This tutorial provides a step-by-step guide to using Feast with Redis as an online feature store for machine learning. It covers deploying a local feature store, building a training dataset, materializing feature values into the Redis online store, and reading the latest features from the Redis online store for inference. The tutorial demonstrates how to use Feast's Python library + optional CLI to install and configure the feature store, and how to use Redis as an online store to deliver real-time predictions at scale with low latency and high throughput.