Validating and Operationalizing Models

This unit teaches practical methods for validating AI/ML features, ensuring models generalize, and integrating human expertise into product workflows. Students learn experimentation (A/B testing), model diagnostics (overfitting/underfitting), product-management perspectives, feature enhancement strategies, and operational challenges to prepare for deployment and monitoring in the next unit.

Learning Objectives

• Analyze A/B test designs and results by computing relevant metrics and statistical significance to determine model or feature impact on product KPIs
• Apply techniques to detect and mitigate overfitting and underfitting (e.g., cross-validation, regularization, train/validation/test splits) and compare model performance across validation scenarios
• Demonstrate the role of a product manager for AI experiences by creating a day-in-the-life plan that includes prioritization, stakeholder communication, experiment scheduling, and monitoring checkpoints
• Evaluate feature enhancement and human-in-the-loop workflows by proposing prioritized improvements, defining annotation/feedback processes, and assessing trade-offs including ethical, operational, and technical risks