Machine Learning Services (R announcement
-
Link
Overview
The process of building an end to end solution typically consists of obtaining and cleaning data, data exploration and feature engineering, model training and tuning, and finally deployment of the model in production. Development and testing of the actual code is best performed using a dedicated development environment. For R, that might mean RStudio or R Tools for Visual Studio.
However, after the solution has been created, you can easily deploy it to SQL Server using Transact-SQL stored procedures in the familiar environment of Management Studio.
In this tutorial, we assume that you have been given all the R code needed for the solution, and focus on building and deploying the solution using SQL Server.
Lesson 1: Download the sample data
Download the sample dataset and the sample SQL script files to a local computer.
Lesson 2: Import data to SQL Server using PowerShell
Execute a PowerShell script that creates a database and a table on the SQL Server 2017 instance and loads the sample data to the table.
Lesson 3: Explore and visualize the data
Perform basic data exploration and visualization, by calling R packages and functions from Transact-SQL stored procedures.
Lesson 4: Create data features using T-SQL
Create new data features using custom SQL functions.
Lesson 5: Train and save an R model using T-SQL
Build a machine learning model using R in stored procedures. Save the model to a SQL Server table.
Lesson 6: Operationalize the model
After the model has been saved to the database, call the model for prediction from Transact-SQL by using stored procedures.
-
Link
What is SQL Server Machine Learning Services?
SQL Server Machine Learning Services is an embedded, predictive analytics and data science engine that can execute R and Python code within a SQL Server database as stored procedures, as T-SQL script containing R or Python statements, or as R or Python code containing T-SQL.
The key value proposition of Machine Learning Services is the power of its proprietary packages to deliver advanced analytics at scale, and the ability to bring calculations and processing to where the data resides, eliminating the need to pull data across the network.
There are two options for using machine learning capabilities in SQL Server:
- SQL Server Machine Learning Services (In-Database) operates within the database engine instance, where the calculation engine is fully integrated with the database engine. Most installations are this option.
- SQL Server Machine Learning Server (Standalone) is a non-SQL installation. Although you use SQL Server Setup to install the server, it is completely decoupled from SQL Server. Functionally, it is equivalent to the non-SQL Microsoft Machine Learning Server for Windows.
R and Python packages
Support for each language is through proprietary Microsoft packages used for creating and training models of various types, scoring data, and parallel processing using the underlying system resources.
Because the proprietary packages are built on open-source R and Python distributions, script or code that you run in SQL Server can also call base functions and use third-party packages compatible with the language version provided in SQL Server (Python 3.5 and recent versions of R, currently 3.3.3).
R Python Description RevoScaleR revoscalepy Functions in these libraries are among the most widely used. Data transformations and manipulation, statistical summarization, visualization, and many forms of modeling and analyses are found in these libraries. Additionally, functions in these libraries automatically distribute workloads across available cores for parallel processing, with the ability to work on chunks of data that are coordinated and managed by the calculation engine. MicrosoftML microsoftml Industry-leading machine learning algorithms for image featurization, classification problems, and more. olapR none Build or execute an MDX query in R script. sqlRUtils none Functions for putting R scripts into a T-SQL stored procedure, registering a stored procedure with a database, and running the stored procedure from an R development environment. mrsdeploy none Primarily used on a non-SQL installation of Machine Learning Server, such as the (Standalone) version. Use this package to deploy and host web services, build scale-out topologies with dedicated web and compute nodes, toggle between local and remote sessions, run diagnostics, and more. For an (In-Database) installation, use this package in a client capacity: for example, to access a web service on a remote server dedicated to running just Machine Learning Services workloads. Portability of your custom R and Python code is addressed through package distribution and interpreters that are built into multiple products. The same packages that ship in SQL Server are also available in several other Microsoft products and services, including a non-SQL version called Microsoft Machine Learning Server. Free clients that include our R and Pyton interpreters include Microsoft R Client and the Python libraries.
