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Duration 14 hours
Course Outline
Foundations: EU AI Act for Technical Teams
- Key obligations and terminology relevant to developers and operators
- A technical understanding of 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
- Dependency and supply-chain management for model components
CI/CD Pipeline Design for Compliance
- Pipeline stages: build, test, validation, packaging, and deployment
- Integrating governance gates and automated policy checks
- Ensuring artifact immutability and tracking provenance
Model Testing, Validation, and Safety Checks
- Data validation and bias detection testing
- Performance, robustness, and adversarial resilience testing
- Automated acceptance criteria and test reporting
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 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 to model training and serving environments
- Securing training and inference data at rest and in transit
- Managing secrets and secure configuration practices
Auditability and Evidence Collection
- Creating machine-readable logs and human-readable summaries
- Compiling evidence packages for conformity assessments and audits
- Managing retention policies and secure storage of compliance artifacts
Incident Response, Reporting, and Remediation
- Identifying potential 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 processes
- Experience with containerization and fundamental Kubernetes concepts
- Familiarity with Git-based version control and CI/CD practices
Target Audience
- Developers creating or maintaining AI components
- DevOps and platform engineers managing deployments
- Administrators overseeing infrastructure and runtime environments
