As you begin exploring the world of machine learning, you'll come across two essential concepts frequently discussed by data scientists: Features and Feature Stores. To understand what a Feature Store is, it's crucial to know what features are. In the context of machine learning, features are individual measurable properties or characteristics of a phenomenon being observed.
Initially, you might think a Feature Store is akin to a database, but instead of storing raw data, it holds data in a specific format. While this idea partially describes a Feature Store, it is only a small part of its purpose. In reality, Feature Stores are much more complex and vital in the machine learning landscape.
Before diving deeper into the details, let's define some essential terminologies to ease the learning curve:
What is a Feature?
A feature is an individual measurable property or characteristic of a phenomenon.
In simple words a measurable property in provided dataset. These property plays a very crucial in machine learning models. So for example in a clothing company following can be feature of a shirt:
