# Register models (/docs/guides/developer-workspace/mlflow/register-models)



The MLflow Model Registry is a central store for versioned models. You can
register a trained model from a dev environment, then retrieve any version of
it in another dev environment or application. All registered models are
accessible to every user on the platform.

Register a model [#register-a-model]

To register a model, log it as an artifact during a run and then call
`mlflow.register_model`. The registry assigns a version number automatically
each time you register under the same model name.

```python
import mlflow
import mlflow.sklearn
from sklearn.linear_model import LogisticRegression

mlflow.set_experiment("shared/my-experiment")

with mlflow.start_run() as run:
    model = LogisticRegression()
    model.fit(X_train, y_train)

    mlflow.sklearn.log_model(model, artifact_path="model")

    run_id = run.info.run_id

model_uri = f"runs:/{run_id}/model"
mlflow.register_model(model_uri=model_uri, name="my-classifier")
```

The model appears in the **Models** section of the MLflow UI under the name
you provided.

Load a registered model [#load-a-registered-model]

You can load any registered model version by name. Use this to run inference
or continue training from a saved checkpoint.

Load the latest version:

```python
import mlflow.sklearn

model = mlflow.sklearn.load_model("models:/my-classifier/latest")
predictions = model.predict(X_test)
```

Load a specific version:

```python
model = mlflow.sklearn.load_model("models:/my-classifier/3")
```

View registered models [#view-registered-models]

Open the MLflow UI from the Developer Workspace sidebar to browse all
registered models, compare versions, and review their associated runs.

Next steps [#next-steps]

* [Open the MLflow UI](open-mlflow-ui) to browse the Model Registry and
  compare runs.