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SQL Server 2017 Machine Learning Services with R

By : Toma≈æ Ka≈°trun Kaštrun, Koesmarno

4.3 (8)

Buy this Book

SQL Server 2017 Machine Learning Services with R

4.3 (8)

By: Toma≈æ Ka≈°trun Kaštrun, Koesmarno

Buy this Book

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.

Preface

Who this book is for

What this book covers

To get the most out of this book

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Free Chapter

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

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

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

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

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

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

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

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

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

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

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Deploying, Managing, and Monitoring Database Solutions containing R Code

Operationalizing R code in a SQL Server database means that data scientists/database developers can also leverage productionizing data science solutions as part of Database Lifecycle Management (DLM). This includes the following:

Checking in R code as part of a SQL Server database project into a version control
Adding the stored procedures for the data science solution as part of SQL Server unit tests
Integrating the data science solution into the Continuous Integration/Continuous Delivery (CI/CD) process
Monitoring performance of the data science solution in the production on a regular basis

In this chapter, we will be using SQL Server Data Tools (SSDT) in Visual Studio 2017 and Visual Studio Team Services to perform this DLM workflow. However, the underlying concept can be applied to any other CI/CD platform...