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.
Course Outline
Foundations: The EU AI Act for Technical Teams
- Key obligations and terminology relevant to developers and operators
- Understanding prohibited practices under Article 4 from a technical standpoint
- Mapping legal requirements to engineering controls
Secure and Compliant Development Lifecycle
- Repository structure and policy-as-code for AI projects
- Code review processes and automated static checks for risky patterns
- Dependency and supply-chain management for model components
CI/CD Pipeline Design for Compliance
- Pipeline stages: build, test, validation, package, and deploy
- Integrating governance gates and automated policy checks
- Ensuring artifact immutability and tracking provenance
Model Testing, Validation, and Safety Checks
- Data validation and bias detection tests
- Performance, robustness, and adversarial resilience testing
- Automated acceptance criteria and test reporting
Model Registry, Versioning, and Provenance
- Using MLflow or equivalent tools for model lineage and metadata
- Versioning models and datasets to ensure reproducibility
- Recording provenance and producing audit-ready artifacts
Runtime Controls, Monitoring, and Observability
- Instrumentation for logging inputs, outputs, and decisions
- Monitoring model drift, data drift, and performance metrics
- Alerting mechanisms, automated rollback, and canary deployments
Security, Access Control, and Data Protection
- Least-privilege IAM policies for model training and serving environments
- Protecting training and inference data both at rest and in transit
- Secrets management and secure configuration practices
Auditability and Evidence Collection
- Generating machine-readable logs and human-readable summaries
- Packaging evidence for conformity assessments and audits
- Retention policies and secure storage of compliance artifacts
Incident Response, Reporting, and Remediation
- Detecting suspected prohibited practices or safety incidents
- Technical steps for containment, rollback, and mitigation
- Preparing technical reports for governance bodies and regulators
Summary and Next Steps
Requirements
- A solid understanding 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 responsible for building or maintaining AI components
- DevOps and platform engineers tasked with deployment
- Administrators managing infrastructure and runtime environments
14 Hours