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10.04.2020
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Machine Learning Platform Average ratng: 5,9/10 5525 votes
-->Jan 21, 2020 Innovate on an open and flexible platform. Get built-in support for open-source tools and frameworks for machine learning model training and inferencing. Use familiar frameworks like PyTorch, TensorFlow, and scikit-learn, or the open and interoperable ONNX format.
Machine Learning Services is a feature in SQL Server that gives the ability to run Python and R scripts with relational data. You can use open-source packages and frameworks, and the Microsoft Python and R packages for predictive analytics and machine learning. The scripts are executed in-database without moving data outside SQL Server or over the network. This article explains the basics of SQL Server Machine Learning Services.
Note
For executing Java in SQL Server, see the Language Extensions documentation.
What is Machine Learning Services?
SQL Server Machine Learning Services lets you execute Python and R scripts in-database. You can use it to prepare and clean data, do feature engineering, and train, evaluate, and deploy machine learning models within a database. The feature runs your scripts where the data resides and eliminates transfer of the data across the network to another server.
Base distributions of Python and R are included in Machine Learning Services. You can install and use open-source packages and frameworks, such as PyTorch, TensorFlow, and scikit-learn, in addition to the Microsoft packages revoscalepy and microsoftml for Python, and RevoScaleR, MicrosoftML, olapR, and sqlrutils for R.
Machine Learning Services uses an extensibility framework to run Python and R scripts in SQL Server. Learn more about how this works:
What can I do with Machine Learning Services?
You can use Machine Learning Services to build and train machine learning and deep learning models within SQL Server. You can also deploy existing models to Machine Learning Services and use relational data for predictions.
Examples of the type of predictions that you can use SQL Server Machine Learning Services for include:
| Classification/Categorization | Automatically divide customer feedback into positive and negative categories |
| Regression/Predict continuous values | Predict the price of houses based on size and location |
| Anomaly Detection | Detect fraudulent banking transactions |
| Recommendations | Suggest products that online shoppers may want to buy, based on their previous purchases |
How to execute Python and R scripts
Visual studio mac vs windows. There are two ways to execute Python and R scripts in Machine Learning Services:
The most common way is to use the T-SQL stored procedure sp_execute_external_script.
You can also use your preferred Python or R client and write scripts that push the execution (referred to as a remote compute context) to a remote SQL Server. See how to set up a data science client for Python development and R development for more information.
Python and R versions
The following lists the versions of Python and R included in Machine Learning Services with each version of SQL Server.
| SQL Server version | Python version | R version |
|---|---|---|
| SQL Server 2017 | 3.5.2 | 3.3.3 |
| SQL Server 2019 | 3.7.3 | 3.5.2 |
For the R version in SQL Server 2016, see the R version section in What is R Services?
Python and R packages
You can use open-source packages and frameworks, in addition to Microsoft's enterprise packages. Most common open-source Python and R packages are pre-installed in Machine Learning Services. The following Python and R packages from Microsoft are also included:
| Language | Package | Description |
|---|---|---|
| Python | revoscalepy | The primary package for scalable Python. Data transformations and manipulation, statistical summarization, visualization, and many forms of modeling. Additionally, functions in this package automatically distribute workloads across available cores for parallel processing. |
| Python | microsoftml | Adds machine learning algorithms to create custom models for text analysis, image analysis, and sentiment analysis. |
| R | RevoScaleR | The primary package for scalable R. Data transformations and manipulation, statistical summarization, visualization, and many forms of modeling. Additionally, functions in this package automatically distribute workloads across available cores for parallel processing. |
| R | MicrosoftML (R) | Adds machine learning algorithms to create custom models for text analysis, image analysis, and sentiment analysis. |
| R | olapR | R functions used for MDX queries against a SQL Server Analysis Services OLAP cube. |
| R | sqlrutils | A mechanism to use R scripts in a T-SQL stored procedure, register that stored procedure with a database, and run the stored procedure from an R development environment. |
| R | Microsoft R Open | Microsoft R Open (MRO) is the enhanced distribution of R from Microsoft. It is a complete open-source platform for statistical analysis and data science. It is based on and 100% compatible with R, and includes additional capabilities for improved performance and reproducibility. |
For more information on which packages are installed with Machine Learning Services and how to install other packages, see:
- Install new R packages with sqlmlutils.
How do I get started with Machine Learning Services?
Configure your development tools. You can use: Aplicacion para descargar videos.
- Azure Data Studio or SQL Server Management Studio (SSMS) to use T-SQL and the stored procedure sp_execute_external_script to execute your Python or R script.
- Python or R on your own development laptop or workstation to execute scripts. You can either pull data down locally or push the execution remotely to SQL Server with revoscalepy and RevoScaleR. See how to set up a data science client for Python development and R development for more information.
Write your first Python or R script
- Quickstart: Run simple Python scripts
- Quickstart: Run simple R scripts
- Tutorial: Use Python in T-SQL: Explore data, perform feature engineering, train and deploy models, and make predictions (five-part series)
- Tutorial: Use R in T-SQL: Explore data, perform feature engineering, train and deploy models, and make predictions (five-part series)
Next steps
- Set up a data science client for Python development and R development
- Author: admin
- Category: Category
Machine Learning Platform Average ratng: 5,9/10 5525 votes
-->Jan 21, 2020 Innovate on an open and flexible platform. Get built-in support for open-source tools and frameworks for machine learning model training and inferencing. Use familiar frameworks like PyTorch, TensorFlow, and scikit-learn, or the open and interoperable ONNX format.
