AI-300: Operationalizing Machine Learning and Generative AI SolutionsDemo

Question

HOTSPOT -

You train a model in Azure Machine Learning. You plan to capture experiment details for later comparison. The training code must log parameters and metrics for each run.

You review the following training script.

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You need to verify whether the training script fulfills the experiment-tracking requirement. For each of the following statements, select Yes if the statement is true. Otherwise, select No.

Yes or No

Statements	Yes	No
The code ensures that training parameters and metrics are tracked for each run.
The code uses the experiment name to define the artifact path of the logged model.
The code associates the run with a named experiment.

Explanation

mlflow.log_param and mlflow.log_metric log values in the active run. mlflow.set_experiment("classification-experiment") associates the subsequent run with that named experiment. The model artifact subpath is explicitly "model" in mlflow.sklearn.log_model(model, "model"), not the experiment name.

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AI-300: Operationalizing Machine Learning and Generative AI SolutionsDemo

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Notes

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