# Automatically register SageMaker AI models with SageMaker Model Registry
<a name="mlflow-track-experiments-model-registration"></a>

You can log MLflow models and automatically register them with SageMaker Model Registry using either the Python SDK or directly through the MLflow UI. 

**Note**  
Do not use spaces in a model name. While MLflow supports model names with spaces, SageMaker AI Model Package does not. The auto-registration process fails if you use spaces in your model name.

## Register models using the SageMaker Python SDK
<a name="mlflow-track-experiments-model-registration-sdk"></a>

Use `create_registered_model` within your MLflow client to automatically create a model package group in SageMaker AI that corresponds to an existing MLflow model of your choice.

```
import mlflow 
from mlflow import MlflowClient

mlflow.set_tracking_uri(arn)

client = MlflowClient()

mlflow_model_name = 'AutoRegisteredModel'
client.create_registered_model(mlflow_model_name, tags={"key1": "value1"})
```

Use `mlflow.register_model()` to automatically register a model with the SageMaker Model Registry during model training. When registering the MLflow model, a corresponding model package group and model package version are created in SageMaker AI. 

```
import mlflow.sklearn
from mlflow.models import infer_signature
from sklearn.datasets import make_regression
from sklearn.ensemble import RandomForestRegressor

mlflow.set_tracking_uri(arn)
params = {"n_estimators": 3, "random_state": 42}
X, y = make_regression(n_features=4, n_informative=2, random_state=0, shuffle=False)

# Log MLflow entities
with mlflow.start_run() as run:
    rfr = RandomForestRegressor(**params).fit(X, y)
    signature = infer_signature(X, rfr.predict(X))
    mlflow.log_params(params)
    mlflow.sklearn.log_model(rfr, artifact_path="sklearn-model", signature=signature)

model_uri = f"runs:/{run.info.run_id}/sklearn-model"
mv = mlflow.register_model(model_uri, "RandomForestRegressionModel")

print(f"Name: {mv.name}")
print(f"Version: {mv.version}")
```

## Register models using the MLflow UI
<a name="mlflow-track-experiments-model-registration-ui"></a>

You can alternatively register a model with the SageMaker Model Registry directly in the MLflow UI. Within the **Models** menu in the MLflow UI, choose **Create Model**. Any models newly created in this way are added to the SageMaker Model Registry.

![\[Model registry creation within the MLflow UI.\]](http://docs.aws.amazon.com/sagemaker/latest/dg/images/mlflow/mlflow-ui-register-model.png)


After logging a model during experiment tracking, navigate to the run page in the MLflow UI. Choose the **Artifacts** pane and choose **Register model** in the upper right corner to register the model version in both MLflow and SageMaker Model Registry. 

![\[Model registry creation within the MLflow UI.\]](http://docs.aws.amazon.com/sagemaker/latest/dg/images/mlflow/mlflow-ui-register-model-2.png)


## View registered models in Studio
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Within the SageMaker Studio landing page, choose **Models** on the left navigation pane to view your registered models. For more information on getting started with Studio, see [Launch Amazon SageMaker Studio](https://docs.aws.amazon.com/sagemaker/latest/dg/studio-updated-launch.html).

![\[MLflow models registered in SageMaker Model Registry in the Studio UI.\]](http://docs.aws.amazon.com/sagemaker/latest/dg/images/mlflow/mlflow-studio-model-registry.png)