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• Leave a review - let other readers know what you think
• Other Books You May Enjoy
• Summary
• Comparing results
• Testing rxLinMod performance on a memory-optimized table with a clustered ColumnStore index
• Testing rxLinMod performance on a memory-optimized table with a primary key

• Leave a review - let other readers know what you think 更新時間：2021-06-24 19:04:10
• Other Books You May Enjoy
• Summary
• Comparing results
• Testing rxLinMod performance on a memory-optimized table with a clustered ColumnStore index
• Testing rxLinMod performance on a memory-optimized table with a primary key
• Testing rxLinMod performance on a table with a clustered ColumnStore index
• Testing rxLinMod performance on a table with a primary key
• High performance using ColumnStore and in memory OLTP
• Accessing external data sources using PolyBase
• Built-in JSON capabilities
• R and SQL Server 2016/2017 Features Extended
• Summary
• Creating predictions with R - disk usage
• Creating a baseline and workloads and replaying
• Exploring and analyzing data
• Gathering relevant data
• Machine Learning Services with R for DBAs
• Summary
• Useful references
• Monitoring the accuracy of the productionized model
• Setting up continuous delivery
• Automating the build for CI
• Adding the test phase to the build definition
• Deploying the build to a local SQL Server instance
• Creating a build definition in VSTS
• Setting up continuous integration
• Using version control
• Adding a unit test against a stored procedure
• Publishing schema changes
• Adding a new stored procedure object
• Importing an existing database into the project
• Creating the SQL Server database project
• Prerequisites for this chapter
• Preparing your environment for the database lifecycle workflow
• Integrating R into the SQL Server Database lifecycle workflow
• Deploying Managing and Monitoring Database Solutions containing R Code
• Summary
• Operationalizing R script as part of SSIS
• Scheduling training and prediction operations
• Executing SQL Server prediction operations via PowerShell
• Integrating R workloads and prediction operations beyond SQL Server
• Operationalizing R code with Visual Studio
• Resource governor
• System configuration and system resources
• Managing SQL Server Machine Learning Services with DMVs
• Viewing an R Services custom report
• Adding the custom reports for the first time
• Using custom reports for SQL Server R Services
• Using SSMS as part of operationalizing R script
• Tools
• External packages
• Fast batch prediction workloads
• Extensibility framework workloads
• Managing roles and permissions for workloads
• Step 2b – Operationalize the model using native scoring
• Step 2a – Operationalize the model using real-time scoring
• Step 1 – Train and save a real-time scoring model using T-SQL
• Integrating the R model for fast batch prediction
• Native scoring
• Real-time scoring
• Prerequisites
• Fast batch prediction
• Step 2 – Operationalize the model
• Step 1 – Train and save a model using T-SQL
• Prerequisite – prepare the data
• Integrating an existing R model
• Operationalizing R Code
• Summary
• Performing predictions with R Services in the SQL Server database
• Deploying and using predictive solutions
• Advanced predictive algorithms and analytics
• Data modeling
• Predictive Modeling
• Summary
• Functions for statistical tests and sampling
• Functions for descriptive statistics
• Dataset merging
• Dataset subsetting
• Variable creation and recoding
• Variable creation and data transformation
• Importing data using ODBC
• Importing SPSS data
• Importing SAS data
• Data import from SAS SPSS and ODBC
• Functions for data preparation
• Scalable and distributive computational environments
• Overcomming R language limitations
• RevoScaleR Package
• Summary
• Integrating R in Power BI
• Integrating R in SSRS reports
• Integrating R code in reports and visualizations
• Example – R data visualization in T-SQL
• Tree diagram
• Scatter plot
• Boxplot
• Histogram
• Plot
• Data visualization in R
• Example - data exploration and munging using R in T-SQL
• Pivot / Unpivot data
• Transpose data
• Finding missing values
• More data munging with dplyr
• Adding/removing rows/columns in data frames
• Data munging in R
• Exploring data in R
• Importing SQL Server data into R
• Data exploration and data munging
• Data frames in R
• Understanding SQL and R data types
• Data Exploration and Data Visualization
• Summary
• Arguments
• Getting to know the sp_execute_external_script external procedure
• Managing SQL Server R Services with PowerShell
• Using the rxInstallPackages function
• Copying files
• Using XP_CMDSHELL
• Using R.exe in CMD
• Using R Tools for Visual Studio (RTVS) 2015 or higher
• Package information
• Installing new R packages
• Resource Governor
• Security
• Configuring the environment and installing R Tools for Visual Studio (RTVS)
• Configuring the database
• Choosing the edition
• Minimum requirements
• Managing Machine Learning Services for SQL Server 2017 and R
• Summary
• Language syntax
• Security aspects
• Memory limitations
• Performance issues
• R Limitations
• The Microsoft Machine Learning R Services architecture
• R Tools for Visual Studio (RTVS)
• Microsoft SQL Server Machine Learning R Services
• Microsoft Machine Learning R Server
• Microsoft R Open (MRO)
• The Microsoft Machine learning R Server platform
• Analytical barriers
• Overview of Microsoft Machine Learning Server and SQL Server
• Summary
• Boosting analytics with SQL Server R integration
• Microsoft's commitment to the open source R language
• Using R prior to SQL Server 2016
• Introduction to R and SQL Server
• Reviews
• Get in touch
• Conventions used
• Download the color images
• Download the example code files
• To get the most out of this book
• What this book covers
• Who this book is for
• Preface
• Packt is searching for authors like you
• About the reviewers
• About the authors
• Contributors
• PacktPub.com
• Why subscribe?
• www.PacktPub.com
• Title Page
• coverpage

• coverpage
• Title Page
• www.PacktPub.com
• Why subscribe?
• PacktPub.com
• Contributors
• About the authors
• About the reviewers
• Packt is searching for authors like you
• Preface
• Who this book is for
• What this book covers
• To get the most out of this book
• Download the example code files
• Download the color images
• Conventions used
• Get in touch
• Reviews
• 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
• Other Books You May Enjoy
• Leave a review - let other readers know what you think 更新時間：2021-06-24 19:04:10