You manage an Azure Machine Learning workspace. You must log multiple metrics by using MLflow. You need to maximize logging performance. What are two possible ways to achieve this goal? Each correct…

The correct answer is A. MLflowClient.log_batch B. mlflow.log_metrics. Performance considerations: If you need to log multiple metrics (or multiple values for the same metric) avoid making calls to mlflow.log_metric in loops. Better performance can be achieved by logging batch of metrics. Use the method mlflow.log_metrics which accepts a…

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Question

You manage an Azure Machine Learning workspace. You must log multiple metrics by using MLflow. You need to maximize logging performance. What are two possible ways to achieve this goal? Each correct answer presents a complete solution. NOTE: Each correct selection is worth one point.

Options

AMLflowClient.log_batch
Bmlflow.log_metrics
Cmlflow.log_metric
Dmlflow.log_param

How the community answered

(37 responses)

A
89% (33)
C
3% (1)
D
8% (3)

Explanation

Performance considerations: If you need to log multiple metrics (or multiple values for the same metric) avoid making calls to mlflow.log_metric in loops. Better performance can be achieved by logging batch of metrics. Use the method mlflow.log_metrics which accepts a dictionary with all the metrics you want to log at once or use MLflowClient.log_batch which accepts multiple type of elements for logging. https://learn.microsoft.com/en-us/azure/machine-learning/how-to-log-view-metrics?view=azureml- api-2&tabs=interactive

Topics

#MLflow#Metric Logging#Performance Optimization#Azure ML

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