Vector databases in general are good for storing large amounts of unstructured data by first converting them into embeddings via ML models. There's also feature

Vector databases in general are good for storing large amounts of unstructured data by first converting them into embeddings via ML models. There's also feature stores, which store and organize features for later use in model training or predictive analytics. Feature stores generally come in _before_ models get trained, while vector databases generally come _after_ (i.e. they use trained models). Milvus (https://milvus.io https://milvus.io) and Feast (https://feast.dev/ https://feast.dev/) are two of the most well known vector databases and feature stores, respectively.