A feature store is a central repository of precomputed features that serve as a single source of truth for machine learning projects, providing several benefits including centralized data management, clean data handling, shareable features across models, and standardized inference to the data. The adoption of feature stores has risen in popularity since Uber introduced the concept in 2017, with organizations utilizing them to streamline their data and ML lifecycle. Feature stores offer a one-stop-shop for data collection, transformation, and access, making it easier for teams to work together and reduce wasteful rework. By applying monitoring and quality checks to feature stores, practitioners can catch common machine learning issues such as missing values, data format changes, and statistical distribution shifts, ensuring better model performance and reduced latency.