Data labelers are emerging because of the development of the artificial intelligence industry.

Data annotation belongs to the basic work in the artificial intelligence industry, requiring a large number of data annotation specialists to engage in the relevant part of the work to meet the demand for artificial intelligence training data. However, with the continuous optimization of future annotation tools, annotators will reduce a large number of repetitive tasks with the help of intelligent auxiliary tools. Data annotators need to teach AI products to recognize and identify items, and annotate raw data such as pictures, voice, text, and video into a structured language that the AI can understand.

Through repeated practice, the AI's labeling accuracy continues to improve, and the quality of the data becomes higher and higher. Internet companies are one of the most prominent data science and big data employment directions. The core business of Internet companies is data collection, processing and analysis, which plays an important role in the decision-making process of the company. The main positions in such companies are big data engineers, data analysts, data mining experts and so on.

Data labeler's work content:

1, data collection: from the Internet, the company's internal, external storage devices and other sources, in accordance with certain rules and standards, to extract the relevant documents, pictures or videos.

2. Data annotation: Classification, evaluation, labeling, quality verification and other standardized operations are performed on data such as text, voice, image, video, and so on, so as to facilitate the screening and analysis for the training of subsequent modeling and machine learning algorithms.

3, data processing: use professional software tools and technical solutions to label, evaluate and organize the data that has been collected.

4, quality control: review and test the accuracy of the marking, timely correction of errors or irregularities in the content of the marking, in order to ensure that the data is accurate and reliable.

5, document management: record the progress of each project, the amount of data to be processed, the software used by the program and other information, and organize and formulate the corresponding development documents.