Data labeling

Data labeling transforms raw, unstructured data (images, text, video) into structured, annotated training sets, providing the 'ground truth' for supervised machine learning models.

This technology is the critical preprocessing step for successful AI development: it assigns meaningful tags to raw data, enabling algorithms to learn patterns and make accurate predictions. The process involves human-in-the-loop (HITL) annotators or semi-automated tools to perform tasks like bounding box creation for computer vision or named entity recognition for natural language processing (NLP). High-quality, accurately labeled datasets are non-negotiable; they directly mitigate 'garbage in, garbage out' scenarios, ensuring models—like those used in autonomous vehicles or healthcare diagnostics—achieve the required 95%+ performance metrics.

https://www.ibm.com/topics/data-labeling