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OPS4194 Mastering ISO 20000 for Senior ML Engineers in AI Infrastructure

$199.00
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What is the ISO 20000 for Senior ML Engineers course about?

ML engineers at scale face mounting evidence demands, SOC 2, ISO 20000, internal policy attestations, that disrupt model deployment cycles. These artefacts aren’t edge cases; they’re recurring, high-stakes deliverables that drain bandwidth when handled reactively. Paul, like many senior practitioners, is expected to deliver both innovation and compliance, but the handoffs between engineering and assurance teams create friction points, especially when evidence.

What situation is the ISO 20000 for Senior ML Engineers for?

ML engineers at scale face mounting evidence demands, SOC 2, ISO 20000, internal policy attestations, that disrupt model deployment cycles. These artefacts aren’t edge cases; they’re recurring, high-stakes deliverables that drain bandwidth when handled reactively. Paul, like many senior practitioners, is expected to deliver both innovation and compliance, but the handoffs between engineering and assurance teams create friction points, especially when evidence.

Who is the ISO 20000 for Senior ML Engineers course for?

Senior ML Engineers in large-scale AI teams who own system resilience, incident response, or model lifecycle governance but aren't formal compliance owners.

Who is the ISO 20000 for Senior ML Engineers course not for?

Junior data scientists doing exploratory modeling, compliance auditors focused solely on controls testing, or software engineers outside of ML infrastructure roles.

What do you take away from the ISO 20000 for Senior ML Engineers course?

Produce ISO 20000-aligned service documentation that passes review without rework Automate evidence collection for change management and incident response cycles Lead internal audit prep without relying on cross-functional chasing Own the narrative when regulators ask follow-ups on model performance incidents Reduce monthly compliance workload by 75% through standardized templates and validation workflows.

How does this map to your situation?

Audit readiness in AI infrastructure Incident response ownership expansion Change control in ML deployment pipelines Configuration management for model systems.

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.

What does the ISO 20000 for Senior ML Engineers cover on delivery and format?

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 9 hours total, self-paced across 12 modules.

Closely related courses: Network Automation for Senior Infrastructure Engineers, Regional Bank Senior Infrastructure Engineer's, Influence Across Business Lines as a Senior, The next role.

More answers: what you get with every course, refund policy, all help answers.

A tailored course, built for your situation

Mastering ISO 20000 for Senior ML Engineers in AI Infrastructure

Build compliant, auditable AI systems with confidence and control

$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.
Audit-ready AI systems shouldn’t require last-minute scrambles

The situation this course is for

ML engineers at scale face mounting evidence demands, SOC 2, ISO 20000, internal policy attestations, that disrupt model deployment cycles. These artefacts aren’t edge cases; they’re recurring, high-stakes deliverables that drain bandwidth when handled reactively. Paul, like many senior practitioners, is expected to deliver both innovation and compliance, but the handoffs between engineering and assurance teams create friction points, especially when evidence needs span model lineage, incident response, and change control. The cost isn’t just time, it’s credibility when audit timelines shift. Yet most training ignores the tactical reality of these deliverables. Generic compliance courses don’t map to the actual artefacts ML teams produce. The gap isn’t knowledge, it’s structure. Practitioners know pieces of the framework but lack a repeatable way to embed compliance into CI/CD workflows. The result? Re-work. Delays. Escalations. Even preventable findings.

Who this is for

Senior ML Engineers in large-scale AI teams who own system resilience, incident response, or model lifecycle governance but aren't formal compliance owners

Who this is not for

Junior data scientists doing exploratory modeling, compliance auditors focused solely on controls testing, or software engineers outside of ML infrastructure roles

What you walk away with

  • Produce ISO 20000-aligned service documentation that passes review without rework
  • Automate evidence collection for change management and incident response cycles
  • Lead internal audit prep without relying on cross-functional chasing
  • Own the narrative when regulators ask follow-ups on model performance incidents
  • Reduce monthly compliance workload by 75% through standardized templates and validation workflows

The 12 modules (with all 144 chapters)

