Operational Excellence and Governance

Healthcare AI projects fail governance reviews — not because models underperform, but because data, privacy controls, and platform configurations don't hold up to scrutiny. This course gives you the skills to make sure yours do. You'll apply data governance controls in Microsoft Foundry (formerly Azure AI Studio) to classify, document, and permission multimodal medical datasets. You'll evaluate dataset lineage and access decisions for auditability and policy alignment. You'll apply de-identification and minimum-necessary practices to imaging metadata, report text, and patient-linked attributes, and evaluate re-identification exposure before a workflow proceeds. You'll review Microsoft Foundry project-configuration scenarios, analyze deployment pathway options, and evaluate monitoring and cost-performance tradeoffs for medical imaging AI workloads. By the end, you'll produce governance artifacts, privacy risk assessments, and configuration review summaries that reflect real healthcare AI governance responsibilities. A working understanding of healthcare data concepts and basic cloud platform familiarity is helpful; no prior governance engineering background is required.