What it is: ML-powered data cleaning library. Automatically finds label errors, outliers, and near-duplicates in your training data using machine learning confidence scores.

What It Does Best

Finding mislabeled data. Uses cross-validation and model uncertainty to identify training examples with wrong labels. Works with any ML framework.

Data-centric AI. Improve model performance by fixing data rather than tweaking hyperparameters. Often gives bigger gains than model optimization.

Works with existing models. Integrates with scikit-learn, PyTorch, TensorFlow, Hugging Face. No need to change your workflow.

Pricing

Open source: Free, AGPL license

Cleanlab Studio: Paid hosted platform with GUI

When to Use It

✅ Training ML models with human-labeled data

✅ Model underperforming and you suspect bad labels

✅ Working with crowdsourced or noisy datasets

✅ Computer vision or NLP classification tasks

When NOT to Use It

❌ No labeled data (unsupervised learning)

❌ Very small datasets (need enough data for cross-validation)

❌ Time-series or regression problems (optimized for classification)

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