Data labeling is the process of tagging or marking data to make it understandable and usable by machine learning models. It involves adding relevant information, such as metadata or annotations, to various data types—like text, audio, images, and video—to help train AI models.
For instance, in text data, labels may include identifying sentiment or categorizing topics, while in images or videos, labels could include identifying objects, people, or actions. These labeled datasets are essential for supervised learning, where AI models use the labeled data to recognize patterns and make predictions. Data labeling is a critical step in preparing high-quality training data for AI systems.