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chatper6, Now that the monitor is available, use the suggest_baseline() method to produce a default baseline for the model … There should be two files saved in the configured S3 bucket: constraints.json and statistics.json.

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2023/01/06 07:25

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from sagemaker.model_monitor.dataset_format import DatasetFormat 
from sagemaker import get_execution_role

s3_path = "s3://monitoring/xgb-churn-data"

monitor.suggest_baseline(
	baseline_dataset=s3_path + "/training-dataset.csv",
	dataset_format=DatasetFormat.csv(header=True),
	output_s3_uri=s3_path + "/baseline/",
	wait=True)
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