What you'll learn

Teams keep shipping AI on data that was never governed for it. The failures are quiet: a credit model that drifts after a data refresh, a customer assistant that leaks something it shouldn't, or a decision nobody can trace back. That's a governance problem, not a model bug.

In this course, Data Governance: AI and Machine Learning, you'll gain the ability to identify, apply, and evaluate the data governance practices that AI and ML workflows demand.

First, you'll explore where governance shows up across the AI lifecycle, how it differs from model governance and responsible AI, and how the risks shift across traditional ML, generative AI, and retrieval-augmented systems.

Next, you'll discover how to make AI data traceable and accountable using metadata, lineage, provenance, dataset datasheets, and clear ownership, and where to place governance checkpoints that align with NIST AI RMF, the EU AI Act, and ISO/IEC 42001.

Finally, you'll learn how to evaluate a real scenario like tracing a bad AI outcome back to weak governance, recommend the controls that would have caught it, and prioritize what to fix first while keeping a clear line between what data governance owns and what model validation, risk, and legal teams are responsible for.

When you're finished with this course, you'll have the skills and knowledge of data governance for AI and ML needed to make AI-relevant data trustworthy, auditable, and defensible across its lifecycle.