What is a Feature Store? A Feature Store is a system that manages features or input features for machine learning models. A feature is a piece of data that is used to train a machine learning model. Examples of 2 characteristics include a person's age, geographic location, purchase history, online browsing behavior, etc. A feature can also be obtained at the end of an ETL process.

Why do we need a Feature Store? The data used in training machine learning models is often large and complex. Managing this data can be difficult and time consuming. A Feature Store helps simplify this process by providing a platform to store, share, and reuse features across different machine learning models.

How does a Feature Store work on AWS? A Feature Store provides a centralized place to store features. Amazon Sagemaker Feature Store is the AWS service that allows you to exercise this functionality in the models that are in Amazon Sagemaker. These features can come from different sources, such as databases, cloud data storage systems, etc. Data scientists can search and select the features they need for a specific machine learning model. Additionally, a Feature Store allows reuse of features across different machine learning models. This can improve the efficiency of model development and reduce the time required to train models.