# XGBoost sample notebooks
<a name="xgboost-sample-notebooks"></a>

The following list contains a variety of sample Jupyter notebooks that address different use cases of Amazon SageMaker AI XGBoost algorithm.
+ [How to Create a Custom XGBoost container](https://sagemaker-examples.readthedocs.io/en/latest/aws_sagemaker_studio/sagemaker_studio_image_build/xgboost_bring_your_own/Batch_Transform_BYO_XGB.html) – This notebook shows you how to build a custom XGBoost Container with Amazon SageMaker AI Batch Transform.
+ [Regression with XGBoost using Parquet](https://sagemaker-examples.readthedocs.io/en/latest/introduction_to_amazon_algorithms/xgboost_abalone/xgboost_parquet_input_training.html) – This notebook shows you how to use the Abalone dataset in Parquet to train a XGBoost model.
+ [How to Train and Host a Multiclass Classification Model](https://sagemaker-examples.readthedocs.io/en/latest/introduction_to_amazon_algorithms/xgboost_mnist/xgboost_mnist.html) – This notebook shows how to use the MNIST dataset to train and host a multiclass classification model.
+ [How to train a Model for Customer Churn Prediction](https://sagemaker-examples.readthedocs.io/en/latest/introduction_to_applying_machine_learning/xgboost_customer_churn/xgboost_customer_churn.html) – This notebook shows you how to train a model to Predict Mobile Customer Departure in an effort to identify unhappy customers.
+ [An Introduction to Amazon SageMaker AI Managed Spot infrastructure for XGBoost Training](https://sagemaker-examples.readthedocs.io/en/latest/introduction_to_amazon_algorithms/xgboost_abalone/xgboost_managed_spot_training.html) – This notebook shows you how to use Spot Instances for training with a XGBoost Container.
+ [How to use Amazon SageMaker Debugger to debug XGBoost Training Jobs](https://sagemaker-examples.readthedocs.io/en/latest/sagemaker-debugger/xgboost_census_explanations/xgboost-census-debugger-rules.html) – This notebook shows you how to use Amazon SageMaker Debugger to monitor training jobs to detect inconsistencies using built-in debugging rules.

For instructions on how to create and access Jupyter notebook instances that you can use to run the example in SageMaker AI, see [Amazon SageMaker notebook instances](nbi.md). After you have created a notebook instance and opened it, choose the **SageMaker AI Examples** tab to see a list of all of the SageMaker AI samples. The topic modeling example notebooks using the linear learning algorithm are located in the **Introduction to Amazon algorithms** section. To open a notebook, choose its **Use** tab and choose **Create copy**.