Skip to main content
Image coming soon

OPS9901 Mastering ISO 20000 for Senior ML Engineers in High-Velocity AI Organizations

$199.00
Adding to cart… The item has been added

A tailored course, built for your situation

Mastering ISO 20000 for Senior ML Engineers in High-Velocity AI Organizations

A complete implementation blueprint for aligning AI infrastructure with service management rigor

$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

Senior ML Engineers in AI-driven organizations who are expanding their influence beyond model build into production integrity, audit readiness, and cross-functional service governance

Who this is not for

Junior data scientists, non-technical compliance staff, or practitioners focused solely on offline experimentation without production deployment

What you walk away with

  • Full command of ISO 20000's service lifecycle structure as it applies to AI/ML pipelines
  • Clear mapping of model governance artifacts to formal service management controls
  • Ability to proactively shape service design inputs before review cycles begin
  • Documented workflows that satisfy both engineering velocity and compliance scrutiny
  • Confidence in producing audit-ready service documentation without external support

The 12 modules (with all 144 chapters)

Module 1. Introduction to ISO 20000 in AI-Driven Environments
Lays the foundation for applying service management standards specifically to machine learning systems, clarifying scope boundaries and value drivers unique to AI organizations.
12 chapters in this module
  1. Defining service management in the context of AI model delivery
  2. Historical evolution of ISO 20000 and its relevance to software intelligence
  3. Key differences between traditional IT services and ML-powered services
  4. How AI velocity challenges conventional service lifecycle timelines
  5. Core principles of service quality applicable to model outputs
  6. The role of the ML engineer in service ownership and accountability
  7. Mapping ISO 20000 clauses to AI infrastructure components
  8. Why service management is no longer just an Ops concern
  9. Common misconceptions about compliance slowing innovation
  10. Establishing baseline terminology across engineering and compliance
  11. Case example: Service incident tracing in an autonomous recommendation system
  12. Preparing your mindset for integrated service design
Module 2. Service Strategy and AI Capability Planning
Teaches how to align AI roadmap planning with formal service strategy requirements, ensuring model initiatives support business capacity goals.
12 chapters in this module
  1. Linking model development cycles to service capacity planning
  2. Defining service value from the perspective of internal stakeholders
  3. Translating AI use cases into service portfolio entries
  4. Assessing demand patterns for model inference workloads
  5. Financial considerations in AI service lifecycle planning
  6. Risk-based prioritization of model deployment initiatives
  7. Balancing innovation speed with service sustainability
  8. Defining service level objectives for ML systems
  9. Engaging product teams in service design conversations
  10. Integrating compliance requirements into initial planning phases
  11. Documenting strategic fit for AI service proposals
  12. Workshop: Drafting a service strategy statement for a new ranking model
Module 3. Service Design and Model Architecture Integration
Covers how to embed service management requirements directly into ML system architecture and design documents.
12 chapters in this module
  1. Incorporating service availability targets into model design
  2. Designing for recoverability in real-time AI systems
  3. Documentation standards for AI service components
  4. Version control strategies aligned with service configuration management
  5. Security by design in model serving infrastructure
  6. Scalability planning based on predicted service demand
  7. Embedding monitoring and logging from initial design stages
  8. Change management implications of model retraining cycles
  9. Data lineage requirements for audit readiness
  10. Designing handoff points between research and MLOps teams
  11. Creating service design packages for AI components
  12. Workshop: Annotating a model architecture diagram with ISO 20000 requirements
Module 4. Service Transition for Machine Learning Models
Provides a structured approach to releasing ML models into production while maintaining service integrity.
12 chapters in this module
  1. Defining service transition milestones for model deployment
  2. Change evaluation processes for model updates
  3. Release planning with rollback safeguards for AI services
  4. Testing strategies that validate both model performance and service behavior
  5. Configuration management for model endpoints and dependencies
  6. Knowledge transfer protocols between ML and operations teams
  7. Service validation checklists for pre-production models
  8. Managing dependencies across data pipelines and serving layers
  9. Retirement planning for obsolete models and datasets
  10. Documenting release packages for audit purposes
  11. Common pitfalls in AI service transitions and how to avoid them
  12. Workshop: Building a release plan for a computer vision model update
Module 5. Service Operation in Real-Time AI Systems
Focuses on maintaining consistent service quality during live model operation, including incident and problem management.
12 chapters in this module
  1. Incident classification for AI model failures
  2. Event monitoring setup for model performance degradation
  3. Request fulfillment processes for model access and tuning
  4. Problem management techniques for recurring model issues
  5. Root cause analysis methods specific to ML systems
  6. Service desk integration for model-related support requests
  7. Daily health checks for AI service components
  8. Managing technical debt in production ML pipelines
  9. Handling model concept drift as a service issue
  10. Maintaining service documentation in dynamic environments
  11. Automation opportunities in AI service operations
  12. Workshop: Simulating an incident response for a sentiment model failure
Module 6. Continual Service Improvement for AI Pipelines
Teaches how to systematically improve AI services using feedback loops and performance data.
12 chapters in this module
  1. Defining metrics that reflect true service improvement
  2. Collecting actionable feedback from model consumers
  3. Analyzing model performance trends over time
  4. Identifying improvement opportunities in inference latency
  5. Benchmarking against internal and external AI service standards
  6. Prioritizing improvements based on business impact
  7. Creating CSI registers for AI model portfolios
  8. Linking model updates to service quality objectives
  9. Measuring the impact of changes on service stability
  10. Documenting improvement initiatives for compliance
  11. Sustaining improvement momentum in fast-moving environments
  12. Workshop: Drafting a CSI initiative for recommendation model refresh
Module 7. Configuration Management for ML Systems
Details how to maintain accurate records of AI service components and their interdependencies.
12 chapters in this module
  1. Defining configuration items in machine learning pipelines
  2. Establishing baselines for model versions and dependencies
  3. Automated discovery of AI service components
  4. Relationship mapping between data sources and model outputs
  5. Change tracking for feature engineering components
  6. Access control for configuration management databases
  7. Audit preparation using configuration records
  8. Integrating MLOps tools with CMDB structures
  9. Version synchronization across model and service records
  10. Handling metadata drift in long-running AI services
  11. Documenting configuration management processes for ISO 20000
  12. Workshop: Building a CMDB schema for a personalization model
Module 8. Change Management in AI Development Cycles
Covers formal change control processes adapted for rapid ML experimentation and deployment.
12 chapters in this module
  1. Classifying changes to AI models and infrastructure
  2. Risk assessment for model retraining and redeployment
  3. Change advisory board roles in AI organizations
  4. Standard change templates for routine model updates
  5. Emergency change procedures for critical model fixes
  6. Change scheduling around model refresh cycles
  7. Backout planning for failed model deployments
  8. Documentation requirements for change records
  9. Integrating A/B testing into change evaluation
  10. Post-implementation review processes for ML changes
  11. Balancing agility with control in high-velocity teams
  12. Workshop: Processing a change request for bias mitigation update
Module 9. Incident and Problem Management for AI Services
Provides frameworks for responding to and preventing issues in production ML systems.
12 chapters in this module
  1. Defining service level agreements for model availability
  2. Incident escalation paths for model performance issues
  3. Categorizing AI-related incidents by impact and urgency
  4. Problem investigation techniques for model degradation
  5. Known error databases for recurring model behaviors
  6. Trend analysis of incident data to prevent future problems
  7. Post-mortem documentation standards for AI incidents
  8. Linking incident data to model monitoring systems
  9. Preventive measures for data quality-related failures
  10. Integrating human feedback into problem management
  11. Metrics for measuring incident resolution effectiveness
  12. Workshop: Conducting a problem analysis for false positive surge
Module 10. Service Level Management for ML Models
Teaches how to define, track, and report on service level objectives for AI systems.
12 chapters in this module
  1. Negotiating realistic SLOs for machine learning services
  2. Defining service scope boundaries for model offerings
  3. Monitoring service performance against agreed targets
  4. Reporting formats for service level achievements
  5. Handling SLO breaches constructively
  6. Review cycles for updating service level agreements
  7. Balancing innovation with service stability commitments
  8. Transparency practices for service consumers
  9. Integrating model performance with service availability
  10. Documentation requirements for service level management
  11. Tools for automating service level reporting
  12. Workshop: Drafting an SLA for a search ranking model
Module 11. Supplier Management in AI Ecosystems
Covers managing third-party dependencies in ML systems, including cloud platforms and data providers.
12 chapters in this module
  1. Defining supplier roles in AI service delivery
  2. Contractual considerations for model hosting services
  3. Performance monitoring of external AI components
  4. Managing dependencies on third-party APIs and data
  5. Risk assessment for vendor lock-in scenarios
  6. Audit rights and access requirements for compliance
  7. Exit strategies for replacing supplier components
  8. Collaboration models between internal and external teams
  9. Documentation of supplier relationships for ISO 20000
  10. Handling service continuity during vendor transitions
  11. Best practices for multi-cloud AI deployments
  12. Workshop: Evaluating a new vector database provider
Module 12. Auditing and Improving AI Service Management
Prepares learners to lead internal audits and drive maturity improvements in AI service governance.
12 chapters in this module
  1. Understanding audit objectives for ISO 20000 compliance
  2. Preparing documentation for service management audits
  3. Internal audit planning for AI service portfolios
  4. Conducting interviews with ML and operations staff
  5. Identifying gaps in service management implementation
  6. Reporting audit findings to technical leadership
  7. Remediation planning for audit recommendations
  8. Building organizational capability for self-auditing
  9. Tracking progress on corrective actions
  10. Integrating audit insights into continual improvement
  11. Demonstrating compliance maturity to external assessors
  12. Workshop: Simulating an internal audit of a recommendation system

