Method and device for generating machine learning model

Abstract

The embodiment of the invention provides a method and a device for generating a machine learning model, which are applied to a machine learning platform, and the method comprises the following steps:obtaining a machine learning demand of a target server, the machine learning demand comprises a training data format of the machine learning model and a target function of the machine learning model;according to machine learning requirements, a preset model generation strategy is utilized, and a target model, used for machine learning, of the target server is configured; And returning the targetmodel to the target server. Therefore, the data uploading operation is simplified, the network flow is saved, the use threshold of the machine learning model is reduced through modular setting, and the generation efficiency of the machine learning model is improved.

Description

technical field [0001] The present application relates to the technical field of machine learning, in particular to a method and device for generating a machine learning model. Background technique [0002] Machine learning refers to the use of data or past experience to discover and adapt the laws in the data so as to achieve the purpose of autonomous learning. The existing machine learning platform is a machine learning platform built on a cloud platform. Users submit data to the cloud platform, draw a flow chart by dragging a series of processes on the machine learning platform, and finally run the machine learning results. Therefore, the problem in the existing technology is that the user's machine learning data is generally stored locally, and the data needs to be uploaded to the cloud platform first. If the amount of data to be analyzed is huge, the uploading process is very time-consuming. At the same time, when the target system to be deployed by the user does not u...

AI Technical Summary

Problems solved by technology

Therefore, the problem in the existing technology is that the user's machine learning data is generally stored locally, and the data needs to be uploaded to the cloud platform first. If the amount of data to be analyzed is huge, the upload process is very time-consuming

At the same time, when the target system to be deployed by the user does not use the cloud server corresponding to the machine learning platform, for example, the Alibaba machine learning platform corresponds to the Alibaba Cloud server, and the deployment of the target system can only be realized by uploading data to the corresponding cloud server, affecting System real-time

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