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Unified storage framework for machine learning datasets

Project description

Space: Unified Storage for Machine Learning

Unify data in your entire machine learning lifecycle with Space, a comprehensive storage solution that seamlessly handles data from ingestion to training.

Key Features:

  • Ground Truth Database
    • Store and manage data locally or in the cloud.
    • Ingest from various sources, including ML datasets, files, and labeling tools.
    • Support data manipulation (append, insert, update, delete) and version control.
  • OLAP Database and Lakehouse
    • Analyze data distribution using SQL engines like DuckDB.
  • Distributed Data Processing Pipelines
    • Integrate with processing frameworks like Ray for efficient data transformation.
    • Store processed results as Materialized Views (MVs), and incrementally update MVs when the source is changed.
  • Seamless Training Framework Integration
    • Access Space datasets and MVs directly via random access interfaces.
    • Convert to popular ML dataset formats (e.g., TFDS, HuggingFace, Ray).

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