A tailored course, built for your situation
Mastering ISO 42001 for Data Science Practitioners
Build AI governance that scales with your impact
$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.
Your AI governance work is critical, but it’s not being seen by leadership
The situation this course is for
Data science teams are building AI systems that shape business outcomes, but without a recognized governance structure, their work remains invisible to executive stakeholders. ISO 42001 changes that, it’s the first international standard for AI management systems, and it’s designed to elevate technical work into strategic visibility.
Who this is for
Senior data science practitioner in a high-growth tech company, working on AI/ML systems with increasing business impact but limited executive visibility
Who this is not for
Entry-level data analysts, non-technical AI ethics researchers, or consultants looking for generic compliance checklists
What you walk away with
- Structure AI governance work to meet ISO 42001 requirements
- Document AI risk assessments and controls in a board-relevant format
- Socialize governance decisions across engineering and product teams
- Build reusable templates for AI system registration and audit readiness
- Position yourself as the internal reference for AI governance
The 12 modules (with all 144 chapters)
Module 1. Introduction to ISO 42001 and the AI Governance Landscape
Understand the foundation of ISO 42001, its structure, and how it aligns with data science workflows in modern enterprises. Learn how this standard elevates technical work to strategic visibility.
12 chapters in this module
- Why ISO 42001 matters for data science teams
- How ISO 42001 differs from other AI ethics guidelines
- Core principles of AI management systems
- Mapping ISO 42001 clauses to data science deliverables
- Real-world examples from early adopters in tech
- How governance maturity affects promotion trajectories
- Key benefits of early ISO 42001 adoption
- Understanding scope and applicability
- Common misconceptions about AI standards
- The role of data scientists in AI governance
- How ISO 42001 supports cross-functional alignment
- First steps in scoping your AI management system
Module 2. Scoping Your AI Management System
Define the boundaries and applicability of your AI governance framework within your current data science initiatives. Focus on practical scoping that aligns with existing projects.
12 chapters in this module
- Identifying which AI systems to include
- Documenting scope with leadership in mind
- Handling edge cases in AI system classification
- Using data lineage to inform scoping decisions
- Aligning scope with business impact assessments
- Avoiding over-scoping common pitfalls
- Getting stakeholder feedback on scope
- Versioning and updating scope documentation
- How scope affects audit readiness
- Examples from large-scale AI deployments
- Integrating scope into sprint planning
- Template for scope declaration
Module 3. Leadership and Organizational Context
Learn how to position AI governance as a leadership responsibility and align it with Shopify's broader organizational goals.
12 chapters in this module
- Defining organizational context for AI
- Identifying key stakeholders in AI governance
- Mapping AI systems to business objectives
- Establishing governance roles and responsibilities
- Creating cross-functional governance councils
- How leadership commitment affects outcomes
- Documenting decision rights for AI systems
- Balancing innovation and control
- Integrating AI governance into leadership routines
- Measuring leadership engagement
- Case study: AI governance in a merchant-facing platform
- Template for leadership commitment statement
Module 4. Risk Assessment and AI Impact Evaluation
Develop structured risk assessments tailored to AI systems, focusing on fairness, transparency, and operational resilience.
12 chapters in this module
- Building an AI-specific risk register
- Assessing bias and fairness in model outputs
- Evaluating transparency and explainability
- Identifying operational risks in AI deployment
- Using data quality as a risk indicator
- Incorporating human oversight points
- Prioritizing risks by business impact
- Documenting risk treatment plans
- Review cycles for ongoing risk monitoring
- Integrating risk assessment into model validation
- How to present risk findings to non-technical leaders
- Template for AI risk assessment
Module 5. AI System Documentation and Register
Create a centralized, living register of AI systems that serves as a single source of truth for governance and audits.
12 chapters in this module
- Designing an AI system inventory
- Defining minimum documentation standards
- Capturing model purpose and intended use
- Tracking data sources and model versions
- Including human-in-the-loop touchpoints
- Version control for governance artifacts
- Automating register updates from CI/CD pipelines
- Access control for sensitive models
- Integrating with existing data catalogs
- Using the register in incident response
- Example: AI register at a global e-commerce platform
- Template for AI system card
Module 6. Data Governance for AI Systems
Extend existing data governance practices to address AI-specific needs like training data provenance and drift detection.
12 chapters in this module
- Defining data quality metrics for AI
- Tracking data lineage for model inputs
- Handling synthetic and augmented data
- Managing data access for model training
- Documenting data bias mitigation steps
- Monitoring for data drift in production
- Integrating with data loss prevention tools
- Auditing data access for AI models
- Balancing privacy and model performance
- Using metadata to improve model interpretability
- Case study: Data governance in personalization systems
- Template for data governance checklist
Module 7. Model Development and Validation
Implement structured validation processes that ensure AI models meet performance, fairness, and safety requirements before deployment.
