Algorithm model operation monitoring method and device, computer equipment and storage medium

Abstract

The invention relates to an algorithm model operation monitoring method and device, computer equipment and a storage medium. The method comprises the steps of obtaining a feature tag and a regression result of an algorithm model in a current statistical period; determining a current performance index according to the feature tag and the regression result, wherein the current performance index is a performance index value of the algorithm model in a current statistical period; and when the current performance index value satisfies a preset alarm condition of the algorithm model, outputting first alarm prompt information. By adopting the method, the abnormal operation condition of the algorithm model can be found in time, the blindness of updating the algorithm model can be avoided, and the performance of the algorithm model can be ensured.

Description

technical field [0001] The present application relates to the technical field of the Internet, in particular to an algorithm model operation monitoring method, device, computer equipment and storage medium. Background technique [0002] With the application and development of artificial intelligence technology in the Internet field, especially the application and development of machine learning, the algorithm model obtained by machine learning is applied to big data risk control, providing a strong security guarantee for Internet finance and other fields. However, how to efficiently and intuitively measure the performance of the algorithm model, monitor the abnormal execution of the algorithm, and guide the flexible iteration and timely operation and maintenance of the algorithm model has become an urgent research problem. [0003] The current solution is mainly to measure the performance of the algorithm model before going online, and to update the algorithm model regularly...

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Problems solved by technology

[0004] However, this method has the following problems. First, if the regular update time is long, the algorithm model cannot be guaranteed to have a low generalization error, which may affect the business

Second, if the regular update time is short, frequent iterations will cause waste of resources

Finally, abnormal operation of the algorithm model cannot be detected in time

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