Hopsworks

The first open-source Feature Store and the first with a DataFrame API. Most data sources (batch/streaming) supported. Ingest features using SQL, Spark, Python, Flink. The only feature store supporting stream processing for writes. Available as managed platform and on-premises.

Vertex AI

A centralized repository for organizing, storing, and serving ML features on the GCP Vertex platform. Vertex AI Feature Store supports BigQuery, GCS as data sources. Separate ingestion jobs after feature engineering in BigQuery. Offline is BigQuery, Online BigTable.

SageMaker

Sagemaker Feature Store integrates with other AWS services like Redshift, S3 as data sources and Sagemaker serving. It has a feature registry UI in Sagemaker, and Python/SQL APIs. Online FS is Dynamo, offline parquet/S3.

Databricks

A Feature Store built around Spark Dataframes. Supports Spark/SQL for feature engineering with a UI in Databricks. Online FS is AWS RDS/MYSQL/Aurora. Offline is Delta Lake.

Zipline

One of the first feature stores from 2018. Feature engineering is using a DSL that implements a variety of functions such as resource efficient and point-in-time correct training set backfills, scheduled updates, feature visualizations and automatic data quality monitoring.

Michelangelo

The mother of feature stores. Michelangelo is an end-to-end ML platfom and Palette is the features store. Features are defined in a DSL that translates into Spark and Flink jobs. Online FS is Redis/Cassandra. Offline is Hive.

Bigabid

A centralized feature store where different data professionals across the company can each create and manage canonical features. This allows data scientists to add features they’ve built into a shared feature store where it is easy to consume both online and offline.

FBLearner

Internal end-to-end ML Facebook platform that includes a feature store. It provides innovative functionality, like automatic generation of UI experiences from pipeline definitions and automatic parallelization of Python code.

Overton

Internal end-to-end ML platform at Apple. It automates the life cycle of model construction, deployment, and monitoring by providing a set of novel high-level, declarative abstractions. It has been used in production to support multiple applications in both near-real-time applications and back-of-house processing.

Featureform

FeatureForm is a virtual feature store platfrom - you plug in your offline and online data stores. It supports Flink, Snowflake, Airflow Kafka, and other frameworks.

Twitter

Twitter's first feature store was a set of shared feature libraries and metadata. Since then, they moved to building their own feature store, which they did by customizin feast for GCP.

Feast

Originally developed as an open-source feature store by Go-JEK, Feast has been taken on by Tecton to be a minimal, configurable feature store. You can connect in different online/offline data stores and it can run on any platform. Feature engineering is done outside of Feast.

Tecton

Tecton.ai is a managed feature store that uses PySpark or SQL (Databricks or EMR) or Snowflake to compute features and DynamoDB to serve online features. It provides a Python-based DSL for orchestration and feature transformations that are computed as a PySpark job. Available on AWS.

Scribble Enrich

The platform handles the complexity of computation and data semantics by providing a python SDK to develop, document and test the feature engineering modules (transforms, pipelines, scheduling, etc) and controlled execution on the server-side.

Jukebox

Spotify built their own ML platform that leverages TensorFlow Extended (TFX) and Kubeflow. They focus on designing and analyzing their ML experiments instead of building and maintaining their own infrastructure, resulting in faster time from prototyping to production.

Intuit

Intuit have built a feature store as part of their data science platform. It was developed for AWS and uses S3 and Dynamo as its offline/online feature serving layers.

Iguazio

A centralized and versioned feature storre built around their MLRun open-source MLOps orchestration framework for ML model management. Uses V3IO as it offline and online feature stores.

Kaskada

The company that first solved temporal streaming joins, enabling running predictive models from event-based data - powered by cutting edge data infrastructure.

Rasgo

A feature store for preparing, understanding, and deploying features using cloud data warehouses (Snowflake) that can be accessed either through a web app for browsing your features and creating modeling data sets or a python package for publishing and deploying your features

Abacus.ai

The platform allows to build real-time machine and deep learning features, upload ipython notebooks, monitor model drift, and set up CI/CD for machine learning systems.

FeatureBase

A database developed to support machine-scale analytics and data science workflows. The core format allows for granular scans at a feature-by-feature level rather than a columnar or tabular data format.

Doordash

A ML Platform with an effective online prediction ecosystem. It serves traffic on a large number of ML Models, including ensemble models, through their Sibyl Prediction Service.They extended Redis with sharding and compression to work as their online feature store.

Salesforce

ML Lake is a shared service that provides the right data, optimizes the right access patterns, and alleviates the machine learning application developer from having to manage data pipelines, storage, security and compliance. Built on an early version of Feast based around Spark.

H2O AI Hybrid Cloud

H2O.ai and AT&T co-created the H2O AI Feature Store to store, update, and share the features data scientists, developers, and engineers need to build AI models.

Continual

Continual is a SQL-centric feature store that aims to maintain relationships between all features in an entity and performs joins when entity is needed. It is available in open beta.

Metarank

A real-time feature-engineering framework and feature store containing with YML support for defining features for online shopping recommendation use cases. Builds on Apache Flnk and optionally Redis. A low-code Learn-to-Rank online recommendation engine.

Feathr

Feathr automatically computes your feature values and joins them to your training data, using point-in-time-correct semantics to avoid data leakage, and supports materializing and deploying your features for use online in production.