A feature store is a software tool that simplifies the flow of raw data into machine-learning models. It is similar to a database, but with a focus on storing and sharing pre-prepared data that can be input straight into an ML model without any additional transformation.
The goals of using a feature store are to:
Make data access quicker and easier for data scientists who are building and training models;
Ensure that data preparation is easily sharable and re-usable across different models; and
Smooth the path between ML research and production, by allowing allowing models to be deployed without extensive data engineering support.
ByteHub is a Python-based feature store designed to be as easy-to-use and familiar to data scientists as possible. Specifically ByteHub provides:
A familiar Pandas-like interface for accessing data and features;
A simple install, without any complex infrastructure to set up;
Compatibility with familiar data science tools, like Jupyter notebooks.