A tailored course, built for your situation
Mastering NIST AI RMF for Data Platform ICs
Build an AI governance foundation that compounds across deployments
$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
Individual Contributor (IC) at a data and AI platform company involved in governance, architecture, or standards implementation for AI workloads
Who this is not for
Managers looking for team-wide compliance training or executives seeking board-level narratives
What you walk away with
- A personal library of AI governance artefacts that compound in value across projects
- Clear authorship on framework extensions tailored to platform-specific AI risks
- Reusable assessment templates adopted across peer teams
- Stronger visibility into cross-functional AI delivery timelines
- Ownership of a documented, evolving AI risk ontology used beyond single projects
The 12 modules (with all 144 chapters)
Module 1. Understanding the NIST AI RMF Core Structure
Break down the NIST AI RMF into actionable layers relevant to data platform ICs. Focus on mapping current Databricks AI patterns to the framework’s four functions: Govern, Map, Measure, and Manage.
12 chapters in this module
- Identifying the core components of NIST AI RMF documentation
- Differentiating Govern function requirements from implementation layers
- Mapping data lineage workflows to the Map function
- Linking model monitoring tools to the Measure function
- Aligning incident response plans with the Manage function
- Recognizing platform-specific gaps in the current RMF draft
- Documenting AI system boundaries using NIST-defined criteria
- Establishing ownership for each RMF function within engineering teams
- Integrating RMF workflows into existing sprint planning cycles
- Tracking AI risk decisions using version-controlled decision logs
- Using RMF language to align with security and privacy teams
- Benchmarking current AI governance maturity against NIST tiers
Module 2. Govern Function: Institutionalizing AI Accountability
Learn how to structure clear accountability for AI systems without centralizing control. This module covers lightweight governance models that scale with platform adoption.
12 chapters in this module
- Defining AI accountability in decentralized engineering environments
- Creating lightweight AI governance charters for project teams
- Assigning AI risk owners across development and operations roles
- Documenting AI system intents and expected behaviors upfront
- Establishing feedback loops between AI developers and compliance
- Linking AI governance decisions to sprint review processes
- Developing escalation paths for high-severity AI incidents
- Maintaining AI oversight without slowing down innovation
- Using version-controlled runbooks for policy consistency
- Incorporating AI ethics review checkpoints in CI/CD pipelines
- Tracking AI system changes with audit-ready metadata
- Aligning AI governance with existing platform-wide controls
Module 3. Map Function: Visualizing AI System Boundaries
Turn complex AI workflows into clear, navigable maps that serve as foundational assets. Focus on creating diagrams and metadata that last beyond single projects.
12 chapters in this module
- Identifying all components in an end-to-end AI pipeline
- Mapping data flow between ingestion, training, and serving layers
- Documenting model dependencies and third-party integrations
- Labeling AI system components with risk impact levels
- Linking model versions to training data snapshots
- Creating dynamic architecture diagrams updated with each release
- Tagging AI components with regulatory relevance flags
- Using metadata to automate compliance documentation
- Generating system boundary reports from live configurations
- Sharing AI maps across security, legal, and operations teams
- Versioning AI architecture diagrams alongside code
- Embedding AI map updates into post-deployment reviews
Module 4. Measure Function: Quantifying AI Risk and Performance
Establish consistent metrics for AI performance, fairness, and reliability. Turn qualitative concerns into trackable indicators that support governance at scale.
12 chapters in this module
- Defining fairness metrics for model outputs by use case
- Measuring model drift using statistical distance metrics
- Tracking accuracy decay over time in production models
- Benchmarking model performance across demographic groups
- Logging confidence intervals for probabilistic predictions
- Creating automated alerts for outlier behavior in AI systems
- Validating data quality at each stage of the pipeline
- Assessing model explainability using SHAP or LIME methods
- Measuring adversarial robustness in high-risk applications
- Documenting model uncertainty with calibrated confidence scores
- Aligning measurement practices with industry benchmarks
- Reporting AI performance metrics to non-technical stakeholders
Module 5. Manage Function: Operationalizing AI Risk Responses
Build structured workflows for detecting, responding to, and learning from AI incidents. Create durable processes that improve with each event.
12 chapters in this module
- Classifying AI incidents by severity and response urgency
- Designing automated detection rules for model anomalies
- Establishing incident reporting workflows for AI systems
- Creating runbooks for common AI failure scenarios
- Documenting post-incident review findings and actions
- Integrating AI incident logs with security monitoring tools
- Tracking resolution timelines for AI-related outages
- Conducting blameless retrospectives on AI system failures
- Updating training data based on incident root causes
- Automating model retraining triggers from incident data
- Sharing lessons across teams through AI incident briefs
- Archiving resolved incidents for audit and training use
Module 6. Integrating RMF into Data Platform Workflows
Embed NIST AI RMF practices into engineering workflows without adding friction. Focus on automation, tooling, and minimal viable compliance.
12 chapters in this module
- Identifying natural integration points for RMF checks
- Embedding AI risk assessments into pull request templates
- Automating documentation generation from code commits
- Linking CI/CD pipelines to RMF compliance checks
- Using metadata tagging to populate audit artifacts
- Generating AI governance reports from operational logs
- Integrating AI fairness checks into model validation steps
- Alerting on deviations from approved AI patterns
- Versioning AI governance policies alongside code
- Syncing AI risk registers with project management tools
- Updating framework mappings after platform upgrades
- Reducing compliance overhead through template reuse
Module 7. Authoring Reusable AI Governance Artefacts
Develop a personal library of templates, runbooks, and decision records that compound in value with each project. Focus on ownership and reuse.
