Update Feast documentation based on 0.4 and new functionality

README.md

@@ -1,17 +1,16 @@
 <p align="center">
-<a href="https://feast.dev/">
-  <img src="docs/.gitbook/assets/feast_logo.png" width="550">
-  </a>
+    <a href="https://feast.dev/">
+      <img src="docs/assets/feast_logo.png" width="550">
+    </a>
 </p>
 <br />
 
 [![Unit Tests](https://github.com/feast-dev/feast/workflows/unit%20tests/badge.svg?branch=master)](https://github.com/feast-dev/feast/actions?query=workflow%3A%22unit+tests%22+branch%3Amaster)
 [![Docker Compose Tests](https://github.com/feast-dev/feast/workflows/docker%20compose%20tests/badge.svg?branch=master)](https://github.com/feast-dev/feast/actions?query=workflow%3A%22docker+compose+tests%22+branch%3Amaster)
 [![Code Standards](https://github.com/feast-dev/feast/workflows/code%20standards/badge.svg?branch=master)](https://github.com/feast-dev/feast/actions?query=workflow%3A%22code+standards%22+branch%3Amaster)
-[![Docs latest](https://img.shields.io/badge/Docs-latest-blue.svg)](https://docs.feast.dev/)
+[![Docs Latest](https://img.shields.io/badge/docs-latest-blue.svg)](https://docs.feast.dev/)
 [![GitHub Release](https://img.shields.io/github/release/feast-dev/feast.svg?style=flat)](https://github.com/feast-dev/feast/releases)
 
-
 ## Overview
 
 Feast (Feature Store) is a tool for managing and serving machine learning features. Feast is the bridge between models and data.
@@ -24,7 +23,7 @@ Feast aims to:
 
 ![](docs/.gitbook/assets/feast-docs-overview-diagram-2.svg)
 
-TL;DR: Feast decouples feature engineering from feature usage. Features that are added to Feast become available immediately for training and serving. Models can retrieve the same features used in training from a low latency online store in production.
+Feast decouples feature engineering from feature usage, allowing independent development of features and consumption of features. Features that are added to Feast become available immediately for training and serving. Models can retrieve the same features used in training from a low latency online store in production.
 This means that new ML projects start with a process of feature selection from a catalog instead of having to do feature engineering from scratch.
 
 ```