Skip to main content
Image coming soon

OPS1649 Mastering ISO 20000 for Principal ML Engineers in AWS-Centric Deployments

$199.00
Adding to cart… The item has been added

A tailored course, built for your situation

Mastering ISO 20000 for Principal ML Engineers in AWS-Centric Deployments

A structured path to engineer service reliability into AI/ML systems with documented, auditable rigor, tailored for senior technical leads at global firms.

$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.
Spending weeks rebuilding service narratives for compliance reviewers instead of engineering them upfront

The situation this course is for

ML teams routinely face last-minute scrambles to produce service operation records when client audits or internal reviews hit. The gap isn’t technical skill, it’s the absence of a structured, repeatable approach to service documentation that satisfies ISO 20000 while scaling with AWS ML deployments. This course closes that gap.

Who this is for

Senior ML engineers at global consulting firms who lead AWS-based AI implementations and face recurring demands for compliance-ready service records but lack a standardized approach to build them into development cycles.

Who this is not for

Entry-level data scientists, DevOps generalists without AI focus, or functional managers without hands-on involvement in ML system design.

What you walk away with

  • Produce ISO 20000-compliant service documentation as a natural output of ML deployment workflows
  • Reduce audit preparation time by structuring service records upfront
  • Gain recognition from AWS client leads and internal governance teams for operational rigor
  • Turn ML service reliability into a repeatable, verifiable asset across engagements
  • Design service operations playbooks that survive team turnover and scale across geographies

The 12 modules (with all 144 chapters)

