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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
- Technical interpretation of prohibited practices under Article 4
- Translating legal requirements into engineering controls
Secure and Compliant Development Lifecycle
- Repository structure and policy-as-code for AI projects
- Code review and automated static analysis for risky patterns
- Managing dependencies and the supply chain for model components
Compliance-Driven CI/CD Pipeline Design
- Pipeline stages: build, test, validation, packaging, and deployment
- Integrating governance gates and automated policy verification
- Ensuring artifact immutability and tracking provenance
Model Testing, Validation, and Safety Verification
- Data validation and bias detection testing
- Assessing performance, robustness, and adversarial resilience
- Defining automated acceptance criteria and generating test reports
Model Registry, Versioning, and Provenance
- Leveraging MLflow or similar tools for model lineage and metadata
- Versioning models and datasets to ensure reproducibility
- Recording provenance and creating audit-ready artifacts
Runtime Controls, Monitoring, and Observability
- Instrumentation for logging inputs, outputs, and decision-making
- Monitoring model drift, data drift, and performance indicators
- Implementing alerting, automated rollback, and canary deployments
Security, Access Control, and Data Protection
- Applying least-privilege IAM for model training and serving environments
- Safeguarding training and inference data at rest and in transit
- Best practices for secrets management and secure configuration
Auditability and Evidence Collection
- Generating machine-readable logs alongside human-readable summaries
- Packaging evidence for conformity assessments and audits
- Establishing retention policies and secure storage for compliance artifacts
Incident Response, Reporting, and Remediation
- Identifying suspected prohibited practices or safety incidents
- Technical procedures for containment, rollback, and mitigation
- Preparing technical reports for governance bodies and regulators
Summary and Next Steps
Requirements
- A solid grasp 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 creating or maintaining AI components
- DevOps and platform engineers overseeing deployment
- Administrators managing infrastructure and runtime environments
