Machine learning algorithm whole-process training method and system based on cloud infrastructure

Abstract

The invention relates to a machine learning algorithm whole-process training method and system based on a cloud infrastructure. The method comprises the steps that a training data set and a to-be-solved problem data set are uploaded to a cloud data server through a web application program, and a preset machine learning algorithm is selected; the training data set is obtained by a cloud computing server through a cloud data server, and model training is conducted by using the training data set to obtain a training model; the to-be-solved problem data set is obtained by the cloud computing server through the cloud data server, solving results are obtained by using the training model according to the to-be-solved problem data set, and the solving results are returned to the cloud data server;the solving results are returned to the web application program by the cloud data server. According to the method, computing environments and computing resources are deployed at cloud ends to help technical researching and developing workers in efficiently and rapidly constructing a machine learning algorithm model and efficiently training the model, and therefore time cost and software and hardware purchasing cost are reduced.

Description

technical field [0001] The present invention relates to the technical field of machine learning software systems, in particular to a whole-process training method and system for machine learning algorithms based on cloud architecture. Background technique [0002] AlphaGo defeats Go masters, watson diagnoses rare cases, and driverless cars hit the road, marking that artificial intelligence, especially machine learning technology, is moving towards the mainstream. At the same time, the power system carries more and more renewable energy and electric vehicles, and is developing into an energy Internet, constantly producing high-dimensional, wide-ranging, and large-volume data resources. However, energy power and machine learning are far from being deeply integrated. Researchers in energy and electric power related fields urgently need highly targeted and high-value systems and software to help them develop machine learning technology research and development in many practical...

AI Technical Summary

Problems solved by technology

For relatively complex training models, it is often necessary to deal with the organization of label data sets, training algorithms for data sets, system architecture for running training, selection and installation of operating systems, configuration of GPU and other resources and drivers, and machine learning projects. The setting of the basic framework, etc., occupy the time cost of researchers and the purchase cost of software and hardware

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