AI Product Management: From Machine Learning Foundations to RAG and MLOps

As artificial intelligence reshapes the technology landscape, product leaders must understand how to leverage machine learning to build high-impact solutions. This written course equips you with the fundamental knowledge to bridge the gap between complex AI technologies and successful product strategy. You will transition from understanding basic machine learning concepts to confidently designing AI product architectures and managing their lifecycle. Through clear text explanations and real-world product scenarios, you will learn how to align technical AI capabilities with actual user needs and business goals. What you'll learn: 1. Understand foundational machine learning concepts, neural networks, and Large Language Models (LLMs). 2. Explore modern AI architectures, including Retrieval-Augmented Generation (RAG) and vector databases. 3. Design multi-agent systems and implement basic prompt engineering strategies for product workflows. 4. Evaluate AI product performance using specialized metrics, validation techniques, and A/B testing. 5. Master MLOps fundamentals to oversee the deployment, monitoring, and scaling of AI models. 6. Analyze how leading technology platforms structure their recommendation engines and predictive systems. The course begins with essential AI terminology and core machine learning definitions before moving into advanced architectures and deployment strategies. You will progress through structured text modules that combine theoretical frameworks with practical product management templates and decision-making exercises. This course is designed for product managers, analysts, project managers, and business leaders who want to transition into the AI space. No prior programming or advanced mathematics background is required to succeed. Start reading today to build the skills needed to lead modern AI product teams.