12 chapters in this module
- Designing modular AI risk assessment templates
- Creating standardized runbooks for common AI issues
- Documenting design decisions in reusable decision records
- Versioning artefacts using Git-based workflows
- Organizing artefacts by use case and risk level
- Adapting templates for different AI deployment scenarios
- Sharing artefacts with peer reviewers before standardization
- Tracking artefact adoption across engineering teams
- Updating templates based on audit findings
- Archiving outdated artefacts with clear deprecation notices
- Demonstrating authorship in cross-functional governance forums
- Measuring time saved through artefact reuse
Module 8. Scaling Governance Across AI Deployments
Design practices that grow with AI adoption. Learn how to maintain consistency without becoming a bottleneck.
12 chapters in this module
- Identifying patterns across multiple AI deployments
- Creating scalable AI oversight models for ICs
- Using platform-level controls to reduce project overhead
- Developing self-service guidance for AI teams
- Automating compliance checks for common AI patterns
- Establishing lightweight review processes for low-risk models
- Prioritizing governance efforts by business impact
- Using historical data to predict future AI risks
- Supporting rapid AI experimentation with guardrails
- Balancing innovation speed with risk management
- Documenting platform-wide AI governance decisions
- Measuring governance effectiveness across projects
Module 9. Collaborating Across Security, Legal, and Engineering
Bridge silos by creating shared artefacts and decision frameworks. Focus on mutual value and durable collaboration models.
12 chapters in this module
- Translating AI risk concepts for non-technical stakeholders
- Creating joint review processes for high-impact models
- Aligning AI governance with security incident response
- Working with legal teams on regulatory compliance
- Documenting AI decisions for external auditor review
- Providing clear escalation paths for AI-related concerns
- Hosting cross-functional AI governance workshops
- Building trust through consistent communication
- Sharing AI risk dashboards with leadership teams
- Creating playbooks for regulator-facing inquiries
- Integrating feedback from legal and security reviews
- Demonstrating compliance through operational evidence
Module 10. Evolving the Framework with New AI Capabilities
Stay ahead of AI advancements by designing feedback loops that improve governance over time. Focus on adaptability and learning.
12 chapters in this module
- Monitoring emerging AI capabilities for risk implications
- Updating risk assessments after new feature releases
- Incorporating lessons from AI incident reviews
- Adapting governance practices to new model architectures
- Evaluating third-party AI tools against RMF criteria
- Assessing impact of new regulations on existing models
- Updating training materials based on real-world events
- Soliciting feedback from peer engineers on governance
- Benchmarking against evolving industry standards
- Documenting framework improvements over time
- Sharing updates with broader platform engineering teams
- Measuring reduction in compliance rework over time
Module 11. Demonstrating Value Without Executive Oversight
Show impact through tangible deliverables and peer recognition. Focus on IC-level influence and quiet authority.
12 chapters in this module
- Tracking artefact adoption across project teams
- Measuring reduction in audit preparation time
- Documenting peer feedback on governance templates
- Highlighting risk mitigations in sprint reviews
- Sharing AI governance improvements in team forums
- Creating lightweight reports for leadership review
- Presenting lessons learned at internal tech talks
- Mentoring junior engineers on AI governance basics
- Contributing to platform-wide AI standards
- Demonstrating consistency across multiple AI projects
- Receiving unsolicited requests for governance advice
- Building reputation as a trusted AI governance resource
Module 12. Sustaining Impact Beyond Individual Projects
Turn project-specific work into lasting contributions. Focus on documentation, ownership, and institutional memory.
12 chapters in this module
- Archiving project-specific AI governance decisions
- Creating onboarding materials for new team members
- Documenting lessons learned for future teams
- Preserving artefacts in central knowledge repositories
- Establishing ownership for long-lived AI systems
- Updating governance materials after system changes
- Creating pathways for community contribution
- Measuring reuse of governance artefacts over time
- Recognizing contributors in governance documentation
- Linking past decisions to current AI system behavior
- Reducing onboarding time for new AI projects
- Ensuring governance continuity during team transitions
How this maps to your situation
- AI system deployment lifecycle
- Cross-functional governance coordination
- Platform-level compliance integration
- Individual Contributor influence pathways
Before vs. after
Before
Working reactively on AI governance tasks as they arise, with limited reuse across projects
After
Producing reusable, owned artefacts that compound in value and visibility with each AI deployment
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 alongside regular work over 6-8 weeks.
If nothing changes
Without a structured approach, AI governance efforts remain project-specific and invisible, limiting recognition and impact beyond immediate deliverables.
How this compares to the alternatives
Unlike generic AI ethics courses or platform-specific training, this course focuses on actionable, reusable governance structures that compound in value across deployments.
Frequently asked
Who is this course for?
Individual Contributors in data and AI platform organizations who influence governance, architecture, or standards but don't have formal authority over policy.
How is the course structured?
12 modules, each containing 12 chapters (144 chapters total).
Can I apply this to existing AI projects?
Yes, each module includes templates and examples designed to retrofit into ongoing or completed AI deployments.
$199 one-time. Approximately 3 hours per module, designed to be completed alongside regular work over 6-8 weeks..
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