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.

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Table of Contents (12 chapters)
Preface
Preface
Who this book is for
What this book covers
To get the most out of this book
Get in touch
Free Chapter
1
Introduction to R and SQL Server
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
2
Overview of Microsoft Machine Learning Server and SQL Server
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
3
Managing Machine Learning Services for SQL Server 2017 and R
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
4
Data Exploration and Data Visualization
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
5
RevoScaleR Package
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
6
Predictive Modeling
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
7
Operationalizing R Code
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
8
Deploying, Managing, and Monitoring Database Solutions containing R Code
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
9
Machine Learning Services with R for DBAs
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
10
R and SQL Server 2016/2017 Features Extended
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
11
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Integrating the R model for fast batch prediction

Before continuing to the next steps, please follow the Prerequisite - Prepare the data section.

Step 1 – Train and save a real-time scoring model using T-SQL

In this step, you can create a predictive model for real-time scoring and native scoring, and optionally for the AUC, into a table via a stored procedure. The goal is to build a model that is reusable. You can skip this step if there is an existing compatible model that was created and stored in a table in SQL Server already.

The following stored procedure uses rxSerializeModel, which lets you serialize an R model in raw format. This then allows you to save the model in the VARBINARY format, which can be loaded...

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