ABSTRACT
Machine learning (ML) models need to be trained on large volumes of data. Traditionally, client applications collect data and transfer it to cloud servers to train machine learning models, with the results returned to the clients. There is a lack of actionable information how these two kinds of learning differ in terms of performance and resource utilization. This chapter addresses this problem; a comprehensive empirical evaluation of these two types of learning is conducted for a widely used clustering learning algorithm. It focuses on detailed analysis of the application in both cloud-based and edge-based setup. The chapter conducts an analysis on the implementation of K-Means clustering technique over both Edge-based learning and Cloud-based learning to decipher some key findings for the resource utilization for both approaches. It guides ML designers in selecting a system which best suit their needs.
