A tailored course, built for your situation
Mastering ISO 20000; A Step-by-Step Guide to Service Management Integration
A tailored 90-minute course for ML Engineers scaling cross-functional systems at scale
$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.
Integration playbooks that require rework across compliance, SRE, and platform teams
The situation this course is for
ML engineers often find themselves rebuilding the same integration artifacts across teams, especially when audit timelines or cross-functional dependencies surface late. The lack of a unified service management standard leads to duplicated effort, inconsistent documentation, and last-minute scrambling before reviews.
Who this is for
ML Engineer at a large tech company shipping AI-driven products at scale, working across infrastructure, compliance, and platform teams to deploy models reliably
Who this is not for
Junior developers focused only on model accuracy, or IT administrators managing helpdesk tickets under ISO 20000 without cross-team integration needs
What you walk away with
- Produce integration-ready service documentation that passes cross-functional review the first time
- Reduce rework cycles by aligning early with ISO 20000 service delivery clauses
- Automate recurring service reporting tasks using ISO 20000-aligned templates
- Gain visibility across infrastructure, SRE, and compliance teams during deployment phases
- Ship model deployment packages faster by reusing standardized service artifacts
The 12 modules (with all 144 chapters)
Module 1. Understanding ISO 20000 in AI and Infrastructure Contexts
Lays the foundation by mapping ISO 20000 principles to real-world AI deployment workflows at companies like Meta. Focuses on how service management standards apply beyond traditional IT, especially in model lifecycle governance.
12 chapters in this module
- How ISO 20000 applies to machine learning deployment pipelines
- Key differences between IT service management and ML infrastructure
- Mapping service delivery clauses to model release cycles
- Why service standards matter even in agile, fast-moving teams
- Common misconceptions about ISO 20000 and engineering speed
- How Meta teams are adapting service standards to AI products
- Integrating ISO 20000 with internal platform governance frameworks
- Service ownership models in decentralized engineering orgs
- Linking service documentation to CI/CD pipeline triggers
- Auditor expectations for service records in AI systems
- How service level agreements differ for internal ML platforms
- Documenting service scope for non-IT systems
Module 2. Service Catalog Design for ML Platforms
Teaches how to define and structure a service catalog that includes ML inference endpoints, training pipelines, and data preprocessing services, ensuring clarity across teams.
12 chapters in this module
- Defining service boundaries for model hosting environments
- Structuring service entries for internal ML platforms
- Including version control in service catalog definitions
- Mapping catalog items to IAM and access policies
- Documenting dependencies between ML services
- Versioning service catalog entries across product lines
- Using tags and metadata for discoverability
- Integrating service catalog with internal developer portals
- Maintaining catalog accuracy during rapid iteration
- Auditing catalog completeness for compliance cycles
- Linking service catalog items to cost centers
- Automating catalog updates via CI/CD hooks
Module 3. Service Level Agreements for Model Performance
Covers how to define realistic, measurable SLAs for ML services, including uptime, latency, and drift detection, that align with business expectations.
12 chapters in this module
- Setting measurable SLAs for inference latency
- Defining availability targets for batch training jobs
- Tracking model drift as a service health metric
- Balancing SLA stringency with innovation speed
- Documenting SLA exceptions for experimental models
- Aligning SLAs with business impact metrics
- Reporting SLA breaches without blame culture
- Using SLAs to prioritize technical debt in ML systems
- Negotiating SLAs across product and infrastructure teams
- Automating SLA reporting using observability tools
- Updating SLAs after model retraining cycles
- Handling SLA variance during traffic spikes
Module 4. Incident Management for ML System Failures
Provides a structured approach to classifying, logging, and resolving incidents specific to ML systems, including silent failures and data drift.
12 chapters in this module
- Classifying ML incidents vs traditional system outages
- Logging silent model degradation events
- Defining incident severity for prediction errors
- Integrating incident logs with model monitoring tools
- Assigning incident ownership in matrixed teams
- Documenting root cause for statistical anomalies
- Linking incidents to model version rollbacks
- Using incident data to improve training pipelines
- Reducing mean time to detection for drift
- Automating incident classification with NLP
- Reporting incident trends to compliance teams
- Auditing incident response for ISO 20000 compliance
Module 5. Change Management for Model Deployments
Teaches how to structure change requests for model updates, A/B tests, and pipeline modifications while maintaining compliance and minimizing risk.
12 chapters in this module
- Defining change types for ML model updates
- Documenting impact assessments for model changes
- Routing change requests across compliance and SRE
- Using automated checks in change approval workflows
- Handling emergency model rollbacks under change policy
- Versioning change records alongside model artifacts
- Linking changes to CI/CD pipeline events
- Auditing change history for regulatory reviews
- Managing peer reviews for high-risk changes
- Integrating change logs with model cards
- Reducing change approval time with templates
- Tracking change success rates over time
Module 6. Configuration Management for ML Systems
Covers how to maintain accurate configuration records for models, datasets, and infrastructure, ensuring traceability and audit readiness.
