大数据分析中的机器学习综述

N. M. Hussien, S. A. Hussain, Khlood Ibraheem Abbas, Yasmin Makki Mohialden
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摘要

如今,大数据是许多大型科技公司成功的关键。随着越来越多的公司使用它来存储、分析并从大量数据中获取价值,他们越来越难以以最佳方式使用所获得的数据。大多数系统都想出了使用机器学习的方法。在实时web系统中,必须基于分散的数据在每个节点以智能的方式处理数据。随着数据隐私成为一个更重要的社会问题,标准化学习已经成为一个热门的研究领域,它使不同的组织能够在考虑隐私的同时共同训练机器学习模型。研究人员越来越有兴趣支持更多以不同方式保护隐私的机器学习模型。有必要建立系统和基础设施,使创建不同的标准化学习算法变得更容易。在这项研究中,我们研究并讨论了用于处理大量数据的统一和分布式机器学习技术。FedML是一个Python程序,可以在任何规模下使用机器学习。它是一个统一的、分布式的机器学习包。
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A review of machine learning for big data analysis
Big data is the key to the success of many large technology companies right now. As more and more companies use it to store, analyze, and get value from their huge amounts of data, it gets harder for them to use the data they get in the best way. Most systems have come up with ways to use machine learning. In a real-time web system, data must be processed in a smart way at each node based on data that is spread out. As data privacy becomes a more important social issue, standardized learning has become a popular area of research to make it possible for different organizations to train machine learning models together while keeping privacy in mind. Researchers are becoming more interested in supporting more machine learning models that keep privacy in different ways. There is a need to build systems and infrastructure that make it easier for different standardized learning algorithms to be created. In this research, we look at and talk about the unified and distributed machine learning technology that is used to process large amounts of data. FedML is a Python program that let machine learning be used at any scale. It is a unified, distributed machine learning package.
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