Cleanlab

Overview

Cleanlab is a data-centric AI platform that automatically finds and fixes issues in ML datasets. Founded by MIT researchers, Cleanlab uses confident learning techniques to detect label errors, outliers, and data quality issues that hurt model performance. The platform helps teams improve model accuracy by improving data quality rather than just tuning models.

The open-source library has gained significant traction in the ML community for its effectiveness at finding data issues. Cleanlab Studio provides an enterprise platform built on top of the open-source foundation, making data quality accessible to broader teams.

Key Features

**Label Error Detection**: Find mislabeled data automatically

**Outlier Detection**: Identify unusual examples

**Data Quality Scoring**: Quantify dataset health

**Auto-Fix**: Suggest corrections for issues

**Open Source**: Core library is free

**Cleanlab Studio**: Enterprise platform

**Framework Agnostic**: Works with any ML framework

**Confident Learning**: Research-backed techniques

When to Use Cleanlab

Cleanlab is ideal for:

Improving model performance through data quality

Cleaning noisy labeled datasets

Auditing existing training data

Reducing labeling costs by finding errors

Academic research on data quality

Teams focused on data-centric AI

Pros

Unique focus on data quality

Open-source library available

Research-backed techniques

Actually improves model performance

Easy to integrate

Works with existing datasets

Good documentation

Active community

Cons

Enterprise platform is expensive

Open-source version has limitations

Best for classification tasks

Requires existing labeled data

May not catch all issues

Smaller company

Limited to certain ML tasks

Still developing features

Pricing

**Open Source**: Free library

**Cleanlab Studio**: $99/month per user

**Enterprise**: Custom pricing

**Academic**: Free for researchers