Machine Learning Services is a feature in SQL Server that gives the ability to run Python and R scripts with relational data. You can use open-source packages and frameworks, and the Microsoft Python and R packages for predictive analytics and machine learning. The scripts are executed in-database without moving data outside SQL Server or over the network. This article explains the basics of SQL Server Machine Learning Services.
Note
For executing Java in SQL Server, see the Language Extensions documentation.
What is Machine Learning Services?
SQL Server Machine Learning Services lets you execute Python and R scripts in-database. You can use it to prepare and clean data, do feature engineering, and train, evaluate, and deploy machine learning models within a database. The feature runs your scripts where the data resides and eliminates transfer of the data across the network to another server.
Base distributions of Python and R are included in Machine Learning Services. You can install and use open-source packages and frameworks, such as PyTorch, TensorFlow, and scikit-learn, in addition to the Microsoft packages revoscalepy and microsoftml for Python, and RevoScaleR, MicrosoftML, olapR, and sqlrutils for R.
Machine Learning Services uses an extensibility framework to run Python and R scripts in SQL Server. Learn more about how this works:
What can I do with Machine Learning Services?
You can use Machine Learning Services to build and train machine learning and deep learning models within SQL Server. You can also deploy existing models to Machine Learning Services and use relational data for predictions.
Examples of the type of predictions that you can use SQL Server Machine Learning Services for include:
| Classification/Categorization | Automatically divide customer feedback into positive and negative categories |
| Regression/Predict continuous values | Predict the price of houses based on size and location |
| Anomaly Detection | Detect fraudulent banking transactions |
| Recommendations | Suggest products that online shoppers may want to buy, based on their previous purchases |
How to execute Python and R scripts
Visual studio mac vs windows. There are two ways to execute Python and R scripts in Machine Learning Services:
The most common way is to use the T-SQL stored procedure sp_execute_external_script.
You can also use your preferred Python or R client and write scripts that push the execution (referred to as a remote compute context) to a remote SQL Server. See how to set up a data science client for Python development and R development for more information.
Python and R versions
The following lists the versions of Python and R included in Machine Learning Services with each version of SQL Server.
| SQL Server version | Python version | R version |
|---|---|---|
| SQL Server 2017 | 3.5.2 | 3.3.3 |
| SQL Server 2019 | 3.7.3 | 3.5.2 |
For the R version in SQL Server 2016, see the R version section in What is R Services?
Python and R packages
You can use open-source packages and frameworks, in addition to Microsoft's enterprise packages. Most common open-source Python and R packages are pre-installed in Machine Learning Services. The following Python and R packages from Microsoft are also included:
| Language | Package | Description |
|---|---|---|
| Python | revoscalepy | The primary package for scalable Python. Data transformations and manipulation, statistical summarization, visualization, and many forms of modeling. Additionally, functions in this package automatically distribute workloads across available cores for parallel processing. |
| Python | microsoftml | Adds machine learning algorithms to create custom models for text analysis, image analysis, and sentiment analysis. |
| R | RevoScaleR | The primary package for scalable R. Data transformations and manipulation, statistical summarization, visualization, and many forms of modeling. Additionally, functions in this package automatically distribute workloads across available cores for parallel processing. |
| R | MicrosoftML (R) | Adds machine learning algorithms to create custom models for text analysis, image analysis, and sentiment analysis. |
| R | olapR | R functions used for MDX queries against a SQL Server Analysis Services OLAP cube. |
| R | sqlrutils | A mechanism to use R scripts in a T-SQL stored procedure, register that stored procedure with a database, and run the stored procedure from an R development environment. |
| R | Microsoft R Open | Microsoft R Open (MRO) is the enhanced distribution of R from Microsoft. It is a complete open-source platform for statistical analysis and data science. It is based on and 100% compatible with R, and includes additional capabilities for improved performance and reproducibility. |
For more information on which packages are installed with Machine Learning Services and how to install other packages, see:
- Install new R packages with sqlmlutils.
How do I get started with Machine Learning Services?
Configure your development tools. You can use: Aplicacion para descargar videos.
- Azure Data Studio or SQL Server Management Studio (SSMS) to use T-SQL and the stored procedure sp_execute_external_script to execute your Python or R script.
- Python or R on your own development laptop or workstation to execute scripts. You can either pull data down locally or push the execution remotely to SQL Server with revoscalepy and RevoScaleR. See how to set up a data science client for Python development and R development for more information.
Write your first Python or R script
- Quickstart: Run simple Python scripts
- Quickstart: Run simple R scripts
- Tutorial: Use Python in T-SQL: Explore data, perform feature engineering, train and deploy models, and make predictions (five-part series)
- Tutorial: Use R in T-SQL: Explore data, perform feature engineering, train and deploy models, and make predictions (five-part series)
Next steps
- Set up a data science client for Python development and R development