12 chapters in this module
- Defining model validation criteria
- Testing for accuracy and robustness
- Assessing fairness across customer segments
- Validating explainability methods
- Ensuring compliance with regional regulations
- Incorporating adversarial testing
- Documenting validation results
- Establishing approval workflows
- Handling model re-validation triggers
- Integrating validation into MLOps pipelines
- Case study: Model validation in fraud detection
- Template for model validation report
Module 8. Deployment and Monitoring of AI Systems
Design deployment strategies and monitoring systems that maintain AI performance and compliance over time.
12 chapters in this module
- Defining deployment readiness criteria
- Implementing canary releases for AI models
- Monitoring model performance in production
- Detecting concept and data drift
- Setting up alerts for model degradation
- Logging model inputs and decisions
- Human oversight in automated workflows
- Handling model rollback procedures
- Integrating monitoring with incident response
- Balancing speed and safety in deployment
- Case study: Monitoring recommendation systems
- Template for deployment checklist
Module 9. Human Oversight and Interaction
Design effective human oversight mechanisms that ensure AI systems remain accountable and controllable.
12 chapters in this module
- Identifying critical decision points
- Designing human-in-the-loop workflows
- Defining escalation paths for AI decisions
- Training staff to interact with AI systems
- Documenting oversight procedures
- Measuring human-AI collaboration effectiveness
- Avoiding automation bias
- Ensuring meaningful human review
- Balancing efficiency and control
- Case study: Human review in merchant support AI
- Template for oversight procedure
- Integrating oversight into UX design
Module 10. Transparency and Explainability
Implement transparency practices that build trust with internal and external stakeholders.
12 chapters in this module
- Defining audience-specific explainability
- Using local and global interpretability methods
- Communicating uncertainty in model outputs
- Documenting model limitations
- Creating public-facing transparency reports
- Balancing IP protection and transparency
- Using dashboards to show model behavior
- Integrating explainability into user workflows
- Case study: Explainability in credit scoring
- Template for model card
- Versioning explainability artifacts
- Training teams to answer model questions
Module 11. Audit Readiness and Continuous Improvement
Prepare for internal and external audits while building a culture of continuous improvement in AI governance.
12 chapters in this module
- Preparing for ISO 42001 certification audits
- Compiling evidence for control mapping
- Conducting internal audits
- Responding to auditor findings
- Implementing corrective actions
- Tracking improvement opportunities
- Using audits to drive innovation
- Integrating feedback into model updates
- Maintaining audit trails
- Case study: First internal ISO 42001 audit
- Template for audit response
- Scheduling continuous improvement cycles
Module 12. Scaling AI Governance Across Teams
Extend your governance framework across multiple data science teams while maintaining consistency and adaptability.
12 chapters in this module
- Identifying governance champions
- Creating reusable templates and playbooks
- Standardizing documentation formats
- Sharing best practices across teams
- Integrating with platform engineering
- Onboarding new teams to the framework
- Measuring governance maturity
- Adapting to new AI technologies
- Balancing standardization and innovation
- Case study: Scaling governance at global tech firm
- Template for governance onboarding
- Roadmap for future ISO standards adoption
How this maps to your situation
- Scoping AI systems within data science workflows
- Integrating governance into MLOps pipelines
- Presenting AI risk to non-technical leaders
- Documenting model decisions for future audits
Before vs. after
Before
AI governance work happens in silos, with limited visibility to leadership and no standardized documentation.
After
You have a structured, ISO 42001-aligned approach to AI governance that makes your contributions visible and valued across the organization.
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 six weeks, with self-paced access to all materials.
If nothing changes
Without a structured governance approach, your team's AI systems may face increased scrutiny, audit findings, or reputational risk , and your individual contributions may remain unseen by leadership.
How this compares to the alternatives
Unlike generic AI ethics courses or compliance checklists, this course is tailored to data science practitioners and focused on ISO 42001 , the first international standard for AI management systems. It provides actionable templates and real-world examples you can apply immediately, not just theoretical frameworks.
Frequently asked
How is the course structured?
12 modules, each containing 12 chapters (144 chapters total).
Is this course suitable for non-technical leaders?
No, it's designed specifically for data science practitioners who build and deploy AI systems.
Does the course cover other AI standards?
Focus is on ISO 42001, but we reference NIST AI RMF and OECD principles where relevant.
$199 one-time. Approximately 90 minutes per week over six weeks, with self-paced access to all materials..
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