A tailored course, built for your situation
Premium engagements with AI Act compliance work secured before competitors move
A tailored course for software engineers leading responsible AI implementation in regulated environments
$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 a regulated tech environment, working on data and AI systems where compliance signaling matters
Who this is not for
Entry-level developers, non-technical compliance staff, or managers without hands-on implementation responsibilities
What you walk away with
- Recognized as the go-to engineer for AI Act-aligned development patterns
- First pick for high-impact projects requiring compliance-by-design
- Clear documentation templates that prove adherence during audits or vendor reviews
- Faster alignment with legal and risk teams using shared technical artefacts
- Strategic positioning for engagements with bigger budgets and broader scope
The 12 modules (with all 144 chapters)
Module 1. AI Act compliance mapping for engineers
Understand the specific obligations in the AI Act that apply to software implementation, focusing on high-risk systems, transparency, and documentation requirements.
12 chapters in this module
- Scope of the AI Act for AI developers
- High-risk system classification criteria
- Obligations for model transparency
- Data governance expectations
- Human oversight requirements
- Technical documentation mandates
- Conformity assessment process
- Role of providers vs deployers
- Third-party integration liabilities
- Recordkeeping for audits
- Penalty thresholds and enforcement
- Exemptions for research and development
Module 2. Designing compliant model workflows
Build development pipelines that inherently satisfy AI Act requirements, reducing rework and increasing credibility with compliance reviewers.
12 chapters in this module
- Model lifecycle tracking from dev to prod
- Versioning data and code together
- Bias assessment integration points
- Drift detection with audit trails
- Explainability implementation patterns
- Logging inference decisions
- Access control for model updates
- Secure model serving configurations
- API-level compliance checks
- Automated documentation generation
- Rollback readiness for non-compliance
- Staging environments for conformity testing
Module 3. Compliance-ready documentation patterns
Create technical artefacts that satisfy auditors and risk teams without slowing development velocity.
12 chapters in this module
- Architecture decision records with compliance intent
- Model cards with regulated fields
- Data lineage diagrams for regulators
- System boundary definitions
- Risk assessment templates for engineers
- Versioned technical specifications
- Change logs with impact rationale
- Vendor dataset compliance checks
- Third-party dependency disclosures
- Model performance thresholds
- Incident response documentation
- Audit package assembly workflow
Module 4. Proving algorithmic transparency
Demonstrate compliance through clear, reproducible explanations of model behavior without exposing IP or core logic.
12 chapters in this module
- Feature importance reporting standards
- Counterfactual explanation patterns
- Input-output traceability
- Model decision boundary documentation
- Bias mitigation reporting formats
- Performance across subgroups
- Confidence score calibration logs
- Right to explanation response workflow
- Redaction-safe transparency
- Human-in-the-loop validation logs
- Automated fairness testing
- Transparency vs secrecy balance
Module 5. Data governance in AI development
Implement data practices that align with AI Act requirements for quality, provenance, and representativeness.
12 chapters in this module
- Data provenance tracking at scale
- Representativeness validation workflows
- Annotation quality assurance
- Bias screening in training sets
- Data cleansing logs
- Data usage rights verification
- Synthetic data compliance status
- High-risk data handling protocols
- Data versioning for reproducibility
- Data retention policies
- Cross-border data flow documentation
- Data subject rights fulfillment paths
Module 6. Human oversight integration
Design systems that maintain human control in high-risk AI applications as required by the AI Act.
12 chapters in this module
- Human-in-the-loop decision points
- Override mechanism design
- Intervention readiness levels
- Monitoring for automation bias
- Escalation workflows
- Human review thresholds
- Training for human monitors
- False positive recovery paths
- Audit trails for human actions
- Responsibility mapping
- Availability requirements
- Fallback procedures
Module 7. Security for AI systems
Apply security controls specific to AI workloads to meet AI Act robustness requirements.
12 chapters in this module
- Model poisoning prevention
- Adversarial attack resilience
- Model theft protection
- API security for inference endpoints
- Input sanitization for LLMs
- Model checksum verification
- Supply chain security for models
- Secure model updates
- Access control for fine-tuning
- Encryption of model weights
- Runtime integrity checks
- Penetration testing for AI systems
Module 8. Conformity assessment preparation
Navigate the formal process for demonstrating compliance with the AI Act as a developer.
12 chapters in this module
- Internal conformity checklist design
- Technical file assembly
- Essential requirements mapping
- Gap assessment workflow
- Notified body engagement prep
- Audit trail completeness
- Evidence collection standards
- Third-party review coordination
- Certification path selection
- Self-declaration documentation
- Post-market monitoring plans
- Continuous conformity tracking
Module 9. Vendor and third-party management
Manage external components and services in a way that preserves compliance accountability.
12 chapters in this module
- Vendor compliance screening
- Third-party model audits
- Subprocessor oversight
- Compliance clauses in contracts
- Dependency tree analysis
- Open source compliance risks
- API provider assurance
- Model marketplace due diligence
- Cloud provider responsibility mapping
- Incident response coordination
- Compliance escalation paths
- Exit strategy documentation
Module 10. Incident response for AI systems
Respond to AI-related incidents in a way that satisfies regulatory obligations and maintains trust.
12 chapters in this module
- AI incident classification
- Reporting timelines
- Stakeholder communication plans
- Model rollback procedures
- Root cause analysis for bias
- Transparency in incident reporting
- Regulator notification workflow
- Public statement preparation
- Post-mortem compliance review
- Systemic risk identification
- Corrective action tracking
- Regulatory follow-up coordination
Module 11. Cross-functional collaboration
Work effectively with legal, compliance, and product teams to deliver AI systems that meet all requirements.
12 chapters in this module
- Common language for risk discussions
- Compliance requirement translation
- Joint design review protocols
- Legal handoff documentation
- Risk register ownership
- Escalation pathways
- Stakeholder mapping
- Product requirement validation
- Timeline negotiation
- Compliance milestone tracking
- Shared artefact repositories
- Post-launch feedback loops
Module 12. Future-proofing AI development
Anticipate upcoming changes in AI regulation and position your work to lead.
12 chapters in this module
- Regulatory horizon scanning
- Global alignment patterns
- Compliance debt management
- Adaptive architecture design
- Policy change impact assessment
- Standards adoption roadmap
- Industry working group participation
- Thought leadership content
- Internal training programs
- Compliance innovation initiatives
- Cross-border deployment strategy
- Long-term accountability frameworks
How this maps to your situation
- When you're drafting model documentation for audit
- Before a new AI feature enters architecture review
- During third-party integration planning
- After a compliance escalation is raised
Before vs. after
Before
Compliance feels like a separate track handled by legal or risk teams, with engineers brought in late or asked to rework designs.
After
Engineers lead with compliance-ready patterns from day one, positioning themselves as the first call for high-impact AI projects.
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 3-4 hours per module, designed to be completed in parallel with ongoing work.
How this compares to the alternatives
Unlike generic AI ethics courses or policy overviews, this course delivers engineer-specific patterns tied directly to AI Act obligations , actionable, code-level guidance that integrates into existing development workflows.
Frequently asked
Is this course focused on policy or technical implementation?
It's focused on technical implementation , how software engineers can build systems that inherently satisfy AI Act requirements.
How is the course structured?
12 modules, each containing 12 chapters (144 chapters total).
Will this help me in my current role at a fast-moving tech company?
Yes , it's designed specifically for engineers at companies like yours, where AI innovation must coexist with regulatory readiness.
$199 one-time. Approximately 3-4 hours per module, designed to be completed in parallel with ongoing work..
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