A tailored course, built for your situation
Broader remit across AI governance initiatives with AI Act mastery
A tailored path to expanded influence in your current role through authoritative command of the AI Act framework
$199 one-time
24-hour access provisioning
30-day money-back guarantee
Hand-built implementation playbook
12 modules. 12 chapters per module. 144 chapters total.
12 modules, each with 12 chapters (144 chapters total), text-based, plus downloadable templates and a hand-built implementation playbook delivered alongside course access.
Who this is for
Senior technical practitioner in data and AI infrastructure seeking to expand governance influence without changing roles
Who this is not for
Entry-level engineers, non-technical compliance staff, or executives seeking board-level oversight frameworks
What you walk away with
- Lead AI compliance planning input within your team without escalation
- Shape internal policy interpretation based on AI Act article-level understanding
- Design audit-ready artefacts that reduce rework and increase reusability
- Coordinate cross-functional alignment between engineering and compliance peers
- Own end-to-end delivery of AI governance controls in your domain
The 12 modules (with all 144 chapters)
Module 1. AI Act structure and scope in engineering context
Understand the regulation’s binding articles as they apply to data pipelines, model training, and deployment workflows.
12 chapters in this module
- Article 5 classification rules
- High-risk system triggers
- Data quality obligations
- Transparency requirements
- Recordkeeping mandates
- Systematic risk assessment
- Human oversight design
- Conformity assessment paths
- Technical documentation scope
- Role of internal audits
- Oversight body expectations
- Compliance timeline mapping
Module 2. Mapping AI Act to DevOps delivery cycles
Align compliance demands with CI/CD pipelines, testing stages, and release gates.
12 chapters in this module
- Pre-commit checklist design
- Linting for compliance flags
- Automated documentation triggers
- Pipeline audit trails
- Version control integration
- Environment parity rules
- Rollback compliance
- Staging gate criteria
- Monitoring for drift
- Logging for accountability
- Incident response alignment
- Post-deployment validation
Module 3. Engineering ownership of conformity claims
Take verified ownership of internal declarations without relying on legal or compliance escalation.
12 chapters in this module
- Evidence collection strategy
- Internal sign-off workflows
- Versioned conformity records
- Change impact analysis
- Risk self-rating calibration
- Peer review protocols
- Documentation templates
- Audit trail linkage
- Cross-team alignment points
- Version rollback implications
- Stakeholder notification
- Retention period rules
Module 4. Designing human oversight mechanisms
Implement effective human-in-the-loop systems that meet regulatory scrutiny.
12 chapters in this module
- Alerting thresholds
- Escalation path design
- Intervention logging
- Duty assignment clarity
- Training for oversight roles
- False positive handling
- Drift detection
- Decision reversibility
- Context capture
- System feedback loops
- Performance metrics
- Review cycle cadence
Module 5. Data governance for AI training pipelines
Ensure compliance with data quality, sourcing, and preprocessing mandates.
12 chapters in this module
- Provenance tracking
- Bias assessment timing
- Representativeness checks
- Data cleansing rules
- Versioned dataset management
- Annotator guidelines
- Third-party data vetting
- License compliance
- Retention policies
- Synthetic data use
- Drift monitoring
- Reprocessing triggers
Module 6. Transparency engineering for model explainability
Build systems that generate compliant outputs for external and internal stakeholders.
12 chapters in this module
- Model card automation
- Performance metric exposure
- Limitation disclosure
- User-facing documentation
- API response clarity
- Version comparison tools
- Error explanation design
- Confidence scoring
- Input/output logging
- Drift alerting
- Human review triggers
- Audit package generation
Module 7. Robustness and accuracy in production models
Implement testing and monitoring that meet AI Act performance expectations.
12 chapters in this module
- Stress testing design
- Edge case coverage
- Performance decay alerts
- Accuracy threshold tracking
- Model calibration
- Input validation
- Output validation
- Adversarial testing
- Drift detection metrics
- Re-evaluation triggers
- Fallback mechanism design
- Incident resolution path
Module 8. Security and resilience for AI systems
Hardening deployment practices to meet AI Act cybersecurity expectations.
