Cleanlab
Automated data quality platform that detects and fixes label errors in ML datasets
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About
Cleanlab is an AI-powered data quality platform that helps machine learning teams find and fix errors in their training data. The core insight behind Cleanlab is that data quality — specifically label quality — has a massive impact on model performance, often more than model architecture choices. Mislabeled data silently corrupts models, and Cleanlab makes this invisible problem visible and fixable.
The platform's Confident Learning algorithm analyzes the relationship between model predictions and existing labels to identify examples that are likely mislabeled, ambiguous, or out-of-distribution. For LLM applications, Cleanlab's Trustworthy Language Model (TLM) product detects hallucinations and unreliable outputs by quantifying model confidence on a per-output basis.
Cleanlab serves data science teams across industries including healthcare (for medical AI reliability), finance (for compliance-grade model accuracy), and technology (for production ML quality). It integrates with popular ML platforms including AWS SageMaker, Azure ML, and Hugging Face.
The platform's Confident Learning algorithm analyzes the relationship between model predictions and existing labels to identify examples that are likely mislabeled, ambiguous, or out-of-distribution. For LLM applications, Cleanlab's Trustworthy Language Model (TLM) product detects hallucinations and unreliable outputs by quantifying model confidence on a per-output basis.
Cleanlab serves data science teams across industries including healthcare (for medical AI reliability), finance (for compliance-grade model accuracy), and technology (for production ML quality). It integrates with popular ML platforms including AWS SageMaker, Azure ML, and Hugging Face.
Product Features
- Automated label error detection in classification datasets
- Confidence scoring for every training example
- Trustworthy LM (TLM): hallucination detection for LLMs
- Out-of-distribution data identification
- Suggested label corrections with human review workflow
- Support for text, image, tabular, and multimodal data
- Integration with AWS SageMaker, Azure ML, Hugging Face
- Python SDK (cleanlab open-source library)
- Auto-fix mode for high-confidence label corrections
- Compliance-grade audit trail for regulated industries
- Confidence scoring for every training example
- Trustworthy LM (TLM): hallucination detection for LLMs
- Out-of-distribution data identification
- Suggested label corrections with human review workflow
- Support for text, image, tabular, and multimodal data
- Integration with AWS SageMaker, Azure ML, Hugging Face
- Python SDK (cleanlab open-source library)
- Auto-fix mode for high-confidence label corrections
- Compliance-grade audit trail for regulated industries
About the Publisher
Cleanlab was founded in 2021 by Curtis Northcutt, Jonas Mueller, and Anish Athalye — all from MIT's CSAIL. The company's research on Confident Learning has been published in top ML venues and cited thousands of times. Backed by Menlo Ventures and other leading investors, Cleanlab has grown to serve enterprise clients in healthcare, finance, and technology who require the highest standards of data quality. The Cleanlab open-source library has been downloaded millions of times by data scientists worldwide.