AI Product Evaluation and Enhancement

This unit covers methods to validate AI/ML impact, ensure generalizable model performance, and plan practical product improvements that blend automation with human expertise. Students will practice experiment design, diagnose model behavior, create feature-enhancement plans, and assess operational and ethical challenges to prepare AI products for safe, effective scaling.

Learning Objectives

• Analyze and design A/B tests for AI/ML features by defining hypotheses, selecting primary and secondary metrics, calculating sample size and duration, and interpreting results to determine statistical and business impact
• Apply model-diagnostic techniques (learning curves, validation metrics, cross-validation) to detect overfitting and underfitting and prescribe measurable remediation steps (e.g., regularization, data augmentation, architecture changes) with expected trade-offs
• Demonstrate a product manager workflow by producing a prioritized feature-enhancement plan that integrates AI/ML improvements, human-in-the-loop checkpoints, acceptance criteria, and success metrics for a day-in-the-life scenario
• Evaluate technical, ethical, and operational challenges (including bias, privacy, reliability, monitoring, and maintenance) and create mitigation, monitoring, and escalation strategies to support deployment readiness