Databricks Certified Machine Learning - Associate
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A senior machine learning engineer is developing a machine learning pipeline. They set up the pipeline to automatically transition a new version of a registered model to the Production stage in the Model Registry once it passes all tests using the MLflow Client API client. Which operation was used to transition the model to the Production stage?
A senior machine learning engineer is developing a machine learning pipeline. They set up the pipeline to automatically transition a new version of a registered model to the Production stage in the Model Registry once it passes all tests using the MLflow Client API client. Which operation was used to transition the model to the Production stage?
Explanation:
The MLflow Client API provides a method called transition_model_version_stage that allows you to transition a model version to a specified stage in the Model Registry. This method requires parameters such as the name of the registered model, the version of the model, and the target stage to which the model version should be transitioned. For example:
import mlflow
# Assuming ‘client‘ is an MLflow Client instance
client.transition_model_version_stage(name=“your_model_name“, version=your_model_version, stage=“Production“)
Other options are incorrect because:
Client.update_model_stagedoes not exist in the MLflow Client API.client.transition_model_versiontransitions a model version without specifying the target stage.client.update_model_versionis not a valid method in the MLflow Client API.