Book Image

SQL Server 2017 Machine Learning Services with R

By : Toma≈æ Ka≈°trun Kaštrun, Koesmarno
Book Image

SQL Server 2017 Machine Learning Services with R

By: Toma≈æ Ka≈°trun Kaštrun, Koesmarno

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)

Summary

In this chapter, you learned the steps required to integrate an existing predictive analytics R code into resides outside of SQL Server R with the Extensibility Framework. You have also seen the simplicity and the power of the new PREDICT function in SQL Server 2017, which allows native scoring without having to install R. Managing the security required to run predictive analytics workloads is also important in prediction operations. You have learned how to add SQL queries to R projects using RTVS. Finally, you've discovered the different possibilities for integrating R code and prediction operations into your existing workflows as SQL Server stored procedures, SQL Server Agent jobs, PowerShell scripts, and SSIS projects.

With these new skills, we are ready for the next building block in managing data science solutions as part of database lifecycle: management practices...