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BCM7878 Mastering ISO 22301 for Senior ML Engineers in Ad Tech

$199.00
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A tailored course, built for your situation

Mastering ISO 22301 for Senior ML Engineers in Ad Tech

Build resilient AI systems that maintain uptime through disruptions

$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.
Regulator escalations landing with tight turnaround and incomplete evidence packs

The situation this course is for

ML teams in high-visibility roles often face unexpected requests during compliance sweeps, especially when AI behavior shifts at scale. Without a documented resilience framework, responses take longer, require cross-team chasing, and increase scrutiny.

Who this is for

Senior ML Engineer in ad tech or platform AI, accountable for model behavior under stress, audit, or incident review

Who this is not for

Junior engineers still mastering fundamentals, non-ML roles in advertising tech, or practitioners outside regulated AI deployment

What you walk away with

  • Produce regulator-facing escalation packages that require no rework
  • Demonstrate compliance with ISO 22301 continuity requirements in AI operations
  • Reduce incident investigation cycles by standardizing resilience evidence
  • Own the narrative when model behavior shifts during high-traffic events
  • Build auditable runbooks that survive team turnover

The 12 modules (with all 144 chapters)

Module 1. Understanding ISO 22301 in AI-Driven Environments
Ground your work in the actual clauses of ISO 22301 as they apply to machine learning systems, not generic IT. Focus on how continuity planning applies to model degradation, data pipeline breaks, and ranking anomalies.
12 chapters in this module
  1. What ISO 22301 means for ML-powered ad delivery
  2. Clause 5.2 context of the organization in ad tech
  3. Clause 6.1 actions to address risks and opportunities
  4. Clause 7.4 communication during model rollback
  5. Clause 8.2 managing disruptions to ranking signals
  6. Clause 8.4 business continuity during A/B test failure
  7. Clause 8.5.1 post-incident evidence collection
  8. Clause 9.1 monitoring performance of fallback models
  9. Clause 9.3 management review of ML uptime
  10. Clause 10.2 continual improvement of resilience runbooks
  11. Clause 4.0 scope definition for AI ranking systems
  12. Clause 5.1 leadership commitment to availability
Module 2. Mapping ML Incident Types to Business Impact
Classify outages by financial and reputational impact so you can prioritize resilience planning for the right scenarios.
12 chapters in this module
  1. Categorizing model drift vs. data corruption vs. infrastructure outage
  2. Assigning business impact scores to ranking failures
  3. Identifying regulator-visible failure modes
  4. Documenting expected behavior during traffic surges
  5. Linking incident type to customer trust metrics
  6. Prioritizing scenarios for runbook development
  7. Estimating revenue impact per minute of downtime
  8. Tracking reputational risk in social sentiment
  9. Aligning with SRE teams on severity thresholds
  10. Creating escalation trees for model rollback events
  11. Defining acceptable degradation paths
  12. Benchmarking against peer platform resilience
Module 3. Building Runbooks for AI System Failures
Turn incident patterns into documented, repeatable response flows that survive team churn and regulator review.
12 chapters in this module
  1. Template for AI incident classification matrix
  2. Step-by-step rollback for underperforming ranking models
  3. Decision log for manual intervention approval
  4. Checklist for data pipeline health verification
  5. Runbook section for stakeholder communication
  6. Version control for model deactivation steps
  7. Integration with on-call alerting systems
  8. Runbook review cycle with legal and compliance
  9. Incident simulation using historical data
  10. Post-mortem integration with runbook updates
  11. Access controls for production model rollback
  12. Runbook validation under regulator scrutiny
Module 4. Documenting Resilience for Regulator Review
Create evidence packages that withstand follow-up questions and reduce reviewer workload.
12 chapters in this module
  1. Structure of a regulator-ready evidence binder
  2. Proving model fallback behavior under load
  3. Demonstrating documented decision chains
  4. Version-controlled runbook sign-off process
  5. Generating compliance-ready runbook summaries
  6. Mapping controls to ISO 22301 clauses
  7. Including test results from chaos engineering
  8. Annotating decisions with timestamped logs
  9. Redacting sensitive data while preserving logic
  10. Formatting appendices for external reviewers
  11. Preparing for follow-up document requests
  12. Maintaining evidence freshness between cycles
Module 5. Testing AI Resilience Without Breaking Production
Run safe, realistic failure simulations that prove continuity without risking live performance.
12 chapters in this module
  1. Designing canary rollbacks for model updates
  2. Simulating data pipeline corruption safely
  3. Stress-testing fallback models under load
  4. Validating alert triggers before deployment
  5. Measuring detection-to-response time
  6. Running tabletop exercises with legal
  7. Chaos engineering for ML infrastructure
  8. Documenting test scope and boundaries
  9. Incorporating red team findings
  10. Testing communication plans under pressure
  11. Capturing lessons in runbook updates
  12. Reporting test results to leadership
Module 6. Automating Continuity Evidence Collection
Reduce manual work by pre-generating proof artifacts that update with system changes.
12 chapters in this module
  1. Automated runbook section generation
  2. Embedding model metadata in continuity logs
  3. API calls to extract uptime during incidents
  4. Scheduled snapshots of fallback readiness
  5. Automated compliance mapping reports
  6. Integrating CI/CD with ISO 22301 checks
