Build lean AI value streams that move models from idea to measurable business impact. This course teaches you how to evaluate and improve AI workflows across data strategy, feature management, model training, deployment, monitoring, and feedback.

You’ll learn to map the AI development lifecycle, identify workflow waste and bottlenecks, and apply feature stores, versioning, and practical MLOps strategies. Explore model deployment approaches including A/B testing, canary releases, and shadow mode, while learning how monitoring and concept drift management help keep models effective in production. You’ll also assess AI security risks, governance needs, cost considerations, and human oversight. Compare batch, real-time, and edge orchestration approaches, then connect technical metrics with business goals to evaluate continuing AI value. Designed for learners who want to understand how AI systems move reliably from development into production, this course uniquely combines lean value-stream thinking with MLOps practices. By the end, you’ll be able to evaluate an AI value stream, identify improvement opportunities, recommend changes that strengthen flow and reliability, and use an AI maturity roadmap to plan future improvements.