Data Annotation is the process of labeling and organizing raw data so that AI and machine learning systems can understand and learn from it. It involves adding labels, tags, categories, or metadata to images, videos, text, audio, and sensor data. For example, images can be annotated by identifying people, vehicles, or objects with labels and bounding boxes.
Data Annotation is essential for training computer vision, natural language processing, speech recognition, and other AI models. Accurate and consistent annotation improves model quality, reliability, and performance. Annotation workflows can combine human expertise with AI-assisted tools to process large datasets efficiently while maintaining the quality needed for effective model training.
