Manual Tagging
Manual tagging is a data annotation process where humans manually assign labels, categories, or metadata to data points such as text, images, audio, or video. It is commonly used to create labeled datasets for training and evaluating machine learning models, particularly in supervised learning tasks. This process ensures high-quality, accurate annotations that are crucial for model performance but can be time-consuming and resource-intensive.
Developers should learn and use manual tagging when building machine learning models that require high-quality, domain-specific training data, such as in natural language processing (NLP) for sentiment analysis or computer vision for object detection. It is essential in scenarios where automated tagging methods are unreliable, such as with ambiguous or complex data, or when establishing ground truth for benchmarking algorithms. This skill is particularly valuable in roles involving data preparation, model validation, or AI system development.