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Course Outline
Foundations: The EU AI Act for Technical Teams
- Key obligations and terminology relevant to developers and operators.
- Technical interpretation of prohibited practices under Article 4.
- Mapping legal requirements to engineering controls.
Secure and Compliant Development Lifecycle
- Repository structure and policy-as-code strategies for AI projects.
- Code review processes and automated static checks for identifying risky patterns.
- Dependency and supply-chain management for model components.
CI/CD Pipeline Design for Compliance
- Pipeline stages: build, test, validation, package, and deploy.
- Integrating governance gates and automated policy checks.
- Ensuring artifact immutability and tracking provenance.
Model Testing, Validation, and Safety Checks
- Data validation and bias detection tests.
- Performance, robustness, and adversarial resilience testing.
- Automated acceptance criteria and test reporting mechanisms.
Model Registry, Versioning, and Provenance
- Utilizing MLflow or equivalent tools for model lineage and metadata management.
- Versioning models and datasets to ensure reproducibility.
- Recording provenance and generating audit-ready artifacts.
Runtime Controls, Monitoring, and Observability
- Instrumentation for logging inputs, outputs, and decision-making processes.
- Monitoring model drift, data drift, and performance metrics.
- Alerting mechanisms, automated rollback procedures, and canary deployments.
Security, Access Control, and Data Protection
- Implementing least-privilege IAM for model training and serving environments.
- Protecting training and inference data both at rest and in transit.
- Secrets management and secure configuration best practices.
Auditability and Evidence Collection
- Generating machine-readable logs and human-readable summaries.
- Packaging evidence for conformity assessments and audits.
- Defining retention policies and secure storage for 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 bodies and regulators.
Summary and Next Steps
Requirements
- A foundational understanding of software development and deployment workflows.
- Experience with containerization technologies and basic Kubernetes concepts.
- Familiarity with Git-based source control and CI/CD practices.
Target Audience
- Developers who build or maintain AI components.
- DevOps and platform engineers responsible for deployment processes.
- Administrators managing infrastructure and runtime environments.
14 Hours
