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

In this chapter, we have covered the extensible functionalities of the RevoScaleR package to deliver fast and good predictions based on the explored datasets. In the previous chapter Statistical learning with RevoScaleR package, we have covered data exploration, preparation and simple and bi-variate statistics. This chapter showed how RevoScaleR package was designed to work with large datasets (that overcome the limitations of RAM and single CPU), enabling spill to disk and multi threading. The same procedures can be used as well in database instances of R, for delivering the predictions to your business and data residing in the database. We have covered this aspect as well, exploring different algorithms and comparing the solutions. Once you have your model selected, you may want to use the PREDICT clause. which is a new feature in SQL Server 2017 with a slightly altered...