Thank you for sending your enquiry! One of our team members will contact you shortly.
Thank you for sending your booking! One of our team members will contact you shortly.
Duration 14 hours
Course Outline
Foundations: Understanding the EU AI Act for Engineering Teams
- Key obligations and terminology specific to developers and operators
- A technical interpretation of prohibited practices defined under Article 4
- Translating legal requirements into concrete engineering controls
Secure and Compliant Development Lifecycle
- Structuring repositories and implementing policy-as-code for AI initiatives
- Conducting code reviews and running automated static analyses to identify risky patterns
- Managing dependencies and supply chains for model components
Designing CI/CD Pipelines for Compliance
- Defining pipeline stages: build, test, validation, packaging, and deployment
- Integrating governance gates and automated policy verification checks
- Ensuring artifact immutability and tracking provenance
Model Testing, Validation, and Safety Verification
- Executing data validation and bias detection tests
- Assessing performance, robustness, and resilience against adversarial attacks
- Establishing automated acceptance criteria and generating test reports
Model Registry, Versioning, and Provenance
- Utilizing MLflow or similar tools for model lineage and metadata management
- Versioning models and datasets to ensure reproducibility
- Documenting provenance and creating audit-ready artifacts
Runtime Controls, Monitoring, and Observability
- Implementing instrumentation to log inputs, outputs, and decision logic
- Monitoring for model drift, data drift, and key performance metrics
- Configuring alerting mechanisms, automated rollbacks, and canary deployments
Security, Access Control, and Data Protection
- Enforcing least-privilege IAM for model training and serving environments
- Safeguarding 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
- Implementing retention policies and secure storage for compliance artifacts
Incident Response, Reporting, and Remediation
- Identifying suspected prohibited practices or safety incidents
- Executing technical containment, rollback, and mitigation procedures
- Drafting technical reports for governance bodies and regulators
Conclusion 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
