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MLOps Academy · Lesson

Promote or Roll Back the Winner

Ship the better model or revert cleanly.

Promote or Roll Back the Winner is a free MLOps Academy lesson on CoddyKit — lesson 4 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.

The Test Is Over

Your A/B test has run its full window and the numbers are in. Now comes the decision: promote the challenger or roll it back to the champion. 🚦

Decide Against Your Plan

Pull up the success criteria you wrote before the test. The challenger wins only if it beats the threshold on the primary metric and breaks no guardrail.

Promotion Means Production

To promote a model is to make it the new champion. With a registry, that often means moving the winning version into the Production stage.

Promote via the Registry

MLflow lets you flip the winning version to Production in one call, so your serving layer picks it up.

from mlflow import MlflowClient

client = MlflowClient()
client.transition_model_version_stage("fraud", version=7, stage="Production")

Ramp, Do Not Slam

Even a clear winner deserves a careful rollout. Raise its traffic from ten percent toward one hundred gradually while you watch the live metrics.

When to Roll Back

Roll back if the challenger loses, ties, or trips a guardrail like a latency spike. A tie is not a win, so the safe default is to keep the champion.

Rollback Must Be Instant

A clean rollback is just pointing traffic back at the old version. Because the champion is still registered, recovery should take seconds, not a rebuild. ⏪

Keep the Old Champion

Never delete the previous champion the moment you promote. Move it to the Archived stage instead, so a fast revert is always one step away.

Watch After You Ship

Promotion is not the finish line. Keep monitoring the new champion, since live behavior at full traffic can still surprise you after the test ends.

Record the Decision

Log who decided, the metrics, and the verdict. This audit trail lets you explain later exactly why this model went live or got reverted.

A Losing Test Still Wins

Rolling back is not failure. You protected users from a worse model and learned something real, which is exactly what the experiment was for. ✅

Quick Check

Let us decide what to do after the results land.

Recap

Decide against your written plan: promote a clear, guardrail-safe winner via the registry, ramp gradually, and keep the old champion for an instant rollback. 🎯

Frequently asked questions

Is the “Promote or Roll Back the Winner” lesson free?

Yes — the full text of “Promote or Roll Back the Winner” 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 “Promote or Roll Back the Winner”?

Ship the better model or revert cleanly. 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 4 of 4, so you can start here or from the beginning and move at your own pace.

How long does the “Promote or Roll Back the Winner” 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

  1. Split Traffic Between Model Versions
  2. Pick Metrics That Matter
  3. Read Significance Without Fooling Yourself
  4. Promote or Roll Back the Winner
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