Data labeling is the process of assigning meaningful tags, categories, or annotations to raw data so that machine learning models can recognize patterns and learn from it. Labels may identify objects in images, classify text by sentiment, mark speech transcripts, or indicate specific features within datasets. The labeling method depends on the type of data and the model being trained.
For example, an autonomous driving system may use labeled images to distinguish pedestrians, vehicles, traffic signs, and road markings. In AI development, the accuracy and consistency of these labels directly affect model performance. Data labeling is closely associated with supervised learning, training datasets, data annotation, and machine learning.
