Enable javascript in your browser for better experience. Need to know to enable it? Go here.
Published : Oct 26, 2022
This blip is not on the current edition of the Radar. If it was on one of the last few editions, it is likely that it is still relevant. If the blip is older, it might no longer be relevant and our assessment might be different today. Unfortunately, we simply don't have the bandwidth to continuously review blips from previous editions of the Radar. Understand more
Oct 2022
Trial ? Worth pursuing. It is important to understand how to build up this capability. Enterprises should try this technology on a project that can handle the risk.

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.

Download the PDF



English | Español | Português | 中文

Sign up for the Technology Radar newsletter


Subscribe now

Visit our archive to read previous volumes