Parallel Machine Learning Algorithms

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.