A tailored course, built for your situation
Mastering AI Act for Software Engineers in Product-Focused Roles
Build compliance-ready AI systems with confidence, clarity, and compounding returns on every delivery
$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.
Starting from scratch on every compliance project wastes time and weakens impact
The situation this course is for
Engineers in regulated AI product roles often rebuild compliance foundations across projects, repeating work, missing reuse opportunities, and slowing time to ship. The AI Act raises the stakes: it demands documentation, risk assessments, and system validation that can’t be faked. Without a compounding asset base, each delivery becomes a net cost instead of a strategic investment.
Who this is for
Software engineer at a high-growth AI or data platform company, working on product-facing systems subject to emerging AI governance rules
Who this is not for
This is not for policy analysts, compliance auditors, or legal generalists without coding responsibility. It’s for builders who ship systems and want those systems to generate reusable value over time.
What you walk away with
- Produce AI Act-mandated documentation that doubles as IP for future projects
- Reuse risk classification patterns across models and services
- Automate conformity evidence collection into CI/CD pipelines
- Turn audit preparation into a byproduct of development, not a separate phase
- Build a personal portfolio of governance-enriched deliveries that compounds in value
The 12 modules (with all 144 chapters)
Module 1. Understanding the AI Act's Core Obligations
Break down the EU AI Act into enforceable technical requirements for developers. Focus on high-risk classification, transparency duties, and documentation mandates that directly impact code design and data pipeline architecture.
12 chapters in this module
- Scope of the AI Act for software systems
- High-risk AI use cases under Annex III
- Provider vs deployer obligations
- Real-world enforcement timelines
- Interaction with GDPR and data laws
- Exemptions for research and development
- Obligations during system training phase
- Post-deployment monitoring duties
- Technical documentation requirements
- Record-keeping expectations
- Human oversight mandates
- Conformity assessment pathways
Module 2. Risk Classification for AI Systems
Develop a repeatable method to classify AI components by risk level. Use decision trees aligned with Annex III to determine which systems require full conformity assessments and which can proceed with lightweight documentation.
12 chapters in this module
- Defining an AI system under the AI Act
- Identifying high-risk domains
- Assessing system autonomy level
- Evaluating impact on fundamental rights
- Determining safety implications
- Third-party dependency risk
- Scoring model inputs for risk tier
- Documenting classification rationale
- Handling edge-case applications
- Version control for reclassification
- Team alignment on risk thresholds
- Integration with sprint planning
Module 3. Building Technical Documentation
Create AI Act-compliant technical documentation that serves both regulatory needs and internal reuse. Structure artefacts to support audits while becoming building blocks for future projects.
12 chapters in this module
- Required documentation sections
- System purpose and specifications
- Data governance description
- Risk management process
- Training data provenance
- Model architecture diagrams
- Performance metrics defined
- Testing results and logs
- Version history tracking
- Human oversight implementation
- Post-market monitoring plan
- Documentation as code approach
Module 4. Designing for Human Oversight
Implement effective human oversight mechanisms that meet AI Act standards. Move beyond checklists to integrate meaningful review points into automated workflows.
12 chapters in this module
- Types of human-in-the-loop
- Alerting thresholds for intervention
- User interface requirements
- Response time expectations
- Override capability design
- Audit trail for decisions
- Training materials for operators
- Fail-safe fallback procedures
- Performance monitoring alerts
- Escalation protocols
- Documentation of oversight
- Testing oversight effectiveness
Module 5. Data Governance and Provenance
Establish data practices that satisfy AI Act transparency and fairness mandates. Trace training data from origin to model input with verifiable lineage.
12 chapters in this module
- Data quality standards
- Bias assessment methods
- Data collection documentation
- Preprocessing transparency
- Data labeling practices
- Representativeness evaluation
- Data version tracking
- Annotator qualifications
- Data retention policies
- Synthetic data documentation
- Data drift detection
- Data update protocols
Module 6. Model Transparency and Explainability
Meet AI Act transparency requirements with practical model explainability techniques. Deliver understandable outputs without compromising performance or IP.
