Cleanlab unlocks the full potential of AI by automatically transforming unreliable real-world data into reliable insights and models for large image, text, and tabular datasets.
Cleanlab, an automation solution that improves the accuracy of enterprise artificial intelligence (AI), LLM and analytics solutions, has announced a $5 million seed investment round led by Bain Capital Ventures. Our flagship product, Cleanlab Studio, is the only enterprise solution that can assess and correct errors in both large-scale structured data (such as tabular data and spreadsheets) and large-scale unstructured data (such as visual data, LLM-generated data, and conversational data).
Most companies today have AI models and business intelligence (BI) solutions, but not all of their data is used to train the models. Data and label quality issues such as outliers, label errors, and data shifts often make data poor as useful input for reliable business intelligence, ML model training, or LLM fine-tuning.
Inaccurate data costs U.S. businesses $3.1 trillion annually, and the losses are rising, according to an IBM study. With Cleanlab, organizations like Amazon, Google, Walmart, Deloitte, and Wells Fargo have dramatically reduced the cost and time spent on data quality by automating the correction of errors in their datasets. Cleanlab is designed to work with most types of datasets including text, images, tabular/CSV/JSON data.
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“Using Cleanlab AI, we increased model accuracy by 15% and reduced the number of training iterations by a factor of 3.”
Cleanlab solves this problem for enterprises by analyzing unreliable real-world datasets to find and fix errors, generate improved datasets, and use the improved datasets and new AI-generated labels to free up valuable engineering resources to focus on problem solving rather than data curation and model training.
Cleanlab already creates the most popular open source libraries for data-centric AI. This library is used by thousands of data scientists to automatically diagnose problems in real-world data through algorithms running on existing ML models. But diagnostics alone won’t work for companies that don’t have a model or interface to solve the problems they identify. To serve this broad market, the company introduced Cleanlab Studio, an enterprise application that seamlessly handles fixing data issues and deploying reliable models.
Curtis Northcutt, Jonas Mueller, and Anish Athalye, all three MIT PhDs, founded Clean Labs after working on a new area of AI known as “self-confidence learning,” which Northcutt invented while working with Isaac Chuan (quantum computing pioneer) during his PhD at MIT.
Cleanlab Studio enables both individual data scientists and corporate teams to extract more value from their data, train more reliable models, and derive more accurate analysis and insights by automating the process of finding and fixing images, text, outliers in tabular datasets, label issues, and other data issues. Unlike other solutions in the space, Cleanlab Studio uses state-of-the-art automated ML to handle model training, requiring no hyperparameter tuning, model selection, code, or machine learning expertise to deliver improved datasets, ML models, and business insights in significantly less time.
“Like humans, we often forget that artificial intelligence solutions also embody imperfections. The next evolution of AI will be to be able to characterize this imperfection so that it can understand, find, and correct errors in the data it is trained on. Cleanlab will resonate with everyone because it works just like you. It produces more accurate models in less time,” said Curtis Northcutt, Co-Founder and CEO of Cleanlab AI. “We do not guarantee perfection, we guarantee improvement. Cleanlab breaks the AI glass ceiling by providing accessibility and reliability for AI solutions.”
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“The main risk of LLM is ‘garbage in, garbage out,'” said Aleph Hillary, partner at Bain Capital Ventures. “As Deepmind’s Chinchilla paper (and others) show, LLM performance still relies heavily on data, so there is also a huge opportunity to improve data curation. Cleanlab is the easiest way to curate data for training and fine-tuning, and is an integral part of the emerging infrastructure stack that supports modern AI.”
“Cleanlab has increased accuracy by 28% and reduced the number of labeled transactions required to train the model by more than 98%,” said David Muelas Recuenco, expert data scientist at BBVA (Bank of Bilbao Vizcaya Argentina), one of the world’s largest financial institutions, describing how Cleanlab reduced the cost of curating datasets and training models by more than 98%. was.
“Using Cleanlab AI improved model accuracy by 15% and reduced the number of training iterations by a factor of 3,” said Steven Gawthorpe, senior managing consultant data scientist at Berkeley Research Group. “Our team is extremely impressed with the accuracy, speed and ease of use Cleanlab provides.”
Prior to Cleanlab, Co-Founder and Principal Investigator Jonas Mueller built Amazon’s automated ML solution, which is now used by all AWS automated ML jobs. Co-Founder and CTO Anish Athalye has earned his 5,000+ citations for some groundbreaking work showing where AI solutions fall short and how to improve them. Combining Curtis’ work auto-correcting problems in most datasets, Jonas’ work auto-training ML models on arbitrary datasets, and Anish’s work on secure systems, the team created Cleanlab Studio to achieve their mission to make AI more accessible and effective for humanity.
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Cleanlab Studio integrates with the most popular data and ML workflows, uploads large datasets in internet bandwidth time, and scales for the enterprise.
On June 1, 2023, Databricks announced a partnership with Cleanlab to enable automated data correction for both structured and unstructured datasets through the Databricks platform through Cleanlab Studio integration.
In 2021, Cleanlab has been nominated for NeurIPS Best Paper Award. In 2022, Cleanlab published his 5 peer-reviewed papers NeurIPS and ICML conference/workshop, and in 2023, Cleanlab management led his MIT Data-Centric AI course.
Cleanlab actively works with organizations that train large-scale models and develop business intelligence and analytics solutions on images, text, tabular, and other types of data. Visit Cleanlab Studio to learn more about data remediation with Cleanlab’s no-code, automated enterprise AI platform.
