Machine learning training system and method based on edge cloud computing

Abstract

The invention discloses a machine learning training system and method based on edge cloud computing. A user terminal continuously collects new data expansion and optimal training samples, an output model is trained by a machine learning platform, the trained output model is quickly issued to the user terminal, the user terminal collects the application status of the model and feeds back to the machine learning platform to complete the retraining of the model, a virtuous circle is formed to continuously improve the training ability of the machine learning platform and model accuracy, computingresources are closer to users, and rapid deployment and unified management of the machine learning platform is achieved with the help of resource pooling and resource management capabilities of a cloud computing platform.

Description

technical field [0001] The invention relates to a machine learning training system and method based on edge cloud computing. Background technique [0002] Currently, there are many machine learning platforms, and the work of building and maintaining machine learning platforms is relatively cumbersome. How to quickly build a machine learning training platform and quickly start training tasks has become an urgent problem to be solved. This problem can be solved with the help of cloud computing platform. [0003] Cloud computing generally refers to an infrastructure platform based on a large-scale data center. By establishing a centralized large-scale data center and computing center, a cloud computing cloud is formed, and the cloud provides the resources and services required by users. Therefore, large-scale cloud Data centers are also known as core clouds. Edge cloud computing refers to an open cloud platform that integrates network, computing, storage, and application cor...

AI Technical Summary

Problems solved by technology

[0008] (1) There are many machine learning platforms and the deployment and implementation process is cumbersome, the maintenance cost is high, and unified management cannot be achieved;

[0009] (2) The computing resources of the training platform are located at the far end of the user data and cannot be close to the user;

[0010] (3) The training model cannot be quickly released to end users;

[0011] (4) The application effect of the training model cannot be collected in time and fed back to the training platform for retraining

[0012] Based on the above shortcomings, the present invention designs a machine learning training method based on edge cloud computing to solve the problems of cumbersome deployment and unified management of machine learning platforms, the problem of computing resources being close to users, the problem of rapid release of training models, and the problem of collecting user Feedback on retraining issues

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