Abstract: An agile artificial intelligence for information technology operations(AIOps)framework and maintenance data quality assessment method are proposed. The agile AIOps framework advances the model construction stage to the test stage, and uses the monitoring data generated during the test stage to replace the data collected online to train the model, thereby realizing the early development and early use of intelligent operation. The maintenance data quality assessment method is based on the maximum mean discrepancy to evaluate the trend, stage, detectability, and diagnosability of training data for health assessment and fault diagnosis, so as to estimate the applicability of the data to the model. Based on the test environment provided by Huawei, the test cases are set up and the experimental data set is constructed. The experimental results on the data set verify the feasibility of the agile AIOps framework and the effectiveness of the data quality assessment method.

Key words: artificial intelligence for information technology operations, agile framework, data quality assessment, maximum mean discrepancy