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 creates a new model titled model_name in the MLflow Model Registry._
B
It adds a new version of the model_name model to the MLflow Model Registry._
C
It removes the need to specify the model name in the next required call to mlflow.register_model._
D
It indicates the name of the Run in the MLflow Experiment.
E
It signifies the name of the logged model in the MLflow Experiment.