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Document Type

Article

Keywords

Big data, Parallel Processing, Deep Learning, Machine Learning

Abstract

To expedite the learning process, a group of algorithms known as parallel machine learning algorithms can be executed simultaneously on several computers or processors. As data grows in both size and complexity, and as businesses seek efficient ways to mine that data for insights, algorithms like these will become increasingly crucial. Data parallelism, model parallelism, and hybrid techniques are just some of the methods described in this article for speeding up machine learning algorithms. We also cover the benefits and threats associated with parallel machine learning, such as data splitting, communication, and scalability. We compare how well various methods perform on a variety of machine learning tasks and datasets, and we talk about the advantages and disadvantages of these methods. Finally, we offer our thoughts on where this field of study is headed and where further research is needed. The importance of parallel machine learning for businesses that want to glean insights from massive datasets is emphasised, and the paper provides a thorough introduction of the discipline.

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