A tailored course, built for your situation
Mastering ISO 20000 for Senior ML Engineers in Regulated Data Environments
A step-by-step implementation path for AI/ML practitioners leading service management integration
$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.
Feeling like compliance is a separate track that slows down ML delivery
The situation this course is for
ML engineers spend 30-40% of deployment time reworking systems to meet service management audits. Most lack a clear bridge between ISO 20000 requirements and model operationalization patterns.
Who this is for
Senior ML Engineers in financial services, healthcare, or credit data firms who own end-to-end deployment and are expected to align with IT service frameworks
Who this is not for
Junior data scientists not involved in deployment, or engineers working in non-regulated environments without formal service management expectations
What you walk away with
- Map ISO 20000 service transition controls directly to MLOps pipelines
- Document service continuity plans that pass internal audit without revision
- Lead cross-functional incident review sessions with ITSM teams using shared terminology
- Generate approved change records for model updates that satisfy ISO 20000 compliance reviewers
- Build self-documenting ML systems that reduce audit prep time by 50%
The 12 modules (with all 144 chapters)
Module 1. Introduction to ISO 20000 in AI-Driven Organizations
Understand how ISO 20000 applies to machine learning systems treated as formal IT services. Learn the core service management obligations relevant to ML engineers.
12 chapters in this module
- What ISO 20000 means for ML systems
- Service lifecycle stages and ML parallels
- Key roles in service management
- Difference between ISO 20000 and ISO 27001
- Service catalog entry for ML models
- Service level agreement basics
- Change management triggers
- Incident response coordination
- Service continuity expectations
- Internal audit readiness
- Documented process requirements
- Compliance evidence types
Module 2. Service Strategy and ML Value Definition
Define the business value of ML systems using ISO 20000 service strategy framework. Align model outcomes with service objectives.
12 chapters in this module
- Identifying service beneficiaries
- ML outcomes as service deliverables
- Service portfolio mapping
- Value proposition framing
- Demand pattern analysis
- Resource planning inputs
- Financial accountability basics
- Cost model alignment
- Service valuation methods
- Pricing principles for internal services
- Budget cycle coordination
- Service investment prioritization
Module 3. Service Design and ML System Architecture
Integrate ISO 20000 service design requirements into ML architecture planning. Ensure compliance from first design decision.
12 chapters in this module
- Service design package structure
- Availability requirements for ML APIs
- Capacity planning for inference workloads
- ML system security integration
- Data management in service design
- Supplier management for ML tools
- Change strategy for models
- Service transition planning
- Risk assessment documentation
- Design coordination meetings
- Architecture review gates
- Design validation techniques
Module 4. Service Transition for ML Model Deployments
Apply ISO 20000 service transition controls to model deployment workflows. Formalize release and change processes.
12 chapters in this module
- Change types in ML systems
- Standard change approvals
- Emergency deployment process
- Release unit definition
- Build configuration management
- Test environment controls
- Deployment scheduling
- Backout planning
- Knowledge transfer for operations
- Change advisory board input
- Post-implementation review
- Deployment audit trail
Module 5. Service Operation and ML Incident Management
Operationalize ML incidents using ISO 20000 service operation framework. Respond to outages and performance drift systematically.
12 chapters in this module
- Incident lifecycle stages
- ML incident classification
- Priority determination rules
- Initial diagnosis steps
- Escalation procedures
- Workaround documentation
- Root cause analysis for models
- Incident closure criteria
- Service desk coordination
- Major incident response
- Event correlation for models
- Automated alert integration
Module 6. Continual Service Improvement for ML Systems
Implement ISO 20000 continual improvement cycles for ML services. Track KPIs and drive model evolution.
12 chapters in this module
- CSI model phases
- KPI identification for ML
- Service reporting cycles
- Benchmarking ML performance
- Process evaluation methods
- Improvement initiative backlog
- Change proposal writing
- Service review meetings
- Performance trend analysis
- Customer feedback integration
- Service retirement planning
- Lessons learned documentation
Module 7. Integration with Existing ITSM Tools
Connect ML workflows to ServiceNow, Jira, or BMC systems used for IT service management.
12 chapters in this module
- API integration patterns
- Ticket creation automation
- Status synchronization
- Field mapping standards
- Authentication setup
- Audit log forwarding
- Custom field creation
- Workflow trigger configuration
- SLA tracking setup
- Reporting dashboard sync
- User role assignment
- Change record linking
Module 8. Compliance Evidence Generation for ML Systems
Generate artefacts that satisfy ISO 20000 auditors without requiring rework.
12 chapters in this module
- Evidence retention policies
- Version control for models
- Deployment logs
- Incident records
- Change approvals
- Review meeting minutes
- Policy sign-offs
- Training records
- Access control lists
- Audit trail completeness
- Documented process adherence
- Third-party review readiness
Module 9. Cross-Functional Leadership in Service Management
Lead service management initiatives across IT, data engineering, and compliance teams.
12 chapters in this module
- Stakeholder identification
- Meeting facilitation
- Consensus building
- Communication templates
- Conflict resolution
- Decision tracking
- Status reporting
- Escalation pathways
- Influence without authority
- Peer alignment
- Executive update preparation
- Cross-team documentation
Module 10. ML System Documentation for Service Management
Create compliant documentation that serves both technical and audit audiences.
12 chapters in this module
- Service catalog content
- Runbook writing
- Architecture diagrams
- Process flowcharts
- Change calendar
- Incident playbook
- Disaster recovery plan
- Knowledge base articles
- User guides
- Training materials
- Compliance mapping table
- Version history log
Module 11. Internal Audit Preparation for ML Services
Prepare for ISO 20000 internal audits with confidence using proven evidence patterns.
12 chapters in this module
- Audit scope definition
- Evidence checklist
- Interview preparation
- Audit trail review
- Control testing methods
- Nonconformance response
- Corrective action tracking
- Management review input
- Audit report response
- Follow-up verification
- Readiness assessment
- Audit simulation
Module 12. Sustaining ISO 20000 Compliance in Evolving ML Environments
Maintain compliance as models and infrastructure evolve. Build self-sustaining practices.
12 chapters in this module
- Change control integration
- Model lifecycle tracking
- Infrastructure drift detection
- Automated compliance checks
- Policy update process
- Training refresh cycles
- Audit readiness maintenance
- Continuous monitoring
- Compliance dashboard
- Stakeholder updates
- Lessons from past audits
- Improvement roadmap
How this maps to your situation
- When leading first ML system into production
- Before internal audit cycle begins
- When joining cross-functional service review meeting
- After model incident requiring formal response
Before vs. after
Before
Compliance feels like a separate track requiring rework and last-minute documentation for ML systems.
After
You produce ISO 20000-aligned artefacts as a natural part of ML deployment, with peer teams citing your work.
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: 6-8 hours total, self-paced, designed for practitioners with live deployment responsibilities.
If nothing changes
Without structured integration, ML systems face repeated compliance rework, delayed deployments, and requests to re-prove reliability that slow innovation velocity.
How this compares to the alternatives
Generic ISO 20000 courses focus on IT operations, not ML systems. This course maps every clause to model deployment decisions, not abstract theory.
Frequently asked
Does this course cover ML-specific ISO 20000 interpretations?
Yes, every module includes examples of how ISO 20000 applies to model training, validation, deployment, and monitoring.
How is the course structured?
12 modules, each containing 12 chapters (144 chapters total).
Is prior ISO 20000 experience required?
No. The course starts with fundamentals and builds to advanced implementation.
$199 one-time. 6-8 hours total, self-paced, designed for practitioners with live deployment responsibilities..
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