  Companies are using machine learning to solve real-world problems and are
developing hundreds to thousands of features in the process. They are building
feature engineering pipelines as part of MLOps life cycle to transform data
from various data sources and materialize the same for future consumption.
Without feature stores, different teams across various business groups would
maintain the above process independently, which can lead to conflicting and
duplicated features in the system. Data scientists find it hard to search for
and reuse existing features and it is painful to maintain version control.
Furthermore, feature correctness violations related to online (inferencing) -
offline (training) skews and data leakage are common. Although the machine
learning community has extensively discussed the need for feature stores and
their purpose, this paper aims to capture the core architectural components
that make up a managed feature store and to share the design learning in
building such a system.
