Repository Index

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

1 Line of code data quality profiling & exploratory data analysis for Pandas and Spark DataFrames.

• pandas-profiling
• pandas-dataframe
• statistics
• jupyter-notebook

Always know what to expect from your data.

• pipeline-tests
• dataquality
• datacleaning
• datacleaner

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.

• weak-supervision
• data-cleaning
• data-quality
• noisy-labels

Evidently is ​​an open-source ML and LLM observability framework. Evaluate, test, and monitor any AI-powered system or data pipeline. From tabular data to Gen AI. 100+ metrics.

• data-drift
• jupyter-notebook
• pandas-dataframe
• machine-learning

A light-weight, flexible, and expressive statistical data testing library

• pandas
• validation
• schema
• dataframes

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.

• machine-learning
• ml
• model-validation
• data-validation

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

• dataquality
• spark
• unit-testing
• scala

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