Load a Model Back by Name and Stage
Fetch the current Production model from your code.
Load a Model Back by Name and Stage 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.
Closing the Loop
Registering a model only pays off when code can load it back. This is where your serving app finally meets the registry. 🔁
The models URI
You fetch registered models through a models:/ URI. It names the model and which version or stage you want, no file paths required.
Load by Exact Version
For a pinned, reproducible load, ask for a specific version. This always returns the same artifact, which is great for tests.
import mlflow
m = mlflow.pyfunc.load_model(
"models:/churn-classifier/4")Load by Stage
In production you usually load by stage instead. Asking for Production means you always get whatever version is live right now.
m = mlflow.pyfunc.load_model(
"models:/churn-classifier/Production")Stage Loading Is Indirect
Loading by stage adds a layer of indirection. Promote a new version to Production and your code picks it up with no edits or redeploy.
Load by Alias
If you use aliases, load with the @ syntax. Asking for the champion alias resolves to whichever version you tagged as champion.
m = mlflow.pyfunc.load_model(
"models:/churn-classifier@champion")pyfunc Is Universal
The pyfunc flavor wraps any framework behind one predict method. Your serving code stays identical whether it is sklearn or PyTorch underneath.
preds = m.predict(input_df)Native Flavor When Needed
Need framework-specific methods? Load the native flavor instead, like mlflow.sklearn, to get the original estimator object back.
model = mlflow.sklearn.load_model(
"models:/churn-classifier/Production")Point at the Server
Loading needs the tracking URI set so MLflow knows which registry to query. Without it, the lookup fails or hits the wrong store.
mlflow.set_tracking_uri(
"http://localhost:5000")Load Once at Startup
Loading is expensive, so do it once when your service boots, not on every request. Reuse the same object across predictions.
Refresh to Pick Up Changes
A long-running service keeps the version it loaded at startup. To adopt a freshly promoted model, reload on a schedule or restart the service.
Quick Check
Decide which URI a long-lived service should load.
Recap
You loaded models with a models:/ URI by version, stage, or alias, and learned to load once at startup. The registry round-trip is complete. 🚀
Frequently asked questions
Is the “Load a Model Back by Name and Stage” lesson free?
Yes — the full text of “Load a Model Back by Name and Stage” 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 “Load a Model Back by Name and Stage”?
Fetch the current Production model from your code. 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 “Load a Model Back by Name and Stage” 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
- Register Your First Model Version
- Stages: Staging, Production, Archived
- Add Tags and Descriptions to Models
- Load a Model Back by Name and Stage