Databricks Certified Machine Learning - Associate
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A machine learning engineer is enhancing a project to automatically refresh the model each time the project runs. The project is connected to an existing model named 'model_name' in the MLflow Model Registry. The following code snippet is part of their strategy:
mlflow.sklearn.log_model(sk_model=model, artifact_path="model", registered_model_name=model_name)
mlflow.sklearn.log_model(sk_model=model, artifact_path="model", registered_model_name=model_name)
Given that 'model_name' is already present in the MLflow Model Registry, what does the parameter registered_model_name=model_name signify?_
A
It removes the need to specify the model name in a subsequent mandatory call to mlflow.register_model._
B
It logs a new model under the title 'model_name' in the MLflow Model Registry._
C
It signifies the name of the logged model within the MLflow Experiment.
D
It adds a new version of the 'model_name' model to the MLflow Model Registry._
E
It indicates the name of the Run in the MLflow Experiment.