The following list contains a variety of sample Jupyter notebooks that address different use cases of Amazon SageMaker AI XGBoost algorithm.
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How to Create a Custom XGBoost container
– This notebook shows you how to build a custom XGBoost Container with Amazon SageMaker AI Batch Transform. -
Regression with XGBoost using Parquet
– 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
– 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
– 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
– 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
– 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. 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.
