AI Project Management: Shipping and Scaling AI Systems

Many AI initiatives stall in the research phase and never make it to production. Successfully managing an artificial intelligence project requires a unique blend of traditional project management, data strategy, and modern deployment practices. This text-only course equips you with the framework needed to guide AI projects from initial concept to scalable production. You will understand how to set realistic expectations, manage data pipelines, align cross-functional teams, and implement basic MLOps practices to ensure your models deliver ongoing business value. In this course, you will: - Learn the foundational terminology and phases of the AI project lifecycle; - Understand how to define clear metrics for model performance and business success; - Manage data collection, labeling, and pipeline requirements effectively; - Apply basic MLOps concepts to transition models from prototypes to scalable systems; - Address modern challenges including LLM evaluation, bias, and deployment risks; - Collaborate confidently with data scientists, engineers, and business stakeholders. You will start with core definitions and the AI lifecycle, then progress through data strategy, model evaluation, and deployment workflows. The reading material concludes with practical frameworks for monitoring and scaling your AI solutions in production. This course is designed for aspiring AI project managers, product managers, and business leaders who want to oversee AI initiatives. No prior coding or advanced mathematical experience is required. Start reading today to master the art of shipping successful, scalable AI systems.