thresholds and KPIs for production models
• Apply engineering and operational practices to design and implement a model monitoring system, including deployment, logging, retraining triggers, and maintenance workflows
• Demonstrate effective collaboration and communication by producing stakeholder-facing reports, runbooks, and handoff documentation that support ongoing model governance
• Evaluate emerging LLM trends and privacy/ethical considerations and develop a personalized roadmap for continued professional development and responsible deployment