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How Top Organizations
Use Cleanlab Software

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.

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
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Amazon AWS Principal Solutions Architect Cher Simon & Chief Evangelist Jeff Barr publishtextbookthat 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

Learn how Amazon also uses Cleanlab to improve Alexahere

Financial Services

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One of the largest financial institutions in the world, Banco Bilbao Vizcaya Argentaria, uses Cleanlab toreduce 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

Technology Consulting

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Berkeley Research Groupincreases ML model accuracy by 15% and reduces time spent by 1/3using 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

Business Intelligence

The Stakeholder Company reduced time spent by 8x in their ML data workflow by using Cleanlab to order data by label quality.

The Stakeholder Company reduced time spent by 8x in their ML data workflow 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.”
- Seah Bei Ying, Data Analyst at The Stakeholder Company

Healthcare