  7. Versioning runbooks with model deployment
  8. Auto-populating evidence templates
  9. Triggering compliance alerts pre-audit
  10. Syncing documentation with configuration
  11. Automating test result aggregation
  12. Auditable trails for evidence integrity
Module 7. Scaling Resilience Across Model Teams
Extend proven patterns from one model to many without duplicating effort.
12 chapters in this module
  1. Standardizing runbook templates across teams
  2. Centralizing incident classification criteria
  3. Shared ownership of continuity framework
  4. Cross-team training on escalation protocols
  5. Harmonizing documentation formats
  6. Common vocabulary for model degradation
  7. Automated resilience scorecards
  8. Benchmarking teams against standards
  9. Rotating audit preparation roles
  10. Building shared resilience libraries
  11. Mentoring junior engineers on continuity
  12. Scaling playbooks to new geographies
Module 8. Integrating with Security and Compliance Teams
Align your resilience work with broader risk functions to avoid duplication and increase trust.
12 chapters in this module
  1. Mapping ISO 22301 to SOC 2 controls
  2. Coordinating with security incident response
  3. Sharing model rollback evidence with auditors
  4. Understanding legal team’s escalation needs
  5. Aligning with privacy team on data breaks
  6. Documenting compliance for cross-border data
  7. Integrating with enterprise risk frameworks
  8. Reporting on uptime during board cycles
  9. Including continuity in vendor assessments
  10. Supporting third-party audit requests
  11. Responding to regulator information requests
  12. Maintaining separation of duties in runbooks
Module 9. Maintaining Runbooks Through Model Evolution
Keep continuity documentation accurate as models and systems change.
12 chapters in this module
  1. Triggering runbook updates with model retraining
  2. Versioning runbooks with model versions
  3. Automated checks for outdated procedures
  4. Review cycles after major ranking changes
  5. Updating runbooks post-incident
  6. Archiving superseded runbook versions
  7. Documenting changes in rollback behavior
  8. Tracking runbook maintenance in sprints
  9. Alerting on unreviewed runbook sections
  10. Incorporating feedback from incident reviews
  11. Linking runbooks to A/B test results
  12. Auditing runbook accuracy quarterly
Module 10. Proving Continuity in Real Incidents
Turn real outages into validation opportunities that strengthen trust.
12 chapters in this module
  1. Activating runbooks during live incidents
  2. Capturing real-time decision logs
  3. Adapting procedures to unexpected conditions
  4. Communicating status to leadership
  5. Preserving evidence during crisis
  6. Balancing speed and documentation
  7. Reporting on incident response effectiveness
  8. Updating runbooks after real events
  9. Sharing learnings across ML teams
  10. Demonstrating improvement over time
  11. Reducing time to first fix
  12. Meeting regulator expectations under pressure
Module 11. Communicating Resilience to Leadership
Translate technical work into strategic value for executives.
12 chapters in this module
  1. Creating executive summaries of runbook readiness
  2. Reporting on uptime during campaign cycles
  3. Mapping continuity to revenue protection
  4. Benchmarking against industry standards
  5. Presenting test results to senior leaders
  6. Summarizing risk reduction from runbooks
  7. Visualizing incident response timelines
  8. Including resilience in performance goals
  9. Linking availability to customer trust
  10. Demonstrating cost savings from automation
  11. Reporting on regulator feedback trends
  12. Positioning your team as continuity leaders
Module 12. Sustaining ISO 22301 Compliance Over Time
Keep your resilience framework alive between audits and incidents.
12 chapters in this module
  1. Scheduling regular runbook reviews
  2. Updating documentation for new ranking models
  3. Revising roles during team reorgs
  4. Training new hires on continuity protocols
  5. Auditing runbook access controls
  6. Refreshing test scenarios annually
  7. Aligning with updated ISO guidance
  8. Incorporating lessons from peer companies
  9. Maintaining evidence freshness
  10. Reporting compliance status to management
  11. Preparing for unannounced regulator reviews
  12. Scaling practices to new product areas

How this maps to your situation

  • Regulator review of AI behavior
  • Model rollback under performance pressure
  • Incident investigation and reporting
  • Cross-functional trust in technical decisions

Before vs. after

Before
Responding to regulator escalations with incomplete runbooks and ad-hoc evidence
After
Producing regulator-ready packages within 48 hours using standardized, auditable runbooks

What's included with your purchase

  • 12 modules with 12 chapters each (144 chapters total)
  • 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 for 12 weeks, or 12 hours total

If nothing changes
Without documented resilience, each incident creates rework, increases scrutiny, and weakens trust in AI systems during high-pressure cycles.

How this compares to the alternatives

Unlike generic compliance courses, this course focuses on ML-specific failure modes, real regulator expectations, and documented patterns used in ad tech.

Frequently asked

Is this course relevant to ML engineers outside of Meta?
Yes. While tailored to Meta's ad ranking context, the framework applies to any ML engineer accountable for system resilience under regulatory scrutiny.
How is the course structured?
12 modules, each containing 12 chapters (144 chapters total).
Does the course cover other ISO standards?
Focus is on ISO 22301, with references to ISO 27001 and ISO 27701 where relevant for continuity in AI systems.
$199 one-time. 90 minutes per week for 12 weeks, or 12 hours total.

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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