What is a data annotator?

Data annotators refer to people who are mainly engaged in the artificial intelligence industry. Popular understanding is editing on the internet. He is mainly responsible for some data labeling tools, which classify, sort, correct and label a large number of text information, picture information, voice information and video information.

In fact, data are generally divided into two types, one is marked and the other is unmarked. Data labeling is to determine the purpose of labeling tasks, set labels according to the purpose, and identify dimensions.

The job responsibility of the data annotator is to annotate and summarize the related contents of data annotation in the R&D team, distribute and review a large number of data outsourcing annotation work, fully understand the background and standards of data annotation required by the R&D team, and provide basis for formulating relevant strategies.

Data annotator's job is an ordinary office job, but it is currently associated with artificial intelligence, so it has been labeled a lot. Common data annotation types include classification annotation, frame annotation, area annotation, tracking annotation and other annotations.

Importance of data annotation

1 to improve the accuracy and efficiency of the machine learning model.

The standard and quality of data annotation is directly related to the accuracy and efficiency of machine learning model. A large number of accurate and reasonable labeled data can effectively reduce the false recognition rate and missed recognition rate of the model, and improve the recognition accuracy and practicability of the model.

2. Reduce the time and cost of model development.

Huge raw data need to be screened, processed and marked, which also consumes a lot of time and labor costs. Planning an effective data labeling process can greatly reduce the time cost of data processing and labeling, improve work efficiency and shorten the development cycle of machine learning model.