# Example: Hyperparameter Tuning Job
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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 AI](xgboost.md) 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 AI high-level [Amazon SageMaker Python SDK](https://sagemaker.readthedocs.io/en/stable) to configure, run, monitor, and analyze hyperparameter tuning jobs. For more information, see [https://github.com/aws/sagemaker-python-sdk](https://github.com/aws/sagemaker-python-sdk).

## Prerequisites
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To run the code in this example, you need
+ [An AWS account and an administrator user](gs-set-up.md)
+ An Amazon S3 bucket for storing your training dataset and the model artifacts created during training
+ [A running SageMaker AI notebook instance](gs-setup-working-env.md)

**Topics**
+ [Prerequisites](#automatic-model-tuning-ex-prereq)
+ [Create a Notebook Instance](automatic-model-tuning-ex-notebook.md)
+ [Get the Amazon SageMaker AI Boto 3 Client](automatic-model-tuning-ex-client.md)
+ [Get the SageMaker AI Execution Role](automatic-model-tuning-ex-role.md)
+ [Use an Amazon S3 bucket for input and output](automatic-model-tuning-ex-bucket.md)
+ [Download, Prepare, and Upload Training Data](automatic-model-tuning-ex-data.md)
+ [Configure and Launch a Hyperparameter Tuning Job](automatic-model-tuning-ex-tuning-job.md)
+ [Clean up](automatic-model-tuning-ex-cleanup.md)