True or False: The feature store can only be used with Spark ML.

The feature store in Databricks is designed to be versatile and is not restricted solely to Spark ML. It can support a variety of machine learning frameworks, including but not limited to TensorFlow, Scikit-learn, and PyTorch. This flexibility enables users to leverage features from the feature store across different tools and libraries that may not specifically be built on Spark ML.

This design allows machine learning practitioners to build, manage, and serve features using the feature store regardless of the ML library they choose, thus enhancing interoperability and driving collaboration among data scientists and machine learning engineers. This capability is key for organizations that may utilize different technologies for different projects or teams.