Data Labeling
Data labeling is the process of annotating raw data (such as images, text, audio, or video) with meaningful tags or labels to create high-quality training datasets for machine learning and AI models. It involves human annotators or automated tools assigning categories, bounding boxes, keypoints, or other metadata to data points, enabling supervised learning algorithms to learn patterns and make predictions. This foundational step is critical for developing accurate and reliable AI systems across various domains.
Developers should learn data labeling when building supervised machine learning models, as it directly impacts model performance by providing labeled data for training, validation, and testing. It is essential in use cases like computer vision (e.g., object detection in autonomous vehicles), natural language processing (e.g., sentiment analysis in customer reviews), and audio processing (e.g., speech recognition in virtual assistants), where models require precise annotations to generalize effectively. Mastering data labeling helps ensure data quality, reduce bias, and accelerate AI project timelines.