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Course Outline

Foundations: Technical Implications of the EU AI Act

  • Key obligations and terminology relevant to engineering personnel and system operators
  • Interpreting prohibited practices outlined in Article 4 from an engineering standpoint
  • Translating legal mandates into specific engineering controls for government applications

Secure and Compliant Software Development Lifecycle

  • Repository architecture and implementation of policy-as-code for AI initiatives
  • Code review processes and automated static analysis to identify high-risk patterns
  • Management of dependencies and supply chain integrity for model components

Continuous Integration and Deployment Pipeline Design for Compliance

  • Pipeline phases: build, test, validation, packaging, and deployment
  • Implementation of governance gates and automated policy enforcement mechanisms
  • Ensuring artifact immutability and maintaining provenance records

Model Testing, Validation, and Safety Assurance

  • Execution of data validation routines and bias detection assessments
  • Testing for performance metrics, robustness, and resilience against adversarial inputs
  • Automated determination of acceptance criteria and generation of test reports

Model Registry, Version Control, and Provenance Management

  • Utilization of MLflow or comparable tools to track model lineage and metadata
  • Versioning protocols for models and datasets to ensure reproducibility in government systems
  • Documentation of provenance and generation of artifacts suitable for audit review

Runtime Controls, Monitoring, and Observability

  • System instrumentation for logging inputs, outputs, and decision-making processes
  • Continuous monitoring of model drift, data distribution shifts, and performance indicators
  • Configuration of alerts, automated rollback procedures, and canary deployment strategies

Security, Identity and Access Management (IAM), and Data Protection

  • Enforcement of least-privilege IAM policies for model training and serving infrastructure
  • Encryption of training and inference data both at rest and in transit
  • Management of secrets and adherence to secure configuration standards

Auditability and Evidence Collection

  • Production of machine-readable logs alongside human-readable compliance summaries
  • Compilation of evidence packages for conformity assessments and external audits
  • Implementation of retention policies and secure storage for compliance documentation

Incident Response, Reporting, and Remediation

  • Detection mechanisms for suspected prohibited activities or safety incidents in government contexts
  • Technical procedures for containment, rollback, and risk mitigation
  • Preparation of technical documentation for internal governance bodies and regulatory agencies

Summary and Strategic Next Steps

Requirements

  • Competency in software engineering and release lifecycle procedures
  • Proficiency in containerization technologies and foundational Kubernetes principles
  • Knowledge of version control systems utilizing Git and continuous integration/deployment methodologies

Audience

  • Developers responsible for the creation or upkeep of artificial intelligence modules
  • DevOps and platform engineers tasked with operational deployment
  • Administrators overseeing infrastructure management and runtime environment stability
 14 Hours

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