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Course Outline
Foundations: EU AI Act for Technical Teams
- Key obligations and terminology relevant to developers and operators
- Gaining a technical understanding of prohibited practices under Article 4
- Mapping legal requirements to engineering controls
Secure and Compliant Development Lifecycle
- Repository structure and policy-as-code implementation for AI projects
- Code review processes and automated static checks to identify risky patterns
- Dependency and supply-chain management for model components
CI/CD Pipeline Design for Compliance
- Essential pipeline stages: build, test, validation, package, deploy
- Integrating governance gates and automated policy checks
- Ensuring artifact immutability and tracking provenance
Model Testing, Validation, and Safety Checks
- Conducting data validation and bias detection tests
- Performing performance, robustness, and adversarial resilience testing
- Establishing automated acceptance criteria and generating test reports
Model Registry, Versioning, and Provenance
- Utilizing MLflow or comparable tools for model lineage and metadata management
- 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 decision processes
- Monitoring model drift, data drift, and key performance metrics
- Configuring alerting mechanisms, automated rollbacks, and canary deployments
Security, Access Control, and Data Protection
- Enforcing least-privilege IAM policies for model training and serving environments
- Securing training and inference data both at rest and in transit
- Implementing secrets management and secure configuration practices
Auditability and Evidence Collection
- Generating machine-readable logs alongside human-readable summaries
- Packaging evidence for conformity assessments and external audits
- Establishing retention policies and secure storage solutions 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
- Experience with containerization and foundational 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 tasked with deployment responsibilities
- Administrators managing infrastructure and runtime environments
14 Hours