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Awesome GitHub RepositoriesRow-Level Feature Transformations
Wraps Python functions with a decorator to define per-row feature transformations that combine stored features with request-time data.
Distinct from Python-Defined Transformations: Distinct from Python-Defined Transformations: focuses on row-level feature computation in a feature store context, not general data transformations.
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Awesome Row-Level Feature Transformations GitHub Repositories
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- feast-dev/feast
feast-dev/feast
6,727在 GitHub 上查看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
