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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 implementation for AI projects
  • Code review processes and automated static analysis for identifying 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
  • Assessing performance, robustness, and adversarial resilience
  • Automated acceptance criteria and comprehensive test reporting

Model Registry, Versioning, and Provenance

  • Utilizing MLflow or equivalent tools for model lineage and metadata management
  • 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 decision-making processes
  • Monitoring model drift, data drift, and performance metrics
  • Setting up alerting, automated rollback mechanisms, and canary deployments

Security, Access Control, and Data Protection

  • Implementing least-privilege IAM policies for model training and serving environments
  • Securing training and inference data both at rest and in transit
  • Best practices for secrets management and secure configuration

Auditability and Evidence Collection

  • Generating machine-readable logs and human-readable summaries
  • Packaging evidence for conformity assessments and audits
  • Establishing retention policies and secure storage for compliance artifacts

Incident Response, Reporting, and Remediation

  • Detecting suspected prohibited practices or safety incidents
  • Executing 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 operations
  • Administrators managing infrastructure and runtime environments
 14 Hours

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