Building Robust Machine Learning Models from Noisy Labeled Data - Improving ML Model

Building Robust Machine Learning Models from Noisy Labeled Data - Improving ML Model

Explore techniques for building robust machine learning models from noisy labeled data in this 58-minute talk. Learn how to properly account for crowdsourced annotations, handle subjective responses from annotators, and track distribution bias through model monitoring. Gain insights into improving ML systems when working with non-expert labelers or user-generated data. Discover practical approaches for addressing key challenges in training and operating models with noisy datasets across various enterprise applications.