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.
Refine high-quality datasets and visual AI models
A Doctor for your data
The data scientist's open-source choice to scale, assess and maintain natural language data. Treat training data like a software artifact.
Interactively explore unstructured datasets from your dataframe.
Resources for Data Centric AI
A curated, but incomplete, list of data-centric AI resources.
Automatically find issues in image datasets and practice data-centric computer vision.
Curated list of open source tooling for data-centric AI on unstructured data.
Lab assignments for Introduction to Data-Centric AI, MIT IAP 2024 π©π½βπ»
Official PyTorch implementation of the paper "Dataset Distillation with Neural Characteristic Function: A Minmax Perspective" (NCFM) in CVPR 2025 (Highlight).
[ICML 2025] Programming Every Example: Lifting Pre-training Data Quality Like Experts at Scale
[NeurIPS 2021] WRENCH: Weak supeRvision bENCHmark
[NeurIPS 2023] This is the code for the paper `Large Language Model as Attributed Training Data Generator: A Tale of Diversity and Bias`.
pyDVL is a library of stable implementations of algorithms for data valuation and influence function computation
Introduction to Data-Centric AI, MIT IAP 2023 π€
OpenDataVal: a Unified Benchmark for Data Valuation in Python (NeurIPS 2023)
Papers about training data quality management for ML models.
Self-Evolved Diverse Data Sampling for Efficient Instruction Tuning
nbsynthetic is simple and robust tabular synthetic data generation library for small and medium size datasets
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