AI data labeling is the fundamental human-driven process of tagging raw data—such as images, text, videos, and sensor readings—with informative labels to provide context and meaning for machine learning algorithms. This critical step involves human annotators identifying and demarcating objects, classifying content, transcribing audio, or highlighting key phrases within a text. For instance, in an autonomous vehicle project, labelers would draw bounding boxes around every car, pedestrian, and traffic sign in thousands of hours of video footage. The quality and precision of this labeling directly determine the model's ability to learn and make accurate predictions; a model trained on poorly labeled data will inevitably perform poorly in the real world.
Locked