12 chapters in this module
- Defining configuration items in ML pipelines
- Tracking model hyperparameters as config records
- Linking config items to data versioning systems
- Automating config updates from CI/CD pipelines
- Using config management databases for audit trails
- Documenting dependencies between ML components
- Handling config drift in development environments
- Validating config accuracy during deployment
- Reporting config completeness to compliance teams
- Integrating config management with feature stores
- Versioning config records across model iterations
- Auditing config history for ISO 20000 reviews
Module 7. Release and Deployment Management
Provides a framework for planning, testing, and deploying ML models in a controlled, auditable way across environments.
12 chapters in this module
- Planning model releases with stakeholder input
- Defining release types for A/B tests and rollouts
- Creating release checklists for compliance teams
- Using canary deployments in model releases
- Documenting rollback procedures for failed releases
- Integrating release plans with sprint cycles
- Automating release documentation from CI/CD
- Tracking release success across environments
- Reporting release metrics to leadership
- Auditing release processes for ISO 20000
- Handling emergency releases under policy
- Reducing release cycle time with templates
Module 8. Service Request Management for Internal Teams
Teaches how to standardize and automate service requests for model access, data pipelines, and platform resources.
12 chapters in this module
- Defining service request types for ML platforms
- Routing requests to correct engineering teams
- Setting SLAs for request fulfillment
- Automating approval workflows for sensitive data
- Documenting request fulfillment for audits
- Integrating request systems with IAM policies
- Reducing manual effort in access provisioning
- Tracking request trends for capacity planning
- Using templates to standardize request intake
- Auditing request history for compliance
- Improving request satisfaction scores
- Linking requests to cost allocation systems
Module 9. Problem Management and Root Cause Analysis
Covers how to identify, log, and resolve recurring issues in ML systems using structured problem management.
12 chapters in this module
- Distinguishing incidents from underlying problems
- Logging recurring model performance issues
- Conducting root cause analysis for drift events
- Linking problems to technical debt backlogs
- Prioritizing problem resolution based on impact
- Using problem records to improve training data
- Documenting permanent fixes for audit trails
- Integrating problem management with Jira
- Reporting problem trends to leadership
- Auditing problem resolution for compliance
- Reducing recurrence with automated checks
- Sharing problem insights across teams
Module 10. Service Continuity and Disaster Recovery
Teaches how to plan for ML service disruptions, including model failure, data loss, and infrastructure outages.
12 chapters in this module
- Identifying critical ML services for recovery planning
- Defining recovery time objectives for models
- Documenting failover procedures for inference endpoints
- Testing disaster recovery plans for ML systems
- Using redundancy in training pipeline design
- Maintaining backup datasets for retraining
- Planning for data center outages in ML hosting
- Linking recovery plans to business continuity
- Auditing recovery readiness for compliance
- Updating plans after model architecture changes
- Reducing recovery time with automated scripts
- Reporting continuity metrics to leadership
Module 11. Supplier Management for Third-Party ML Tools
Covers how to manage vendors and third-party tools used in ML pipelines, ensuring compliance and performance.
12 chapters in this module
- Assessing third-party ML platforms for compliance
- Documenting vendor SLAs for model hosting
- Tracking license usage for commercial ML tools
- Managing vendor onboarding for audit readiness
- Evaluating security controls in vendor offerings
- Using contracts to enforce data privacy terms
- Monitoring vendor performance against SLAs
- Auditing vendor activity for compliance reviews
- Handling vendor outages in ML pipelines
- Integrating vendor management with procurement
- Reducing vendor risk with fallback plans
- Reporting vendor metrics to compliance teams
Module 12. Implementing ISO 20000 in Practice
Guides the learner through a full implementation of ISO 20000 principles in an ML engineering context, with templates and real-world examples.
12 chapters in this module
- Assessing current service management maturity
- Gap analysis against ISO 20000 requirements
- Prioritizing improvements based on risk
- Creating an implementation roadmap
- Engaging stakeholders across teams
- Piloting changes in a non-production environment
- Gathering feedback from compliance teams
- Scaling improvements across product lines
- Documenting implementation for audits
- Maintaining ISO 20000 alignment over time
- Training teams on new service processes
- Celebrating successful implementation milestones
How this maps to your situation
- Model deployment integration
- Cross-team service documentation
- Audit-ready compliance records
- Scalable service management for AI products
Before vs. after
Before
Spending weeks aligning service documentation across SRE, compliance, and platform teams, with last-minute rework before audits
After
Producing ISO 20000-aligned integration playbooks in hours, accepted across teams without revision
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: 90 minutes total, designed to be completed in a single Sunday morning session
If nothing changes
Without structured service management, ML engineers risk duplicated effort, audit findings, and slower deployment cycles as systems scale.
How this compares to the alternatives
Unlike generic ISO 20000 training, this course is tailored to ML engineers in large tech companies, focusing on real integration points with AI systems, not theoretical IT service desks.
Frequently asked
Is this course relevant for non-IT roles?
Yes. ISO 20000 applies to any service delivery, including ML platforms. This course focuses on its application beyond traditional IT.
How is the course structured?
12 modules, each containing 12 chapters (144 chapters total).
Can I apply this to other frameworks?
Yes. The patterns transfer to SOC 2, ISO 27001, and other standards requiring service documentation.
$199 one-time. 90 minutes total, designed to be completed in a single Sunday morning session.
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