What is Incident Response Playbook?

Success metrics include system uptime, model performance stability, deployment velocity, and operational cost efficiency.

For each incident type, document detection criteria and alert sources, initial triage steps including which dashboards to check, escalation paths with contact information, diagnosis procedures to identify root cause, remediation actions including rollback procedures, communication templates for stakeholders, and post-incident review requirements. Include runbooks for the 10 most common ML failures: model serving outages, prediction quality degradation, feature pipeline failures, and data quality alerts. Keep procedures specific and actionable rather than generic.

Run quarterly game day exercises where the team practices incident response on simulated failures. Rotate through on-call responsibilities so every team member has experience with the playbook. Review and update playbooks after every real incident to incorporate lessons learned. Keep playbooks accessible in a single, well-known location rather than scattered across wikis. Practice with new team members during their first on-call rotation. The best playbooks are written for someone at 3am who has never seen the issue before.

ML-specific playbooks need more detail than standard software playbooks because ML failures are often subtle. Include specific metric thresholds that distinguish normal variance from actual degradation. Document which model versions are known-good for rollback targets. Include data quality diagnostic queries that check for common issues. Specify when to engage data scientists versus infrastructure engineers. Provide decision trees for ambiguous situations rather than requiring judgment calls from fatigued on-call engineers.

For each incident type, document detection criteria and alert sources, initial triage steps including which dashboards to check, escalation paths with contact information, diagnosis procedures to identify root cause, remediation actions including rollback procedures, communication templates for stakeholders, and post-incident review requirements. Include runbooks for the 10 most common ML failures: model serving outages, prediction quality degradation, feature pipeline failures, and data quality alerts. Keep procedures specific and actionable rather than generic.

Run quarterly game day exercises where the team practices incident response on simulated failures. Rotate through on-call responsibilities so every team member has experience with the playbook. Review and update playbooks after every real incident to incorporate lessons learned. Keep playbooks accessible in a single, well-known location rather than scattered across wikis. Practice with new team members during their first on-call rotation. The best playbooks are written for someone at 3am who has never seen the issue before.

ML-specific playbooks need more detail than standard software playbooks because ML failures are often subtle. Include specific metric thresholds that distinguish normal variance from actual degradation. Document which model versions are known-good for rollback targets. Include data quality diagnostic queries that check for common issues. Specify when to engage data scientists versus infrastructure engineers. Provide decision trees for ambiguous situations rather than requiring judgment calls from fatigued on-call engineers.

For each incident type, document detection criteria and alert sources, initial triage steps including which dashboards to check, escalation paths with contact information, diagnosis procedures to identify root cause, remediation actions including rollback procedures, communication templates for stakeholders, and post-incident review requirements. Include runbooks for the 10 most common ML failures: model serving outages, prediction quality degradation, feature pipeline failures, and data quality alerts. Keep procedures specific and actionable rather than generic.

Run quarterly game day exercises where the team practices incident response on simulated failures. Rotate through on-call responsibilities so every team member has experience with the playbook. Review and update playbooks after every real incident to incorporate lessons learned. Keep playbooks accessible in a single, well-known location rather than scattered across wikis. Practice with new team members during their first on-call rotation. The best playbooks are written for someone at 3am who has never seen the issue before.

ML-specific playbooks need more detail than standard software playbooks because ML failures are often subtle. Include specific metric thresholds that distinguish normal variance from actual degradation. Document which model versions are known-good for rollback targets. Include data quality diagnostic queries that check for common issues. Specify when to engage data scientists versus infrastructure engineers. Provide decision trees for ambiguous situations rather than requiring judgment calls from fatigued on-call engineers.