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Awesome GitHub RepositoriesFeature Store Document Ingestion
Chunks, embeds, and writes documents to the online feature store in a single step using a configurable pipeline.
Distinct from Document Ingestion Pipelines: Distinct from Document Ingestion Pipelines: focuses on ingesting documents specifically into a feature store for ML serving, not general document processing.
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Awesome Feature Store Document Ingestion GitHub Repositories
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- feast-dev/feast
feast-dev/feast
6,727Auf GitHub ansehenFeast 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 chunks, embeds, and writes documents to the online store in a single step using a configurable pipeline.
Pythonbig-datadata-engineeringdata-quality
