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Duration 14 hours
Course Outline
Foundational Concepts: EU AI Act for Technical Teams
- Key obligations and terminology applicable to developers and operators
- Analysis of prohibited practices under Article 4 from a technical standpoint
- Aligning legal mandates with specific engineering controls
Secure and Compliant Development Lifecycle
- Repository architecture and policy-as-code implementation for AI initiatives
- Code review processes and automated static analysis for high-risk patterns
- Dependency and supply-chain oversight for model components
CI/CD Pipeline Design for Regulatory Adherence
- Pipeline phases: build, test, validation, packaging, and deployment
- Integration of governance checkpoints and automated policy verification
- Ensuring artifact integrity and comprehensive provenance tracking
Model Testing, Validation, and Safety Verification
- Data integrity checks and bias detection protocols
- Assessment of performance, robustness, and resilience against adversarial attacks
- Definition of automated acceptance criteria and test reporting standards
Model Registry, Versioning, and Provenance Management
- Utilizing MLflow or equivalent tools for model lineage and metadata management
- Version control for models and datasets to ensure reproducibility
- Documenting provenance and generating audit-ready artifacts
Runtime Controls, Monitoring, and Observability
- Instrumentation for logging inputs, outputs, and decision logic
- Continuous monitoring of model drift, data shifts, and performance indicators
- Implementation of alerting systems, automated rollback mechanisms, and canary deployments
Security, Access Control, and Data Protection
- Application of least-privilege IAM policies for model training and serving environments
- Safeguarding training and inference data both at rest and in transit
- Effective secrets management and secure configuration practices
Auditability and Evidence Preservation
- Generation of machine-readable logs and human-readable summaries
- Compilation of evidence packages for conformity assessments and audits
- Enforcement of retention policies and secure storage of compliance artifacts
Incident Response, Reporting, and Remediation
- Detection of potential prohibited practices or safety incidents
- Technical procedures for containment, rollback, and mitigation
- Preparation of technical reports for governance bodies and regulatory authorities
Conclusion and Actionable 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 practices
Target Audience
- Developers constructing or maintaining AI components
- DevOps and platform engineers responsible for deployment
- Administrators managing infrastructure and runtime environments