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SQL Server 2017 Machine Learning Services with R
Thisbookisfordataanalysts,datascientists,anddatabaseadministratorswithsomeornoexperienceinRbutwhoareeagertoeasilydeliverpracticaldatasciencesolutionsintheirday-to-daywork(orfutureprojects)usingSQLServer.
目錄(168章)
倒序
- 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
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