The Open Source Feature Store for AI/ML

🌀 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

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

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

A tool for building feature stores.

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

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

ByteHub: making feature stores simple

Python - Java/Scala API for the Hopsworks feature store

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

Build your feature store with macros right within your dbt repository

This repo contains a plugin for feast to run an offline store on Spark

A demo pipeline of using Redis as an online feature store with Feast for orchestration and Ray for training and model serving

Quality Aware Feature Store

Get started with our Solution Accelerator for Propensity Scoring to build effective propensity scoring pipelines that: Enable the persistence, discovery and sharing of features across various model training exercises Quickly generate models by leveraging industry best practices Track and analyze the various model iterations generated

This is a repository created to explore different tools and technologies related to feature stores to build and serve ML models.

This is a learning repository about Databricks and Recommendation Systems

Modular, Reusable, Trackable ML Development and Deployment

Feature Store implementation, which provides both historical and real-time feature serving capabilities for machine learning models.