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Annotating is any action that deliberately interacts with a text to enhance the reader's understanding of, recall of, and reaction to the text. Sometimes called "close reading," annotating usually involves highlighting or underlining key pieces of text and making notes in the margins of the text.

Data labeling can be applied in innumerable use cases like natural language processing, computer vision, and speech recognition. Data annotation is the process of labeling data with different metadata forms like audio, text, images to train ML models like chatbots, autonomous vehicles, and more.

Data annotation is the process of labeling individual elements of training data (whether text, images, audio, or video) to help machines understand what exactly is in it and what is important. This annotated data is then used for model training.

Summary. Annotation is a process used in supervised machine learning for training data sets to help machines understand and recognize the input data and act ingly. Labeling is used to identify key features present in the data while minimizing human involvement.

Examples of data annotation methods include semantic, text classification, and image and video annotation. Text classification is one of the most common data annotation techniques we encounter, such as putting tags on blog posts to group them by topic.

The biggest difference between annotations and labels in Kubernetes is this: while labels attach identifying metadata to a K8s object, annotations attach additional information that is not identifying.

? Annotated data is a prerequisite for training machine learning models, whereas the goal of labelling is to find meaningful features in a dataset. ? Annotation facilitates in the identification of pertinent material and labeling facilitates in the recognition of patterns so that algorithms can be trained.