A tailored course, built for your situation
Mastering EU AI Act Compliance for Analytics & AI Engineering Leaders
A structured path to becoming the recognized expert on AI governance in industrial tech environments.
$199 one-time
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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.
Spending too much time rewriting AI documentation for compliance reviews?
The situation this course is for
AI engineering leaders face increasing pressure to produce auditable, regulator-ready documentation, but most teams treat this as a last-minute overhead rather than a repeatable process. The result is rework, delays, and missed opportunities to demonstrate leadership.
Who this is for
Mid-senior level AI/ML engineers and analytics leads in EU-based industrial technology firms who own or contribute to AI system documentation and compliance processes.
Who this is not for
This course is not for product managers writing high-level AI policies, nor for legal teams interpreting regulations without technical context. It’s also not for practitioners outside regulated industrial domains where AI assurance matters.
What you walk away with
- Produce EU AI Act-compliant technical documentation on demand, reducing audit cycle time
- Establish yourself as the internal reference for AI governance readiness across projects
- Anticipate auditor questions before they arise using standardized evidence patterns
- Streamline cross-functional coordination between engineering, compliance, and legal teams
- Build reusable templates for model cards, risk assessments, and data provenance trails
The 12 modules (with all 144 chapters)
Module 1. Understanding the EU AI Act Scope for Industrial Systems
Clarify which AI applications fall under high-risk classification and how engineering decisions impact compliance scope.
12 chapters in this module
- Defining AI systems under Title III of the EU AI Act
- Mapping industrial use cases to Annex III high-risk categories
- Differentiating between AI components and full systems
- How sensor fusion models trigger additional obligations
- Boundary conditions for real-time inference pipelines
- When IMU-based decision logic becomes safety-critical
- Exemptions for research, testing, and internal tools
- Geographic applicability beyond EU member states
- Integration points with existing functional safety standards
- Role of notified bodies in future conformity checks
- Timeline expectations for enforcement rollout
- Tracking delegated acts and implementing rules
Module 2. Building the Technical Documentation Framework
Create a living repository of evidence that satisfies Article 16 and Annex IV requirements.
12 chapters in this module
- Structure of the minimum required technical file
- Version-controlled model cards with change rationale
- Data lineage descriptions from collection to training
- Performance metrics valid across operational conditions
- Robustness testing protocols for edge deployment
- Human oversight mechanisms built into UI layers
- Failure mode analysis for autonomous adjustments
- Update and rollback procedures with audit trail
- Conformity assessment checklist per deployment cycle
- Linking documentation to development sprints
- Automated snapshot generation at milestone gates
- Redaction strategies for IP protection in submissions
Module 3. Risk Classification and Management Protocols
Apply consistent methodology to classify AI system risk and justify mitigation choices.
12 chapters in this module
- Four-tier risk scale from minimal to unacceptable
- Determining safety objective for control loop models
- Thresholds for physical injury or operational disruption
- Impact of false positives/negatives on process stability
- Environmental harm potential in automated responses
- Societal risks in workforce monitoring applications
- Justifying low-risk classification with evidence
- Escalation paths when uncertainty remains
- Third-party validation options for contested cases
- Documenting assumptions behind risk estimates
- Updating classifications after incident feedback
- Cross-referencing with ISO 12100 risk principles
Module 4. Data Governance for Training and Validation
Ensure data quality, representativeness, and provenance meet regulatory expectations.
12 chapters in this module
- Provenance tracking from raw sensor input to cleaned set
- Metadata standards for time-series IMU recordings
- Bias assessment in motion capture scenarios
- Representativeness checks across environmental variables
- Anonymization techniques for operator behavior logs
- Validation set independence from training distribution
- Drift detection thresholds in production feedback
- Label consistency audits across annotation teams
- Handling missing or corrupted measurement windows
- Simulation data inclusion criteria and limits
- Documentation of synthetic data generation rules
- Retention periods aligned with GDPR and sector norms
Module 5. Transparency and User Information Design
Craft clear instructions for deployers and end users that fulfill disclosure obligations.
12 chapters in this module
- Minimum content required in user manuals
- Explaining system capabilities without overclaiming
- Describing known limitations in operational contexts
- Providing meaningful human override instructions
- Designing alerts for degraded performance modes
- Language requirements for multi-country deployments
- Formatting guidance for maintenance personnel
- Digital vs printed material compliance equivalence
- Version synchronization with software updates
- Logging user interactions with help systems
- Feedback channels for reporting issues
- Updating materials after field experience
Module 6. Human Oversight Mechanisms Implementation
Integrate effective human-in-the-loop controls that satisfy oversight requirements.
12 chapters in this module
- Identifying critical intervention points in workflows
- Response time budgets for operator action
- Alert prioritization to prevent overload
- Interface design for situational awareness
- Training programs for supervisory staff
- Fallback procedures during autonomy failure
- Monitoring dashboards for remote operators
- Audit trails of human interventions
- Simulated drills for rare event response
- Workload assessment during sustained operation
- Role-based access to override functions
- Post-event review protocols for learning
Module 7. Accuracy, Robustness, and Cybersecurity Standards
Meet quantitative and qualitative benchmarks for reliable system behavior.