12 chapters in this module
- Threat modeling
- Access control integration
- Model poisoning defenses
- API security
- Model signing
- Tamper detection
- Backup strategies
- Recovery testing
- Incident response
- Penetration testing
- Vulnerability patching
- Zero-day preparedness
Module 9. Third-party AI component oversight
Manage vendor-supplied models and tools within compliance boundaries.
12 chapters in this module
- Vendor risk assessment
- Contractual obligations
- Audit rights
- Documentation requirements
- Performance guarantees
- Update process review
- Compliance verification
- Escalation paths
- Exit strategy
- Dependency mapping
- Transparency gaps
- Fallback plans
Module 10. Internal audit preparation and coordination
Lead readiness efforts and shape the audit experience from an engineering standpoint.
12 chapters in this module
- Audit scope definition
- Document collection
- Stakeholder coordination
- Gap identification
- Remediation tracking
- Evidence presentation
- Follow-up response
- Process refinement
- Cross-team alignment
- Tooling integration
- Reporting cadence
- Lessons learned
Module 11. Cross-functional influence without formal authority
Shape decisions in compliance, product, and risk teams through technical credibility.
12 chapters in this module
- Credibility foundations
- Framing technical trade-offs
- Building coalitions
- Anticipating pushback
- Strategic documentation
- Timing interventions
- Leveraging precedent
- Creating reusable artefacts
- Consensus pathways
- Escalation alternatives
- Decision trail logging
- Visibility amplification
Module 12. Sustaining governance leadership in evolving environments
Maintain influence as regulations, teams, and systems change.
12 chapters in this module
- Change impact forecasting
- Update anticipation
- Knowledge transfer
- Succession planning
- Documentation evolution
- Practice community building
- Trend monitoring
- Regulatory change alerts
- Internal advocacy
- Leadership engagement
- Resource prioritization
- Legacy system adaptation
How this maps to your situation
- When scoping a new AI-enabled feature
- Before a regulatory audit cycle begins
- During vendor selection for AI tooling
- After a model performance incident
Before vs. after
Before
Awaiting direction on compliance requirements and reacting to audit demands
After
Proactively shaping AI governance inputs and leading implementation design
What's included with your purchase
- 12 modules with 12 chapters each (144 chapters)
- Downloadable templates and worked examples for every module
- Hand-built implementation playbook delivered alongside course access
- 30-day money-back guarantee
Delivery and format
- Course and learning environment access provisioned within 24 hours of purchase
- Hand-built implementation playbook delivered alongside course access
Format: Text-based modules and chapters in the Art of Service learning environment, plus downloadable templates and worked examples for every chapter, plus the hand-built implementation playbook delivered alongside course access.
Time investment: Approximately 3 hours per module, designed for integration with ongoing work cycles.
If nothing changes
Continuing to operate in reactive mode risks being bypassed when governance decisions are made, limiting your ability to influence system design and reducing recognition for your contributions.
How this compares to the alternatives
Unlike generic compliance overviews or executive summaries, this course delivers engineering-grade implementation patterns tied directly to AI Act articles, enabling immediate application in data pipeline and model deployment workflows.
Frequently asked
Is this course technical enough for a hands-on engineer?
Yes. Every module includes code-adjacent examples, configuration patterns, and system design decisions relevant to data and AI infrastructure.
How is the course structured?
12 modules, each containing 12 chapters (144 chapters total).
Does this course cover the EU AI Act only?
Yes, it focuses on the EU AI Act as the first major regulatory framework, whose structure is shaping global expectations.
$199 one-time. Approximately 3 hours per module, designed for integration with ongoing work cycles..
Within 24 hours your account in the learning environment is provisioned and the tailored implementation playbook is delivered alongside it.
30-day money-back guarantee·
144 chapters·
Hand-built playbook included·
Account access within 24 hours