A machine learning engineer is manually refreshing a model in an existing machine learning pipeline.

The mlflow.register_model operation can be used to add a new version of an MLflow Model to an existing MLflow Model Registry model. This operation takes the URI of the model source (such as an MLflow run or a local path) and the name of the registered model as arguments, and returns a ModelVersion object that contains metadata about the registered model version. The mlflow.register_model operation can also create a new registered model if the name does not exist in the Model Registry.

The other options are incorrect for the following reasons:

B. MlflowClient.update_registered_model is used to update the metadata of an existing registered model, such as the name or description, not to add a new version of the model.

C. mlflow.add_model_version does not exist as a valid MLflow operation.

D. MlflowClient.get_model_version is used to fetch the metadata of a specific version of a registered model, not to add a new version of the model.

E. The machine learning engineer does not need to create an entirely new MLflow Model Registry model, as they can use the mlflow.register_model operation to add a new version of the model to the existing “project” model.

References: Adding an MLflow Model to the Model Registry, mlflow.register_model