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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