Get in Touch
 Duration 14 hours

Course Outline

Foundations: The EU AI Act for Technical Teams

  • Key obligations and terminology for developers and operators
  • Tech-centric understanding of prohibited practices under Article 4
  • Translating legal requirements into engineering controls

Secure and Compliant Development Lifecycle

  • Repository structure and policy-as-code for AI projects
  • Code reviews and automated static checks for risky patterns
  • Dependency and supply-chain management for model components

Designing CI/CD Pipelines for Compliance

  • Pipeline stages: build, test, validation, packaging, and deployment
  • Integration of governance gates and automated policy checks
  • Ensuring artifact immutability and tracking provenance

Model Testing, Validation, and Safety Checks

  • Tests for data validation and bias detection
  • Assessing performance, robustness, and adversarial resilience
  • Defining automated acceptance criteria and generating test reports

Model Registry, Versioning, and Provenance

  • Utilizing MLflow or similar tools for model lineage and metadata
  • Versioning models and datasets to ensure reproducibility
  • Recording provenance and generating audit-ready artifacts

Runtime Controls, Monitoring, and Observability

  • Instrumentation for logging inputs, outputs, and decisions
  • Monitoring model drift, data drift, and performance metrics
  • Implementing alerting, automated rollback, and canary deployments

Security, Access Control, and Data Protection

  • Applying least-privilege IAM to model training and serving environments
  • Safeguarding training and inference data both at rest and in transit
  • Managing secrets and adhering to secure configuration practices

Auditability and Evidence Collection

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

Incident Response, Reporting, and Remediation

  • Identifying suspected prohibited practices or safety incidents
  • Technical procedures for containment, rollback, and mitigation
  • Drafting technical reports for governance bodies and regulators

Summary and Next Steps

Requirements

  • A solid grasp 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 creating or maintaining AI components
  • DevOps and platform engineers overseeing deployments
  • Administrators managing infrastructure and runtime environments

Number of participants


Price per participant

Upcoming Courses

Related Categories