Top AI Repos — open-source AI, indexed and scored
Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Open-Source Software, Tutorials, and Research on Data-Centric AI 🤖
| Date | Stars |
|---|---|
| 2026-07-24 | 355 |
| 2026-07-25 | 355 |
| 2026-07-28 | 355 |
| 2026-07-30 | 355 |
| 2026-08-06 | 355 |
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[](https://discord.gg/mw7xjJ7b7s) [](https://medium.com/data-centric-ai-community) [](https://www.youtube.com/@datacentricai) [](https://awesome.re) [](https://github.com/EthicalML/awesome-production-machine-learning/graphs/commit-activity)   [](https://twitter.com/YData_ai/) # *Awesome* Data-Centric AI **Data-Centric AI is the approach to AI development that considers the training dataset as the centerpiece of the solution instead of the model.** This repository contains a curated list of awesome resources such as open-source libraries, tutorials, and articles that will help you understand the concepts, and start your journey on Data-Centric AI development. We do value open collaboration and knowledge sharing, so we recommend not to limit yourself to this repository alone and check other amazing projects such as [Andrew Ng's Resources Hub](https://datacentricai.org/). ## 📊 Data Profiling - [YData Profiling](https://github.com/ydataai/ydata-profiling) - YData Profiling supports both Pandas and Spark DataFrames, providing a fast and straightforward visual data understanding. - [SweetViz](https://github.com/fbdesignpro/sweetviz) - SweetViz is an open-source Python library that generates beautiful, high-density visualizations to kickstart EDA (Exploratory Data Analysis) with just two lines of code. - [DataPrep.EDA](https://github.com/sfu-db/dataprep) - DataPrep.EDA is an EDA (Exploratory Data Analysis) tool in Python that allows you to understand a Pandas/Dask DataFrame with a few lines of code in seconds. - [AutoViz](https://github.com/AutoViML/AutoViz) - Automatically Visualize any dataset, any size with a single line of code. - [Lux](https://github.com/lux-org/lux) - Lux is a Python library that facilitates fast and easy data exploration by automating the visualization and data analysis process. - [Great Expectations](https://github.com/great-expectations/great_expectations) - Great Expectations helps data teams eliminate pipeline debt, through data testing, documentation, and profiling. - [D-Tale](https://github.com/man-group/dtale) - Is an open-source python auto-visualization library that brings you an easy way to view & analyze Pandas data structures. It integrates seamlessly with ipython notebooks & python/ipython terminals. - [Data Profiler](https://github.com/capitalone/DataProfiler) - The DataProfiler is a Python library designed to make data analysis, monitoring, and sensitive data detection easy. - [whylogs](https://github.com/whylabs/whylogs) - whylogs is the open source standard for data logging. It uses data profiling techniques to create *whylogs profiles*, which can be used as logs to enable monitoring and observability for data pipelines and ML models ## 🔐 Synthetic Data - [YData Synthetic](https://github.com/ydataai/ydata-synthetic) - Structured Synthetic Data Generators using Generative Adversarial Networks specifically for tabular and time-series data. - [Synthpop](https://cran.r-project.org/web/packages/synthpop/index.html) - A tool for producing synthetic versions of microdata containing confidential information so that they are safe to be released to users for exploratory analysis. - [DataSynthesizer](https://github.com/DataResponsibly/DataSynthesizer) - DataSynthesizer generates synthetic data that simulates a given dataset. It applies Differential Privacy techniques to achieve a
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Read on GitHubWould you bet a product on this? Bounded 0–100 and slow moving.
matched fp:3209416708bce52a, topic:awesome, topic:awesome-list, readme:curated list
matched fp:3209416708bce52a, topic:synthetic-data, readme:synthetic data