One of the first feature stores from 2018, orginially called Zipline and part of the Bighead ML Platform. Feature engineering is using a DSL that includes point-in-time correct training set backfills, scheduled updates, feature visualizations and automatic data quality monitoring.
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.
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.
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.
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.
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 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.
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.
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.
Nexus supports batch, near real-time, and real-time feature computation and has global scale for serving online and offline features from Redis and Delta Lake-s3, respectively.
Robinhood built their own event-based real-time feature store based on Kafka and Flink.