This ebook offers a pragmatic framework for implementing AI governance without slowing delivery. It explains why AI governance is often confusing and overly focused on ethics without actionable processes. The document introduces lightweight templates for model review, documentation, escalation and periodic audit, emphasizing clear ownership across developers, product owners and governance boards. It highlights the overlap between data and AI governance and the role of MLOps in ensuring quality and accountability. By embedding governance into everyday workflows, organizations can manage risk, comply with regulation and scale AI responsibly while maintaining speed and innovation.

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