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Kursplan
Foundations: EU AI Act for Technical Teams
- Relevant obligations and terminology for developers and operators
- Understanding prohibited practices under Article 4 from a technical perspective
- Mapping legal requirements to engineering controls
Secure and Compliant Development Lifecycle
- Repository structure and policy-as-code for AI projects
- Code review and automated static checks for risky patterns
- Dependency and supply-chain management for model components
CI/CD Pipeline Design for Compliance
- Pipeline stages: build, test, validation, package, deploy
- Integrating governance gates and automated policy checks
- Artifact immutability and provenance tracking
Model Testing, Validation, and Safety Checks
- Data validation and bias detection tests
- Performance, robustness, and adversarial resilience testing
- Automated acceptance criteria and test reporting
Model Registry, Versioning, and Provenance
- Using MLflow or equivalent for model lineage and metadata
- Versioning models and datasets for reproducibility
- Recording provenance and producing audit-ready artifacts
Runtime Controls, Monitoring, and Observability
- Instrumentation for logging inputs, outputs, and decisions
- Monitoring model drift, data drift, and performance metrics
- Alerting, automated rollback, and canary deployments
Security, Access Control, and Data Protection
- Least-privilege IAM for model training and serving environments
- Protecting training and inference data at rest and in transit
- Secrets management and secure configuration practices
Auditability and Evidence Collection
- Generating machine-readable logs and human-readable summaries
- Packaging evidence for conformity assessments and audits
- Retention policies and secure storage of compliance artifacts
Incident Response, Reporting, and Remediation
- Detecting suspected prohibited practices or safety incidents
- Technical steps for containment, rollback, and mitigation
- Preparing technical reports for governance and regulators
Summary and Next Steps
Krav
- An understanding of software development and deployment workflows
- Experience with containerization and basic Kubernetes concepts
- Familiarity with Git-based source control and CI/CD practices
Audience
- Developers building or maintaining AI components
- DevOps and platform engineers responsible for deployment
- Administrators managing infrastructure and runtime environments
14 timer
