> tpl_aim_028
ML Incident-Response Runbook
Emergency production incident response runbook for machine learning services detailing triage workflows, automated shadow fallbacks, heuristic kill-switches, upstream data contamination isolation, concept drift mitigation, and model rollback procedures.
Production ML incident response runbook detailing triage for model degradation, automated fallback to heuristics, data corruption rollback, and retraining.
Important Tech Document Template & Operational Notice
TinyCTO.tv Tech Document Template Notice: This template is a general educational and operational starting point. It is not legal, tax, accounting, investment, procurement, regulatory, security or certification advice. Requirements vary by jurisdiction, organization, contract and risk. Review and adapt it with qualified professionals before relying on it.
Problem Solved
Production ML models degrade silently when upstream data pipelines change formats or external market dynamics shift, outputting nonsense predictions for days while traditional server health monitors report 100% green.
When to Use
- •Triage and remediating catastrophic model prediction degradation, latency spikes, or sudden accuracy drops
- •Activating emergency fallback mechanisms (reverting from a neural network to a heuristic rule engine)
- •Isolating poisoned or corrupted training datasets and rolling back serving pods to the last known good champion model
When NOT to Use
- •For general infrastructure virtual machine or container networking outages (use TPL-OPS-007)
- •For planned routine model retraining scheduled via CI/CD (use TPL-AIM-025)
5 Template Sections & Structural Outline
Distinguishing hard outages (pod crashes, OOM, latency p99 > 500ms) from silent statistical failures (PSI drift, collapsed prediction distribution, degenerate outputs).
Activating the emergency heuristic kill-switch: diverting live traffic from the failing ML model to a deterministic, deterministic rule engine within 180 seconds.
Diagnosing null value spikes, shifted schema types, or broken third-party data feeds; isolating corrupted feature store partitions.
Executing zero-downtime rollback in Triton/KServe by redirecting the `@champion` pointer in MLflow to the previous audited model version.
Quarantining contaminated prediction logs, rebuilding feature tables, conducting blameless postmortem, and executing audited retraining.
Completion Instructions
Independent Review Checklist
- All mandatory sections completed
- No secrets or passwords included
- Executive sponsor sign-off obtained
ML Incident-Response Runbook - Worked Case Study
Fictional Entity: Sovereign Payments Real-Time Fraud Engine Silent Degradation Incident Response
Real-world production case study demonstrating complete operational adoption for Sovereign Payments Real-Time Fraud Engine Silent Degradation Incident Response.
- •Detected silent feature drift at 02:15 AM (upstream currency code format change caused 94% fraud score drop)
- •Activated deterministic rule fallback within 110 seconds, preventing an estimated $420K in unauthorized transactions
- •Rolled back Triton serving cluster to Champion v12 within 4 minutes and quarantined contaminated Kafka queues
Frequently Asked Questions
What is "Silent Model Degradation" and why does traditional monitoring miss it?
Silent model degradation occurs when the service responds with HTTP 200 OK and sub-10ms latency, but the predictions are statistically garbage (e.g. predicting 0.00 fraud score for all transactions due to a missing feature). Traditional APM tools only monitor CPU, memory, and HTTP codes. Specialized ML monitors (like Evidently AI or Prometheus metrics on output distributions) are required.
Why should teams never attempt to re-train a model during an active Sev-1 incident?
Model training is stochastic, computationally expensive, and requires extensive offline validation. Trying to retrain and deploy a new model in the middle of an outage almost always introduces new untested bugs. The only safe operational response is rolling back to a known-good model version or falling back to deterministic heuristics.
What is the role of a deterministic heuristic fallback in machine learning architectures?
A heuristic fallback is a lightweight, hardcoded rule engine (e.g. "If transaction > $10,000 and user age < 1 day, require SMS OTP"). When an ML model crashes, exceeds latency timeouts, or exhibits extreme statistical drift, traffic instantly falls back to heuristics, preserving business continuity without failing open.
Download Tech Document Pack
Auth RequiredDownload all blank templates, worked scenarios, and verification manifests in a single verified archive.
Authoritative Sources
- Google SRE: Machine Learning Incident ManagementGoogle SRE • OFFICIAL REQUIREMENT
- NIST AI Risk Management Framework (Playbook)NIST • OFFICIAL REQUIREMENT