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Duration 14 hours
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 specific engineering controls
Secure and Compliant Development Lifecycle
- Repository structure and policy-as-code implementation for AI projects
- Code review processes 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, and deploy
- Integrating governance gates and automated policy checks
- Ensuring artifact immutability and provenance tracking
Model Testing, Validation, and Safety Checks
- 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
- Utilizing MLflow or equivalent tools for model lineage and metadata
- Versioning models and datasets to ensure reproducibility
- Recording provenance and producing audit-ready artifacts
Runtime Controls, Monitoring, and Observability
- Instrumenting systems to log inputs, outputs, and decisions
- Monitoring model drift, data drift, and key performance metrics
- Implementing alerting, automated rollback, and canary deployments
Security, Access Control, and Data Protection
- Applying least-privilege IAM policies for model training and serving environments
- Protecting training and inference data both at rest and in transit
- Managing secrets and adhering to secure configuration practices
Auditability and Evidence Collection
- Generating both machine-readable logs and human-readable summaries
- Packaging evidence for conformity assessments and audits
- Establishing retention policies and secure storage for compliance artifacts
Incident Response, Reporting, and Remediation
- Detecting suspected prohibited practices or safety incidents
- Executing technical steps for containment, rollback, and mitigation
- Preparing technical reports for governance bodies and regulators
Summary and Next Steps
Requirements
- A solid understanding of software development and deployment workflows
- Practical experience with containerization and fundamental Kubernetes concepts
- Familiarity with Git-based source control and CI/CD practices
Target Audience
- Developers responsible for building or maintaining AI components
- DevOps and platform engineers overseeing deployment strategies
- Administrators managing infrastructure and runtime environments
