Production MLOps for AI Systems

О курсе

Production MLOps for AI Systems prepares you to take machine learning and generative AI models from experimentation to reliable, scalable production. By completing this course, you will learn how to design and manage deployment pipelines using MLOps and LLMOps principles, analyze and optimize system performance, detect data and concept drift in deployed models, build production-grade feature engineering pipelines, and implement monitoring and observability solutions that ensure long-term reliability. What makes this course unique is its end-to-end focus on real production challenges. Rather than treating deployment, testing, data engineering, and monitoring as isolated topics, the course connects them into a cohesive lifecycle that mirrors how modern AI systems are built and maintained in industry. You will gain hands-on and conceptual experience across cloud-based deployment, performance optimization, adaptable system design, scalable data pipelines, and model evaluation. The course benefits from the expertise of partners including Microsoft, Google Cloud, and IBM, bringing multiple perspectives, tools, and best practices into a single learning journey. This multi-author approach helps you develop flexible, platform-aware skills that transfer across environments—an essential capability for anyone responsible for running AI systems in production.

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