AI Product Management Grow Your Career

This course equips participants with the skills and knowledge to excel in AI Product Management. Through a comprehensive exploration of foundational principles, practical frameworks, and hands-on activities, learners will gain expertise in developing and managing AI-driven products that align with business goals and deliver measurable outcomes. Module 1: Introduction to AI Product Management Topics Covered: What is AI Product Management? How AI Product Management differs from traditional Product Management. Key responsibilities, skills, and competencies of AI Product Managers. Key Takeaways: Understand the framework for creating model-driven products. Identify the unique value AI Product Managers bring to organizations. Module 2: Adopting the Product Operating Model Topics Covered: Overview of the Product Operating Model. Roles of Product Managers, Data Scientists, Tech Leads, and Product Leadership. How to organize teams around value, usability, feasibility, and viability.Key Takeaways: Learn how to deliver measurable outcomes using the Product Operating ModelModule 3: Foundations of AI ProductsTopics Covered: Overview of AI branches (e.g., machine learning, NLP, vision, generative AI). How AI algorithms and workflows function. Managing AI products effectively.Key Takeaways: Build a foundational understanding of AI concepts and applications.Module 4: AI Product StrategyTopics Covered: Principles of AI Product Strategy: Focus, Data-Driven Insights, Transparency, and Placing Bets. How to align AI goals with business objectives.Key Takeaways: Develop strategic thinking to identify and prioritize high-impact problems. Module 5: AI Product Discovery Topics Covered: Validating problems, assessing risks, and rapid experimentation. Principles of discovery: minimize waste, assess risks, and validate solutions. Key Takeaways: Understand how product discovery ensures solutions are valuable, usable, feasible, and viable. Module 6: AI Product Delivery & ML Ops Topics Covered: CI/CD and iterative development in AI product delivery. ML Ops lifecycle: Development, Deployment, Monitoring, and Retraining. Key Takeaways: Learn how to ensure reliability, scalability, and adaptability of AI solutions. Module 7: Growing Your Career in AI Product ManagementTopics Covered: Building expertise as an AI Product Manager. Leadership in AI Product Management and ethical AI practices. Strategies for continuous learning and staying updated. Key Takeaways: Develop a roadmap for career advancement in AI Product ManagementModule 8: Key Takeaways & Course ProjectTopics Covered: Summary of course modules and frameworks. Real-world application of the AI Product Management FrameworkKey Takeaways: Synthesize key learnings into a portfolio-ready course projectCourse Project: Design a recommendation system for an e-commerce platform, focusing on strategy, discovery, and delivery phases. Create a final presentation summarizing your findings, solutions, and outcomes. Learning Outcomes By the end of this course, participants will: Understand the unique aspects of AI Product Management and its differences from traditional Product Management. Apply the Product Operating Model to organize teams and achieve outcomes. Develop AI product strategies that align with business goals.Execute discovery processes to validate problems and identify effective solutions. Deliver and manage AI products using CI/CD principles and ML Ops frameworks.Build leadership and expertise in AI Product Management to grow their careers.Thank you,Eric Dabols, Founder Data As A Product