Data labeling identifies objects on raw data such as images, text, videos, and audio. The goal is to provide one or more informative labels to provide context so that a machine learning model can learn from it.
For example, labels might indicate whether a photo contains a train or a horse, which words were said in a voice recording, or if a lab image contains a tumor. Data labeling is required for various use cases, including computer vision, natural language processing, and speech recognition. Supervised machine learning models are built on large volumes of high-quality labeled data.