1. Data preprocessing: Data annotators need to preprocess the original data, including data cleaning, screening and conversion, to ensure the quality and availability of the data. This process may need to deal with a lot of data, so data annotators need to master some skills and methods of data processing, such as data screening, data cleaning, data conversion and so on.

2. Learn and use labeling tools: Data annotators need to learn and use labeling tools to label data. The spread of labeling tools often includes various image labeling software and voice labeling software. These tools need some training and learning from data annotators to ensure accurate data annotation.

3. Data annotation: Data annotators need to annotate data, including image, voice, text and other data. Specifically, for image data, data annotators need to annotate various objects, scenes and textures in the image; For speech data, data annotators need to annotate various sounds, intonations and tones in speech; For text folding data, the data annotator needs to annotate various words, sentences and paragraphs in the text.

4. Inspection of labeling quality: Data annotators need to check the labeled data to ensure the quality and accuracy of labeling. This process may require some auditing and verification to ensure that the labeling results meet the task requirements and the needs of data users.

5. Data management: Data annotators need to manage the marked data, including data storage, backup and update. This process needs to ensure the accessibility, readability and security of the data cube ruler.

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Importance of data annotation: Data annotation plays an important role in the field of artificial intelligence, because it is the basis of training machine learning models. Through data annotation, human beings can provide a large number of labeled data to the machine learning model, which can use these data to train the model and optimize the algorithm, thus improving the accuracy and reliability of the model.