Need for Data Labeling/Annotation
Data labeling is the process of adding meaningful tags or annotations to raw data enabling machine learning algorithms to train and fine tune the models more effectively and generate accurate predictions. Machine learning, computer vision, natural language processing. Without proper labels, machine learning algorithms cannot effectively process the context for the training data leading to inaccurate predictions and classifications. Challenges – consistency across labels, handling subjective labeling, sourcing qualified labelers, data privacy, time and labour intensive, prone to human errors, added layer of management
