Example: Hyperparameter Tuning Job
This example shows how to create a new notebook for configuring and launching a hyperparameter tuning job. The tuning job uses the XGBoost algorithm with Amazon SageMaker to train a model to predict whether a customer will enroll for a term deposit at a bank after being contacted by phone.
You use the low-level SDK for Python (Boto3) to configure and launch the hyperparameter tuning job, and
the AWS Management Console to monitor the status of hyperparameter tuning jobs. You can also use the
Amazon SageMaker high-level Amazon SageMaker Python SDK
Prerequisites
To run the code in this example, you need
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An Amazon S3 bucket for storing your training dataset and the model artifacts created during training
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