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This article provides a comprehensive list of the tutorials, demos, and sample applications that use machine learning features in SQL Server 2016 or SQL Server 2017. Start here to learn how to run R or Python from T-SQL, how to use remote and local compute contexts, and how to optimize your R and Python code for a SQL production environment.
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This article provides a comprehensive list of the tutorials, demos, and sample applications that use machine learning features in SQL Server 2016 or SQL Server 2017. Start here to learn how to run R or Python from T-SQL, how to use remote and local compute contexts, and how to operationalize your R and Python code for a SQL production environment.
These real-world scenarios from the SQL Server development team demonstrate how to embed machine learning in applications. All samples include code that you can download, modify, and use in production.
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+[Solutions](#bkmk_solutions)
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Templates from the Microsoft Data Science team are customizable, to get you started fast with machine learning. Each solution is tailored to a specific task or industry problem. Most of the solutions are designed to run either in SQL Server, or in a cloud environment such as Azure Machine Learning. Other solutions can run on Linux or in Spark or Hadoop clusters, by using Microsoft R Server or Machine Learning Server.
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### <aname ="bkmk_samples"></a>SQL Server product samples
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## <aname ="bkmk_samples"></a>R and Python samples
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These samples and demos provided by the SQL Server and R Server development team highlight ways that you can use embedded analytics in real-world applications.
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|[Build a predictive model using Python and SQL Server](https://microsoft.github.io/sql-ml-tutorials/python/rentalprediction/)| Build the ski rental analysis application using Python, to help plan for future demand. This example uses the new Python library, **revoscalepy**, to create a linear regression model. | SQL Server 2017 |
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|[How to use Tableau with SQL Server Machine Learning Services](https://blogs.msdn.microsoft.com/mlserver/2017/12/14/how-to-use-tableau-with-sql-server-machine-learning-services-with-r-and-python/)| Analyze social media and create Tableau graphs, using SQL Server and R. | SQL Server 2016 or SQL Server 2017 |
The Microsoft Data Science Team has provided customizable solution templates that can be used to jump-start solutions for common scenarios. Each solution is tailored to a specific task or industry problem. Most of the solutions are designed to run either in SQL Server, or in a cloud environment such as Azure Machine Learning. Other solutions can run on Linux or in Spark or Hadoop clusters, by using Microsoft R Server or Machine Learning Server.
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The Microsoft Data Science Team has provided solution templates that can be used to jump-start solutions for common scenarios. All code is provided, along with instructions on how to train and deploy a model for scoring using SQL Server stored procedures.
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All code is provided, along with instructions on how to train and deploy a model for scoring using SQL Server stored procedures.
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title: Use a Python model in SQL Server for training and predictions | Microsoft Docs
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description: Create and train a model using Python and the classic Iris data set. Save the model to SQL Server, and then use it to generate predicted outcomes.
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title: Python models in SQL Server for training and predictions using stored procedures | Microsoft Docs
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description: Embed Python code in SQL Server stored procedures to create, train, and use a Python model with the classic Iris data set. Save a trained model to SQL Server, and then use it to generate predicted outcomes.
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ms.prod: sql
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ms.technology: machine-learning
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ms.author: heidist
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manager: cgronlun
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# Use a Python model in SQL Server for training and scoring
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# Create, train, and use a Python model with stored procedures in SQL Server
This exercise demonstrates the capabilities of Python integration with SQL Server when you [add machine learning](../install/sql-machine-learning-services-windows-install.md) to a database engine instance. On a Python-integrated instance, you can wrap Python code inside a [stored procedure](../../relational-databases/stored-procedures/stored-procedures-database-engine.md), enabling one of the major mechanisms for operationalizing production code in SQL Server. The ability to embed code in a procedure has tangible benefits in how you design, test, and manage data science and machine learning workloads in SQL Server.
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This exercise demonstrates the capabilities of Python integration with SQL Server when you [add machine learning](../install/sql-machine-learning-services-windows-install.md) to a database engine instance. On a Python-integrated instance, you can wrap Python code inside a [stored procedure](../../relational-databases/stored-procedures/stored-procedures-database-engine.md), leveraging one of the major mechanisms for operationalizing production code in SQL Server. The ability to embed code in a procedure has tangible benefits in how you design, test, and manage data science and machine learning workloads in SQL Server.
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In this Python exercise, you will create and execute two procedures. The first one uses the classic Iris flower data set and generates a Naïve Bayes model to predict an Iris species based on flower characteristics. The second procedure is for scoring. It calls the model generated in the first procedure to output a set of predictions. By placing code in a stored procedure, operations are contained, reusable, and callable by other stored procedures and client applications.
