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Answer: mlflow.log_metrics(my_dict)
The question asks for logging dictionary-type artifacts in MLflow within Azure Machine Learning. The correct method is mlflow.log_metrics(my_dict) because it specifically accepts a dictionary of key-value pairs where keys are metric names and values are numeric metric values. This aligns with the community consensus where option C received 100% support and multiple comments with upvotes confirmed this is the proper method for logging multiple metrics from a dictionary. Option A (mlflow.log_input) is for logging dataset information, not metrics. Option B (mlflow.log_metric) only accepts single metric values, not dictionaries. Option D (mlflow.log_text) is for logging text strings, not metric dictionaries. While one comment mentioned log_params, that's for parameters, not metrics, and isn't relevant to the dictionary artifact logging requirement.
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You are working with an MLflow model in an Azure Machine Learning workspace and training it interactively in a notebook. You need to log artifacts of the experiments that are of a dictionary type.
Which syntax should you use?
A
mlflow.log_input(my_dict)
B
mlflow.log_metric("my_metric", my_dict)
C
mlflow.log_metrics(my_dict)
D
mlflow.log_text("my_metric", my_dict)