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Evaluation of a PMML-Based GPR Scoring Engine on a Cloud Platform and Microcomputer Board for Smart Manufacturing



Yung-Tsun T. Lee, Max Ferguson, Kincho H. Law, Jinkyoo Park, Raunak Bhinge


The use of data-driven predictive models is becoming increasingly popular in engineering and manufacturing sectors. This paper discusses the deployment of Gaussian Process Regression (GPR) predictive models for smart manufacturing. A scoring engine is developed based on the Predictive Model Markup Language (PMML) standard, to illustrate the portability of predictive models among different statistical tools and different platforms. Specifically, we evaluate the tradeoffs between embedding GPR-based predictive models on a physical device and executing the predictive models on a managed cloud platform like the Google Compute Engine. We compare the performance of the two deployment strategies with two predictive models, namely an energy consumption model and a milling tool condition model, that are built with data from a Mori Seiki CNC milling machine. We describe how the response time of the two deployment strategies is related to the network latency and computational speed of the scoring machine hardware. It is shown that the time required to calculate model predictions is a significant factor in the overall response time of the embedded scoring engine. We demonstrate that the scoring engine on the cloud platform can achieve a lower response time and higher prediction rate than the microcomputer, due to the superior computational performance of the cloud-based hardware.
Proceedings Title
2016 IEEE International Confernece on Big Data(Big Data 2016)
Conference Dates
December 5-8, 2016
Conference Location
Washington, DC


scoring engine, predictive model markup language, energy prediction model, milling machine.


Lee, Y. , Ferguson, M. , Law, K. , Park, J. and Bhinge, R. (2016), Evaluation of a PMML-Based GPR Scoring Engine on a Cloud Platform and Microcomputer Board for Smart Manufacturing, 2016 IEEE International Confernece on Big Data(Big Data 2016), Washington, DC, [online], (Accessed May 26, 2024)


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Created December 5, 2016, Updated June 14, 2017