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)

Summary

Starting with SQL Server 2016, R integration became a very important part of the SQL Server platform. Since the public release of SQL server 2016, until February 2018 (the time of writing this), the community had embraced R as well as Python very well, making data exploration and data analysis part of the general database task. Microsoft addressed many of the issues, and broadened the SQL Server as a product. With SQL Server 2017, Python was added as a secondary analytical language, reaching to an even broader community as well as businesses, and at the same time, taking are of data scalability, performance, and security.

In the next chapter, we will cover different R distributions and IDE tools for using R as a standalone or within the SQL Server, and what the differences among them are when deciding which one to choose.