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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 mandates into specific engineering controls
Secure and Compliant Development Lifecycle
- Structuring repositories and implementing policy-as-code for AI projects
- Code reviews and automated static analysis for identifying risky patterns
- Managing dependencies and supply chains for model components
Designing CI/CD Pipelines for Compliance
- Defining pipeline stages: build, test, validation, packaging, and deployment
- Embedding governance gates and automated policy checks
- Ensuring artifact immutability and tracking provenance
Model Testing, Validation, and Safety Verification
- Conducting data validation and bias detection tests
- Evaluating performance, robustness, and resilience against adversarial attacks
- Defining automated acceptance criteria and generating test reports
Model Registry, Versioning, and Provenance Tracking
- Leveraging MLflow or similar tools for model lineage and metadata management
- Versioning models and datasets to ensure reproducibility
- Recording provenance data and creating audit-ready artifacts
Runtime Controls, Monitoring, and Observability
- Instrumenting systems to log inputs, outputs, and decision-making processes
- Monitoring for model drift, data drift, and performance metrics
- Implementing alerting, automated rollback mechanisms, and canary deployments
Security, Access Control, and Data Protection
- Applying least-privilege IAM policies for training and serving environments
- Safeguarding training and inference data both 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
- Executing technical steps 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
- Practical experience with containerization and fundamental Kubernetes concepts
- Working knowledge of Git-based version control and CI/CD methodologies
Target Audience
- Developers responsible for creating or maintaining AI components
- DevOps and platform engineers overseeing deployment operations
- Administrators managing underlying infrastructure and runtime environments
