A tailored course, built for your situation
Mastering AI Act for Senior Developer Relations Practitioners
Turn regulatory requirements into trusted guidance for engineering teams adopting AI
$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.
Engineering teams ignore compliance memos, but act on developer relations guidance
The situation this course is for
AI governance rolls down from regulators, but implementation stalls when engineering teams don't trust the source. Developer relations leaders are uniquely positioned to close the gap, but only if their materials feel native to development workflows and sprint realities.
Who this is for
Senior developer relations lead at a cloud or AI platform company, regularly pulled into cross-functional AI governance discussions, expected to translate compliance requirements into actionable guidance for engineering teams, but without formal authority over those teams
Who this is not for
This is not for compliance officers writing policy, or engineering managers building AI systems. It’s for developer relations leads who bridge between governance mandates and development teams.
What you walk away with
- Produce regulator-facing documentation that passes initial review
- Establish clear escalation paths from engineering teams to compliance sponsors
- Deliver implementation checklists adopted by peer developer advocates
- Respond to AI Act inquiries with authoritative references
- Shape internal AI governance playbooks as a named contributor
The 12 modules (with all 144 chapters)
Module 1. Mapping the AI Act to Developer Workflows
Translate high-level AI Act requirements into sprint-level tasks engineering teams recognize and act on. Identify which provisions trigger changes in code review, model logging, or dependency tracking.
12 chapters in this module
- How Article 5 on high-risk systems impacts CI/CD pipelines
- Mapping transparency obligations to API documentation practices
- Developer-facing interpretation of model registration requirements
- Linking data provenance rules to existing data lineage tools
- Translating banned-use cases into pre-commit hook validations
- Connecting human oversight rules to incident response runbooks
- Mapping accuracy benchmarks to model testing cadence
- Turning record-keeping mandates into automated logging triggers
- Aligning version control practices with model update tracking
- Mapping conformity assessment steps to release gates
- Identifying developer tasks under 'post-market monitoring'
- Translating general-purpose AI obligations into sandbox rules
Module 2. Building Trust in Compliance Guidance
Design materials that earn developer buy-in by matching their workflow rhythms, tools, and decision logic , not forcing compliance templates onto engineering reality.
12 chapters in this module
- Why developers dismiss top-down compliance memos
- Matching tone to developer documentation standards
- Using pull request templates to embed compliance checks
- Integrating requirements into issue tracker workflows
- Benchmarking against open-source AI governance guides
- Avoiding overreach in developer guidance
- Timing releases to sprint planning cycles
- Using code comments as compliance anchors
- Linking policies to linter rule documentation
- Embedding reminders in developer onboarding flows
- Aligning language with internal style guides
- Structuring guidance like RFCs, not mandates
Module 3. Escalation Paths for Grey-Zone Decisions
Define clear handoffs when developers encounter ambiguous AI Act interpretations , ensuring timely input from legal, compliance, and risk without blocking progress.
12 chapters in this module
- Identifying when a model crosses into high-risk category
- Criteria for triggering a legal review on training data
- Escalation thresholds for third-party model use
- When to pause development for conformity assessment
- Routing decisions involving biometric or children's data
- Handling requests to bypass logging requirements
- Escalation triggers for adversarial testing gaps
- Documenting exceptions during incident remediation
- Peer review thresholds for model fine-tuning
- When documentation efforts exceed reasonable burden
- Clarifying ownership of post-market monitoring
- Routing ambiguous general-purpose AI use cases
Module 4. Developer-Ready AI Act Playbooks
Shift from abstract policy summaries to executable workflows developers can adopt , with versioned templates, tool integrations, and clear ownership.
12 chapters in this module
- Versioning compliance playbooks in Git
- Integrating checklists into CI/CD pipelines
- Automating model card generation from metadata
- Using feature flags for regulated functionality
- Template for AI system documentation in MD format
- Building audit trails into existing logging systems
- Standardizing model registry submission forms
- Creating runbooks for model update validation
- Developing internal training snippets for new hires
- Embedding compliance checks in on-call rotations
- Maintaining up-to-date dependency inventories
- Versioning dataset documentation alongside code
Module 5. Peer Review in AI Governance
Structure peer review processes that catch compliance gaps early without slowing innovation , using familiar code review patterns and developer-owned tooling.
12 chapters in this module
- Applying pull request standards to model changes
- Using linters to catch prohibited patterns
- Peer sign-off requirements for high-risk models
- Reviewing training data descriptions like code
- Using pull-based notifications for compliance updates
- Automating initial compliance screening in CI
- Routing complex questions to named reviewers
- Documenting review rationale in merge commits
- Standardizing comments on compliance gaps
- Integrating feedback from compliance teams
- Handling disputed interpretations in code review
- Archiving review history for auditor access
Module 6. Internal Training That Sticks
Design developer training that aligns with how engineers learn , through tools, templates, and just-in-time resources rather than slide decks or lectures.