Module 1. Introduction to ISO 20000 in AI-Driven Environments
Lay the foundation for service management in machine learning systems. Understand how ISO 20000 principles apply to model deployment, monitoring, and incident response workflows. This module maps standard clauses to real ML infrastructure artefacts, showing where compliance intersects with reliability engineering. Learn to distinguish between advisory practices and mandatory requirements, focusing on regulatory accountability rather than checklist compliance.
12 chapters in this module
  1. Understanding the scope of ISO 20000 for AI systems
  2. Key differences between ITIL and ML service operations
  3. Mapping ISO 20000 clauses to model lifecycle stages
  4. Identifying service boundaries in distributed inference pipelines
  5. Defining availability SLAs for real-time ML services
  6. Incident classification thresholds for model drift events
  7. Change advisory board roles in automated deployment gates
  8. Service level reporting for internal audit consumption
  9. Integrating SLOs into ISO 20000 service agreements
  10. Vendor management for third-party model APIs
  11. Data lineage tracking as a service continuity requirement
  12. Role-based access reviews in shared ML platforms
Module 2. Service Management System Design for ML Infrastructure
Design a compliant service management backbone tailored to ML systems. This module walks through creating a documented SMS that satisfies ISO 20000 while remaining agile. Focus areas include policy integration, risk assessment alignment, and resourcing strategies for incident response. Learn how senior engineers can own key components without becoming full-time compliance staff.
12 chapters in this module
  1. Establishing policy alignment across AI teams
  2. Documenting service management objectives clearly
  3. Risk assessment integration with model risk frameworks
  4. Resource planning for incident response capacity
  5. Budget justification for observability tooling
  6. Stakeholder communication cadence planning
  7. Version control for service documentation
  8. Internal audit readiness checklist creation
  9. Training plan development for on-call engineers
  10. Toolchain selection for service reporting
  11. Automated service status dashboards
  12. Document retention policy for audit trails
Module 3. Incident Management in Real-Time ML Systems
Build an ISO 20000-compliant incident response process that works at ML scale. This module covers categorization, escalation paths, resolution timeframes, and post-mortem workflows specific to model failures. Learn how to structure runbooks, define severity levels, and integrate with existing alerting systems without introducing latency.
12 chapters in this module
  1. Defining incident categories for model performance drops
  2. Severity classification based on business impact
  3. Escalation protocols during peak traffic hours
  4. Duty rotation schedules for ML support teams
  5. Initial response time benchmarks for latency spikes
  6. Automated incident ticket creation from monitoring tools
  7. Root cause analysis templates for model drift
  8. Service restoration verification steps
  9. Post-mortem meeting facilitation techniques
  10. Action item tracking for reliability improvements
  11. Integration with PagerDuty and Opsgenie
  12. Compliance logging for audit evidence
Module 4. Change Control in ML Deployment Pipelines
Implement ISO 20000-compliant change management in CI/CD workflows. This module covers standardizing deployment changes, managing emergency changes, and documenting rollback procedures. Learn how to embed compliance into automated pipelines without slowing innovation.
12 chapters in this module
  1. Classifying changes in automated deployment systems
  2. Standard change templates for model updates
  3. Emergency change approval workflows
  4. Change advisory board meeting structure
  5. Pre-deployment risk assessment checklists
  6. Post-implementation review documentation
  7. Rollback procedure standardization
  8. Automated change notification systems
  9. Version compatibility testing protocols
  10. Change calendar integration with team planning
  11. Audit trail generation for deployment records
  12. Lessons learned from failed change rollouts
Module 5. Configuration Management for Model Lineage
Establish ISO 20000-compliant configuration management focused on model and data lineage. This module introduces tools and processes for tracking ML assets, versions, dependencies, and relationships. Learn how to maintain an accurate CMS without overburdening development workflows.
12 chapters in this module
  1. Defining configuration items in ML systems
  2. Model version tracking with unique identifiers
  3. Data pipeline dependency mapping
  4. Feature store integration with CMS
  5. Automated lineage capture at inference time
  6. Version comparison tools for model updates
  7. Access control for configuration records
  8. CMS integration with CI/CD platforms
  9. Drift detection in training-serving skew
  10. Periodic configuration audit procedures
  11. Reconciliation with infrastructure as code
  12. CMS reporting for internal audits
Module 6. Service Level Management for ML Services
Define and manage service level agreements and objectives for ML systems. This module guides you through setting realistic SLAs, monitoring compliance, and reporting performance. Learn how to balance user needs with operational feasibility in high-velocity environments.
12 chapters in this module
  1. Service catalog creation for ML offerings
  2. SLA negotiation with product teams
  3. SLO definition for model accuracy and latency
  4. Tolerance thresholds for prediction drift
  5. Reporting frequency for SLA compliance
  6. Service credit policies for downtime
  7. Customer communication during outages
  8. SLA review and update processes
  9. Monitoring integration with Prometheus/Grafana
  10. Alert tuning to reduce false positives
  11. Capacity planning from usage trends
  12. Performance review meeting templates
Module 7. Supplier Management in ML Ecosystems
Manage third-party risk in ML supply chains. This module covers vendor selection, contract alignment, performance monitoring, and offboarding. Learn how to maintain ISO 20000 compliance when relying on external APIs, datasets, or cloud services.
12 chapters in this module
  1. Vendor due diligence for ML model providers
  2. Contractual SLAs for third-party APIs
  3. Risk assessment of open-source model dependencies
  4. Performance monitoring of external services
  5. Right-to-audit clauses in vendor agreements
  6. Compliance evidence collection from suppliers
  7. Onboarding process for new ML vendors
  8. Offboarding checklist for decommissioned APIs
  9. Multi-cloud vendor management strategy
  10. Incident coordination with external teams
  11. Vendor risk scoring methodology
  12. Annual review templates for supplier contracts
Module 8. Capacity Management for ML Workloads
Plan and optimize capacity for ML systems in alignment with ISO 20000. This module covers forecasting demand, modeling resource needs, and scaling strategies. Learn how to build capacity plans that support growth while maintaining compliance.
12 chapters in this module
  1. Workload forecasting for model inference
  2. Historical usage trend analysis
  3. Stress testing protocols for peak loads
  4. Auto-scaling configuration best practices
  5. Cost-benefit analysis of over-provisioning
  6. Spot instance usage policies
  7. Cold start mitigation strategies
  8. GPU/TPU utilization monitoring
  9. Load balancing across regions
  10. Disaster recovery capacity planning
  11. Capacity review meeting structure
  12. Reporting templates for finance teams
Module 9. Information Security Management Integration
Integrate information security practices into ML service management. This module covers access control, encryption, data protection, and incident response coordination. Learn how to meet ISO 20000 requirements while maintaining data confidentiality and integrity.
12 chapters in this module
  1. Role-based access control for ML platforms
  2. Encryption requirements for model artifacts
  3. Data anonymization in testing environments
  4. Penetration testing of inference APIs
  5. Security patching cadence for dependencies
  6. Logging and monitoring for suspicious activity
  7. Incident response coordination with security teams
  8. Data retention and deletion policies
  9. Compliance with privacy regulations
  10. Security training for ML engineers
  11. Vulnerability scanning in CI/CD
  12. Audit trail protection mechanisms
Module 10. Business Continuity in ML Systems
Ensure ML service resilience during disruptions. This module covers disaster recovery planning, failover testing, and continuity strategies. Learn how to design systems that maintain compliance during outages or migrations.
12 chapters in this module
  1. Disaster recovery plan documentation
  2. Failover testing schedules and procedures
  3. Data backup strategies for model weights
  4. Cross-region model deployment patterns
  5. Recovery time objectives for ML services
  6. Recovery point objectives for training data
  7. Manual override procedures during outages
  8. Communication plan for service disruptions
  9. Third-party dependency continuity planning
  10. Post-failure restoration validation
  11. Continuity testing reporting
  12. Lessons learned from past incidents
Module 11. Compliance Evidence Automation
Automate ISO 20000 evidence collection and reporting. This module introduces tools and templates for reducing manual effort. Learn how to generate audit-ready documentation from operational data without interrupting workflows.
12 chapters in this module
  1. Automated evidence collection workflows
  2. Template generation for audit packages
  3. Integration with internal ticketing systems
  4. Scheduled evidence reporting
  5. Validation checks for completeness
  6. Version control for submitted evidence
  7. Access logs for compliance reviewers
  8. Redaction tools for sensitive data
  9. Evidence retention policies
  10. Cross-team collaboration features
  11. Pre-audit self-assessment tools
  12. Final evidence package compilation
Module 12. Continuous Improvement in ML Service Management
Implement a feedback loop for ongoing service enhancement. This module covers metrics analysis, improvement planning, and change execution. Learn how to use audit findings and performance data to strengthen ML systems over time.
12 chapters in this module
  1. Identifying improvement opportunities from incident data
  2. Root cause analysis for recurring issues
  3. Prioritization framework for service changes
  4. Improvement plan documentation
  5. Stakeholder alignment on changes
  6. Implementation tracking for initiatives
  7. Post-implementation benefit measurement
  8. Service review meeting facilitation
  9. Benchmarking against industry standards
  10. Knowledge sharing across teams
  11. Lessons learned repository
  12. Year-over-year improvement reporting

