AUTHORS: Keon Myung Lee, Jaesoo Yoo, Jiman Hong

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ABSTRACT: Machine learning is an approach to develop some algorithm for problem solving from data of the problem domain without coding programs. Although there are various machine learning tools with which machine learning applications can be developed relatively easily, non-experts have yet difficulties in developing machine learning applications. To be a successful developer, it is required to understand machine learning algorithms and to make right design choices. This paper addresses the decision choices to be made and which tasks need to be automated by the platform for non-expert developers to get an effective and efficient machine learning application. It presents the autonomicity levels which specify the level of automation in machine learning application development. It describes the requirements of an autonomic machine learning platform which helps non-expert developers build a machine learning application. It also introduces an architecture of an autonomic machine learning platform.

KEYWORDS: machine learning, distributed computing, autonomic computing, machine learning platform

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