A machine learning engineer is manually refreshing a model in an existing machine learning pipeline. The pipeline uses the MLflow Model Registry model "project". The machine learning engineer would like to add a new version of the model to "project".
Which of the following MLflow operations can the machine learning engineer use to accomplish this task?
To add a new version of a model to the MLflow Model Registry, the operation 'mlflow.register_model' should be used. This function registers a new model version in the model registry based on the specified model URI. This operation allows the machine learning engineer to create a new version of the model within the already existing registry entry for the project. The other options are either not applicable for adding a new model version, such as updating metadata or retrieving a model version, or incorrectly suggest that a new registry model needs to be created.
Answer is A import mlflow model_uri = "runs:/d16076a3ec534311817565e6527539c0/model" model_details = mlflow.register_model( model_uri=model_uri, name="project" )
mlflow.register_model will create a new model version in the model registry for the model files specified by model_uri. Doc: https://mlflow.org/docs/latest/python_api/mlflow.html#mlflow.register_model
I think the model is already registered, the answer should be B
Because the model name already exists, meaning it's registered already, and it's being refreshed: per docs: Updates metadata for RegisteredModel entity. Input field description should be non-None. Backend raises exception if a registered model with given name does not exist.
Strike that, it's A...we're registering a new model, not updating the existing.
update_registered_model allows you to update model description, the right choice should be A