awesome-repositories.com
Blog
awesome-repositories.com

Découvrez les meilleurs dépôts open-source grâce à notre recherche par IA.

ExplorerRecherches sélectionnéesAlternatives open sourceLogiciels auto-hébergésBlogPlan du site
ProjetÀ proposNotre méthodologiePresseServeur MCP
Mentions légalesConfidentialitéConditions d'utilisation
© 2026 Bringes Technology SRL·VAT RO45896025·hello@awesome-repositories.com
·

1 dépôt

Awesome GitHub RepositoriesRequest-Time Data Sources

Input columns supplied alongside entity rows or DataFrames so on-demand transformations can incorporate values provided at query time.

Distinct from Row-Level Feature Transformations: Distinct from Row-Level Feature Transformations: focuses on the data source mechanism (passing request-time columns) rather than the transformation logic itself.

Explore 1 awesome GitHub repository matching data & databases · Request-Time Data Sources. Refine with filters or upvote what's useful.

Awesome Request-Time Data Sources GitHub Repositories

Trouvez les meilleurs dépôts grâce à l'IA.Nous recherchons les dépôts les plus pertinents grâce à l'IA.
  • feast-dev/feastAvatar de feast-dev

    feast-dev/feast

    6,727Voir sur GitHub↗

    Feast 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 supplies input columns alongside entity rows or entity DataFrames so transformations can incorporate values provided at query time.

    Pythonbig-datadata-engineeringdata-quality
    Voir sur GitHub↗6,727
  1. Home
  2. Data & Databases
  3. Data Transformation Functions
  4. Python-Defined Transformations
  5. Row-Level Feature Transformations
  6. Request-Time Data Sources