The State of the Art in Implementing Machine Learning for Mobile Apps: A Survey
Xiangfeng Dai, Irena Spasić, Samuel Chapman, Bradley Meyer
Abstract
Mobile applications based on machine learning are reshaping and affecting many aspects of our lives. Implementing machine learning on mobile devices faces various challenges, including computational power, energy, latency, low memory, and privacy risks. In this article, we investigate the current state of implementing machine learning for mobile applications, providing an overview of five architectures commonly used for this purpose and the ways in which they address the given challenges. We also discuss their pros and cons, providing recommendations for each architecture. Additionally, we review recent studies, popular toolkits, cloud services, and platforms supporting machine learning as a service. This survey will, therefore, bring mobile developers up to speed on the latest trends in implementing machine learning for mobile applications.