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 Duration 14 hours

Course Outline

Foundations: The EU AI Act for Technical Teams

  • Key obligations and terminology relevant to developers and operators
  • Technical interpretation of prohibited practices under Article 4
  • Translating legal requirements into concrete engineering controls

Secure and Compliant Development Lifecycle

  • Repository structures and policy-as-code strategies for AI projects
  • Code reviews and automated static analysis for risky patterns
  • Managing dependencies and supply chains for model components

Designing CI/CD Pipelines for Compliance

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

Model Testing, Validation, and Safety Protocols

  • Tests for data validation and bias detection
  • Evaluating performance, robustness, and adversarial resilience
  • Automating acceptance criteria and generating test reports

Model Registry, Versioning, and Provenance

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

Runtime Controls, Monitoring, and Observability

  • Instrumenting systems to log inputs, outputs, and decision logic
  • Monitoring for model drift, data drift, and performance metrics
  • Implementing alerting, automated rollback, and canary deployments

Security, Access Control, and Data Protection

  • Applying least-privilege IAM for training and serving environments
  • Safeguarding training and inference data at rest and in transit
  • Best practices in secrets management and secure configuration

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
  • Executing technical steps 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
  • Proficiency in Git-based source control and CI/CD practices

Target Audience

  • Developers building or maintaining AI components
  • DevOps and platform engineers overseeing deployment processes
  • Administrators managing infrastructure and runtime environments

Number of participants


Price per participant

Provisional Upcoming Courses (Require 5+ participants)

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