The Open Source Feature Store for Machine Learning
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Updated
Dec 24, 2024 - Python
The Open Source Feature Store for Machine Learning
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
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.
High-performance key-value store for ML inference. 100x faster than Redis.
✨ A curated list of awesome community resources, integrations, and examples of Redis in the AI ecosystem.
👕 An open-source course that will teach you how to build and deploy a real-time personalized recommender for H&M fashion articles.
Compute and store real-time features for crypto trading using Bytwax (stream processing) and Hopsworks (Feature Store)
Pixeltable — AI Data infrastructure providing a declarative, incremental approach for multimodal workloads.
Examples for Deep Learning/Feature Store/Spark/Flink/Hive/Kafka jobs and Jupyter notebooks on Hops
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.
ByteHub: making feature stores simple
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