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Microsoft

DP-100 · Question #514

You manage an Azure Machine Learning workspace. You build an image recognition training pipeline, which includes hyperparameter tuning. For each epoch run, you plan to log the following metrics…

The correct answer is B. mlflow.log_param() D. mlflow.log_artifacts(). mlflow.log_param() mlflow.log_param() logs a single key-value param in the currently active run. The key and value are both strings. Use mlflow.log_params() to log multiple params at once. mlflow.log_artifacts() mlflow.log_artifacts() logs all the files in a given directory as…

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Question

You manage an Azure Machine Learning workspace. You build an image recognition training pipeline, which includes hyperparameter tuning. For each epoch run, you plan to log the following metrics: - the transformed images used for training in an existing folder - a description to explain the hyperparameter changes You need to configure logging for the experiment. Which two functions should you use? Each correct answer presents part of the solution. (Choose two.) NOTE: Each correct selection is worth one point.

Options

  • Amlflow.log_artifact()
  • Bmlflow.log_param()
  • Cmlflow.log_metric()
  • Dmlflow.log_artifacts()

How the community answered

(43 responses)
  • A
    12% (5)
  • B
    81% (35)
  • C
    7% (3)

Explanation

mlflow.log_param() mlflow.log_param() logs a single key-value param in the currently active run. The key and value are both strings. Use mlflow.log_params() to log multiple params at once. mlflow.log_artifacts() mlflow.log_artifacts() logs all the files in a given directory as artifacts, again taking an optional https://mlflow.org/docs/latest/tracking/tracking-api/

Topics

#MLflow#Experiment Logging#Hyperparameter Tuning#Azure Machine Learning

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