You will learn how to use Amazon SageMaker to create, train, and deploy a machine learning (ML) model using the XGBoost ML algorithm in this lesson. Amazon SageMaker is a fully managed service that allows any developer or data scientist to swiftly construct, train, and deploy machine learning (ML) models.
This guide will teach you how to:
1. Create a SageMaker notebook instance
2. Prepare the data
3. Train the model to learn from the data
4. Deploy the model
5. Evaluate your ML model's performance
The Bank Marketing Data Set, which includes data on customer demographics, reactions to marketing events, and external factors, is used to train the model. The data has been labeled, and a column in the dataset indicates if the client has signed up for a service provided by the bank. The University of California, Irvine's Machine Learning Repository hosts a version of this dataset that is open to the public.
The resources developed and used in this tutorial are eligible for AWS Free Tier.
Important: Terminating resources that are no longer being used saves money and is a smart practice. If you do not terminate your resources, you will incur charges to your account.