Registering a model using MLflow SDK
You can register a model using the user interface or the MLFlow SDK.
Using MLflow SDK to register a model
Registering a model enables you to track your model and upload and share the model. Registering a model stores the model archives in the model registry with a version tag. The first time you register a model, Model Registry automatically creates a model repository with the first version of the model.
To register a model using MLFlow SDK, specify the
registered_model_nameand assign a value:
mlflow.sklearn.log_model(lr, "model", registered_model_name="ElasticnetWineModel")
If you run the Python code again with the same
model_nameit will create an additional version for the