12 chapters in this module
- Embedding training in developer portal dashboards
- Using interactive tutorials for AI Act concepts
- Linking documentation to error messages
- Creating short video snippets for common workflows
- Building sandbox environments for testing
- Using quizzes that validate practical understanding
- Structuring learning like API documentation
- Integrating reminders into local development flows
- Benchmarking completion against peer teams
- Tracking progress through Git activity
- Reinforcing training through code reviews
- Updating materials based on incident reports
Module 7. Documentation as Code for Compliance
Treat compliance documentation like code , versioned, reviewed, tested, and updated in lockstep with system changes , so it remains accurate and trustworthy.
12 chapters in this module
- Storing AI documentation in Git repos
- Applying pull request workflows to doc changes
- Automating documentation generation from code
- Using CI pipelines to validate completeness
- Linking model cards to specific commits
- Versioning dataset cards with data updates
- Using tests to verify documentation accuracy
- Integrating doc checks into release gates
- Auditing doc changes like code changes
- Routing doc updates through peer review
- Generating compliance reports from source
- Archiving documentation snapshots for audits
Module 8. Balancing Innovation and Oversight
Support rapid experimentation while ensuring developers don’t accidentally cross into regulated territory , using sandboxed environments and clear boundaries.
12 chapters in this module
- Defining safe-to-fail experimentation zones
- Using feature flags to isolate regulated functions
- Sandbox rules for third-party model testing
- Logging requirements for experimental models
- When sandbox activity triggers formal review
- Tracking experimental usage via telemetry
- Reviewing sandbox outputs for compliance risk
- Documenting lessons from sandboxed projects
- Updating guidelines based on sandbox findings
- Enforcing data usage boundaries in test
- Handling accidental processing of sensitive data
- Transitioning projects from sandbox to production
Module 9. Vendor and Third-Party Model Governance
Establish clear rules for using third-party models and tools , so developers can innovate without importing undetected compliance risks.
12 chapters in this module
- Criteria for accepting third-party AI services
- Assessing third-party model conformity assessments
- Reviewing terms of service for compliance clauses
- Tracking model updates from external providers
- Validating documentation from vendor sources
- Handling black-box models in regulated contexts
- Assessing open-weight model compliance posture
- Documenting model fine-tuning decisions
- Managing dependencies on third-party APIs
- Auditing third-party model performance claims
- Establishing fallback plans for discontinued models
- Enforcing local logging on third-party model calls
Module 10. Incident Response for AI Systems
Prepare for AI-related incidents with response playbooks that integrate seamlessly into existing on-call workflows and escalation chains.
12 chapters in this module
- Defining AI incident types and severity levels
- Integrating AI alerts into existing monitoring tools
- Playbooks for model bias detection events
- Response steps for data leakage incidents
- Handling model drift beyond thresholds
- Documenting root cause analysis for regulators
- Updating training data after incidents
- Communicating with stakeholders during outages
- Reviewing model updates post-incident
- Updating compliance documentation
- Coordinating with legal and PR teams
- Archiving incident records for audits
Module 11. Metrics That Build Confidence
Track and report on developer adoption, compliance coverage, and incident trends using metrics that resonate with both engineering and executive audiences.
12 chapters in this module
- Measuring compliance workflow adoption rate
- Tracking peer review completion times
- Monitoring sandbox compliance exception rates
- Calculating model documentation completeness
- Benchmarking incident detection speed
- Measuring training completion across teams
- Tracking third-party model review backlog
- Assessing documentation accuracy in audits
- Reporting on model update validation success
- Monitoring escalation path utilization
- Calculating developer satisfaction with guidance
- Tracking repeat incidents by category
Module 12. Sustaining Momentum Across Cycles
Ensure AI governance guidance evolves with developer needs, tooling changes, and regulatory updates , without creating ongoing maintenance debt.
12 chapters in this module
- Scheduling regular playbook updates
- Automating updates from regulatory sources
- Incorporating feedback from developer surveys
- Rotating maintainership across teams
- Archiving outdated guidance clearly
- Deprecating templates with clear notices
- Updating materials after internal incidents
- Versioning changes for audit trails
- Indexing guidance for searchability
- Linking to active discussions and RFCs
- Measuring usage of updated materials
- Retiring deprecated compliance workflows
How this maps to your situation
- When engineering leads question AI Act applicability
- After a regulator requests AI system documentation
- Before launching a new AI-powered feature
- When onboarding third-party AI models
Before vs. after
Before
Compliance questions from developers go unanswered or escalate to legal teams, creating delays and friction.
After
Developer relations provides timely, trusted, and actionable guidance , resolving 80% of AI Act questions at first contact.
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 of focused reading and reflection, designed to fit within a single Sunday morning.
If nothing changes
Without trusted internal guidance, engineering teams either ignore compliance requirements or create inconsistent, audit-prone workarounds.
How this compares to the alternatives
Generic AI governance courses focus on policy writing and compliance frameworks , this course is built for developer relations leads who must make those policies actionable for engineering teams.
Frequently asked
Is this course about writing compliance policy?
No. It’s for translating existing AI Act requirements into developer workflows, templates, and guidance that engineering teams actually follow.
How is the course structured?
12 modules, each containing 12 chapters (144 chapters total).
Will this help me respond to regulator inquiries?
Yes. You'll gain templates for documentation, escalation summaries, and peer-reviewed implementation checklists that stand up under review.
$199 one-time. Approximately 90 minutes of focused reading and reflection, designed to fit within a single Sunday morning..
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