DP-100: Possible ways to maximize MLflow logging performance

Optimize your MLflow logging with batch and multiple metrics methods for improved performance in Azure Machine Learning.

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.

A. MLflowClient.log_batch
B. mlflow.log_metrics
C. mlflow.log_metric
D. mlflow.log_param

Answer

A. MLflowClient.log_batch
B. mlflow.log_metrics

Explanation

To maximize logging performance with MLflow, the two methods that can be used are A. MLflowClient.log_batch and B. mlflow.log_metrics. MLflowClient.log_batch allows for sending multiple metrics, parameters, and tags in a single API call, reducing the number of calls made to the tracking servermlflow.log_metrics enables logging multiple metrics simultaneously, which is more efficient than logging each metric individually.

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