Labeling Tools are Great, but What About Quality Checks?

Explore the critical aspect of quality checks in data labeling tools through this 29-minute conference talk presented by Marcus Edel and Jakub Piotr Cłapa from Collabora Ltd. at a Linux Foundation event. Delve into the importance of ensuring accuracy and reliability in labeled data, which is crucial for developing robust machine learning models. Learn about various quality control methods, best practices, and potential pitfalls to avoid when implementing quality checks in your data labeling workflow. Gain insights on how to improve the overall quality of your labeled datasets and ultimately enhance the performance of your AI and machine learning projects.