The Open Source Feature Store for AI/ML

Feathr – A scalable, unified data and AI engineering platform for enterprise

The Virtual Feature Store. Turn your existing data infrastructure into a feature store.

OpenMLDB is an open-source machine learning database that provides a feature platform computing consistent features for training and inference.

Hopsworks - Data-Intensive AI platform with a Feature Store

🌀 𝗧𝗵𝗲 𝗙𝘂𝗹𝗹 𝗦𝘁𝗮𝗰𝗸 𝟳-𝗦𝘁𝗲𝗽𝘀 𝗠𝗟𝗢𝗽𝘀 𝗙𝗿𝗮𝗺𝗲𝘄𝗼𝗿𝗸 | 𝗟𝗲𝗮𝗿𝗻 𝗠𝗟𝗘 & 𝗠𝗟𝗢𝗽𝘀 for free by designing, building and deploying an end-to-end ML batch system ~ 𝘴𝘰𝘶𝘳𝘤𝘦 𝘤𝘰𝘥𝘦 + 2.5 𝘩𝘰𝘶𝘳𝘴 𝘰𝘧 𝘳𝘦𝘢𝘥𝘪𝘯𝘨 & 𝘷𝘪𝘥𝘦𝘰 𝘮𝘢𝘵𝘦𝘳𝘪𝘢𝘭𝘴

Serverless Machine Learning Course for building AI-enabled Prediction Services from models and features

👕 Open-source course on architecting, building and deploying a real-time personalized recommender for H&M fashion articles.

ML/AI meta-model, used in MLRun/Iguazio/Nuclio, see qgate-sln-<MLRun | solution>

FeatHub - A stream-batch unified feature store for real-time machine learning

A tool for building feature stores.

MLRun/Iguazio/Nuclio quality gate solution. The solution checks a quality of MLRun implementation/delivery.

Pixeltable — AI Data infrastructure providing a declarative, incremental approach for multimodal workloads.

✨ A curated list of awesome community resources, integrations, and examples of Redis in the AI ecosystem.

High-performance key-value store for ML inference. 100x faster than Redis.

Compute and store real-time features for crypto trading using Bytwax (stream processing) and Hopsworks (Feature Store)

A detailed summary of "Designing Machine Learning Systems" by Chip Huyen. This book gives you and end-to-end view of all the steps required to build AND OPERATE ML products in production. It is a must-read for ML practitioners and Software Engineers Transitioning into ML.

Examples for Deep Learning/Feature Store/Spark/Flink/Hive/Kafka jobs and Jupyter notebooks on Hops

ByteHub: making feature stores simple

The DBT of ML, as Aligned describes data dependencies in ML systems, and reduce technical data debt