Technical requirements
To follow the code examples in the chapter, the resources created in Chapter 4, Adding Feature Store to ML Models, and Chapter 5, Model Training and Inference, are required. You will need familiarity with Docker and any notebook environment, which could be a local setup, such as Jupyter, or an online notebook environment, such as Google Colab, Kaggle, or SageMaker. You will also need an AWS account with full access to some of the resources, such as Redshift, S3, Glue, DynamoDB, and the IAM console. You can create a new account and use all the services for free during the trial period. You can find the code examples of the book and feature repository in the following GitHub links:
- https://github.com/PacktPublishing/Feature-Store-for-Machine-Learning/tree/main/Chapter06
- https://github.com/PacktPublishing/Feature-Store-for-Machine-Learning/tree/main/customer_segmentation