No code. Automated.
Data you can trust.
Turn unreliable data into reliable models and insights. Automatically find and fix errors for LLMs and the modern AI stack.
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Reliable data. Reliable models (or insights). Reliable revenue.
Cleanlab Studio handles the entire data quality and data-centric AI pipeline in a single framework for analytics and machine learning tasks.

Data Curation

Automatically improve your dataset. No code required.
  • Find and fix data issues (e.g. outliers, ambiguous data)
  • Find and fix label issues (e.g. incorrectly labeled data)
  • Know when to trust your data: Avoid spending time/cost to review data that’s already high quality, by automatically validating all datasets in Cleanlab Studio

Auto ML

Automatically train and tune robust models via the world’s most advanced AutoML.
  • Automated pipeline does all ML for you: data preprocessing, foundation model fine-tuning, hyperparameter tuning, and model selection
  • ML models are used to diagnose data issues, and then can be re-trained on your corrected dataset with one click

Model Deployment

Seamless model deployment: Just a few clicks to get accurate predictions.
  • Train and deploy a robust ML model without writing code
  • Access real-time and batch inference through a web interface or REST API
  • Get predictions for unlabeled data or evaluate models on labeled test data

Diagnostics

Explore analytics, summaries, and specific issues within your datasets.
  • Find the classes in your dataset with the most label issues
  • Explore the entire heatmap of suggested corrections for all classes in your dataset
  • Cleanlab Studio provides all of this information and more for free as soon as you upload your dataset
Pioneered at MIT and trusted by hundreds of top organizations.
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Pioneered at MIT
Cleanlab’s Chief Executive Officer, Curtis Northcutt, invented confident learning during his PhD at MIT while working with the inventor of the quantum computer
Built on the world’s cutting edge AutoML @ AI layer
Prior to Cleanlab, Chief Scientist Jonas Mueller developed Amazon's AutoML platform, used today to train and deploy many models on AWS SageMaker
Designed for security and scalability for enterprise from the ground up
Cleanlab’s Chief Technology Officer, Anish Athalye, is well cited for his PhD work at MIT in the world’s top systems lab (PDOS)
Founded by the instructors of the MIT course on Data-centric AI

Enterprise Ready Integration

Cleanlab Studio interfaces directly with your data, no matter how it is stored.

Local Data Files

Programmatically

Data Warehouse

Cloud Storage

Testimonials

Testimonials from top organizations using Cleanlab technology
Amazon AWS Principal Solutions Architect Cher Simon & Chief Evangelist Jeff Barr publish textbook that features Cleanlab in hands on exercises.
Manually inspecting and fixing potential label errors can be time-consuming. We can train a better model using Cleanlab to filter noisy data.
-Cher Simon,Amazon AWS Principal Solutions Architect at Amazon
Google used Cleanlab to find and fix label errors in millions of speech samples across different languages, to quantify annotator accuracy, and provide clean data for training speech models.
Cleanlab is well-designed, scalable and theoretically grounded: it accurately finds data errors, even on well-known and established datasets. After using it for a successful pilot project at Google, Cleanlab is now one of my go-to libraries for dataset cleanup.
-Patrick Violette,Senior Software Engineer at Google
One of the largest financial institutions in the world, Banco Bilbao Vizcaya Argentaria, uses Cleanlab to reduce label costs by over 98% and boost model accuracy by 28%.
Cleanlab helped us reduce the uncertainty of noise in the tags. This process enabled us to train the model, update the training set, and optimize its performance. The goal was to reduce the number of labeled transactions and make the model more efficient, requiring less time and dedication. With the current model, we were able to improve accuracy by 28%, while reducing the number of labeled transactions required to train the model by more than 98%.
-David Muelas Recuenco,Expert Data Scientist at BBVA
Berkeley Research Group increases ML model accuracy by 15% and reduces time spent by 1/3 using Cleanlab Studio.
We've started relying on Cleanlab to improve our ML and AI models at Berkeley Research Group LLC for over a month... I have to say, I'm impressed. Here's what we found: Increased model accuracy by 15%, Improved explainability & addressed performance impediments, Cut out training iterations by one-third, Overall performance improvement for our Data Science team.
-Steven Gawthorpe,Senior Managing Consultant Data Scientist at Berkeley Research Group


Good data builds great businesses
Data Issues by Type
2307
label issues
764
outliers
2311
ambiguous
1311
near duplicates
Dark
Light
NSFW
Suggested: Swimsuits
Excluded
Auto-fix
Keep as is
Needs review
Exclude
Label Error
Given: Apparel
Suggested: Electronics
Corrected
648/800Issues resolved
Unlabeled
Can my pin be changed in any cash machine?
Suggested: change_pin
Labeled
PII
Review
toxic
Exclude
NSFW
Exclude