Predictive Maintenance Modeling Guide using SQL R Services

In this template, we demonstrate how to develop a Predictive Maintenance solution with SQL Server 2016 R Services where the process is aligned with the existing [R Notebook][1] published in the Cortana Intelligence Gallery but works with a larger dataset. In this scenario, there are 5 data sources namely: telemetry, errors, maintanence, machines, failures. The data ingestion (for the template we upload the local CSV files to SQL Server), feature engineering and data preparation is done using SQL code on the server where the data resides for most users which circumvents the issues of moving sensitive big data. Then various aspects of the data are visualized and models built using R code on SQL Server which enables users to scale their operations. At the end of the template implementation, the user would have had a hands-on experience about how to formulate the problem and work with SQL Server R Services to solve their data science problems even when working with Big Data. [1]: https://gallery.cortanaintelligence.com/Collection/Predictive-Maintenance-Modelling-Guide-1