Understanding the concept of a feature store

Consider the following scenario: you are a data scientist working on an ML project in the automotive industry with a fellow data scientist and a few data engineers. You are responsible for modeling vehicle fuel efficiency, while your fellow data scientist is responsible for modeling vehicle performance. Both of you are using data coming from car manufacturers that your company is working with that is preprocessed and stored in the cloud by the data engineers in the team as input to the models.

The data is stored in disparate sources, such as Amazon S3, Amazon Relational Database Service (RDS), and a data lake built on AWS, depending on the nature of the source data. You and your fellow data scientist have been reaching out separately to the data engineering team to get the data processed in certain ways that work best for your respective modeling exercises. You do not realize that your fellow data scientist's models actually share...