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Duration 14 hours
Course Outline
Foundations: Understanding the EU AI Act for Technical Teams
- Key obligations and terminology relevant to developers and operators.
- A technical perspective on understanding prohibited practices under Article 4.
- Translating legal requirements into concrete engineering controls.
Secure and Compliant Development Lifecycle
- Optimizing repository structure and implementing policy-as-code for AI projects.
- Utilizing code review and automated static analysis to identify risky patterns.
- Managing dependencies and supply-chain security 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 into the workflow.
- Ensuring artifact immutability and tracking provenance.
Model Testing, Validation, and Safety Assurance
- Conducting data validation and bias detection tests.
- Testing for performance, robustness, and resilience against adversarial attacks.
- Establishing automated acceptance criteria and generating comprehensive test reports.
Model Registry, Versioning, and Provenance
- Leveraging MLflow or equivalent tools for tracking model lineage and metadata.
- Implementing versioning for models and datasets to ensure reproducibility.
- Recording provenance details to produce audit-ready artifacts.
Runtime Controls, Monitoring, and Observability
- Implementing instrumentation to log inputs, outputs, and decision-making processes.
- Monitoring for model drift, data drift, and key performance metrics.
- Configuring alerting systems, automated rollbacks, and canary deployments.
Security, Access Control, and Data Protection
- Applying least-privilege IAM policies for 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 Management
- Generating machine-readable logs alongside 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 suspected prohibited practices or safety-related incidents.
- Executing technical steps for containment, rollback, and mitigation.
- Drafting technical reports for governance bodies and regulators.
Summary and Recommended Next Steps
Requirements
- A solid understanding of software development and deployment workflows
- Experience with containerization and fundamental 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
