Feature stores para Machine Learning
For un feature store para características de ML, the strongest matches are gojek/feast (Feast is a leading open-source feature store that provides), feast-dev/feast (Feast is a purpose-built open-source feature store that directly) and logicalclocks/hopsworks (Hopsworks is a dedicated feature store platform that provides). featureform/featureform and linkedin/feathr round out the shortlist. Each is ranked by relevance to your query, popularity and recent activity.
Plataformas de código abierto para gestionar, almacenar y servir características de datos consistentes para el entrenamiento de modelos de machine learning.
Feature stores para Machine Learning
Encuentra los mejores repositorios con IA.Buscaremos los repositorios que mejor coincidan usando IA.
- gojek/feast
gojek/feast
7,095Ver en GitHubFeast is a machine learning feature store and MLOps data infrastructure layer. It provides a centralized system for managing and serving features across offline training and online production environments, utilizing an online feature serving layer for low-latency retrieval. The project centers on a feature registry that acts as a central catalog for defining, governing, and discovering feature services. It employs a unified data access layer to decouple feature retrieval from physical storage and includes a point-in-time data generator to create historically accurate training datasets that pr
Feast is a leading open-source feature store that provides a feature registry, low-latency online serving, point-in-time correct joins, and training data generation, making it exactly the kind of system you need for managing ML features across training and inference.
PythonFeature RegistriesStream IngestionTemporal Join Alignment - feast-dev/feast
feast-dev/feast
6,727Ver en GitHubFeast is an open-source feature store for machine learning that provides a central platform for defining, storing, and serving features across both training and inference workflows. It operates as a declarative system where feature definitions are written as code in Python files, synchronized to a central registry, and made available for low-latency online retrieval or point-in-time correct historical joins for training datasets. The project abstracts storage behind a pluggable architecture, allowing offline and online backends to be swapped without changing retrieval logic, and coordinates ma
Feast is a purpose-built open-source feature store that directly covers low-latency online serving, point-in-time correct joins, a feature registry, and offline/online store separation, making it an ideal match for your machine learning feature management needs.
PythonFeature RegistriesFeature View CatalogsReal-Time Prediction Serving - logicalclocks/hopsworks
logicalclocks/hopsworks
1,302Ver en GitHubHopsworks - Data-Intensive AI platform with a Feature Store
Hopsworks is a dedicated feature store platform that provides offline and online stores, point-in-time correct joins, streaming ingestion, and a feature registry, directly matching your need for consistent low-latency feature serving and training data generation.
JavaData Science ToolsDeep Learning FrameworksFeature Stores - featureform/featureform
featureform/featureform
1,979Ver en GitHubThe Virtual Feature Store. Turn your existing data infrastructure into a feature store.
Featureform is a virtual feature store that layers on top of your existing data infrastructure, providing a unified feature registry, low‑latency online serving, point‑in‑time joins, and training data generation — exactly what this search is asking for.
GoFeature EngineeringFeature StoreFeature Stores - linkedin/feathr
linkedin/feathr
1,929Ver en GitHubFeathr – A scalable, unified data and AI engineering platform for enterprise
Feathr is a purpose-built feature store that offers low-latency online serving, point-in-time correct joins, training data generation, a feature registry, streaming ingestion, and separate offline/online stores, matching all the required capabilities for ML feature management.
ScalaFeature Store
