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Duration 14 hours
Course Outline
Foundations: Navigating the EU AI Act for Technical Teams
- Identifying key obligations and terminology relevant to developers and system operators
- Analyzing prohibited practices under Article 4 through a technical implementation lens
- Translating legal mandates into concrete engineering controls
Building a Secure and Compliant Development Lifecycle
- Structuring repositories and enforcing policy-as-code within AI projects
- Conducting code reviews and automated static analysis to detect risky patterns
- Managing dependencies and supply-chain integrity for model components
Designing CI/CD Pipelines for Regulatory Compliance
- Defining pipeline stages: building, testing, validating, packaging, and deployment
- Embedding governance gates and automated policy checks into the workflow
- Ensuring artifact immutability and tracking full provenance
Testing, Validation, and Safety Verification for Models
- Executing data validation and bias detection tests
- Assessing performance, robustness, and resilience against adversarial attacks
- Establishing automated acceptance criteria and generating test reports
Managing Model Registry, Versioning, and Provenance
- Leveraging MLflow or similar tools to maintain model lineage and metadata
- Implementing versioning for models and datasets to ensure reproducibility
- Documenting provenance and creating artifacts ready for audit
Implementing Runtime Controls, Monitoring, and Observability
- Instrumenting systems to log inputs, outputs, and decision-making processes
- Tracking model drift, data drift, and key performance metrics
- Configuring alerting systems, automated rollback mechanisms, and canary deployments
Enhancing Security, Access Control, and Data Protection
- Applying least-privilege IAM policies for model training and serving environments
- Safeguarding training and inference data both at rest and in transit
- Adopting best practices for secrets management and secure configuration
Ensuring Auditability and Effective Evidence Collection
- Generating both machine-readable logs and human-readable summaries
- Packaging comprehensive evidence for conformity assessments and regulatory audits
- Defining retention policies and securely storing compliance artifacts
Managing Incident Response, Reporting, and Remediation
- Detecting potential prohibited practices or critical safety incidents
- Executing technical procedures for containment, rollback, and mitigation
- Drafting technical reports for internal governance and regulatory bodies
Conclusion and Recommended Next Steps
Requirements
- A solid grasp of software development and deployment workflows
- Practical experience with containerization and fundamental Kubernetes concepts
- Proficiency in Git-based source control and CI/CD methodologies
Target Audience
- Developers responsible for building or maintaining AI components
- DevOps and platform engineers overseeing deployment processes
- Administrators managing infrastructure and runtime environments
