Data Labeling
Data labeling is the process of annotating raw data, such as text, images, audio, or video, with meaningful tags or classifications so it can be used to train supervised machine learning models. These labels provide the "ground truth" that helps models learn to recognize patterns and make accurate predictions.
Why it Matters:
Labeled data is essential for training accurate AI models. The quality of labeling directly impacts model performance.
In software projects, QAT Global can manage labeling pipelines and quality assurance as part of ML solution delivery. For IT staffing, our recruiting teams often source annotators and data engineers to support high-volume labeling and dataset curation initiatives.
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