Why this matters
Verify: Per-channel recall back within 5% of baseline; OOV stabilized; sample 100 ad queries.
Learn: Update training with new campaign data; add segment-specific drift alert.
Example 2: Toxicity filter misses new slang
Signal: Human moderation reports +3x; toxicity false negatives up; length, latency normal.
Triage: Quality issue; language drift; confined to EN locale and gaming community.
Mitigate: Tighten threshold for high-risk segments; add lexical rules for new slang; route uncertain cases to human review queue.
Verify: False negatives down; reviewer load manageable.
Learn: Incremental fine-tune with new examples; schedule weekly slang sampling; add targeted lexical drift check.
Example 3: Latency spike in RAG pipeline
Signal: p95 latency 800ms→1700ms; 5xx up; GPU utilization steady; embedding service timeouts increase.
Triage: Dependency issue in external embedding service.
Mitigate: Switch to cached embeddings; fail open to smaller local model for embeddings; reduce context window to cap retrieval time.
Verify: p95 back under 900ms; error rates normalized.
Learn: Add health probe and circuit breaker for the dependency; keep warm replica; canary dependency upgrades.
Hands-on checklist
Use this when an alert fires:
- [ ] Confirm alert context (metric, segment, baseline, time)
- [ ] Check recent deploys (model, rules, feature, infra)
- [ ] Validate input schema and volume per segment
- [ ] Compare current vs. last-known-good sample outputs
- [ ] Decide SEV and open incident record
- [ ] Choose mitigation: rollback, fallback, traffic shaping
- [ ] Announce ETA and next update time
- [ ] Verify recovery on SLO metrics and samples
- [ ] Conduct RCA and update runbook
Exercises
Exercise 1: Set thresholds and actions
You operate a multilingual intent classifier. Current baselines: macro F1 0.87; per-class recall for "Billing" 0.82; p95 latency 700ms. Propose alert thresholds and immediate actions for:
- A. Macro F1 drops below 0.80 for 15 min
- B. "Billing" recall drops by 20% in FR locale
- C. p95 latency exceeds 1000ms for 10 min
Expected output
A clear list of thresholds and first actions (rollback vs. fallback vs. canary), plus verification criteria.
Hints
- Use both global and per-segment alerts.
- Prefer safe rollback for quality; traffic shaping for latency.
Exercise 2: Draft a rollback decision tree
Given: Latest model improves accuracy by +2% but shows increased toxicity risk in EN gaming segment. Annotator bandwidth is limited for 48 hours. Create a simple if-else decision tree for when to rollback, tighten filters, or gate traffic.
Expected output
A compact tree covering severity, segment gating, and time-bound review plan.
Hints
- Gate by segment when impact is localized.
- Use timeboxed mitigations until annotation is available.
Common mistakes and self-check
- Mistake: Treating global averages only. Fix: Monitor per-segment metrics.
- Mistake: Delaying mitigation to perfect diagnosis. Fix: Prefer reversible, low-risk mitigations (rollback/fallback).
- Mistake: No verification window. Fix: Require post-change monitoring period.
- Mistake: Alert fatigue. Fix: Deduplicate alerts and tune thresholds.
- Mistake: Missing comms. Fix: Assign owner and update cadence.
Self-check
- Can you identify the incident surface (quality/data/infra/dependency)?
- Is there a safe immediate mitigation you can apply now?
- What specific metric and segment will prove recovery?
Practical projects
- Build a runbook: Create a one-page incident playbook for your current NLP system with thresholds, owners, and fallbacks.
- Simulated incident drill: Replay a historical metric dip (synthetic) and practice triage, mitigation, and verification in 60 minutes.
- Canary + rollback pipeline: Configure a canary release policy with automatic rollback if p95 latency or macro F1 regress by more than 5%.
Quick Test
Take the quick test below. Anyone can take it; if you log in, your progress is saved.
Mini challenge
Your genAI summarizer starts truncating outputs after a library upgrade. Outline a 30-minute mitigation and a 24-hour plan. Include metrics to watch and your rollback trigger.
Learning path
- Before this: Monitoring and alerting for NLP, data quality checks, basic canary deployments.
- Now: Incident response for model degradation (this lesson).
- Next: Automated retraining, evaluation gates, and continuous delivery for NLP models.
Who this is for
NLP Engineers, ML Engineers, and Data Scientists deploying or maintaining NLP systems in production.
Prerequisites
- Comfort with NLP metrics (F1, ROUGE) and logging.
- Basic understanding of CI/CD and canary releases.
- Familiarity with data drift concepts.
Next steps
- Customize the runbook template for your stack.
- Schedule a monthly incident drill.
- Add one new per-segment alert this week.