Operationalizing Data-Centric AI - Practical Tools to Quickly Improve ML Datasets

Explore practical tools for operationalizing data-centric AI in this 30-minute conference talk from MLOps World: Machine Learning in Production. Gain insights from Jonas Mueller, Chief Scientist at Cleanlab, as he discusses efficient and systematic approaches to improving machine learning datasets. Learn about novel algorithmic strategies for automatically identifying various data issues across image, text, and tabular datasets. Discover how to detect label errors, bad data annotators, out-of-distribution examples, and other dataset problems that can significantly impact model performance. Examine case studies showcasing the effectiveness of data-centric AI software used by thousands of data scientists. Conclude with a discussion on the future of the data-centric AI movement and key challenges that require further attention in the field.

Operationalizing Data-Centric AI: Practical Tools to Quickly Improve ML Datasets