Versioned Endpoints And Canary Releases

Why this matters

As a Machine Learning Engineer, you will routinely deploy new model versions and API behaviors. Versioned endpoints prevent breaking clients, and canary releases let you ship improvements safely, limiting blast radius while you verify metrics like accuracy, latency, and error rates. These practices reduce incidents, speed up iteration, and build trust with product teams.

• Real tasks you will do: expose /v1, /v2 model endpoints; maintain backward compatibility; gradually shift 1% → 5% → 25% → 50% → 100% traffic to a new model; roll back fast if metrics degrade.
• Success looks like: stable SLAs, predictable rollouts, quick rollbacks, clear deprecation timelines for clients.

Concept explained simply

Versioned endpoints mean your API path or header includes a version (e.g., /v1/predict, /v2/predict). Clients choose when to adopt a new version. You can keep old versions running during a migration period.

Canary releases send a small, controlled percentage of traffic to a new version (the canary). If metrics look good, increase the share; if not, instantly revert to the stable version.

Mental model

Imagine two taps feeding water: one old (stable), one new (experimental). You open the new tap slightly to sample water quality. If good, open wider; if bad, close it immediately. Endpoint versions label the taps; canary controls how far you open the new one.

Key design choices

• Versioning style: Path-based (/v1, /v2) is simplest; header-based (Accept: application/vnd.myapi.v2+json) keeps clean URLs but adds complexity.
• Semantic versioning policy: Major (breaking), Minor (backward compatible), Patch (bug fixes). For serving, expose only major in the URL; track minor/patch internally.
• Traffic splitting: Percentage-based, user-hash sticky routing, or per-tenant gating. Sticky routing keeps a user on the same model for consistent experience.
• Metrics to guard: p95 latency, error rate, offline/online performance gap, key business metric (e.g., CTR, conversion).
• Rollback triggers: Threshold breaches on metrics, incident alerts, or downstream error spikes.

Worked examples

{
  "v1": {"path": "/v1/predict", "response": {"score": float}},
  "v2": {"path": "/v2/predict", "response": {"score": float, "reason": string}},
  "deprecation_policy": {"v1": {"announce": "T0", "freeze": "T0+60d", "shutdown": "T0+120d"}}
}

// Pseudocode
assign(user_id):
  bucket = hash(user_id) % 100
  if bucket < 5: route to v2  // 5% canary
  else: route to v1

Step-by-step: Rolling out a new model safely

1. Prepare versions: Implement /v2 alongside /v1. Keep response fields stable or add-only. Document differences.
2. Shadow test (optional): Mirror traffic to v2 without user impact. Compare outputs and latency.
3. Start canary at 1–5%: Use sticky routing by user/tenant. Monitor p50/p95 latency, error rate, model-specific metric.
4. Expand in steps: If metrics stay healthy, increase to 25% → 50% → 100%. Pause if any anomaly appears.
5. Communicate deprecation: Announce v1 end-of-life dates. Provide migration notes.
6. Retire old version: After the deprecation window and 100% traffic on v2, remove v1 and clean infra.

Exercises

Complete these practical tasks. You can compare your answers with the solutions.

Exercise 1: Version plan (matches ex1)

Design a versioning and deprecation plan for a prediction API that adds a new response field in v2. Include paths, timeline, and compatibility notes.

Exercise 2: Canary rollout policy (matches ex2)

Define a 4-step canary rollout with metrics, thresholds, and rollback rules. Include sticky routing logic and hold times.

Common mistakes and how to self-check

• Breaking changes in-place: Changing /v1 behavior without bumping the version. Self-check: Any client contract change must create /v2.
• No sticky routing: Users bounce between models, causing inconsistent experiences. Self-check: Verify assignment uses a stable key (user/tenant/request-id grouping).
• Vague thresholds: “Looks fine” is not a gate. Self-check: Write numeric thresholds for latency, errors, and a business metric.
• Skipping rollback drills: Teams panic during incidents. Self-check: Time a rollback from 25% to 0% in staging. Aim for seconds.
• Infinite legacy: Never retiring old versions. Self-check: Every version has an announced end-of-life date.

Practical projects

• Build a demo service exposing /v1/predict and /v2/predict with add-only response changes. Add a simple request logger.
• Implement a local traffic router that assigns requests by user hash into 5% or 95% buckets and prints the chosen version.
• Create a metrics simulator that randomly injects latency and error spikes, then triggers an automatic rollback rule.

Who this is for

• Machine Learning Engineers deploying models to production
• Backend/Platform engineers supporting ML inference services
• Data Scientists shipping models with API contracts

Prerequisites

• Basic HTTP/REST knowledge (methods, status codes, JSON)
• Familiarity with model inference and latency/error metrics
• Comfort with configuration files and environment-based rollouts

Learning path

1. Review API versioning strategies and pick path-based or header-based.
2. Define a deprecation policy template (announce, freeze, shutdown).
3. Set canary metrics, thresholds, and routing keys (user/tenant).
4. Practice a 4-step canary rollout in staging and time your rollback.
5. Apply the process to your next model update and collect lessons learned.

Mini challenge

Your v2 reduces latency by 20% but slightly lowers precision in a low-traffic region. Propose a region-specific canary plan, including thresholds to ensure no major precision drop, and a rule to pause global rollout until regional metrics stabilize.

Ready to test yourself?

Take the quick test below. It is available to everyone; sign in to save your progress and see your history over time.

Next steps

• Turn today’s plan into a reusable rollout checklist for your team.
• Automate traffic shifting and rollback thresholds in your environment.
• Schedule deprecation reminders for legacy versions.