Automatically find and fix errors in your dataset and train more accurate ML models on real-world data.
AI for Data Correction.
Stop spending 90% of your time dealing with messy data.
Cleanlab Studio is a no-code data correction solution for ML and data teams. Extending technology invented at MIT and used by Google, Amazon, and data scientists around the world, Cleanlab Studio automatically finds and fixes issues in ML datasets. Quickly improve the quality of your data and labels with just a few clicks!
Why we built Cleanlab Studio
AI solutions can do a lot for the world, but AI relies on good data and real-world data can be messy. We built Cleanlab Studio so that you can build reliable AI solutions. We take care of your data, so you can take care of business.
This image was labeled “road” in a dataset uploaded to Cleanlab Studio. Entirely automated, Cleanlab Studio suggests “crosswalk”, another label from the dataset.
This positive review on Amazon was erroneously marked “1 star”. Cleanlab Studio found this issue automatically and suggested a more appropriate label of “5 stars”.
Cleanlab Studio automatically found this entry error in a healthcare records dataset, where a patient had fever and high blood pressure, but was marked as “healthy”.
For any supervised learning dataset (image, text, tabular data), Cleanlab Studio will
Find label errors and other data issues automatically
Enable easy data editing to fix these issues and produce a better dataset
Score and track data quality over time as you make improvements
Cleanlab Studio supports image, text, and tabular/CSV/Excel/JSON data. Audio and other modalities are on the way!
Testimonials from top organizations using Cleanlab technology
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.”
Wells Fargo used Cleanlab to train accurate (F1 score = 80) ML classifiers on financial data with with 40% label noise.
“We used cleanlab for finding label errors in financial text data, helping us find label errors in our human annotation process. I like cleanlab more than alternative solutions because it's like 'bring your own model' & 'bring your own data', by acting like a wrapper around your model, it's superbly easy to implement, and it works well even when the model itself is not decent in classification due to relatively high noise rate (40% noisy) to achieve a consistent f1 score around.”
The CEO of OpenTeams and Founder of Anaconda shares OpenTeams success using Cleanlab for data preparation.
“I am excited by the cleanlab 2.0 project. We use this successfully at OpenTeams. I haven't seen something this interesting in the space of data-preparation and labeling since snorkel.”
The Stakeholder Company reduced their time spent dealing with ML training data by 8x by using Cleanlab to order data by label quality.
“We used Cleanlab to quickly validate one of our classifier models' predictions for a dataset. This is typically a very time-consuming task since we would have to check thousands of examples by hand. However, since Cleanlab helped us identify the data points that were most likely to have label errors, we only had to inspect an eighth of our dataset to see that our model was problematic. We later realized that this was due to a post-processing error in the dataset — something that would otherwise have taken a much longer time to notice.”
How Cleanlab Studio works
Cleanlab Studio lets you create Cleansets, cleaned versions of your datasets.
Use Cleanlab Studio to fix your data
You were excited to work on interesting ML and data science problems, until you realized 90% of your time is spent dealing with data and label issues. Your model performance is lower than expected and your data analysis is inaccurate because unlike curated benchmarks, real-world ML datasets contain incorrect labels/annotations, out of distribution examples, and many other types of bad data.
This is where Cleanlab Studio comes in. Cleanlab Studio automates most of the work needed to deal with data and label issues. Some of our users think cleanlab is black magic, but it’s mostly math and science published in top conferences and journals.