Feature Stores play a critical role in data science infrastructure by providing a stable pipeline for machine learning applications, addressing common challenges such as inefficient feature engineering, redundancy, and the gap between experimentation and production environments. They serve as both online and offline databases, enabling machine learning pipelines and applications to access data in real-time or batch mode, thereby ensuring data consistency and reducing redundant efforts by maintaining a single source of truth for features. Feature Stores are distinguished from Data Lakes and Data Warehouses by their specialized focus on storing and managing features for machine learning, supporting transparency and explainability. They facilitate the integration of MLOps practices into machine learning workflows, allowing for efficient model training, validation, and deployment. Examples of feature stores include Uber’s Michelangelo, Google’s Feast, Hopsworks’ Feature Store, and Tecton’s Feature Store, which offer diverse functionalities and integrations to streamline AI product development and operational efficiency.