SQL Server 2017 Machine Learning Services with R

Develop and run efficient R scripts and predictive models for SQL Server 2017
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SQL Server 2017 Machine Learning Services with R

Tomaž Kaštrun, Julie Koesmarno

2 customer reviews
Develop and run efficient R scripts and predictive models for SQL Server 2017
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Book Details

ISBN 139781787283572
Paperback338 pages

Book Description

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

Chapter 1: Introduction to R and SQL Server
Using R prior to SQL Server 2016
Microsoft's commitment to the open source R language
Boosting analytics with SQL Server R integration
Summary 
Chapter 2: Overview of Microsoft Machine Learning Server and SQL Server
Analytical barriers
The Microsoft Machine learning R Server platform
The Microsoft Machine Learning R Services architecture
Summary
Chapter 3: Managing Machine Learning Services for SQL Server 2017 and R
Minimum requirements
Choosing the edition
Configuring the environment and installing R Tools for Visual Studio (RTVS)
Security
Package information
Summary
Chapter 4: Data Exploration and Data Visualization
Understanding SQL and R data types
Data exploration and data munging
Data visualization in R
Integrating R code in reports and visualizations
Summary
Chapter 5: RevoScaleR Package
Overcomming R language limitations
Scalable and distributive computational environments
Functions for data preparation
Variable creation and data transformation
Variable creation and recoding
Dataset subsetting
Dataset merging
Functions for descriptive statistics
Functions for statistical tests and sampling
Summary
Chapter 6: Predictive Modeling
Data modeling
Advanced predictive algorithms and analytics
Deploying and using predictive solutions
Performing predictions with R Services in the SQL Server database
Summary
Chapter 7: Operationalizing R Code
Integrating an existing R model
Fast batch prediction
Integrating the R model for fast batch prediction
Managing roles and permissions for workloads
Tools
Integrating R workloads and prediction operations beyond SQL Server
Summary
Chapter 8: Deploying, Managing, and Monitoring Database Solutions containing R Code
Integrating R into the SQL Server Database lifecycle workflow
Prerequisites for this chapter
Using version control
Setting up continuous integration
Setting up continuous delivery
Monitoring the accuracy of the productionized model
Summary
Chapter 9: Machine Learning Services with R for DBAs
Gathering relevant data
Exploring and analyzing data
Creating a baseline and workloads, and replaying
Creating predictions with R - disk usage
Summary
Chapter 10: R and SQL Server 2016/2017 Features Extended
Built-in JSON capabilities
Accessing external data sources using PolyBase
High performance using ColumnStore and in memory OLTP
Summary

What You Will Learn

  • Get an overview of SQL Server 2017 Machine Learning Services with R
  • Manage SQL Server Machine Learning Services from installation to configuration and maintenance
  • Handle and operationalize R code
  • Explore RevoScaleR R algorithms and create predictive models
  • Deploy, manage, and monitor database solutions with R
  • Extend R with SQL Server 2017 features
  • Explore the power of R for database administrators

Authors

Table of Contents

Chapter 1: Introduction to R and SQL Server
Using R prior to SQL Server 2016
Microsoft's commitment to the open source R language
Boosting analytics with SQL Server R integration
Summary 
Chapter 2: Overview of Microsoft Machine Learning Server and SQL Server
Analytical barriers
The Microsoft Machine learning R Server platform
The Microsoft Machine Learning R Services architecture
Summary
Chapter 3: Managing Machine Learning Services for SQL Server 2017 and R
Minimum requirements
Choosing the edition
Configuring the environment and installing R Tools for Visual Studio (RTVS)
Security
Package information
Summary
Chapter 4: Data Exploration and Data Visualization
Understanding SQL and R data types
Data exploration and data munging
Data visualization in R
Integrating R code in reports and visualizations
Summary
Chapter 5: RevoScaleR Package
Overcomming R language limitations
Scalable and distributive computational environments
Functions for data preparation
Variable creation and data transformation
Variable creation and recoding
Dataset subsetting
Dataset merging
Functions for descriptive statistics
Functions for statistical tests and sampling
Summary
Chapter 6: Predictive Modeling
Data modeling
Advanced predictive algorithms and analytics
Deploying and using predictive solutions
Performing predictions with R Services in the SQL Server database
Summary
Chapter 7: Operationalizing R Code
Integrating an existing R model
Fast batch prediction
Integrating the R model for fast batch prediction
Managing roles and permissions for workloads
Tools
Integrating R workloads and prediction operations beyond SQL Server
Summary
Chapter 8: Deploying, Managing, and Monitoring Database Solutions containing R Code
Integrating R into the SQL Server Database lifecycle workflow
Prerequisites for this chapter
Using version control
Setting up continuous integration
Setting up continuous delivery
Monitoring the accuracy of the productionized model
Summary
Chapter 9: Machine Learning Services with R for DBAs
Gathering relevant data
Exploring and analyzing data
Creating a baseline and workloads, and replaying
Creating predictions with R - disk usage
Summary
Chapter 10: R and SQL Server 2016/2017 Features Extended
Built-in JSON capabilities
Accessing external data sources using PolyBase
High performance using ColumnStore and in memory OLTP
Summary

Book Details

ISBN 139781787283572
Paperback338 pages
Read More
From 2 reviews

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