How this maps to your situation

  • AI model lifecycle governance
  • Enterprise service management integration
  • Production system reliability
  • Cross-functional compliance readiness

Before vs. after

Before
Working reactively to compliance requests and audit timelines
After
Proactively shaping service governance frameworks from the ML team outward

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 6 hours of focused reading and implementation exercises, designed for completion in short sessions over a weekend or across weekday evenings.

If nothing changes
Without structured command of service management standards, even advanced AI systems may face operational rejection, audit findings, or governance delays that slow deployment velocity and reduce engineering influence.

How this compares to the alternatives

Unlike generic compliance courses, this program is tailored specifically to ML engineers in AI-forward organizations, focusing on practical implementation of ISO 20000 within real-world model delivery contexts rather than theoretical frameworks or checklist compliance.

Frequently asked

How is the course structured?
12 modules, each containing 12 chapters (144 chapters total).
Is prior experience with ISO 20000 required?
No , the course starts from foundational concepts and builds to advanced implementation, assuming only general familiarity with enterprise systems.
Can I apply this to non-AI machine learning systems?
Yes , the principles apply to any ML system in production, though examples focus on high-velocity AI contexts.
$199 one-time. Approximately 6 hours of focused reading and implementation exercises, designed for completion in short sessions over a weekend or across weekday evenings..

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
!