The AI Engineer's Guide to Surviving the EU AI Act

Appendix C. The Integration of AI Governance and MLOps

Integrating AI governance into the MLOps Stack Canvas (Figure C-1) enables teams to proactively identify and mitigate risks related to bias, fairness, privacy, and security, while also supporting compliance with relevant regulations and standards. This integrated approach enhances model quality, promotes reliable predictions, and reinforces responsible AI development. It scales alongside technical capabilities, encourages cross-functional collaboration, and facilitates audits and accountability.

In this appendix, we’ll review  the components of the MLOps Stack Canvas (introduced in Chapter 2) and extend the framework to incorporate key AI governance concepts.

Figure C-1. The MLOps Stack Canvas framework

Value Proposition

In addition to formulating a general value proposition and data governance goals for the MLOps platform, consider how AI governance contributes to the overall value of the ML project. For instance:

• Add a “Compliance Requirements” section to outline applicable regulations and standards (e.g., the EU AI Act, GDPR, industry-specific AI regulations).

• Include a “Risk Classification” component aligned with the EU AI Act categories (unacceptable, high, limited, or minimal risk) to assess the potential risks and expected benefits of the AI system.

• Add an “Ethical Impact Assessment” section to evaluate the potential ...