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‎.openpublishing.redirection.json‎

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"redirect_url": "/sql/linux/sql-server-linux-manage-ssms",
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"redirect_document_id": false
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},
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"source_path": "docs/advanced-analytics/r/install-pretrained-models-sql-server.md",
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"redirect_url": "https://docs.microsoft.com/sql/advanced-analytics/install/sql-pretrained-models-install",
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{
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"source_path": "docs/advanced-analytics/tutorials/walkthrough-prerequisites-for-data-science-walkthroughs.md",
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"redirect_url": "https://docs.microsoft.com/sql/advanced-analytics/tutorials/walkthrough-data-science-end-to-end-walkthrough",

‎docs/advanced-analytics/TOC.md‎

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## SQL Server 2016
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### [R Services (In-Database)](install/sql-r-services-windows-install.md)
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### [R Server (Standalone)](install/sql-r-standalone-windows-install.md)
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## [Pre-trained models](r/install-pretrained-models-sql-server.md)
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## [Pre-trained models](install/sql-pretrained-models-install.md)
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## [Command-prompt setup](install/sql-ml-component-commandline-install.md)
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## [Offline setup (no internet)](install/sql-ml-component-install-without-internet-access.md)
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## [Upgrade R and Python](r/use-sqlbindr-exe-to-upgrade-an-instance-of-sql-server.md)

‎docs/advanced-analytics/install/sql-pretrained-models-install.md‎

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# Install pre-trained machine learning models on SQL Server
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[!INCLUDE[appliesto-ss-xxxx-xxxx-xxx-md-winonly](../../includes/appliesto-ss-xxxx-xxxx-xxx-md-winonly.md)]
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This article explains how to add free, pre-trained machine learning models for *sentiment analysis* and *image featurization* to a SQL Server database engine instance having R or Python integration. The pre-trained models are built by Microsoft and ready-to-use. You can add them to an existing instance using a PowerShell script. For more information about these models, see the [Resources](#bkmk_resources) section of this article.
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This article explains how to add free pre-trained machine learning models for *sentiment analysis* and *image featurization* to a SQL Server database engine instance having R or Python integration. The pre-trained models are built by Microsoft and ready-to-use, easily added to an existing instance using a PowerShell script. For more information about these models, see the [Resources](#bkmk_resources) section of this article.
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Once installed, the pre-trained models are considered an implementation detail that power specific functions in the MicrosoftML (R) and microsoftml (Python) libraries. You should not (and cannot) view or modify the models, or treat them as an independent resource in custom code or paired other functions.
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Once installed, the pre-trained models are considered an implementation detail that power specific functions in the MicrosoftML (R) and microsoftml (Python) libraries. You should not (and cannot) view, customize, or retrain the models, nor can you treat them as an independent resource in custom code or paired other functions.
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Functions invoking the pretrained models are listed in the following table.
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| R function (MicrosoftML) | Python function (microsoftml) | Usage |
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|--------------------------|-------------------------------|-------|
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| [getSentiment](https://docs.microsoft.com/machine-learning-server/r-reference/microsoftml/getsentiment) | [get_sentiment](https://docs.microsoft.com//machine-learning-server/python-reference/microsoftml/get-sentiment) | Generates positive-negative sentiment score over text inputs. For more information, see this [blog post](https://blogs.msdn.microsoft.com/sqlserverstorageengine/2017/11/01/sentiment-analysis-with-python-in-sql-server-machine-learning-services/).|
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| [featurizeImage](https://docs.microsoft.com/machine-learning-server/r-reference/microsoftml/featurizeimage) | [featurize_image](https://docs.microsoft.com/machine-learning-server/python-reference/microsoftml/featurize-image) | Extracts text information from image file inputs. For more information, see this [blog post](https://blogs.msdn.microsoft.com/mlserver/2017/04/12/image-featurization-with-a-pre-trained-deep-neural-network-model/). |