12 chapters in this module
- Defining accuracy thresholds by use case severity
- Stress testing under environmental extremes
- Input perturbation tolerance for sensor noise
- Fail-safe states during communication loss
- Cyberattack resilience in networked models
- Secure boot and update mechanisms for edge AI
- Runtime integrity verification methods
- Adversarial example defenses in image streams
- Resource exhaustion protections in constrained devices
- Monitoring for anomalous inference patterns
- Penetration testing scope for AI-enabled systems
- Patch management integration with OT networks
Module 8. Conformity Assessment Pathways
Navigate self-declaration versus third-party evaluation routes based on risk level.
12 chapters in this module
- Internal audit prerequisites for self-certification
- Selecting accredited conformity assessment bodies
- Preparing for unannounced inspection visits
- Evidence packaging for remote review
- Handling requests for supplementary information
- Timeline management across multiple submissions
- Coordinating with supply chain partners
- Leveraging existing certifications (e.g., IEC 61508)
- Maintaining post-market surveillance linkage
- Updating certificates after major modifications
- Cost-benefit analysis of external validation
- Building institutional memory from past assessments
Module 9. Recordkeeping and Traceability Systems
Implement versioned, searchable archives of all AI lifecycle artifacts.
12 chapters in this module
- Unique identifier assignment for models and datasets
- Immutable logging of training runs and parameters
- Change request tracking with approval chains
- Linking code commits to documentation updates
- Storage formats compatible with long-term retrieval
- Access controls for sensitive configuration files
- Automated export for regulatory requests
- Retention schedules tied to product lifecycle
- Disaster recovery planning for digital assets
- Cross-reference indexing for audit navigation
- Search functionality for keyword and date ranges
- Integration with PLM and ALM platforms
Module 10. Supply Chain and Third-Party Component Management
Extend compliance responsibility across vendors and open-source dependencies.
12 chapters in this module
- Due diligence requirements for AI component suppliers
- Contractual clauses for documentation delivery
- Verification of third-party conformity claims
- Open-source license compatibility checks
- Vulnerability monitoring for pre-trained models
- Re-training rights and data ownership terms
- Component substitution protocols
- Interoperability testing with external systems
- Managing dependencies in containerized deployments
- Attestation templates for supplier inputs
- Incident response coordination agreements
- Exit strategies for discontinued components
Module 11. Post-Market Monitoring and Incident Response
Establish ongoing surveillance and corrective action processes after deployment.
12 chapters in this module
- Real-world performance tracking KPIs
- User-reported issue intake workflows
- Automated anomaly detection in telemetry
- Classification of incidents by severity level
- Root cause analysis procedures for failures
- Corrective action timelines by risk tier
- Field update deployment mechanics
- Notification obligations to authorities
- Trend analysis across fleet-wide operations
- Feedback loops into next-generation designs
- Annual compliance status reporting
- Decommissioning plans with data deletion
Module 12. Organizational Readiness and Internal Advocacy
Position yourself as the go-to resource within your organization for AI governance excellence.
12 chapters in this module
- Creating internal training materials for peers
- Developing lightweight checklists for project leads
- Hosting brown bag sessions on recent changes
- Curating a knowledge base of common pitfalls
- Responding to peer challenges with framework-backed reasoning
- Building credibility through early wins
- Gaining informal influence across departments
- Shaping internal AI governance policy drafts
- Presenting success stories to senior leadership
- Mentoring junior engineers on compliance basics
- Establishing recognition as the default reviewer
- Scaling impact through reusable artefact libraries
How this maps to your situation
- EU AI Act compliance for industrial AI systems
- Technical documentation for high-risk AI
- Risk management in sensor-driven models
- Governance of IMU-based decision systems
Before vs. after
Before
Spending weeks assembling compliance documentation only when auditors ask, reacting to last-minute requests, and missing chances to lead internally.
After
Producing regulator-ready AI dossiers on demand, shaping team practices, and being sought out as the internal authority on AI governance.
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 90 minutes per week over four weeks, designed for completion on weekends or quiet evenings.
If nothing changes
Without a structured approach, AI projects will continue to face delays during compliance checks, eroding trust in technical teams and ceding strategic influence to non-technical stakeholders.
How this compares to the alternatives
Unlike generic AI ethics courses, this program focuses exclusively on actionable, regulator-facing deliverables required under the EU AI Act, tailored to industrial engineering contexts.
Frequently asked
Is this relevant for non-medical industrial AI applications?
Yes. The course emphasizes mechanical, energy, and automation systems like those in Siemens' domains, not just healthcare or automotive.
How is the course structured?
12 modules, each containing 12 chapters (144 chapters total).
Can I share templates with my team?
Yes. All downloadable resources are licensed for team use within your organization.
$199 one-time. Approximately 90 minutes per week over four weeks, designed for completion on weekends or quiet evenings..
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