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)

The Microsoft Machine Learning R Services architecture

The architecture of R Server covers many components needed in order for the communication between R IDE or SQL Server and R engine to work properly.

Several components are involved in order to properly execute Transact SQL, R script, and return all the results back to T-SQL:

Figure 7

Launchpad is a new service in SQL Server 2016 that supports execution of external scripts using the external stored procedure from SQL Server. However, in SQL Server 2017, the Python launcher has also been introduced, making Launchpad generally available to the second non-SQL Server language. The idea behind Launchpad is that since the infrastructure is already prepared, the SQL Server should, in the future, support other languages as well, such as JavaScript and C++, opening this service not only to predictive analytics and machine learning...