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By completing this tutorial, you will learn how to:
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By completing this tutorial, you will learn:
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> [!div class="checklist"]
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> *Use stored procedures to operationalize code
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> *Embed code in a stored procedure
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> *Pass inputs to your code through inputs on the stored procedure
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> *How stored procedures are used to operationalize code
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> *How to embed code in a stored procedure
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> *How to pass inputs to your code through inputs on the stored procedure
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title: Capture a trace with Database Experimentation Assistant for SQL Server upgrades
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description: Capture a trace with Database Experimentation Assistant
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ms.custom: ""
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ms.date: 10/12/2018
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ms.date: 10/22/2018
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ms.prod: sql
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ms.prod_service: dea
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ms.suite: sql
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ms.topic: conceptual
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author: HJToland3
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ms.author: ajaykar
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- Confirm the SQL Server name is valid. To confirm, try connecting to the server using SSMS.
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- Confirm your firewall configuration isn't blocking connections to SQL Server.
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- Confirm user has the permissions listed in the permissions blog.
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- Confirm user has the permissions listed in the blog posting [Replay FAQ](https://blogs.msdn.microsoft.com/datamigration/2017/03/24/dea-2-0-replay-faq/).
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- Confirm that trace name doesn't follow the standard rollover convention (that is, 'Capture\_1'). You can instead try trace names like 'Capture\_1A' or 'Capture1'.
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Following are some possible errors you might see and solutions for resolving them:
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title: Create Analysis Reports with Database Experimentation Assistant for SQL Server upgrades
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description: Create Analysis Reports with Database Experimentation Assistant
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ms.custom: ""
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ms.date: 10/22/2018
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|Possible Errors|Solution|
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|Unable to connect to the database. Make sure you have sysadmin privileges for analyzing and viewing the reports.|You might not have access or sysadmin permissions to the server or database. Confirm your login permissions and privileges and try again|
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|Unable to generate Report Name on server Server Name. For details, check the Report Name report.|You might not have sysadmin privileges needed to generate a new report. Detailed errors can be found by clicking on the errored out report (see #4) and by checking the logs found at %temp%\\DEA.|
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|Unable to generate Report Name on server Server Name. For details, check the Report Name report.|You might not have sysadmin privileges needed to generate a new report. Detailed errors can be found by clicking on the errored out report and by checking the logs found at %temp%\\DEA.|
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|The current user doesn't have the required permissions to run the operation. Make sure you have sysadmin privileges for performing trace and analyzing the reports.|You don't have sysadmin privileges needed to generate a new report.|
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title: Replay a trace with Database Experimentation Assistant for SQL Server upgrades
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description: Replay a trace with Database Experimentation Assistant
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ms.date: 10/22/2018
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### Verify setup
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-**SQL Server install path:** Provide the path to where SQL Server is installed. For example, [C:\\Program](/Program) Files (x86)\\Microsoft SQL Server\\120.
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-**SQL Server install path:** Provide the path to where SQL Server is installed. For example, C:\\Program Files (x86)\\Microsoft SQL Server\\120.
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-**Controller machine name:** Provide the name of the machine that has been set up as the controller. This machine is the one running the Windows service named SQL Server Distributed Replay controller. The Distributed Replay controller orchestrates the actions of the Distributed Replay clients. There can only be one controller instance in each Distributed Replay environment.
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-**Client machine names:** Provide the name for each client machine, separated by commas, for example client1, client2. You can have up to five client controllers. Clients are one or more machines, physical or virtual, running the Windows service named SQL Server Distributed Replay client. The Distributed Replay clients work together to simulate workloads against an instance of SQL Server. There can be one or more clients in each Distributed Replay environment.
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- Select **Next**.
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- Verify that the Distributed Replay controller service is running on the controller machine using the Distributed Replay Management Tools (run command `dreplay.exe status -f 1`).
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- If the replay is being started remotely:
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- Confirm that the machine running DEA can ping the controller successfully. Confirm firewall settings to allow connections per instructions in the Configure Replay Environment page.
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- Confirm that the machine running DEA can ping the controller successfully. Confirm firewall settings to allow connections per instructions in the Configure Replay Environment page. For more infomation, see the article [SQL Server Distributed Replay](https://docs.microsoft.com/sql/tools/distributed-replay/sql-server-distributed-replay?view=sql-server-2017).
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- Make sure that the DCOM Remote Launch and Remote Activation are allowed for the user of the Distributed Replay controller.
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- Make sure that the DCOM Remote Access permissions are allowed for the user of Distributed Replay controller.
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