• Text annotations. A metadata tag is used to show up properties of a dataset. The data uses tags that emphasise criteria such as keywords, sentences, or different feelings in text, such as "angry", to inform the computer on how to differentiate meaning or emotion behind words.
• Video annotations. Adding metadata to unlabeled video files to develop and train an ML system.
• Image annotations. Classifying photos in a dataset to train an ML model.
• Content annotations. Monitoring and applying a pre-determined set of rules and standards to user-generated contributions to assess if a message is allowed.

Data annotation is a groundbreaking data analysis tool that could help businesses take their operations to new levels.

But annotations are time-consuming and complex and requires a highly skilled workplace that can turn out huge volumes of annotated data. This means that outsourcing to a top data labelling firm can result in considerable advantages in the form of dependable AI engines.