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Duration 14 hours
Course Outline
Foundations: The EU AI Act for Technical Teams
- Key obligations and terminology relevant to developers and operators
- A technical perspective on understanding prohibited practices under Article 4
- Aligning legal requirements with specific engineering controls
Secure and Compliant Development Lifecycle
- Repository structure and policy-as-code implementation for AI projects
- Code reviews and automated static checks to identify risky patterns
- Managing dependencies and the supply chain for model components
CI/CD Pipeline Design for Compliance
- Defining pipeline stages: build, test, validation, package, and deploy
- Incorporating governance gates and automated policy checks
- Ensuring artifact immutability and tracking provenance
Model Testing, Validation, and Safety Checks
- Data validation and bias detection testing
- Assessing performance, robustness, and adversarial resilience
- Establishing automated acceptance criteria and test reporting
Model Registry, Versioning, and Provenance
- Utilising MLflow or equivalent tools for model lineage and metadata
- Versioning models and datasets to ensure reproducibility
- Recording provenance details and creating audit-ready artifacts
Runtime Controls, Monitoring, and Observability
- Instrumenting systems for logging inputs, outputs, and decisions
- Monitoring model drift, data drift, and key performance metrics
- Implementing alerting, automated rollback, and canary deployments
Security, Access Control, and Data Protection
- Applying least-privilege IAM to model training and serving environments
- Safeguarding training and inference data both at rest and in transit
- Managing secrets and implementing secure configuration practices
Auditability and Evidence Collection
- Generating machine-readable logs and human-readable summaries
- Compiling evidence for conformity assessments and audits
- Managing retention policies and secure storage for compliance artifacts
Incident Response, Reporting, and Remediation
- Identifying suspected prohibited practices or safety incidents
- Executing technical steps for containment, rollback, and mitigation
- Drafting technical reports for governance bodies and regulators
Summary and Next Steps
Requirements
- A solid understanding of software development and deployment workflows
- Experience with containerization and fundamental Kubernetes concepts
- Familiarity with Git-based source control and CI/CD practices
Target Audience
- Developers responsible for building or maintaining AI components
- DevOps and platform engineers managing deployment processes
- Administrators overseeing infrastructure and runtime environments
