Abstract

CASE STUDIES ON GE: APPROACHES FOR BUILDING VALUABLE MACHINE LEARNING APPLICATIONS FOR THE INDUSTRIAL INTERNET

Yunyao Gu*

ABSTRACT

By 2020 the industrial internet will have more than 50 billion connected machines. This is an area of tremendous opportunity because even a 1% gain in efficiency can translate into tens of billions of dollars saved within business units, especially for corporations like GE.1 Based on information provided from GE employees and engineers in Wise.io, this essay aims to analyse approaches for building valuable Machine Learning applications for the Industrial Internet, from looking into three case studies of GE, and how the company resolves time-consuming, labour intensive asset monitoring workflows, with the help of a start-up called Wise.io to deploy machine learning for itself as well as its customers across all of GE‟s business units.

Keywords: Machine Learning, Industrial Internet, Application, Time-Consuming, Labour Intensive.