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Course Outline
Foundations: The 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 tools 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
Requirements
- A solid understanding of software development and deployment workflows.
- Experience with containerization and foundational 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 Hours
