A machine learning engineer wants to move their model version model_version for the MLflow Model Registry model model from the Staging stage to the Production stage using MLflow Client client. At the same time, they would like to archive any model versions that are already in the Production stage.
Which of the following code blocks can they use to accomplish the task?
To move a model version from the Staging stage to the Production stage and simultaneously archive any existing model versions in the Production stage, the correct function to use is transition_model_version_stage from MLflow Client, with the archive_existing_versions flag set to True. This ensures that any current models in Production are archived as the new model version is promoted to Production.
The answer is D. In the transition_model_version_stage function, when setting the archive_existing_versions flag to True, all existing model versions in the specified stage will be automatically moved to the archived stage. https://mlflow.org/docs/latest/python_api/mlflow.client.html#mlflow.client.MlflowClient.transition_model_version_stage
Answer: C
client.transition_model_stage does not exist, so it is not C.
sorry it's D
Answer is A