Train and Log to the Registry
Run training and register the resulting model.
Train and Log to the Registry 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.
One Run, Fully Captured
This lesson ties training and the registry together. You train a model, then push it into the MLflow Model Registry so it becomes a versioned, reusable artifact. 🚀
Start a Tracking Run
Wrap your training in mlflow.start_run(). Everything you log inside that block belongs to one run, with its own id and timestamp.
import mlflow
with mlflow.start_run() as run:
# train and log here
print(run.info.run_id)Train Your Model
Inside the run, fit your estimator like normal. The training code does not change just because MLflow is watching.
from sklearn.ensemble import RandomForestClassifier
model = RandomForestClassifier(n_estimators=100)
model.fit(X_train, y_train)Log the Parameters
Record the choices you made with log_param. Months later, this is how you remember exactly which settings produced this model.
mlflow.log_param("n_estimators", 100)
mlflow.log_param("max_depth", None)Log the Metrics
Capture how well it did with log_metric. Metrics are the numbers you will sort by when comparing runs later.
acc = model.score(X_test, y_test)
mlflow.log_metric("accuracy", acc)Log the Model Artifact
Use mlflow.sklearn.log_model to save the fitted model into the run. MLflow stores the weights, flavor, and environment together.
mlflow.sklearn.log_model(
sk_model=model,
name="model",
)Register While Logging
Pass registered_model_name and MLflow logs the model and registers a new version in one step. This is the cleanest path.
mlflow.sklearn.log_model(
sk_model=model,
name="model",
registered_model_name="churn-classifier",
)Register After the Fact
Already logged a model? Call register_model with the run URI to add it to the registry without retraining anything.
uri = "runs:/<run_id>/model"
mlflow.register_model(uri, "churn-classifier")Versions Auto-Increment
Register the same name again and you get version 2, then 3, and so on. The registry keeps every version, never overwriting an old one.
Quick Check
You log a model under a name that already exists. What happens?
The Run, Confirmed
After the block exits, the run is marked FINISHED. Your params, metrics, and registered model version are now permanently linked.
Find It in the UI
Open the MLflow UI and your run appears under its experiment, with the new model version listed in the Models tab. Nothing is lost.
Recap: Train, Log, Register
You trained inside a run, logged params and metrics, saved the model, and registered a version. Train, log, register: that is the start of a real pipeline. ✅
Frequently asked questions
Is the “Train and Log to the Registry” lesson free?
Yes — the full text of “Train and Log to the Registry” 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 “Train and Log to the Registry”?
Run training and register the resulting 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 “Train and Log to the Registry” 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
- Train and Log to the Registry
- Promote the Best Model to Production
- Serve the Production Model
- Trace a Prediction Round-Trip