Artificial Intelligence Governance
DataCamp
Last updated June 30, 2026
A guide to building responsible, scalable governance for AI inside an organization, developed with experts from the data-governance company Collibra. It starts with scope and stakeholders — bringing legal, risk, data science, and the business to the same table — and uses readiness assessments and maturity models to set governance objectives that serve both compliance and strategy. It then shows how to make governance part of daily work through checklists, approval gates, and automated documentation, and how to fit it into machine-learning operations (MLOps) pipelines. Finally it looks at scaling governance across teams and regions, tracking governance metrics, and improving continuously. The course is conceptual, with no coding required. You can sample the opening chapter before subscribing; the full course and its Statement of Accomplishment are part of DataCamp Premium.
What you'll learn
- Defining the scope of AI governance and aligning stakeholders
- Setting objectives with readiness assessments and maturity models
- Embedding governance into daily workflows and MLOps pipelines
- Choosing lightweight vs heavyweight governance by risk and scale
- Scaling across teams and regions
- Tracking metrics, traceability, and continuous improvement
Frequently asked questions about Artificial Intelligence Governance
Is Artificial Intelligence Governance free?
No — Artificial Intelligence Governance is a paid course.
What are the prerequisites for Artificial Intelligence Governance?
None.
Does Artificial Intelligence Governance offer a certificate?
Yes. DataCamp Statement of Accomplishment on completion (requires DataCamp Premium).
Why we suggest this course
A strong choice for anyone setting up or maturing an AI governance program — data leaders, risk and compliance professionals, and the legal and business stakeholders they work alongside. Built with practitioners from Collibra, it is unusually practical: it gets into the everyday mechanics (approval gates, traceability, documentation) and the choice between lightweight and heavyweight models depending on risk and scale, rather than staying at the level of principles. One thing to know: the opening chapter is free to try, but the full course and its Statement of Accomplishment are part of DataCamp Premium.