Automated Labeling
Automated labeling is a technique in machine learning and data science that uses algorithms to automatically assign labels or annotations to data, such as images, text, or audio, without requiring manual human effort. It leverages pre-trained models, heuristics, or active learning to generate training data for supervised learning tasks. This approach is commonly used to accelerate the creation of large labeled datasets for tasks like object detection, sentiment analysis, or speech recognition.
Developers should learn automated labeling when working on machine learning projects that require large amounts of labeled data, as it reduces time and cost compared to manual annotation. It is particularly useful in scenarios like semi-supervised learning, where limited labeled data is available, or in domains like computer vision and natural language processing where labeling can be labor-intensive. For example, it can be applied in autonomous vehicle development to label road scenes or in customer service chatbots to classify user intents.