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This guide brings together EU AI Act requirements with practical engineering solutions.
New to the AI Act? Start with our comprehensive introduction to understand the regulatory landscape.
What you'll find here:
- AI Act requirements decoded for technical teams
- Working code examples demonstrating compliance in practice
- Engineering practices that build trustworthy AI systems
- Clear connections between legal obligations and technical implementation
To ensure compliance, trust must be built into the engineering process of AI systems. This guide demonstrates how modern MLOps practices provide a strong foundation for meeting regulatory requirements while building robust, trustworthy AI solutions.
This is a living resource that will be continuously updated as more legal and technical information becomes available. Your feedback and discussions are highly encouraged to help us refine the content and ensure its relevance to the community.
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Find out how to navigate the resources on this website
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A worked end-to-end ML pipeline for a (hypothetical) high-risk AI system
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Engineering techniques for trustworthy AI systems
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Requirements originating from the AI Act and connections to engineering practice
Acknowledgment
The Bavarian AI Act Accelerator is a two-year project funded by the Bavarian State Ministry of Digital Affairs to support SMEs, start-ups, and the public sector in Bavaria in complying with the EU AI Act. Under the leadership of the appliedAI Institute for Europe and in collaboration with Ludwig Maximilian University, the Technical University of Munich, and the Technical University of Nuremberg, training, resources, and events are being offered. The project objectives include reducing compliance costs, shortening the time to compliance, and strengthening AI innovation. To achieve these objectives, the project is divided into five work packages: project management, research, education, tools and infrastructure, and community.