Auto-Promote Only If It Beats Baseline
Gate promotion on champion-challenger comparison.
Auto-Promote Only If It Beats Baseline is a free MLOps Academy lesson on CoddyKit — lesson 3 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.
Newer Is Not Always Better
A freshly retrained model can be worse than the one in production. Never promote a new model blindly just because it is newer.
Champion vs Challenger
The live model is the champion; the new candidate is the challenger. Promotion is a contest the challenger must win on the metrics.
Define the Baseline
Your baseline is the current champion score on a fixed evaluation set. The challenger has to clearly beat that number to earn promotion.
Evaluate on the Same Data
Score both models on the identical, held-out test set. A fair comparison only counts when nothing else changed between them.
A Promotion Gate
Wrap the decision in a check that returns true only if the challenger wins. This gate compares the two scores directly.
def should_promote(new, baseline):
return new > baselineAdd a Margin
Tiny gains can be noise, so require a real margin. Demanding at least a 1% lift avoids churning the production model for nothing.
def should_promote(new, baseline):
return new >= baseline + 0.01Pick the Right Metric
Compare on the metric that matters for your task, like F1 or AUC, not just raw accuracy. The wrong metric promotes the wrong model.
Wire It Into the Pipeline
Put the gate right after evaluation in your DAG. If the challenger loses, the pipeline stops and the champion stays live.
Promote in the Registry
Winning means moving the model stage to Production in the registry. This transition is the one action that changes what users get served.
client.transition_model_version_stage(
name="fraud", version=7, stage="Production")Log the Decision
Record both scores and the outcome every run. That audit trail explains later why a model was or was not promoted.
Guard Against Bad Data
A challenger can look amazing because the test set leaked or shrank. Sanity-check the evaluation before trusting any promotion.
Quick Check
When should the new challenger model be auto-promoted?
Recap
You gated promotion on results: compare challenger to champion on the same data, require a margin, then transition the winner in the registry. ✅
Frequently asked questions
Is the “Auto-Promote Only If It Beats Baseline” lesson free?
Yes — the full text of “Auto-Promote Only If It Beats Baseline” 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 “Auto-Promote Only If It Beats Baseline”?
Gate promotion on champion-challenger comparison. 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 3 of 4, so you can start here or from the beginning and move at your own pace.
How long does the “Auto-Promote Only If It Beats Baseline” 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
- Schedule vs Trigger-Based Retraining
- Orchestrate Retraining with Airflow
- Auto-Promote Only If It Beats Baseline
- Keep a Human in the Loop