Feast is an open-source Feature Store for machine learning. It has several useful properties, including generating point-in-time correct feature sets — so error-prone future feature values do not leak to models during training — and supporting both streaming and batch data sources. However, it currently only supports timestamped structured data and therefore may not be suitable if you work with unstructured data in your models. We've successfully used Feast at a significant scale as an offline store during model training and as an online store during prediction.