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)

Deploying, Managing, and Monitoring Database Solutions containing R Code

Operationalizing R code in a SQL Server database means that data scientists/database developers can also leverage productionizing data science solutions as part of Database Lifecycle Management (DLM). This includes the following:

  • Checking in R code as part of a SQL Server database project into a version control
  • Adding the stored procedures for the data science solution as part of SQL Server unit tests
  • Integrating the data science solution into the Continuous Integration/Continuous Delivery (CI/CD) process
  • Monitoring performance of the data science solution in the production on a regular basis

In this chapter, we will be using SQL Server Data Tools (SSDT) in Visual Studio 2017 and Visual Studio Team Services to perform this DLM workflow. However, the underlying concept can be applied to any other CI/CD platform...