Model adaptive training method and device, equipment, medium and program product

A technology of model self-adaptation and training method, which is applied in the fields of devices, equipment, digital model self-adaptive training methods, media and program products, to achieve the effect of improving performance stability and reducing the amount of manual labeling

Pending Publication Date: 2021-11-16
JINGDONG CITY BEIJING DIGITS TECH CO LTD
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  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0005] This application provides a model adaptive training method, device, equipment, medium and program product, which solv...

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  • Model adaptive training method and device, equipment, medium and program product
  • Model adaptive training method and device, equipment, medium and program product
  • Model adaptive training method and device, equipment, medium and program product

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Embodiment Construction

[0099] In order to make the purposes, technical solutions and advantages of the embodiments of the present application clearer, the technical solutions in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments It is only a part of the embodiments of the present application, but not all the embodiments. Based on the embodiments in this application, all other embodiments obtained by persons of ordinary skill in the art without creative work, including but not limited to combinations of multiple embodiments, all fall within the protection scope of this application.

[0100] The terms "first", "second", "third", "fourth", etc. (if any) in the specification and claims of the present application and the above drawings are used to distinguish similar objects, and not necessarily Used to describe a specific sequence or sequence. It is...

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Abstract

The invention provides a model adaptive training method and device, equipment, a medium and a program product, and the method comprises the steps: obtaining all operation data of an original model during actual operation, and detecting a first concept drift value of the original model during actual operation according to the operation data; and then, according to the value model and the first concept drift value, distributing each operation data to a tagged data set and/or a non-tagged data set, and when the data size of the tagged data set is greater than or equal to a preset threshold value, utilizing an adaptive training model, and according to the tagged data set and the non-tagged data set, performing adaptive training on the original model to determine a new trained model. The technical problem of how to enable the AI model to carry out adaptive training under the condition that human intervention is as little as possible is solved. The technical effects that the manual annotation amount needed when the developers update the models is reduced, and the performance stability of the models is improved in the mode of integrating the multiple models are achieved.

Description

technical field [0001] The present application relates to the field of computer data processing, in particular to a numerical model adaptive training method, device, equipment, medium and program product. Background technique [0002] At present, for the AI ​​(Artificial Intelligence, artificial intelligence) model, the existing historical data is generally used for training and then goes online for production services to predict new actual operating data. [0003] However, as time goes by, the phenomenon that the sample distribution changes due to environmental changes is unavoidable, and this phenomenon is called concept drift. At this point, the performance of the AI ​​model will gradually degrade. To this end, we need to retrain the AI ​​model regularly using the latest data to continuously update the AI ​​model, and monitor the model performance in real time to ensure the stable performance of the online AI model. [0004] Therefore, the post-performance maintenance o...

Claims

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Application Information

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IPC IPC(8): G06K9/62G06N3/08G06N3/04
CPCG06N3/08G06N3/045G06F18/2155
Inventor 王小波尹泽夏林锋张钧波
Owner JINGDONG CITY BEIJING DIGITS TECH CO LTD
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