This course provides a comprehensive overview of the principles and best practices for successful AI product management in real-world scenarios. Students will explore the end-to-end lifecycle of AI models, from problem identification through deployment, monitoring, maintenance, and continuous improvement. We will examine applications and constraints in government, healthcare, finance, and other industries. Topics include scoping, scalability, data availability, ethical considerations, and compliance requirements. We will also discuss bias, fairness, and unintended consequences of AI systems. Through case studies and hands-on team projects, students will learn how to identify potential AI solutions, manage risks, and ensure the positive impact of their AI products. Students will prepare product documentation suitable for use in the industry. By the end of the course, students will have developed a product pitch, a minimum viable product definition, and a risk assessment, preparing them to lead AI-driven innovation in their organizations.Prerequisites: Basic knowledge of machine learning, software engineering, or cloud computing is recommended.