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Course Outline

Foundations: EU AI Act for Technical Teams

  • Key obligations and terminology relevant to developers and operators
  • Gaining a technical understanding of prohibited practices under Article 4
  • 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 checks to identify risky patterns
  • Dependency and supply-chain management for model components

CI/CD Pipeline Design for Compliance

  • Essential pipeline stages: build, test, validation, package, deploy
  • Integrating governance gates and automated policy checks
  • Ensuring artifact immutability and tracking provenance

Model Testing, Validation, and Safety Checks

  • Conducting data validation and bias detection tests
  • Performing performance, robustness, and adversarial resilience testing
  • Establishing automated acceptance criteria and generating test reports

Model Registry, Versioning, and Provenance

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

Runtime Controls, Monitoring, and Observability

  • Instrumenting systems to log inputs, outputs, and decision processes
  • Monitoring 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 policies for model training and serving environments
  • Securing training and inference data both at rest and in transit
  • Implementing secrets management and secure configuration practices

Auditability and Evidence Collection

  • Generating machine-readable logs alongside human-readable summaries
  • Packaging evidence for conformity assessments and external audits
  • Establishing retention policies and secure storage solutions 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 responsibilities
  • Administrators managing infrastructure and runtime environments
 14 Hours

Number of participants


Price per participant

Provisional Upcoming Courses (Require 5+ participants)

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