Feature stores have become essential in enhancing machine learning productivity and operations (MLOps) by serving as centralized repositories for storing and managing features, the independent variables used in training models. They streamline the machine learning pipeline by enabling feature reuse, improving collaboration between data science and engineering teams, and maintaining model performance through consistent feature definitions and real-time updates. Feature stores also enhance data governance by standardizing feature definitions and allowing version control, which helps in maintaining data integrity and reducing the time required for model development. Additionally, they address the 'online/offline skew' problem by ensuring consistency between model training and inference environments, and they offer scalability and integration with existing data systems, making them adaptable to various organizational needs. An example of a feature store integrated into a larger ML infrastructure is JFrog ML, which offers a fully-managed platform that simplifies model deployment and offers high velocity and agility for machine learning initiatives.