Book Image

SQL Server 2017 Machine Learning Services with R.

By : Julie Koesmarno, Tomaž Kaštrun
Book Image

SQL Server 2017 Machine Learning Services with R.

By: Julie Koesmarno, Tomaž Kaštrun

Overview of this book

R Services was one of the most anticipated features in SQL Server 2016, improved significantly and rebranded as SQL Server 2017 Machine Learning Services. Prior to SQL Server 2016, many developers and data scientists were already using R to connect to SQL Server in siloed environments that left a lot to be desired, in order to do additional data analysis, superseding SSAS Data Mining or additional CLR programming functions. With R integrated within SQL Server 2017, these developers and data scientists can now benefit from its integrated, effective, efficient, and more streamlined analytics environment. This book gives you foundational knowledge and insights to help you understand SQL Server 2017 Machine Learning Services with R. First and foremost, the book provides practical examples on how to implement, use, and understand SQL Server and R integration in corporate environments, and also provides explanations and underlying motivations. It covers installing Machine Learning Services;maintaining, deploying, and managing code;and monitoring your services. Delving more deeply into predictive modeling and the RevoScaleR package, this book also provides insights into operationalizing code and exploring and visualizing data. To complete the journey, this book covers the new features in SQL Server 2017 and how they are compatible with R, amplifying their combined power.
Table of Contents (12 chapters)

Integrating an existing R model

This section takes an existing R code that generates the R model and runs against the SQL Server dataset into a workflow, where the model can be refreshed and evaluated on a regular basis, then used for predictive analysis. The following figure shows a typical predictive modeling workflow in an R script:

Figure 7.1: Typical predictive modeling workflow

To integrate this script in SQL Server, you'll need to organize the workflow into three steps:

  1. Prepare the data for training
  2. Train and save the model using T-SQL
  3. Operationalize the model

In this section, the last two steps will use sp_execute_external_script, which invokes an R process. These steps are using the SQL Server extensibility framework, described later on.

Prerequisite – prepare...