Owning the AI Lifecycle in Azure

Owning the AI Lifecycle in Azure focuses on managing AI system delivery from build through deployment and ongoing operations. AI initiatives introduce new complexities in data architecture, model development, performance evaluation, and production monitoring. This course equips you to coordinate those moving parts within enterprise environments. You’ll examine cloud-native AI architecture decisions, data readiness requirements, and model development workflows using Azure Machine Learning and Microsoft Foundry models. The course explores how AutoML, generative AI, AI agents, and Copilot deployments fit into structured delivery processes. You will also learn how to interpret model performance metrics, support MLOps practices, and guide production monitoring strategies to ensure AI systems remain reliable and aligned with business objectives. By the end of this course, you’ll be able to coordinate AI delivery across development and operational stages while supporting scalable, production-ready AI systems within the Microsoft Azure ecosystem.