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| [getSentiment](https://docs.microsoft.com/machine-learning-server/r-reference/microsoftml/getsentiment) | [get_sentiment](https://docs.microsoft.com//machine-learning-server/python-reference/microsoftml/get-sentiment) | Generates positive-negative sentiment score over text inputs. [Learn more...](https://blogs.msdn.microsoft.com/sqlserverstorageengine/2017/11/01/sentiment-analysis-with-python-in-sql-server-machine-learning-services/).|
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| [featurizeImage](https://docs.microsoft.com/machine-learning-server/r-reference/microsoftml/featurizeimage) | [featurize_image](https://docs.microsoft.com/machine-learning-server/python-reference/microsoftml/featurize-image) | Extracts text information from image file inputs. [Learn more...](https://blogs.msdn.microsoft.com/mlserver/2017/04/12/image-featurization-with-a-pre-trained-deep-neural-network-model/). |
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## Prerequisites
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SQL Server with R or Python, with the [MicrosoftML package for R](https://docs.microsoft.com/machine-learning-server/r-reference/microsoftml/microsoftml-package) or [microsoftml package for Python](https://docs.microsoft.com/machine-learning-server/python-reference/microsoftml/microsoftml-package).
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+ [SQL Server 2017 Machine Learning Services](sql-machine-learning-services-windows-install.md) with R, Python, or both. This version includes the required libraries. Component upgrade is not necessary for this version.
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+ [SQL Server 2016 R Services](sql-r-services-windows-install.md), with upgraded R components as documented in [Upgrade machine learning (R and Python) components](../r/use-sqlbindr-exe-to-upgrade-an-instance-of-sql-server.md).
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This step is necessary for adding MicrosoftML to your installation. Recall that SQL Server 2016 is R-only so only the R components can be upgraded. To add Python integration, you must have SQL Server 2017.
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+ [SQL Server 2017 Machine Learning Services](sql-machine-learning-services-windows-install.md) with R, Python, or both. This version includes the required libraries. Component upgrade is not necessary for this version.
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This step is necessary for adding MicrosoftML to your installation. Recall that SQL Server 2016 is R-only so only the R components can be upgraded. For Python integration, you must have SQL Server 2017.
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External scripts must be enabled and SQL Server LaunchPad service must be running. Installation instructions provide the steps for additional configuration and verification.
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On an internet-connected SQL Server 2017 Machine Learning default instance with R and Python, you should see messages similar to the following.
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```powershell
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MSSQL14.MSSQLSERVER
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Verifying R models [9.2.0.24]
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Downloading R models [C:\Users\<user-name>\AppData\Local\Temp]
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Installing R models [C:\Program Files\Microsoft SQL Server\MSSQL14.MSSQLSERVER\R_SERVICES\]
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Verifying Python models [9.2.0.24]
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Installing Python models [C:\Program Files\Microsoft SQL Server\MSSQL14.MSSQLSERVER\PYTHON_SERVICES\]
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PS C:\WINDOWS\system32>
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```
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```powershell
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MSSQL14.MSSQLSERVER
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Verifying R models [9.2.0.24]
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Downloading R models [C:\Users\<user-name>\AppData\Local\Temp]
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Installing R models [C:\Program Files\Microsoft SQL Server\MSSQL14.MSSQLSERVER\R_SERVICES\]
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Verifying Python models [9.2.0.24]
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Installing Python models [C:\Program Files\Microsoft SQL Server\MSSQL14.MSSQLSERVER\PYTHON_SERVICES\]
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PS C:\WINDOWS\system32>
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```
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<a name="verify"> </a>
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‎docs/advanced-analytics/r/r-server-standalone.md‎

