A method and apparatus for machine learning based on cloud computing

Abstract

A machine learning method based on cloud computing includes pulling corresponding images according to a selected depth learning framework, creating containers according to the images and configurationinformation including a specified number of processor resources, and binding a storage path of an input model file and a storage path of training data with the containers; in the training process, the container is used to execute the model file obtained according to the storage path of the model file, the training data is read according to the storage path of the training data, the model established by the model file is trained and executed by the read training data, and the trained model is published. A device for machine learning based on cloud computing is also disclosed. The scheme can eliminate the complexity in the process of machine learning and reduce the threshold of learning and using.

Description

technical field [0001] The present invention relates to cloud computing technology, in particular to a method and device for machine learning based on cloud computing. Background technique [0002] Machine learning (Machine Learning, referred to as ML) is a multi-field interdisciplinary subject that specializes in the study of how computers simulate or implement human learning behaviors to acquire new knowledge or skills, reorganize existing knowledge structures, and continuously improve their own knowledge. performance. It is the core of artificial intelligence and the fundamental way to make computers intelligent. Its application pervades all fields of artificial intelligence. It mainly uses induction and synthesis rather than deduction. [0003] Currently, most developers generally feel that machine learning is much harder than it is because the process of building and training a model and then deploying it to production is too complex and slow. The process of using mac...

AI Technical Summary

Problems solved by technology

[0003] Currently, most developers generally feel that machine learning is much harder than it is because the process of building and training a model and then deploying it to production is too complex and slow

The process of using machine learning mainly has the following parts: collecting and preparing training data, selecting the algorithm and framework to be used, teaching the model how to make predictions through training, adjusting the model, integrating the model with the application and deploying several major steps, each step Both require extremely tedious manual work, combined with extensive expertise, access to large amounts of compute and storage, and time spent experimenting and optimizing every aspect of the process, factors that prevent most developers from using machine learning alone

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