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Tags: Data quality
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1–14 of 14 · Most stars

The Open Context Layer for Data and AI , OpenMetadata is the open platform for building trusted data context and business semantics for humans, AI assistants, and agents.

  • metadata
  • datadiscovery
  • dataquality
  • data-profiling

Cleanlab's open-source library is the standard data-centric AI package for data quality and machine learning with messy, real-world data and labels.

Deepchecks: Tests for Continuous Validation of ML Models & Data. Deepchecks is a holistic open-source solution for all of your AI & ML validation needs, enabling to thoroughly test your data and models from research to production.

Deequ is a library built on top of Apache Spark for defining "unit tests for data", which measure data quality in large datasets.

Build data pipelines with SQL and Python, ingest data from different sources, add quality checks, and build end-to-end flows.

TranslatedOntiCards is an enterprise-oriented AI Data Hub that offers one-stop capabilities around business databases: data source connection, metadata understanding, natural language query, cross-source fusion, data quality inspection, query auditing, data asset management, and more

Lightweight DataFrame validation decorators for Pandas, Polars, Modin, and PyArrow. No custom types required.