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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
- Interpreting prohibited practices under Article 4 from a technical standpoint
- Translating legal requirements into concrete engineering controls
Secure and Compliant Development Lifecycle
- Repository structures and policy-as-code strategies for AI projects
- Code review processes and automated static checks for risky patterns
- Managing dependencies and the supply chain for model components
Designing CI/CD Pipelines for Compliance
- Defining pipeline stages: build, test, validation, package, and deploy
- Integrating governance gates and automated policy verification
- Ensuring artifact immutability and tracking provenance
Model Testing, Validation, and Safety Checks
- Data validation and bias detection methodologies
- 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 generating 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 deployment strategies
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
- Best practices for secrets management and secure configuration
Auditability and Evidence Collection
- Generating machine-readable logs and human-readable summaries
- Packaging evidence for conformity assessments and regulatory audits
- Implementing 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 boards 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
Target Audience
- Developers building or maintaining AI components
- DevOps and platform engineers responsible for deployment
- Administrators managing infrastructure and runtime environments
