Feature stores are a cornerstone of production ML pipelines.Our mission is to offer an independent review and comparison of the products on the market for data scientists, data engineers and ML engineers.
Databricks Feature Store is a centralized repository of features. It enables feature sharing and discovery across your organization and also ensures that the same feature computation code is used for model training and inference.
Feast is an open-source framework that enables you to access data from your machine learning models. It allows teams to register, ingest, serve, and monitor features in production. Test does not provide a UI or support for feature engineering - it only ingests ready-made features.
The Iguazio feature store is a centralized and versioned catalog where everyone can engineer and store features along with their metadata and statistics, share them and reuse them, and analyze their impact on existing models
Tecton.ai is a managed feature store that uses PySpark (Databricks or EMR) to compute features and DynamoDB to serve online features. It provides a Python-based DSL for feature transformations that is computed as a PySpark job.
Forget costly rewrites of your database or limitations on the machine learning libraries you can use. As a virtual feature store, we integrate with your current infrastructure, giving you ultimate flexibility.