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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
- Technical interpretation of prohibited practices under Article 4
- Translating legal requirements into actionable engineering controls
Secure and Compliant Development Lifecycle
- Structuring repositories and applying policy-as-code for AI projects
- Conducting code reviews and automated static analysis for risky patterns
- Managing dependencies and the supply chain for model components
Designing CI/CD Pipelines for Compliance
- Defining 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
- Executing data validation and bias detection tests
- Assessing performance, robustness, and adversarial resilience
- Establishing automated acceptance criteria and generating test reports
Model Registry, Versioning, and Provenance
- Leveraging MLflow or equivalent tools for model lineage and metadata management
- Implementing version control for models and datasets to ensure reproducibility
- Documenting provenance and creating audit-ready artifacts
Runtime Controls, Monitoring, and Observability
- Instrumenting systems to log inputs, outputs, and decision-making processes
- Monitoring for model drift, data drift, and key performance metrics
- Implementing alerting systems, automated rollbacks, and canary deployments
Security, Access Control, and Data Protection
- Applying least-privilege IAM policies to model training and serving environments
- Securing training and inference data both at rest and in transit
- Managing secrets and adhering to secure configuration practices
Auditability and Evidence Collection
- Generating machine-readable logs alongside human-readable summaries
- Packaging evidence for conformity assessments and regulatory audits
- Defining retention policies and securing the storage of compliance artifacts
Incident Response, Reporting, and Remediation
- Identifying suspected prohibited practices or safety incidents
- Executing technical steps for containment, rollback, and mitigation
- Drafting technical reports for governance bodies and regulators
Summary and Next Steps
Requirements
- Comprehensive understanding of software development and deployment workflows
- Practical experience with containerization and foundational Kubernetes concepts
- Proficiency in Git-based source control and CI/CD practices
Target Audience
- Developers responsible for building or maintaining AI components
- DevOps and platform engineers overseeing deployment processes
- Administrators managing infrastructure and runtime environments
