Answer: MLflowClient.log_batch, mlflow.log_metrics

The question asks for two methods to maximize logging performance when logging multiple metrics with MLflow. Based on the community discussion and Microsoft documentation, the optimal approaches are batch logging methods that avoid multiple individual API calls. MLflowClient.log_batch (A) allows logging multiple metrics, parameters, and artifacts in a single batch operation, significantly reducing network overhead. mlflow.log_metrics (B) accepts a dictionary of metrics to log them all at once, which is more efficient than calling mlflow.log_metric (C) multiple times in a loop. While some community members incorrectly selected AC due to confusion about method names (mlflow.log_metrics vs mlflow.log_metric), the consensus and higher upvotes support AB, with references to official Microsoft documentation confirming that both A and B are valid batch logging methods for performance optimization. mlflow.log_param (D) is for parameters, not metrics, and doesn't address the performance requirement.

Author: LeetQuiz Editorial Team