Distributed Machine Learning

Distributed machine learning refers to multi-node machine learning algorithms and systems that are designed to improve performance, increase accuracy, and scale to larger input data sizes. Increasing the input data size for many algorithms can significantly reduce the learning error and can often be more effective than using more complex methods [8]. Distributed machine learning allows companies, researchers, and individuals to make informed decisions and draw meaningful conclusions from large amounts of data. Many systems exist for performing machine learning tasks in a distributed environment. These systems fall into three primary categories: database, general, and purpose-built systems. Each type of system has distinct advantages and disadvantages, but all are used in practice depending upon individual use cases, performance requirements, input data sizes, and the amount of implementation effort.