Evaluating AI Product Performance: A Guide for Product Managers

Launching an AI product is only the first step; ensuring it continues to deliver accurate results in production is where the real challenge begins. As a product manager, you need to understand how to evaluate, maintain, and improve your AI models over time. This text-only course equips you with the foundational knowledge to manage AI product performance effectively, helping you collaborate with engineering teams to implement robust MLOps practices, interpret key evaluation metrics, and maintain user trust. What you'll learn: - Understand foundational AI evaluation metrics and the core principles of MLOps. - Analyze model performance using hyperparameter tuning and modern retraining strategies. - Implement continuous testing frameworks to catch performance issues before they impact users. - Identify data drift and model degradation using modern observability concepts. - Define key performance indicators (KPIs) that align technical model metrics with business goals. You will start by mastering essential terminology and the lifecycle of AI products. From there, you will explore practical strategies for continuous testing, feedback loops, and performance optimization through clear written explanations and conceptual exercises. This course is designed specifically for aspiring and practicing product managers, project managers, and business analysts who want to lead AI initiatives. No prior coding or advanced mathematics experience is required. Start reading today to bridge the gap between AI technology and product success.