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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
- Analyzing prohibited practices under Article 4 from a technical standpoint
- Translating legal requirements into concrete engineering controls
Secure and Compliant Development Lifecycle
- Repository structuring and policy-as-code implementation for AI projects
- Code review processes and automated static checks for detecting risky patterns
- Managing dependencies and supply chains for model components
Compliance-Focused CI/CD Pipeline Design
- Defining 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 Assurance
- Conducting data validation and bias detection tests
- Assessing performance, robustness, and adversarial resilience
- Establishing automated acceptance criteria and generating test reports
Model Registry, Versioning, and Lineage
- Leveraging MLflow or similar tools for model lineage and metadata management
- Implementing versioning 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, automated rollback, and canary deployment strategies
Security, Access Control, and Data Protection
- Applying least-privilege IAM policies to model training and serving environments
- Safeguarding training and inference data both at rest and in transit
- Adopting best practices for secrets management and secure configuration
Auditability and Evidence Management
- Generating both machine-readable logs and human-readable summaries
- Packaging evidence for conformity assessments and regulatory audits
- Defining retention policies and ensuring secure storage of compliance artifacts
Incident Response, Reporting, and Remediation
- Identifying potential prohibited practices or safety incidents
- Executing technical procedures for containment, rollback, and mitigation
- Drafting technical reports for governance bodies and regulators
Summary and Future Steps
Requirements
- A solid understanding of software development and deployment workflows
- Experience with containerization and fundamental 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 managing deployment processes
- Administrators overseeing infrastructure and runtime environments
