Enterprise Model Monitoring and Evaluation

This unit teaches students how to design, implement, and maintain enterprise-grade model monitoring and evaluation systems in the cloud. Students will compare AI/ML platform offerings (including the Microsoft stack), use workbook templates for use-case planning and platform evaluation, and learn optimization and endpoint-monitoring techniques needed for reliable production AI.

Learning Objectives

• Apply model monitoring principles to build a monitoring system that includes endpoint health checks, performance and drift metrics, alerting thresholds, and documentation using the Model Monitoring Tracking Sheet
• Evaluate and compare enterprise AI/ML cloud platforms (including Microsoft AI/ML services and alternative providers) using the AI/ML Platform Evaluation Sheet, assessing features, scalability, security/compliance, and cost trade-offs
• Demonstrate methods to optimize model performance and stability by recommending adjustments such as retraining cadence, feature-management, and resource scaling in response to monitoring signals
• Analyze generative AI use cases with the Generative AI Use Case Planner and justify deployment and governance decisions that align with project management constraints and enterprise risk considerations