How this maps to your situation

  • Audit readiness in AI infrastructure
  • Incident response ownership expansion
  • Change control in ML deployment pipelines
  • Configuration management for model systems

Before vs. after

Before
Spending 80+ hours per month compiling evidence, chasing teams, and fixing last-minute audit gaps
After
Leading compliance with structured templates, automated evidence, and ownership of the full audit narrative

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 9 hours total, self-paced across 12 modules

If nothing changes
Without structured compliance integration, ML teams face recurring rework, delayed launches, and growing friction with assurance teams , especially under regulatory scrutiny cycles.

How this compares to the alternatives

Unlike generic ITIL or ISO 20000 courses, this program is tailored to ML infrastructure engineers , focusing on practical evidence, change control, and incident management in real AI systems. No theory, no abstraction , just templates and playbooks that plug into your existing workflows.

Frequently asked

Is this course relevant if I'm not in a formal compliance role?
Yes. This course is designed for senior engineers who own system resilience and need to produce audit-ready artefacts without becoming compliance specialists.
How is the course structured?
12 modules, each containing 12 chapters (144 chapters total).
Can I apply this to non-ISO 20000 frameworks?
Yes. The methods transfer to SOC 2, ISO 27001, and internal policy audits , especially around evidence structure and automation.
$199 one-time. Approximately 9 hours total, self-paced across 12 modules.

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
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