Cleanlab automates data error detection and correction using AI-powered algorithms to enhance the quality of datasets for machine learning and analytics. This technology addresses issues such as label noise, outliers, and data drift, significantly reducing the time and cost associated with data management while improving model performance.
Funding
$30M raised to dateRaised to date based on public sources. This may differ from the amount the company actually raised and is based only on what is publicly available on the internet.
Founders
Product
Problem
Machine learning models and analytics rely on high-quality data, but real-world datasets often contain errors such as incorrect labels, outliers, and duplicates. Identifying and correcting these data issues is a time-consuming and expensive process, hindering model performance and the reliability of insights.
Solution
Cleanlab provides an AI-powered data curation platform that automates the detection and correction of errors in datasets. The platform identifies label errors, outliers, personally identifiable information (PII), near duplicates, data drift, and low-quality examples. It then allows users to automatically fix these issues, add intelligent metadata, and improve the reliability of data for training machine learning models, business intelligence, and analytics. Cleanlab also offers a Trustworthy Language Model (TLM) that detects hallucinations in GenAI systems, providing trustworthiness scores for every LLM output.
Target Audience
Cleanlab's primary customers are data scientists, machine learning engineers, and business analysts who need to improve the quality and reliability of their datasets for AI model training and analytics.
Features
- Automated detection of label errors, outliers, PII, NSFW content, near duplicates, and data drift
- Identification of low-quality image examples, such as dark, blurry, under-exposed, and over-exposed images
- AI-automated data labeling using foundation model confidence scores and active learning
- Trustworthy Language Model (TLM) for hallucination detection in GenAI systems
- Data analytics and summaries to explore issues within datasets
- AutoML pipeline for automated training, tuning, and deployment of robust models
- Integration with local data files, data warehouses, cloud storage, and programmatic access
- VPC deployment option for enhanced security of sensitive data