-
1 Votes
Python in TSQL
Are there any advantages to run python scripts within a TSQL ??Answered | 5 Replies | 1744 Views | Created by sakurai_db - Wednesday, July 18, 2018 2:11 AM | Last reply by elenaflorence - Tuesday, August 18, 2020 7:34 AM -
0 Votes
Microsoft SQL Server Management Studio
I have SQL Server 2012 installed and I don't have the SQL Server Agent which I need to allow me to do auto backup to my database. Please help, thanks. I have a sql server 2012 installation ... -
0 Votes
Error in visual studio python project
selected "Python" -> selected "Migrate" Executing manage.py migrate Traceback ... -
0 Votes
Machine Learning Server (SQL): how to backup and restore Python and Packages
After installation of numpy-1.18.1-cp35-cp35m-win_amd64.whl package in Python 3.5 (machine learning server 9.4.7 for windows x64, SQL Management Studio v17.9.1, MSSQL Server 2017) I recieved the ... -
0 Votes
use R to open connection to remote SQL Server in script executed by sp_execute_external_script
This simple script runs as expected from RGui in the same R environment that was installed with SQL Server 2017 in-database R ... -
0 Votes
Python script fails on SQL Server 2017
conda list | findstr xgboost and the xgboost package was not found. I then ran ran conda install -c anaconda py-xgboost and the package was ...Unanswered | 1 Replies | 2413 Views | Created by CBrauer - Friday, April 5, 2019 7:08 PM | Last reply by BQAD - Saturday, June 15, 2019 9:26 AM -
0 Votes
Visual Studio run just the highlighted code
Hi All, Is it possible while in Visual Studio (a Python console project) to highlight some Python code in the *.py file and run just the highlighted ... -
0 Votes
Trying to do Machine learning on 2017 SQL Server
We are doing machine learning on a sql server 2017 instance. We have to re-install sql server 2017 each time after we receive this error: The exact error I am ... -
0 Votes
Running External Script with an AD Account
Hi all, I am using SQL Server 2019 2.1. and running some Python Scripts that write the output to a File. However, since the scripts are run per default with user ... -
0 Votes
R Services support for Always-On Clustering
Hi, Has anybody had a definiative answer as to if R Services for SQL 2016 is supported on Always-On Clusters? I know it isn't supported on Failover Clusters but ...Unanswered | 1 Replies | 1341 Views | Created by glennda37 - Wednesday, December 12, 2018 2:56 PM | Last reply by glennda37 - Thursday, January 10, 2019 11:23 AM -
0 Votes
SQL Server Machine Learning Services on Ubuntu
Hello, I've successfully installed SQL Server 2019 preview on Ubuntu 18.04 and I'd like to use RevoScalePy library which is a part of Microsoft MLlib for ... -
0 Votes
RevoScaleR package isn't available
Hi, I've installed SQL Server 2016 Enterprise Edition along with R Client. The package RevoScaleR should be available, but neither within R Studio nor Visual Studio. Running R ... -
0 Votes
Error while installing SQL Server 2017 Cumulative update 9
Hello I'm trying to install the Cumulative Update 9 but I keep getting the below error message while installing the update. Action ...Answered | 1 Replies | 3674 Views | Created by Rao G - Wednesday, July 25, 2018 2:08 AM | Last reply by Rao G - Friday, July 27, 2018 1:52 PM
Machine Learning Services (R announcement
-
Link
Overview
The process of building an end to end solution typically consists of obtaining and cleaning data, data exploration and feature engineering, model training and tuning, and finally deployment of the model in production. Development and testing of the actual code is best performed using a dedicated development environment. For R, that might mean RStudio or R Tools for Visual Studio.
However, after the solution has been created, you can easily deploy it to SQL Server using Transact-SQL stored procedures in the familiar environment of Management Studio.
In this tutorial, we assume that you have been given all the R code needed for the solution, and focus on building and deploying the solution using SQL Server.
Lesson 1: Download the sample data
Download the sample dataset and the sample SQL script files to a local computer.
Lesson 2: Import data to SQL Server using PowerShell
Execute a PowerShell script that creates a database and a table on the SQL Server 2017 instance and loads the sample data to the table.
Lesson 3: Explore and visualize the data
Perform basic data exploration and visualization, by calling R packages and functions from Transact-SQL stored procedures.
Lesson 4: Create data features using T-SQL
Create new data features using custom SQL functions.
Lesson 5: Train and save an R model using T-SQL
Build a machine learning model using R in stored procedures. Save the model to a SQL Server table.
Lesson 6: Operationalize the model
After the model has been saved to the database, call the model for prediction from Transact-SQL by using stored procedures.
-
Link
What is SQL Server Machine Learning Services?
SQL Server Machine Learning Services is an embedded, predictive analytics and data science engine that can execute R and Python code within a SQL Server database as stored procedures, as T-SQL script containing R or Python statements, or as R or Python code containing T-SQL.
The key value proposition of Machine Learning Services is the power of its proprietary packages to deliver advanced analytics at scale, and the ability to bring calculations and processing to where the data resides, eliminating the need to pull data across the network.
There are two options for using machine learning capabilities in SQL Server:
- SQL Server Machine Learning Services (In-Database) operates within the database engine instance, where the calculation engine is fully integrated with the database engine. Most installations are this option.
- SQL Server Machine Learning Server (Standalone) is a non-SQL installation. Although you use SQL Server Setup to install the server, it is completely decoupled from SQL Server. Functionally, it is equivalent to the non-SQL Microsoft Machine Learning Server for Windows.
R and Python packages
Support for each language is through proprietary Microsoft packages used for creating and training models of various types, scoring data, and parallel processing using the underlying system resources.
Because the proprietary packages are built on open-source R and Python distributions, script or code that you run in SQL Server can also call base functions and use third-party packages compatible with the language version provided in SQL Server (Python 3.5 and recent versions of R, currently 3.3.3).
R Python Description RevoScaleR revoscalepy Functions in these libraries are among the most widely used. Data transformations and manipulation, statistical summarization, visualization, and many forms of modeling and analyses are found in these libraries. Additionally, functions in these libraries automatically distribute workloads across available cores for parallel processing, with the ability to work on chunks of data that are coordinated and managed by the calculation engine. MicrosoftML microsoftml Industry-leading machine learning algorithms for image featurization, classification problems, and more. olapR none Build or execute an MDX query in R script. sqlRUtils none Functions for putting R scripts into a T-SQL stored procedure, registering a stored procedure with a database, and running the stored procedure from an R development environment. mrsdeploy none Primarily used on a non-SQL installation of Machine Learning Server, such as the (Standalone) version. Use this package to deploy and host web services, build scale-out topologies with dedicated web and compute nodes, toggle between local and remote sessions, run diagnostics, and more. For an (In-Database) installation, use this package in a client capacity: for example, to access a web service on a remote server dedicated to running just Machine Learning Services workloads. Portability of your custom R and Python code is addressed through package distribution and interpreters that are built into multiple products. The same packages that ship in SQL Server are also available in several other Microsoft products and services, including a non-SQL version called Microsoft Machine Learning Server. Free clients that include our R and Pyton interpreters include Microsoft R Client and the Python libraries.