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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 operators
- Interpreting prohibited practices under Article 4 from a technical standpoint
- Aligning legal mandates with concrete engineering controls
Building a Secure and Compliant Development Lifecycle
- Structuring repositories and implementing policy-as-code for AI initiatives
- Enforcing code reviews and automated static checks to detect risky patterns
- Managing dependencies and the supply chain for model components
Designing CI/CD Pipelines with Compliance in Mind
- Defining pipeline stages: build, test, validation, packaging, and deployment
- Embedding governance gates and automated policy checks into the workflow
- Ensuring artifact immutability and tracking provenance
Testing, Validation, and Safety Verification of Models
- Executing data validation and bias detection tests
- Assessing performance, robustness, and resistance to adversarial attacks
- Establishing automated acceptance criteria and generating test reports
Model Registry, Versioning, and Provenance Management
- Leveraging MLflow or similar tools for model lineage and metadata
- Versioning models and datasets to ensure reproducibility
- Documenting provenance and creating audit-ready artifacts
Runtime Controls, Monitoring, and Observability
- Instrumenting systems to log inputs, outputs, and decision-making processes
- Monitoring model drift, data drift, and key performance indicators
- Configuring alerting mechanisms, automated rollbacks, and canary deployments
Security, Access Control, and Data Protection
- Applying least-privilege IAM principles to model training and serving environments
- Safeguarding training and inference data both at rest and in transit
- Implementing secrets management and secure configuration standards
Auditability and Evidence Collection
- Generating machine-readable logs alongside human-readable summaries
- Compiling evidence packages for conformity assessments and audits
- Defining retention policies and ensuring secure storage of compliance artifacts
Incident Response, Reporting, and Remediation
- Detecting potential violations of prohibited practices or safety incidents
- Executing technical steps for containment, rollback, and mitigation
- Drafting technical reports for governance bodies and regulatory authorities
Summary and Path Forward
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 building or maintaining AI components
- DevOps and platform engineers responsible for deployment
- Administrators managing infrastructure and runtime environments
