What you’ll learn
- How to version NLP models clearly so teams know what changed and why.
- How to release new models safely using canary strategies, metrics, and rollback rules.
- How to keep APIs and clients stable while models evolve.
Who this is for
- NLP Engineers and MLEs deploying models to production.
- Data Scientists handing off models to platform teams.
- Tech Leads who own model quality, latency, and reliability.
Prerequisites
- Basic experience packaging and serving models (REST/gRPC or batch).
- Familiarity with metrics like latency, error rate, accuracy/F1.
- Comfort with config files (YAML/JSON) and Git.
Why this matters
Example 4: Shadow before canary
Shadow v2 for a day to confirm p95 latency and memory headroom. If stable, proceed to a 10% canary to validate user-facing KPIs.
Step-by-step canary runbook
- Define success: target quality/latency thresholds and a maximum acceptable regression.
- Prepare measurement: logging for version tag, request IDs, and metrics dashboards.
- Start small: 5–10% traffic for at least one statistical window.
- Compare apples-to-apples: segment by locale, device, traffic pattern.
- Decide: increase, hold, or rollback based on guardrails.
- Document: update changelog and model registry with outcomes.
Guardrail checklist (tick as you go)
- p95 latency within threshold
- Error rate within threshold
- Quality stable or improved
- Business KPI stable or improved
- Rollback plan rehearsed
Common mistakes and self-check
- No MAJOR bump on breaking changes: If output schema/labels changed, that is MAJOR. Self-check: could any client break? If yes, MAJOR.
- Skipping shadow tests for heavy models: Leads to surprise latency. Self-check: did you run shadow at target QPS?
- Short canary windows: Rare traffic segments go unseen. Self-check: did you cover peak hours and key locales?
- Comparing different cohorts: Canary on mobile vs baseline on desktop is misleading. Self-check: ensure cohort parity.
- Not pinning dataset/code: Repro fails later. Self-check: data_hash and code_hash present?
Learning path
- Versioning basics: semantic versions, changelogs, registry entries.
- Routing strategies: shadow, canary, A/B; weighted traffic.
- Metrics and guardrails: latency, errors, quality, business impact.
- Rollback/roll-forward playbooks.
- Automation: CI triggers to update registry and deploy canaries.
Practical projects
- Build a model registry entry: Write JSON for two versions of the same classifier with data/code hashes and metrics.
- Simulate a canary: Create a config that moves from 5% → 25% → 50% with guardrails and a rollback condition.
- Backward-compat wrapper: Implement a small mapping layer that converts v2 labels back to v1 for legacy clients.
Exercises
Complete these, then check your answers. The Quick Test is available to everyone; logged-in users get saved progress.
- Exercise 1 — Version and route
Produce a semantic version for a newly retrained intent classifier (same API), and a 10% canary routing policy with latency and error guardrails. - Exercise 2 — Decide the rollout
Given canary metrics after 60 minutes, decide whether to increase, hold, or rollback, and justify using guardrails.
Exercise checklist
- Version reflects change type correctly
- Routing weights sum to 100%
- Guardrails include latency and error rate at minimum
- Decision uses observed vs threshold comparison
Mini challenge
In five lines, write a rollback plan that any on-call engineer can execute within 2 minutes, including where to find the version tag and the exact action to revert traffic.
Next steps
- Automate registry updates during CI when a new model artifact is produced.
- Add a pre-deploy shadow stage for large models to catch latency regressions.
- Define organization-wide versioning rules and guardrail thresholds to standardize deployments.