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In this talk, we demonstrate how to develop and deploy end-to-end machine learning solutions for predictive maintenance in manufacturing industry with R. For predictive maintenance, the following questions regarding when a machine fails are typically asked: what's the Remaining Useful Life (RUL) of an asset? Will an asset fail within a given time frame? Which time window will an asset likely fail? We formulate the above questions to regression, binary classification and multiclass classification problems respectively, and use a public aircraft engine data to demonstrate the complete modeling steps in R: data labeling, processing, feature engineering, model training and evaluation. R users are often challenged with productizing the models they built. After model development, we will show two ways of productization: 1) deploy with SQL server as stored procedures using the new R services; 2) deploy it by publishing as a web service restful API; Either approach would enable user to call the deployed scoring engine from any applications. The presentation will be followed by a live demo during the talk.
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