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| Python tools | The built-in Python command line tool is useful for ad hoc testing and tasks. Find the tool at \Program files\Microsoft SQL Server\140\PYTHON_SERVER\python.exe. |
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| Anaconda | Anaconda is an open-source distribution of Python and essential packages. |
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| Python samples and scripts | As with R, Python includes built-in data sets and scripts. Find the revoscalepy data at \Program files\Microsoft SQL Server\140\PYTHON_SERVER\lib\site-packages\revoscalepy\data\sample-data. |
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| Pre-trained models in R and Python | Pre-trained models are supported and usable on a standalone server, but you cannot install them through SQL Server Setup. The setup program for Microsoft Machine Learning Server provides the models, which you can install free of charge. For more information, see [Install pretrained machine learning models on SQL Server](install-pretrained-models-sql-server.md). |
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| Pre-trained models in R and Python | Pre-trained models are supported and usable on a standalone server, but you cannot install them through SQL Server Setup. The setup program for Microsoft Machine Learning Server provides the models, which you can install free of charge. For more information, see [Install pretrained machine learning models on SQL Server](../install/sql-pretrained-models-install.md). |
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## Get started step-by-step
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‎docs/advanced-analytics/r/sql-server-r-services.md‎

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| Python tools | The built-in Python command line tool is useful for ad hoc testing and tasks. Find the tool at \Program files\Microsoft SQL Server\140\PYTHON_SERVER\python.exe. |
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| Anaconda | Anaconda is an open-source distribution of Python and essential packages. |
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| Python samples and scripts | As with R, Python includes built-in data sets and scripts. Find the revoscalepy data at \Program files\Microsoft SQL Server\140\PYTHON_SERVER\lib\site-packages\revoscalepy\data\sample-data. |
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| Pre-trained models in R and Python | Pre-trained models are supported and usable on a standalone server, but you cannot install them through SQL Server Setup. The setup program for Microsoft Machine Learning Server provides the models, which you can install free of charge. For more information, see [Install pretrained machine learning models on SQL Server](install-pretrained-models-sql-server.md). |
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| Pre-trained models in R and Python | Pre-trained models are supported and usable on a standalone server, but you cannot install them through SQL Server Setup. The setup program for Microsoft Machine Learning Server provides the models, which you can install free of charge. For more information, see [Install pretrained machine learning models on SQL Server](../install/sql-pretrained-models-install.md). |
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## Get started step-by-step
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‎docs/advanced-analytics/what-s-new-in-sql-server-machine-learning-services.md‎

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| CU 1 | In revoscalepy, adds rx_create_col_info for returning schema information from a SQL Server data source, similar to [rxCreateColInfo](https://docs.microsoft.com/machine-learning-server/r-reference/revoscaler/rxcreatecolinfo) for R. <br/><br/>Enhancements to [rx_exec](https://docs.microsoft.com/machine-learning-server/python-reference/revoscalepy/rx-exec) to support parallel scenarios using the `RxLocalParallel` compute context.|
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| Initial release |[**Python integration for in-database analytics**](https://blogs.technet.microsoft.com/dataplatforminsider/2017/04/19/python-in-sql-server-2017-enhanced-in-database-machine-learning/) <br/><br/>The [revoscalepy](python/what-is-revoscalepy.md) package is the Python-equivalent of RevoScaleR. You can create Python models for linear and logistic regressions, decision trees, boosted trees, and random forests, all parallelizable, and capable of being run in remote compute contexts. This package supports use of multiple data sources and remote compute contexts. The data scientist or developer can execute Python code on a remote SQL Server, to explore data or build models without moving data. <br/><br/>The [microsoftml](https://docs.microsoft.com/machine-learning-server/python-reference/microsoftml/microsoftml-package) package is the Python-equivalent of the MicrosoftML R package.<br/><br/>T-SQL and Python integration through [sp_execute_external_script](https://docs.microsoft.com/sql/relational-databases/system-stored-procedures/sp-execute-external-script-transact-sql). You can call any Python code using this stored procedure. This secure infrastructure enables enterprise-grade deployment of Python models and scripts that can be called from an application using a simple stored procedure. Additional performance gains are achieved by streaming data from SQL to Python processes and MPI ring parallelization. <br/><br/>You can use the T-SQL [PREDICT](../t-sql/queries/predict-transact-sql.md) function to perform [native scoring](sql-native-scoring.md) on a pre-trained model that has been previously saved in the required binary format.|
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| Initial release | [**MicrosoftML (R)**](using-the-microsoftml-package.md) contains state-of-the-art machine learning algorithms and data transformation that can be scaled or run in remote compute contexts. Algorithms include customizable deep neural networks, fast decision trees and decision forests, linear regression, and logistic regression. |
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| Initial release | [**Pre-trained models**](r/install-pretrained-models-sql-server.md) for image recognition and positive-negative sentiment analysis. Use these models to generate predictions on your own data. |
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| Initial release | [**Pre-trained models**](install/sql-pretrained-models-install.md) for image recognition and positive-negative sentiment analysis. Use these models to generate predictions on your own data. |
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| Initial release | [**R package management**](r/install-additional-r-packages-on-sql-server.md), including the following highlights: database roles to help the DBA manage packages and assign permissions to install packages, [CREATE EXTERNAL LIBRARY](https://docs.microsoft.com/sql/t-sql/statements/create-external-library-transact-sql) statement in T-SQL to help DBAs manage packages without needing to know R, and a rich set of R functions in [RevoScaleR](r/use-revoscaler-to-manage-r-packages.md) to help install, remove, or list packages owned by users. |
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| Initial release | [**Operationalization through mrsdeploy**](https://docs.microsoft.com/machine-learning-server/r-reference/mrsdeploy/mrsdeploy-package) for deploying and hosting R script as a web service. Applies to R script only (no Python equivalent). Intended for the (Standalone) server option to avoid resource competition with other SQL Server operations. |
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