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Duration 14 hours
Course Outline
Foundations: Understanding the EU AI Act for Engineering Teams
- Key regulatory obligations and terminology relevant to developers and system operators
- Technical interpretation of prohibited practices under Article 4
- Translating legal mandates into concrete engineering controls
The Secure and Compliant Development Lifecycle
- Structuring repositories and applying policy-as-code principles to AI projects
- Integrating code reviews and automated static analysis to identify risky patterns
- Managing dependencies and supply chain integrity for model components
Designing CI/CD Pipelines with Compliance in Mind
- Defining pipeline stages: build, test, validation, packaging, and deployment
- Embedding governance gates and automated policy checks into the workflow
- Ensuring artifact immutability and maintaining accurate provenance tracking
Testing, Validation, and Safety Assurance for Models
- Conducting data validation and bias detection tests
- Assessing performance, robustness, and resilience against adversarial attacks
- Defining automated acceptance criteria and generating comprehensive test reports
Model Registry, Versioning, and Provenance Management
- Utilizing MLflow or equivalent tools for model lineage and metadata management
- Versioning models and datasets to ensure reproducibility
- Documenting provenance and generating audit-ready artifacts
Runtime Controls, Monitoring, and Observability
- Implementing instrumentation to log inputs, outputs, and decision logic
- Monitoring for model drift, data drift, and performance degradation
- Configuring 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 best practices
Auditability and Evidence Collection
- Generating machine-readable logs alongside human-readable summaries
- Packaging evidence for conformity assessments and regulatory audits
- Establishing retention policies and secure storage for compliance artifacts
Incident Response, Reporting, and Remediation
- Detecting suspected violations of prohibited practices or safety incidents
- Executing technical steps for containment, rollback, and mitigation
- Drafting technical reports for internal governance and regulatory bodies
Wrap-up and Future Actions
Requirements
- A solid grasp 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 building or maintaining AI components
- DevOps and platform engineers responsible for deployment
- Administrators managing infrastructure and runtime environments
