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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
- A technical perspective on prohibited practices under Article 4
- Translating legal requirements into engineering controls
Secure and Compliant Development Lifecycle
- Repository structures and policy-as-code for AI projects
- Code reviews and automated static analysis for risky patterns
- Managing dependencies and supply chains for model components
CI/CD Pipeline Design for Compliance
- 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 Checks
- Data validation and bias detection testing
- Testing 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
- Documenting 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 metrics
- Implementing alerting, automated rollbacks, 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
- Managing secrets and enforcing secure configuration practices
Auditability and Evidence Collection
- Generating 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
- Identifying suspected prohibited practices or safety incidents
- Executing technical containment, rollback, and mitigation steps
- Drafting technical reports for governance bodies and regulators
Summary and Next Steps
Requirements
- Knowledge of software development and deployment workflows
- Experience with containerization and fundamental Kubernetes concepts
- Familiarity with Git-based source control and CI/CD methodologies
Target Audience
- Developers responsible for building or maintaining AI components
- DevOps and platform engineers overseeing deployment processes
- Administrators managing infrastructure and runtime environments
