Data labeling refers to the process of manually annotating or tagging data to provide context and meaning. This is often done to train machine learning models to recognize patterns and make predictions. Data labeling can involve categorizing images, transcribing audio recordings, identifying objects in videos, or any other task that helps create labeled datasets for training algorithms. Data labeling is a crucial step in the machine learning pipeline, as the quality and accuracy of the labeled data directly impact the performance of the trained model. This field is rapidly growing, with many companies and organizations specializing in data labeling services to support the development of AI and machine learning technologies.