Automatically Fix Data Issues & Label Errors in Most ML Datasets | Cleanlab

ABOUT THE TALK:
In this talk, we discuss cleanlab open-source (github.com/cleanlab/cleanlab) and Cleanlab Studio (https://cleanlab.ai/studio). Cleanlab open-source is a fast-growing python framework for data-centric AI that automatically detects issues in ML datasets. Cleanlab Studio is a no-code web interface used by universities and fortune 500 companies for dataset issue detection and fixing. Cleanlab algorithms have theoretical support for improved accuracy on real-world, messy data.

ABOUT THE SPEAKER:
Curtis Northcutt is an American computer scientist and entrepreneur focusing on machine learning and AI to empower people. He is the CEO and co-founder of Cleanlab, an AI software company that improves machine learning model performance by automatically fixing data and label issues in real-world, messy datasets. Curtis completed his PhD at MIT where he invented Cleanlab’s algorithms for automatically finding and fixing label issues in any dataset.

ABOUT DATA COUNCIL:
Data Council (https://www.datacouncil.ai/) is a community and conference series that provides data professionals with the learning and networking opportunities they need to grow their careers.

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