A tailored course, built for your situation
Premium engagement picks with AI Act readiness
How practitioners are securing higher-margin AI governance work by leading with AI Act compliance
$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 Software Engineer in AI/ML infrastructure or platform governance, working at scale with regulatory adjacent systems
Who this is not for
Entry-level developers, compliance auditors without technical background, or professionals outside regulated AI system development
What you walk away with
- First access to cross-functional AI governance projects with executive sponsorship
- Clear documentation strategies that satisfy AI Act conformity assessments
- Influence in early scoping of AI systems to avoid costly rework
- Differentiated positioning for higher-margin internal and client-facing engagements
- Faster path from AI policy to implemented, audit-ready controls
The 12 modules (with all 144 chapters)
Module 1. AI Act scope boundaries and system classification
Learn how to classify AI systems under the AI Act using technical criteria, focusing on risk tiers and obligations for high-risk systems. Understand what triggers enhanced documentation and third-party assessment.
12 chapters in this module
- Definition of AI system under EU law
- High-risk classification triggers
- Exemptions for research and development
- Product lifecycle applicability
- Integration with existing data governance
- Thresholds for real-time biometrics
- Automated evaluation tools
- Versioning and update obligations
- Open-source considerations
- Substantial modification criteria
- Supply chain notification rules
- Documentation for classification decisions
Module 2. Data quality requirements for training and validation
Master the data governance obligations under the AI Act, including data provenance, bias assessment, and documentation standards necessary for audit readiness.
12 chapters in this module
- Data lineage documentation
- Bias testing pre-deployment
- Representativeness thresholds
- Data split traceability
- Version-controlled datasets
- Human oversight in annotation
- Geographic data representation
- Temporal drift detection
- Data retention policies
- Synthetic data compliance
- Third-party data sourcing
- Documentation for data practices
Module 3. Technical documentation for conformity assessment
Build comprehensive technical files that satisfy AI Act Article 11 requirements, aligning engineering output with regulatory evidence needs.
12 chapters in this module
- System purpose and intended use
- Architecture diagrams for auditors
- Performance metrics reporting
- Robustness testing results
- Cybersecurity logging
- Fail-safe mechanisms description
- Version control integration
- Accuracy reporting templates
- Interpretability documentation
- Monitoring system design
- Update process tracking
- Conformity self-declaration
Module 4. Risk management system integration
Embed continuous risk assessment into AI development workflows to meet AI Act lifecycle requirements.
12 chapters in this module
- Risk identification triggers
- Hazard scenario modeling
- Severity classification matrix
- Residual risk evaluation
- Failure mode mitigation
- Incident escalation paths
- Risk register maintenance
- Risk-acceptance documentation
- Third-party risk oversight
- Dynamic risk reassessment
- Integration with DevOps
- Evidence for periodic review
Module 5. Transparency and user information strategies
Design user-facing disclosures that meet AI Act obligations while maintaining usability and trust.
12 chapters in this module
- High-risk system labeling
- User instruction templates
- Human oversight notice
- Performance limitation disclosure
- Language accessibility standards
- Machine-readable notices
- API documentation requirements
- End-user rights communication
- Marketing claim boundaries
- Change notification protocols
- Multi-jurisdiction alignment
- Version update disclosures
Module 6. Human oversight mechanisms in AI systems
Implement effective human-in-the-loop designs that satisfy AI Act requirements for meaningful control.
12 chapters in this module
- Oversight role definition
- Intervention timing requirements
- Training for human operators
- Monitoring dashboard design
- Override capability testing
- Escalation workflow
- Error feedback loops
- Oversight logging
- Role-based access control
- Simulation testing
- Intervention success metrics
- Audit trail preservation
Module 7. Accuracy and performance benchmarking
Establish performance validation processes that meet AI Act standards for reliability and consistency.
12 chapters in this module
- Test dataset independence
- Performance metric selection
- Drift detection thresholds
- Edge case evaluation
- Stress testing scenarios
- Benchmarking against baselines
- Cross-validation protocols
- Adversarial testing
- Performance decay alerts
- Accuracy reporting intervals
- Model collapse prevention
- Version comparison metrics
Module 8. Robustness and cybersecurity integration
Strengthen AI system resilience against attacks and failures to meet AI Act security expectations.
12 chapters in this module
- Adversarial attack resistance
- Input sanitization rules
- Model integrity checks
- Fail-open vs fail-safe
- Cyberattack simulation
- Penetration testing
- Threat model updates
- Incident response
- Model theft prevention
- Data poisoning detection
- System degradation alerts
- Recovery process testing
Module 9. Quality management system alignment
Align existing engineering practices with AI Act quality assurance requirements for development pipelines.
12 chapters in this module
- Development lifecycle documentation
- Version control standards
- Change approval workflows
- Code review integration
- Testing coverage thresholds
- Incident root cause analysis
- Corrective action tracking
- Internal audit processes
- Continuous improvement
- Toolchain validation
- Supplier quality oversight
- Certification readiness
Module 10. Conformity assessment pathways
Navigate the AI Act conformity process based on system classification, including self-declaration and notified body routes.
12 chapters in this module
- Internal conformity checklist
- Notified body selection
- Audit preparation
- Technical file submission
- Gap assessment process
- Remediation planning
- Surveillance cycle
- Post-market monitoring
- Complaint handling
- Non-compliance resolution
- Recall procedures
- Certification maintenance
Module 11. Post-market monitoring and updates
Establish ongoing monitoring and update practices that meet AI Act lifecycle obligations.
12 chapters in this module
- Performance degradation alerts
- Incident logging
- User feedback channels
- Model retraining triggers
- Update impact assessment
- Version deprecation
- Change documentation
- Patch deployment
- Rollback procedures
- Security vulnerability tracking
- Third-party dependency updates
- Annual review process
Module 12. Implementation playbook integration
Apply course concepts to real-world scenarios using tailored templates and decision guides.
12 chapters in this module
- AI system intake form
- Classification decision tree
- Documentation checklist
- Risk register template
- Data quality report
- Technical file builder
- Transparency notice generator
- Oversight workflow
- Performance dashboard
- Security test plan
- Conformity roadmap
- Stakeholder briefing pack
How this maps to your situation
- New AI project scoping
- Pre-deployment compliance review
- Post-market audit preparation
- Cross-team governance alignment
Before vs. after
Before
Waiting for assignments to come through standard channels with limited influence on scope or selection
After
Positioned first for premium engagements with documented AI Act readiness and influence in high-margin project scoping
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 to be completed alongside active projects
If nothing changes
Continuing to miss first-access opportunities on high-impact AI governance work that shapes platform direction and draws executive attention
How this compares to the alternatives
Generic AI ethics courses provide conceptual frameworks but lack actionable AI Act implementation steps. This course delivers specific, auditable artefacts aligned with EU regulatory requirements.
Frequently asked
How is the course structured?
12 modules, each containing 12 chapters (144 chapters total).
Is this course technical enough for engineers?
Yes , it's designed for practitioners implementing AI systems, with code-adjacent documentation standards and engineering controls.
Can I use this for client-facing work?
Yes , the templates and playbooks help position you as the go-to expert for AI Act readiness in technical engagements.
$199 one-time. Approximately 3 hours per module, designed to be completed alongside active projects.
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