Feature Store Milestones
This article is the result of a collaboration with Jim Dowling.
The feature store space is booming! From the introduction of the feature store concept by Uber in 2017, to the launch of the first Enterprise-ready feature store, by Logical Clocks the following year, to the entrance of tech giants Google, Amazon and Databricks, the need for platform support for feature management has never been clearer.
Featurestore.org was born from the realization that the list of in-house and Enterprise-ready feature stores was more extensive than one might have thought, but there was no single place on the Internet to learn about feature stores. Our mission is to give visibility to these great platforms and to make sure that you, our reader, are kept up to date on the latest and greatest in feature stores. Here, we summarize the most important feature store milestones, as we see it. This is far from an exhaustive list, so feel free to reach out to us and suggest improvements.
What’s next for feature stores?
As feature store platforms mature, we anticipate they will gain new capabilities to improve the computation of features and management of data for AI. Here are some of our predictions on what will become increasingly important in the coming months and years:
- Custom Metadata: an essential mechanism to describe and aid the discovery of features.
- Provenance: platform support for tracking the dependencies between features, training data created from features, and the models that use features.
- Tighter Serving Integration: automate and ease the burden on application developers who need to perform real-time transformations and join features from the online feature store, before sending feature vectors to online models for predictions.
Feature Store Summit
We want to bring visibility to great data platforms and discuss the latest and greatest in feature stores. Attend the Feature Store Summit, on October 12–13, to discover the latest technologies, best practices, use cases for putting ML models into production environments in the upcoming.
References
- Meet Michelangelo: Uber’s Machine Learning Platform
- Zipline: Airbnb’s Machine Learning Data Management Platform — Nikhil Simha and Varant Zanoyan
- Feature Store: The Missing Data Layer in ML Pipelines?
- Introducing Feast: an open source feature store for machine learning
- Kaskada Accelerates ML Workflow with Its Feature Store
- Logical Clocks Launches Hopsworks.ai: The World’s First Artificial Intelligence Cloud Platform with a Feature Store
- Founded by Creators of Uber Michelangelo, Tecton.ai Launches to Make World-Class Machine Learning Accessible to Every Company With $25 Million From Andreessen Horowitz and Sequoia
- Tecton Becomes Feast Core Contributor to Build the Most Advanced Open Source Feature Store for Machine Learning
- Hopsworks Feature Store Now Available for Microsoft Azure
- Building a Gigascale ML Feature Store with Redis, Binary Serialization, String Hashing, and Compression
- Abacus.AI raises another $22M and launches new AI modules
- Feature Stores for ML Global Meetup Group
- Iguazio Launches the First Integrated Feature Store within its Data Science Platform to Accelerate Deployment of AI in Any Cloud Environment
- AWS Announces Amazon SageMaker Feature Store
- Splice Machine Launches the Splice Machine Feature Store to Simplify Feature Engineering and Democratize Machine Learning
- Molecula raises $17.6 million for its AI feature store technology
- Logical Clocks Introduces RonDB, the World’s Fastest Database in the Cloud
- Databricks Announces the First Feature Store Co-designed with a Data and MLOps Platform
- What is Vertex Feature Store?
- Alteryx announces new AutoML product and Intelligence Suite
- MLOps startup Iterative.ai nabs $20M
