Study On The Quality Management Of M Company’s Data Annotation Project Management

With the rapid development of artificial intelligence in our country,it has been elevated to the level of a national strategy.Data is a crucial foundation for the learning and training of artificial intelligence algorithm models,and therefore,data quality has a direct impact on the effectiveness of algorithms.Data annotation is the process of processing data for use in artificial intelligence algorithm models.This paper conducts research on quality management in data annotation projects using relevant theoretical methods and technical tools of project quality management.This research helps to improve the theoretical and practical abilities of data annotation project managers,assisting them in addressing quality management issues that may arise during the project process,thereby ensuring the achievement of quality management goals in data annotation projects,which has significant practical significance.This paper mainly introduces the data annotation project of M Company and the production process of the data annotation project,and proposes the current situation and problems of quality management in the data annotation project.The specific quality management defects can be interpreted from the following three aspects:firstly,quality control is not in place,quality management does not carry out pre control and in process control,there is no targeted quality inspection ability,and there is no global quality situation summary;Secondly,there is no personnel quality control mechanism,no selection and control mechanism for labeled personnel,and no classification and grading mechanism for quality inspectors;Thirdly,the production process of the data annotation project is incomplete,and a multi round quality inspection system has not been established.This paper focuses on the quality management issues of M company’s data annotation project,elaborates on the quality improvement ideas of M company’s data annotation project,and designs in detail the quality improvement measures of M company’s data annotation project,including improving the quality control of annotation projects,establishing personnel quality control mechanisms,and establishing multiple rounds of quality inspection systems to improve the ability level of M company’s data annotation project quality management.In the management of data annotation projects,human-machine collaboration has been innovatively introduced,which includes the introduction of artificial intelligence models to assist annotation and audit,further improving the overall quality of data annotation projects.After five months of quality management improvement practice,the qualification rate of data annotation project delivery results has increased from the average word accuracy rate before improvement of 90.6%to 97.7%,significantly improving the quality evaluation indicators,proving the effectiveness of the quality management improvement plan.