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Course Description
This course on AI Governance offers a guide to building responsible, scalable governance systems for AI. Featuring insights from experts at Collibra, you’ll learn how to align ethics, compliance, and business goals in real-world AI programs.
Start with the Why and Who
You’ll begin by defining the scope of AI governance and aligning key stakeholders, from legal and risk to data science and business. Then, using tools like readiness assessments and maturity models, you'll learn how to set governance objectives that support both compliance needs and organizational strategy.Make Governance Work Every Day
Discover how to embed governance into your daily workflows through checklists, approval gates, and automated documentation. Learn to integrate governance into MLOps pipelines and tailor your approach using lightweight or heavyweight models depending on risk and scale.Scale Smarter Stay Accountable
Explore how to scale governance across teams and regions using federated models and governance platforms like Collibra’s. You’ll also learn to track governance KPIs, maintain traceability, and drive continuous improvement through monitoring and feedback loops.Training 2 or more people?
Get your team access to the full DataCamp platform, including all the features.- 1
Foundations of AI Governance
FreeExplore what AI governance is, how it differs from ethics and risk management, and why it’s essential for responsible AI. Learn the key components of governance systems, roles and responsibilities across teams, and how to embed accountability and oversight throughout the AI lifecycle.
What is AI Governance?50 xpDefining AI governance in practice50 xpDistinguishing governance from ethics50 xpResponding to external AI pressures50 xpWho walks the talk?100 xpComponents of AI governance systems50 xpEmbedding governance across the AI lifecycle50 xpCoordinating roles and responsibilities50 xpMatch the parts to their purpose100 xpGovernance in motion100 xpGovernance roles and responsibilities50 xpSharing responsibility for AI governance50 xpRoles in AI governance50 xpCollaborating through governance committees50 xp - 2
Regulations and Frameworks in Practice
Dive into global AI regulations, including the EU AI Act and U.S. Executive Order, and learn how to identify and manage high-risk systems. Explore key governance actions like conformity assessments, model documentation, and impact assessments, and understand how self-regulation and traceability build compliance, trust, and long-term accountability.
Key regulatory frameworks50 xpUnderstanding regulatory reach50 xpCore elements of high-risk AI regulation50 xpHigh-risk AI in healthcare50 xpGovernance requirements by risk100 xpCredit scoring: compliance order100 xpSelf-regulation versus government oversight50 xpIn-game governance50 xpVoluntary standards: match the framework100 xpProactive versus reactive50 xpGovernance by sector50 xpApplying governance requirements to systems50 xpHiring risk50 xpLegal actions required50 xpRisk classification100 xpAI compliance in practice100 xpGovernance documentation and traceability50 xpModel cards50 xpGovernance artifacts50 xpFrom training to traceability100 xp - 3
Implementing Governance in Organizations
Learn how to design, embed, and scale AI governance in real-world settings. This chapter covers stakeholder alignment, workflow integration via MLOps, lightweight vs. heavyweight governance models, automation for scalability, and KPI-based monitoring strategies to drive continuous improvement and accountability across your AI systems.
Designing an AI governance strategy50 xpFoundational steps50 xpDefine governance objectives100 xpAssessing organizational readiness50 xpOperationalizing governance workflows50 xpGovernance in MLOps workflows50 xpLightweight versus heavyweight100 xpChecklist items50 xpGovernance at scale50 xpScaling governance across teams50 xpGovernance platform capabilities100 xpWhy automation matters for scale50 xpMonitoring and continuous improvement50 xpPost-deployment issues50 xpMonitoring governance actions100 xpGovernance KPI examples50 xpCollibra in action50 xp
Training 2 or more people?
Get your team access to the full DataCamp platform, including all the features.collaborators
Simla SivanandanSenior Manager, Data Intelligence at Collibra
Simla is a seasoned data governance and AI governance professional with over 15 years of experience. She leads the Data Intelligence function at Collibra, where she connects product, go-to-market, and enablement teams to drive customer-centric outcomes. Simla fosters innovation, champions ethical data practices, and thrives on shaping data offices as centers of enablement. Simla is passionate about empowering teams with the clarity, trust, and adoption needed to build sustainable data and AI governance programs.
Alexandre T'KintSenior Product Manager, Collibra
Alexandre T’Kint is Senior Product Manager for AI Governance at Collibra. Over the past six years, he has helped shape the company’s Data Office and spearheaded the GenAI initiative. For the past three years, he has been a key driver of Collibra’s AI efforts and now contributes to the team leading the roadmap for enterprise-scale AI governance.
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