Canary Rollouts: Ship to a Few First
Shift a small slice of traffic to the new model.
Canary Rollouts: Ship to a Few First is a free MLOps Academy lesson on CoddyKit — lesson 1 of 4. You can read the complete lesson below for free — then practise it hands-on in the browser with a built-in code editor and a 24/7 AI tutor. It is part of the MLOps Academy learning path, one of 4 lessons in the course, and your progress syncs across the web and the CoddyKit app.
What a Canary Is
A canary rollout sends your new model to a small slice of real traffic first, so any damage stays tiny while you watch. 🐤
Why Not Ship to Everyone
Flipping all traffic to a fresh model at once is a big-bang deploy: if it is wrong, every single user feels it instantly.
Start Small on Purpose
Canaries usually begin around 5% of traffic. That is enough to gather real signal, but small enough that a bad model barely dents your users.
Champion and Challenger
The stable model is the champion; the canary is the challenger. Both run live at once so you can compare them on the same real requests.
Watch Before You Widen
While the canary serves its slice, you monitor error rate, latency, and key business metrics, side by side against the champion.
Ramp Up in Steps
If the canary looks healthy, you ramp its share up in stages, say 5%, 25%, 50%, then 100%, pausing to check at each level.
Splitting the Traffic
A router or proxy decides which model each request hits. This simple split sends one in twenty calls to the canary version.
import random
def route(request, canary_pct=5):
if random.uniform(0, 100) < canary_pct:
return canary_model.predict(request)
return champion_model.predict(request)Tag Who Served It
Log which model handled each request. Without a version tag on every prediction, you cannot tell whose metrics are whose later.
log.info("prediction", extra={
"model_version": "v2-canary",
"latency_ms": latency,
})Keep the Slices Comparable
Route by a stable hash of user id so the same person always lands on the same model. Random per-request splits make a fair comparison harder.
Bail Out Fast
The whole point is a quick exit. If the canary misbehaves, you drop its traffic back to 0% and users never noticed a thing.
Canary vs Blue-Green
Blue-green keeps two full environments and flips between them. A canary instead leaks traffic gradually, giving you finer control and earlier warning.
Quick Check
Let us check the core idea behind a canary.
Recap
A canary ships your new model to a tiny slice first, compares it against the champion, ramps up only if healthy, and rolls back instantly if not. ✅
Frequently asked questions
Is the “Canary Rollouts: Ship to a Few First” lesson free?
Yes — the full text of “Canary Rollouts: Ship to a Few First” is free to read here on the web, and the MLOps Academy course includes 4 lessons in total. To practise it interactively (a built-in code editor and a 24/7 AI tutor) and unlock the rest of the MLOps Academy course, upgrade to CoddyKit PRO.
What will I learn in “Canary Rollouts: Ship to a Few First”?
Shift a small slice of traffic to the new model. You practise MLOps Academy with hands-on code you run directly in the browser, and a 24/7 AI tutor answers your questions as you work through the lesson.
Do I need any experience to start MLOps Academy?
No prior experience is required. MLOps Academy on CoddyKit is structured for beginners through advanced learners; this is — lesson 1 of 4, so you can start here or from the beginning and move at your own pace.
How long does the “Canary Rollouts: Ship to a Few First” lesson take?
Most CoddyKit lessons take about 5–10 minutes. Each one is bite-sized and interactive, so you make steady progress and pick up exactly where you left off across the web and the app.
Can I write and run code in this MLOps Academy lesson?
Yes. Every MLOps Academy lesson includes a built-in code editor, so you write and run real code right in your browser and get instant AI feedback — no local setup required.
All lessons in this course
- Canary Rollouts: Ship to a Few First
- Shadow Traffic Without User Impact
- Define Automatic Rollback Criteria
- Progressive Delivery with Argo Rollouts