A tailored course, built for your situation
Mastering ISO 22301 for AI and Machine Learning Practitioners
Build resilient AI systems with structured business continuity planning
$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.
Even mature AI programs face unplanned downtime because continuity planning is treated as compliance, not engineering.
The situation this course is for
Most data science teams implement AI models without formal resilience frameworks, leading to reactive fixes during outages. When regulators or internal auditors ask about recovery capabilities, the team scrambles to reconstruct documentation. This delays scaling, increases rework, and pushes ownership to risk teams who don’t understand the model logic.
Who this is for
Senior data science practitioner at a regulated enterprise who teaches or leads AI implementation and wants to own resilience decisions without deferring to compliance or risk teams.
Who this is not for
Junior developers, non-technical compliance officers, or consultants without hands-on AI deployment experience.
What you walk away with
- Own the full continuity lifecycle for AI models from design to audit
- Produce ISO 22301-compliant documentation without support from risk teams
- Define recovery point and recovery time objectives for model-serving infrastructure
- Lead internal continuity testing with data science and MLOps teams
- Integrate automated resilience checks into model deployment pipelines
The 12 modules (with all 144 chapters)
Module 1. Understanding ISO 22301 in AI Contexts
Learn how ISO 22301 applies to machine learning systems, not just traditional IT services. Focus on recovery objectives for data pipelines, model inference, and retraining cycles.
12 chapters in this module
- Scope of ISO 22301 for non-traditional workloads
- AI system criticality assessment
- Mapping model dependencies
- Identifying single points of failure
- Recovery time vs recovery point for ML models
- Continuity requirements in training vs serving
- Defining minimum viable functionality
- Stakeholder expectations for uptime
- Regulatory triggers for continuity
- Documenting assumptions and tolerances
- Integrating with MLOps lifecycle
- Common gaps in AI continuity planning
Module 2. Business Impact Analysis for ML Systems
Conduct precise impact analyses tailored to AI components, identifying which models and pipelines require formal continuity planning based on operational and financial exposure.
12 chapters in this module
- Quantifying model downtime cost
- Service level agreements for AI outputs
- Downstream dependencies on predictions
- Data freshness thresholds
- Human oversight breakpoints
- Financial exposure per model class
- Legal and compliance implications
- Customer experience degradation
- Model drift tolerance
- Cascading failure scenarios
- Prioritizing models by impact
- Output: BIA register for AI assets
Module 3. Risk Assessment and Threat Modeling
Apply ISO 22301-aligned risk assessment to AI infrastructure, identifying threats specific to model operations and data pipelines.
12 chapters in this module
- Threat actors in ML systems
- Data poisoning scenarios
- Model hosting vulnerabilities
- Dependency chain risks
- Cloud provider outages
- Retraining data unavailability
- Model weight corruption
- API denial of service
- Authentication bypass
- Adversarial input attacks
- Inference latency spikes
- Risk register with mitigation paths
Module 4. Establishing Resilience Objectives
Define measurable recovery objectives that align with business needs and technical feasibility for each AI component.
12 chapters in this module
- Setting recovery time objectives
- Defining recovery point objectives
- Model version rollback strategy
- Data snapshot intervals
- Stateful vs stateless inference
- Warm vs cold start expectations
- Human-in-the-loop reactivation
- Automated failover triggers
- Monitoring for continuity events
- Graceful degradation planning
- Recovery SLAs by model tier
- Documenting recovery expectations
Module 5. Designing Resilient Architectures
Architect AI systems with built-in resilience, including redundancy, failover, and monitoring tailored to model operations.
12 chapters in this module
- Multi-region model deployment
- Load-balanced inference endpoints
- Model caching strategies
- Fallback model design
- Retraining pipeline redundancy
- Data pipeline checkpointing
- Distributed feature stores
- Redundant monitoring systems
- Health probe design
- Capacity planning for failover
- Infrastructure as code for resilience
- Architecture review checklist
Module 6. Continuity Plan Development
Build actionable continuity plans for AI systems that guide response during outages, with clear roles and escalation paths.
12 chapters in this module
- Incident response workflow for models
- Model containment procedures
- Data quarantine steps
- Emergency retraining protocols
- Human override mechanisms
- Communication plan for downtime
- Stakeholder notification tree
- Legal and regulator outreach
- Post-mortem documentation
- Plan version control
- Integration with IT incident response
- Output: Model-specific continuity playbooks
Module 7. Testing and Validation
Run realistic tests of AI continuity plans, from tabletop exercises to full failovers, ensuring reliability under pressure.
