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Duration 14 hours
Course Outline
Foundations: The EU AI Act for Technical Teams
- Key obligations and terminology for developers and operators
- Tech-centric understanding 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 reviews and automated static checks for risky patterns
- Dependency and supply-chain management for model components
Designing CI/CD Pipelines for Compliance
- Pipeline stages: build, test, validation, packaging, and deployment
- Integration of governance gates and automated policy checks
- Ensuring artifact immutability and tracking provenance
Model Testing, Validation, and Safety Checks
- Tests for data validation and bias detection
- Assessing performance, robustness, and adversarial resilience
- Defining automated acceptance criteria and generating test reports
Model Registry, Versioning, and Provenance
- Utilizing MLflow or similar tools for model lineage and metadata
- 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 decisions
- Monitoring model drift, data drift, and 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 adhering to secure configuration practices
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
- Drafting 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 deployments
- Administrators managing infrastructure and runtime environments