SQL Server 2017 Machine Learning Services with R电子书

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SQL Server 2017 Machine Learning Services with R

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Contributors

About the authors

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Preface

Who this book is for

What this book covers

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Conventions used

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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

Microsoft R Open (MRO)

Microsoft Machine Learning R Server

Microsoft SQL Server Machine Learning R Services

R Tools for Visual Studio (RTVS)

The Microsoft Machine Learning R Services architecture

R Limitations

Performance issues

Memory limitations

Security aspects

Language syntax

Summary

Managing Machine Learning Services for SQL Server 2017 and R

Minimum requirements

Choosing the edition

Configuring the database

Configuring the environment and installing R Tools for Visual Studio (RTVS)

Security

Resource Governor

Installing new R packages

Package information

Using R Tools for Visual Studio (RTVS) 2015 or higher

Using R.exe in CMD

Using XP_CMDSHELL

Copying files

Using the rxInstallPackages function

Managing SQL Server R Services with PowerShell

Getting to know the sp_execute_external_script external procedure

Arguments

Summary

Data Exploration and Data Visualization

Understanding SQL and R data types

Data frames in R

Data exploration and data munging

Importing SQL Server data into R

Exploring data in R

Data munging in R

Adding/removing rows/columns in data frames

More data munging with dplyr

Finding missing values

Transpose data

Pivot / Unpivot data

Example - data exploration and munging using R in T-SQL

Data visualization in R

Plot

Histogram

Boxplot

Scatter plot

Tree diagram

Example – R data visualization in T-SQL

Integrating R code in reports and visualizations

Integrating R in SSRS reports

Integrating R in Power BI

Summary

RevoScaleR Package

Overcomming R language limitations

Scalable and distributive computational environments

Functions for data preparation

Data import from SAS, SPSS, and ODBC

Importing SAS data

Importing SPSS data

Importing data using ODBC

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

Prerequisite – prepare the data

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

Step 2 – Operationalize the model

Fast batch prediction

Prerequisites

Real-time scoring

Native scoring

Integrating the R model for fast batch prediction

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

Step 2a – Operationalize the model using real-time scoring

Step 2b – Operationalize the model using native scoring

Managing roles and permissions for workloads

Extensibility framework workloads

Fast batch prediction workloads

External packages

Tools

Using SSMS as part of operationalizing R script

Using custom reports for SQL Server R Services

Adding the custom reports for the first time

Viewing an R Services custom report

Managing SQL Server Machine Learning Services with DMVs

System configuration and system resources

Resource governor

Operationalizing R code with Visual Studio

Integrating R workloads and prediction operations beyond SQL Server

Executing SQL Server prediction operations via PowerShell

Scheduling training and prediction operations

Operationalizing R script as part of SSIS

Summary

Deploying, Managing, and Monitoring Database Solutions containing R Code

Integrating R into the SQL Server Database lifecycle workflow

Preparing your environment for the database lifecycle workflow

Prerequisites for this chapter

Creating the SQL Server database project

Importing an existing database into the project

Adding a new stored procedure object

Publishing schema changes

Adding a unit test against a stored procedure

Using version control

Setting up continuous integration

Creating a build definition in VSTS

Deploying the build to a local SQL Server instance

Adding the test phase to the build definition

Automating the build for CI

Setting up continuous delivery

Monitoring the accuracy of the productionized model

Useful references

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

Testing rxLinMod performance on a table with a primary key

Testing rxLinMod performance on a table with a clustered ColumnStore index

Testing rxLinMod performance on a memory-optimized table with a primary key

Testing rxLinMod performance on a memory-optimized table with a clustered ColumnStore index

Comparing results

Summary

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