12 chapters in this module
- Tabletop exercise design
- Simulated data pipeline failure
- Model serving node outage
- Cross-region failover test
- Automated recovery validation
- Rollback verification
- Performance benchmarking
- Monitoring under stress
- Team coordination drills
- Test frequency by model tier
- Reporting test results
- Audit-ready validation records
Module 8. ISO 22301 Documentation for Audits
Create complete, auditor-ready documentation that proves compliance with ISO 22301 requirements for AI continuity.
12 chapters in this module
- Required clauses for AI systems
- Evidence collection strategy
- Audit trail design
- Version-controlled policy documents
- Roles and responsibilities matrix
- Change management records
- Test result documentation
- Gap analysis reporting
- Management review minutes
- Corrective action logs
- Pre-audit readiness checklist
- Response to auditor follow-ups
Module 9. Integration with MLOps Pipelines
Embed continuity checks and documentation generation directly into model development and deployment workflows.
12 chapters in this module
- Pre-deployment resilience check
- Automated BIA update
- Model tagging for continuity
- CI/CD integration points
- Infrastructure provisioning checks
- Automated test scheduling
- Documentation auto-generation
- Policy compliance gates
- Version rollback automation
- Alerting for drift
- Pipeline audit trail
- Monitoring continuity health
Module 10. Leadership Communication and Reporting
Communicate continuity posture to technical and non-technical stakeholders with clarity and confidence.
12 chapters in this module
- Executive summary templates
- Technical deep dive structure
- Visualizing recovery readiness
- Risk communication framework
- Incident briefing decks
- Post-mortem reporting
- Regulatory disclosure prep
- Stakeholder update frequency
- Metrics for leadership
- Escalation protocols
- Crisis communication plan
- Report distribution workflow
Module 11. Continuous Improvement and Updates
Maintain and evolve AI continuity plans as models change, ensuring long-term resilience.
12 chapters in this module
- Change impact assessment
- Model update review workflow
- Dependency mapping refresh
- Recovery objective reassessment
- Test plan updates
- Documentation versioning
- Stakeholder revalidation
- Lessons learned integration
- Feedback loop design
- Plan obsolescence detection
- Annual review cycle
- Improvement tracking dashboard
Module 12. Scaling Resilience Across AI Portfolios
Extend continuity practices across multiple models and teams, creating consistent, organization-wide standards.
12 chapters in this module
- Framework standardization
- Template library creation
- Centralized monitoring
- Cross-team coordination
- Best practice sharing
- Maturity assessment model
- Training program design
- Audit consistency
- Vendor continuity alignment
- Third-party model oversight
- Global implementation roadmap
- Future trends in AI resilience
How this maps to your situation
- Implementing new AI models in regulated environments
- Responding to internal audit requests on model reliability
- Scaling AI systems across business units
- Preparing for external regulator inquiries
Before vs. after
Before
Reliance on others to define recovery standards, reactive responses to downtime, fragmented documentation, and deferred decisions on critical AI continuity.
After
Own the full lifecycle of AI system resilience, from design to audit, with structured ISO 22301 implementation that enables faster, more confident scaling.
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 in parallel with ongoing AI projects. Most learners finish within 8 weeks.
If nothing changes
Without structured continuity planning, AI systems will remain vulnerable to unexpected outages, leading to prolonged downtime, regulatory scrutiny, and loss of stakeholder trust. As AI adoption grows, the cost of failure increases, both operationally and reputationally.
How this compares to the alternatives
Unlike generic compliance courses, this program is built specifically for AI engineers and data scientists. It avoids abstract theory and focuses on implementable steps for real systems. Compared to consultant-led workshops, it’s 95% lower cost and available on demand, with templates you keep forever.
Frequently asked
Is this course technical or compliance-focused?
It’s built for engineers who need to meet compliance standards. Content is technical, with direct application to AI system design and MLOps.
How is the course structured?
12 modules, each containing 12 chapters (144 chapters total).
Does this apply to cloud-hosted AI services?
Yes. The course covers hybrid and cloud-native deployments, with specific guidance for AWS, GCP, and Azure AI services.
$199 one-time. Approximately 3 hours per module, designed to be completed in parallel with ongoing AI projects. Most learners finish within 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