Module 1. Mapping ISO 20000 to Real-World ML Service Deliverables
Break down ISO 20000 clauses into concrete ML system components like model monitoring, incident response, and deployment rollback , avoiding abstract theory.
12 chapters in this module
  1. Translating ISO 20000 clause 5.1 to ML service ownership
  2. Defining service scope for AWS-hosted inference endpoints
  3. Documenting service level agreements for real-time ML APIs
  4. Capturing availability targets for batch prediction pipelines
  5. Mapping roles in cross-functional ML service teams
  6. Using AWS CloudTrail data as service evidence
  7. Integrating incident response with SageMaker monitoring
  8. Setting up service continuity for model retraining cycles
  9. Documenting supplier relationships in third-party ML tooling
  10. Establishing configuration baselines for ML environments
  11. Linking change management to model versioning
  12. Proving compliance without over-engineering
Module 2. Designing Service Operation Plans for ML Systems
Build service plans that reflect how ML systems actually run , not generic templates , with AWS-native observability baked in.
12 chapters in this module
  1. Structuring service operation guides for SageMaker pipelines
  2. Documenting model drift detection thresholds
  3. Creating incident playbooks for prediction failures
  4. Integrating CloudWatch alarms into service workflows
  5. Defining rollback procedures for bad deployments
  6. Setting up capacity planning for inference loads
  7. Managing dependencies on external data sources
  8. Documenting model retraining triggers
  9. Versioning service documentation alongside models
  10. Using AWS CodePipeline for audit readiness
  11. Capturing lessons from model incidents
  12. Automating service log updates
Module 3. Incident Management for AI/ML Production Environments
Shift from firefighting to structured response by aligning ML incidents with ISO 20000 incident lifecycle stages.
12 chapters in this module
  1. Classifying ML incidents by business impact
  2. Setting up logging standards for model errors
  3. Using AWS X-Ray for root cause analysis
  4. Defining escalation paths for critical failures
  5. Linking incidents to model version rollbacks
  6. Documenting resolution steps for recurring issues
  7. Integrating with ITSM tools like ServiceNow
  8. Proving timeliness of ML incident response
  9. Tracking mean time to recovery for models
  10. Using incident data to improve monitoring
  11. Avoiding over-documentation during outages
  12. Proving compliance after incident closure
Module 4. Problem Management and Root Cause Analysis in ML Systems
Go beyond incident fixes to engineer out systemic weaknesses in model behavior and infrastructure.
12 chapters in this module
  1. Identifying patterns in model prediction errors
  2. Linking model drift to upstream data issues
  3. Using AWS Glue data quality insights
  4. Documenting root cause findings
  5. Creating known error databases for ML
  6. Setting up triggers for problem reviews
  7. Integrating feedback loops from monitoring
  8. Tracking recurring model failures
  9. Assigning ownership for model fixes
  10. Linking problem records to change requests
  11. Using problem data to inform model updates
  12. Proving proactive improvement to auditors
Module 5. Change Management for Model and Infrastructure Updates
Implement disciplined change control that supports agility without sacrificing compliance.
12 chapters in this module
  1. Classifying changes to ML models and pipelines
  2. Documenting risk assessments for new models
  3. Setting up CAB approval for high-risk changes
  4. Using AWS CodePipeline stages as review gates
  5. Capturing backout plans for model updates
  6. Scheduling changes around business cycles
  7. Linking model changes to incident history
  8. Automating change record creation
  9. Integrating with AWS Audit Manager
  10. Proving change authorization for auditors
  11. Handling emergency model rollbacks
  12. Tracking change success rates
Module 6. Configuration Management for ML Environments
Build a living record of ML system components that supports deployment, audit, and recovery.
12 chapters in this module
  1. Defining configuration items for ML pipelines
  2. Using AWS Config to track resource changes
  3. Versioning model containers in ECR
  4. Documenting model hyperparameters as CIs
  5. Tracking data schema versions
  6. Linking CI records to deployment pipelines
  7. Using tags for environment classification
  8. Auditing configuration drift in staging
  9. Mapping dependencies between ML services
  10. Integrating with CMDB tools
  11. Proving configuration accuracy during audits
  12. Automating CI updates with CI/CD
Module 7. Release and Deployment Management for ML Models
Structure model releases to ensure quality, traceability, and rollback readiness.
12 chapters in this module
  1. Defining release types for ML models
  2. Creating release policies for production models
  3. Using SageMaker Model Registry for approval
  4. Documenting deployment checklists
  5. Scheduling releases to minimize downtime
  6. Capturing post-release validation steps
  7. Integrating with AWS SageMaker Pipelines
  8. Tracking release success metrics
  9. Managing parallel model versions
  10. Proving release completeness to auditors
  11. Handling failed deployments
  12. Improving release processes from feedback
Module 8. Service Level Management in ML Operations
Define and track meaningful SLAs that reflect real business needs , not just uptime.
12 chapters in this module
  1. Setting availability targets for ML APIs
  2. Measuring prediction latency in production
  3. Defining accuracy thresholds for models
  4. Tracking data freshness in feature stores
  5. Using CloudWatch metrics for SLA reporting
  6. Creating service level agreements with clients
  7. Documenting SLA exceptions and waivers
  8. Reviewing SLA performance monthly
  9. Linking SLAs to incident management
  10. Proving SLA compliance during audits
  11. Adjusting SLAs based on usage patterns
  12. Communicating SLA changes to stakeholders
Module 9. Service Reporting and Evidence Packaging for Audits
Generate concise, auditor-ready reports from ML operations without last-minute scrambling.
12 chapters in this module
  1. Identifying required evidence for ISO 20000
  2. Extracting logs from AWS CloudTrail and CloudWatch
  3. Compiling incident response records
  4. Packaging change and release histories
  5. Creating configuration snapshots
  6. Using AWS Audit Manager templates
  7. Proving incident resolution timelines
  8. Demonstrating problem recurrence reduction
  9. Showing configuration consistency
  10. Automating evidence collection
  11. Formatting reports for client reviewers
  12. Reducing auditor follow-up questions
Module 10. Integrating ISO 20000 with AWS Well-Architected Framework
Align service management with cloud best practices to satisfy both compliance and engineering rigor.
12 chapters in this module
  1. Mapping reliability pillars to ISO 20000
  2. Using operational excellence principles
  3. Aligning security checks with access controls
  4. Integrating performance efficiency metrics
  5. Linking cost optimization to service design
  6. Using AWS Well-Architected Tool reviews
  7. Documenting trade-offs in model design
  8. Proving architectural alignment
  9. Merging audit narratives
  10. Reducing duplication in evidence
  11. Scaling reviews across clients
  12. Demonstrating cloud-native compliance
Module 11. Scaling Service Management Across Global ML Engagements
Replicate proven service practices across teams without creating silos or overhead.
12 chapters in this module
  1. Standardizing service documentation templates
  2. Creating reusable playbook modules
  3. Training junior engineers on service norms
  4. Using shared configuration repositories
  5. Implementing centralized monitoring
  6. Setting up cross-team review cycles
  7. Managing localization requirements
  8. Adapting for regional compliance needs
  9. Sharing lessons across geographies
  10. Enforcing consistency without stifling innovation
  11. Proving scalability during audits
  12. Reducing onboarding time for new teams
Module 12. Sustaining ISO 20000 Compliance in Evolving ML Landscapes
Keep service management current as models, infrastructure, and requirements change.
12 chapters in this module
  1. Tracking changes in AWS ML services
  2. Updating service documentation for new features
  3. Revising SLAs based on business shifts
  4. Refreshing incident playbooks quarterly
  5. Auditing configuration baselines annually
  6. Reviewing change management effectiveness
  7. Updating problem management from incident trends
  8. Proving continuous improvement
  9. Integrating feedback from client reviews
  10. Preparing for new auditor expectations
  11. Maintaining certification validity
  12. Handing over service ownership smoothly

How this maps to your situation

  • AWS ML deployments at global consulting firms
  • Compliance demands from enterprise clients
  • High-visibility AI projects with strict SLAs
  • Cross-regional service delivery expectations

Before vs. after

Before
Spending weeks assembling service records after audits begin, relying on tribal knowledge, and facing rework due to inconsistent documentation.
After
Producing auditor-ready service records as a natural byproduct of ML deployment , reducing prep time and increasing confidence in compliance posture.

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 per week over 12 weeks, or self-paced with full access from day one.

If nothing changes
Continuing to treat service documentation as an afterthought risks repeated audit scrambles, client escalations, and missed opportunities to position ML work as strategically visible.

How this compares to the alternatives

Generic ITIL courses lack ML context. Internal training is fragmented. Consultants charge $10k+ to build what this course teaches you to create yourself.

Frequently asked

Is this relevant for non-AWS ML deployments?
The core ISO 20000 structure transfers, but examples and templates are optimized for AWS ML services like SageMaker, CloudWatch, and CodePipeline.
How is the course structured?
12 modules, each containing 12 chapters (144 chapters total).
Can I use this for ISO 27001 or SOC 2?
The service management foundation supports broader compliance, but this course focuses on ISO 20000 for ML operations.
$199 one-time. 90 minutes per week over 12 weeks, or self-paced with full access from day one..

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
!