You manage an Azure Machine Learning workspace. You experiment with an MLflow model that trains interactively by using a notebook in the workspace. You need to log dictionary type artifacts of the…

The correct answer is D. mlflow.log_text("my_metric", my_dict). The correct syntax is mlflow.log_text. When you need to log dictionary-type data as an artifact in MLflow, you convert or represent the dictionary as a string/text and log it using mlflow.log_text, which stores the content as a text artifact file associated with the run. The…

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Question

You manage an Azure Machine Learning workspace. You experiment with an MLflow model that trains interactively by using a notebook in the workspace. You need to log dictionary type artifacts of the experiments in Azure Machine Learning by using MLflow. Which syntax should you use?

Options

Amlflow.log_input(my_dict)
Bmlflow.log_metric("my_metric", my_dict)
Cmlflow.log_metrics(my_dict)
Dmlflow.log_text("my_metric", my_dict)

How the community answered

(48 responses)

A
4% (2)
B
10% (5)
C
15% (7)
D
71% (34)

Explanation

The correct syntax is mlflow.log_text. When you need to log dictionary-type data as an artifact in MLflow, you convert or represent the dictionary as a string/text and log it using mlflow.log_text, which stores the content as a text artifact file associated with the run. The other options are unsuitable: mlflow.log_input is for logging dataset inputs, mlflow.log_metric accepts only a single numeric value (not a dict), and mlflow.log_metrics expects a flat dictionary of string-to-float numeric metric values - not arbitrary dictionary artifacts.

Topics

#MLflow#Experiment Tracking#Artifact Logging#Azure Machine Learning

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