AI Models and Product Practices

This unit develops practical skills for product managers to understand how AI models learn, how to map AI/ML capabilities to real product problems, and how to evaluate model outcomes in the context of user journeys. Through a k‑NN example, a churn prediction case study, and a PM toolkit for day‑to‑day activities, learners prepare to prioritize, design, and assess AI solutions and hand off into implementation and governance in subsequent units.

Learning Objectives

• Analyze a k‑Nearest Neighbors (k‑NN) example to explain how supervised models make predictions, identify the role of features and distance metrics, and recognize common failure modes (e.g., bias, variance, data quality issues).
• Apply capability‑matching techniques to map specific product problems to appropriate AI/ML approaches and prioritize solutions based on technical feasibility, user impact, and business value.
• Demonstrate the design of an end‑to‑end churn prediction solution by defining the prediction target, selecting features and evaluation strategy, choosing candidate models, and outlining deployment and monitoring considerations.
• Evaluate model performance using relevant metrics (e.g., precision, recall, AUC, calibration), interpret trade‑offs for product decisions, and identify opportunities in the user journey where AI interventions and PM skills can create value.