12 chapters in this module
- Right to explanation
- Model summary requirements
- User-facing documentation
- Feature importance methods
- Local vs global explanations
- Post-hoc interpretation tools
- Trade secrets protection
- Documentation templates
- User education materials
- Performance-explainability balance
- Third-party model transparency
- Versioned explanation artifacts
Module 7. Robustness, Accuracy, and Cybersecurity
Ensure AI systems are resilient to errors, attacks, and degradation. Align model performance with AI Act reliability expectations across deployment environments.
12 chapters in this module
- Performance under stress
- Adversarial attack resistance
- Model drift detection
- Fail-safe mechanisms
- Security testing protocols
- Threat modeling approach
- Penetration testing scope
- Incident response planning
- Accuracy monitoring
- Calibration validation
- Model retraining triggers
- Confidence threshold setting
Module 8. Conformity Assessment Process
Navigate the AI Act conformity assessment with a structured, repeatable approach. Prepare for both internal checks and notified body involvement.
12 chapters in this module
- Determining assessment path
- Internal review steps
- Notified body selection
- Assessment timeline planning
- Document readiness check
- Gap analysis technique
- Remediation tracking
- Evidence compilation
- Stakeholder alignment
- Legal entity verification
- Quality management review
- Final declaration process
Module 9. Post-Market Monitoring Systems
Build ongoing monitoring into AI deployments to meet AI Act lifecycle obligations. Automate data collection and anomaly detection for continuous compliance.
12 chapters in this module
- Performance degradation alerts
- User feedback integration
- Model drift detection
- Error rate thresholds
- Incident logging
- Automated reporting
- Version tracking
- Retraining triggers
- Model withdrawal criteria
- User notification process
- Update validation
- Compliance dashboard
Module 10. Version Control and Change Management
Implement change control processes that preserve AI Act compliance across updates. Ensure every deployment meets original standards or justifies deviations.
12 chapters in this module
- Change impact assessment
- Version documentation
- Rollback capability
- Approval workflows
- Testing new versions
- User notification
- Model registry setup
- Deprecation policy
- Hotfix procedures
- Patch validation
- Audit trail completeness
- Change log maintenance
Module 11. Supply Chain and Third-Party Risk
Manage AI Act obligations when using third-party models or services. Establish due diligence and contractual requirements to maintain compliance downstream.
12 chapters in this module
- Provider due diligence
- Contractual obligations
- Subcontractor oversight
- Model provenance verification
- License compliance
- Security audit rights
- Performance guarantees
- Liability allocation
- Compliance certification
- Transparency requirements
- Update responsibility
- Exit strategy planning
Module 12. Scaling Compliance Across a Portfolio
Turn individual project compliance into a compounding asset library. Reuse templates, patterns, and documentation frameworks across teams and products.
12 chapters in this module
- Template standardization
- Centralized documentation
- Cross-team review process
- Knowledge transfer
- Training materials
- Tooling integration
- Automated checks
- Lessons learned capture
- IP ownership
- Compounding value tracking
- Leadership reporting
- Future-proofing strategy
How this maps to your situation
- Delivering first AI product under regulatory scrutiny
- Supporting compliance in a fast-moving product team
- Scaling AI governance across multiple services
- Preparing for external audit or certification
Before vs. after
Before
Starting from zero on each compliance effort, repeating work, and struggling to prove due diligence
After
Shipping faster with reusable documentation, repeatable patterns, and a growing library of compliance assets
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 over 6-8 weeks with steady progress.
If nothing changes
Without compounding compliance assets, every new project starts at ground zero, slowing delivery, increasing risk, and missing opportunities to build technical authority.
How this compares to the alternatives
Unlike generic AI ethics courses, this program delivers code-level compliance patterns tied directly to the AI Act. Compared to vendor-led training, it’s independent, practical, and focused on reusable engineering outcomes, not product promotion.
Frequently asked
Is this course relevant if I’m not based in the EU?
Yes. The AI Act is becoming a global benchmark, like GDPR, influencing AI regulations worldwide. Building to its standard positions your work ahead of local requirements.
How is the course structured?
12 modules, each containing 12 chapters (144 chapters total).
Will I get something I can show to my manager?
Yes. The implementation playbook and documentation templates become tangible evidence of your compounding contributions.
$199 one-time. Approximately 3 hours per module, designed to be completed over 6-8 weeks with steady progress..
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