Shadow Traffic Without User Impact
Mirror live requests to a candidate silently.
Shadow Traffic Without User Impact is a free MLOps Academy lesson on CoddyKit — lesson 2 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 Shadow Mode Is
In shadow mode, a candidate model sees real production requests but its answers are never shown to users. It watches from the sidelines. 👀
Mirror, Do Not Serve
Every live request is mirrored to the shadow model. The champion still produces the response users actually receive.
Zero User Impact
Because shadow output is discarded, even a wildly broken candidate cannot hurt a single user. That is the whole appeal of shadow testing.
Test on Real Data
Shadow mode feeds the candidate the exact production traffic it will face, messy edge cases included, which offline test sets often miss.
How the Mirror Works
Send the same input to both models, return only the champion, and store the shadow result for later. The user waits on the champion alone.
def handle(request):
primary = champion.predict(request)
shadow = candidate.predict(request)
store_shadow(request, primary, shadow)
return primaryRun Shadow Off the Hot Path
Call the shadow model asynchronously so it never adds to the latency users feel. A slow shadow must not slow real responses.
Compare the Two Outputs
Later, line up shadow predictions against the champion to spot disagreements. Big gaps flag where the candidate behaves differently.
Catch Crashes Early
Shadow runs reveal practical problems like timeouts, memory spikes, or schema mismatches under real load, all before any user is exposed.
The Ground-Truth Gap
Shadow shows what the model predicts, not whether it was right. You still need labels later to judge true accuracy.
Mind the Side Effects
If your model triggers writes, emails, or charges, shadow calls must skip those actions. Otherwise a silent test causes very loud real effects.
Shadow Then Canary
Shadow proves the candidate runs safely; a canary then proves it serves well. Many teams shadow first, then canary with real users.
Quick Check
What truly defines shadow deployment?
Recap
Shadow mode mirrors live traffic to a candidate, discards its answers, and runs it off the hot path, so you test on real data with zero user risk. ✅
Frequently asked questions
Is the “Shadow Traffic Without User Impact” lesson free?
Yes — the full text of “Shadow Traffic Without User Impact” 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 “Shadow Traffic Without User Impact”?
Mirror live requests to a candidate silently. 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 2 of 4, so you can start here or from the beginning and move at your own pace.
How long does the “Shadow Traffic Without User Impact” 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