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 Duration 14 hours

Course Outline

Foundational Concepts: EU AI Act for Technical Teams

  • Key obligations and terminology applicable to developers and operators
  • Analysis of prohibited practices under Article 4 from a technical standpoint
  • Aligning legal mandates with specific engineering controls

Secure and Compliant Development Lifecycle

  • Repository architecture and policy-as-code implementation for AI initiatives
  • Code review processes and automated static analysis for high-risk patterns
  • Dependency and supply-chain oversight for model components

CI/CD Pipeline Design for Regulatory Adherence

  • Pipeline phases: build, test, validation, packaging, and deployment
  • Integration of governance checkpoints and automated policy verification
  • Ensuring artifact integrity and comprehensive provenance tracking

Model Testing, Validation, and Safety Verification

  • Data integrity checks and bias detection protocols
  • Assessment of performance, robustness, and resilience against adversarial attacks
  • Definition of automated acceptance criteria and test reporting standards

Model Registry, Versioning, and Provenance Management

  • Utilizing MLflow or equivalent tools for model lineage and metadata management
  • Version control for models and datasets to ensure reproducibility
  • Documenting provenance and generating audit-ready artifacts

Runtime Controls, Monitoring, and Observability

  • Instrumentation for logging inputs, outputs, and decision logic
  • Continuous monitoring of model drift, data shifts, and performance indicators
  • Implementation of alerting systems, automated rollback mechanisms, and canary deployments

Security, Access Control, and Data Protection

  • Application of least-privilege IAM policies for model training and serving environments
  • Safeguarding training and inference data both at rest and in transit
  • Effective secrets management and secure configuration practices

Auditability and Evidence Preservation

  • Generation of machine-readable logs and human-readable summaries
  • Compilation of evidence packages for conformity assessments and audits
  • Enforcement of retention policies and secure storage of compliance artifacts

Incident Response, Reporting, and Remediation

  • Detection of potential prohibited practices or safety incidents
  • Technical procedures for containment, rollback, and mitigation
  • Preparation of technical reports for governance bodies and regulatory authorities

Conclusion and Actionable Next Steps

Requirements

  • Knowledge 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 constructing or maintaining AI components
  • DevOps and platform engineers responsible for deployment
  • Administrators managing infrastructure and runtime environments

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