AI-300: Operationalizing Machine Learning and Generative AI SolutionsDemo

Question

You monitor an Azure Machine Learning classification-training experiment named train_classification on Azure Notebooks.

You must store a table named table as an artifact in Azure Machine Learning Studio during model training.

You need to collect and list the metrics by using MLflow.

How should you complete the code segment?

Select

from mlflow.tracking import MlflowClient
import json
mlflow.set_experiment("train_classification")
mlflow_run = mlflow.start_run()
row1 = {"table.col1": 5, "table.col2": 10}
mlflow.(row1)

with open("table.json", 'w') as f:
    json.dump(table, f)
mlflow.("table.json")

client = MlflowClient()
fetched_mlflow_run =
MlflowClient().get_run()

Explanation

mlflow.log_metrics logs a dictionary of metric names and numeric values. mlflow.log_artifact logs a local file as a run artifact. MlflowClient.get_run requires the run ID; mlflow_run.info.run_id supplies that identifier.

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AI-300: Operationalizing Machine Learning and Generative AI SolutionsDemo

AstroTutor

Notes

Readiness

QuestionQ115

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