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Getting started with machine learning in SQL Server

Microsoft provides an integrated, scalable set of machine learning solutions for both on-premises and the cloud: This article provides links to resources for each product in the Microsoft Machine Learning platform.

The option to install Machine Learning Server is provided in SQL Server 2017 to support enterprise customers who want to run distributed, scalable machine learning jobs, but who don't need the integration with the SQL Server database engine, such as the use of SQL compute contexts.

Install SQL Server machine learning components without internet access

By default, installers connect to Microsoft download sites to get required and updated components for machine learning on SQL Server.

If firewall constraints prevent the installer from reaching these sites, you can use an internet-connected device to download files, transfer files to an offline server, and then run setup.

If you would like to view the source code for Microsoft R, it is available for download as an archive in .tar format: Download R Server installers Depending on your environment, you might need to make local copies of installers for the following prerequisites.

For answers to common questions and known issues, see the following article: To check the installation status of the instance and fix common issues, try these custom reports.

For your next step, see the following links: Python developers can learn how to use Python with SQL Server by following these tutorials: To view examples of machine learning that are based on real-world scenarios, see Machine learning tutorials.

Install SQL Server 2016 R Services (In-Database)

Common scenarios that require additional changes include: During setup, some new Windows user accounts are created for running tasks under the security token of the SQL Server Trusted Launchpad service.

When a user sends an R script from an external client, SQL Server activates an available worker account, maps it to the identity of the calling user, and runs the R script on behalf of the user.

However, if you need to run R scripts from a remote data science client and are using Windows authentication, you must grant these worker accounts permission to sign in to the SQL Server instance on your behalf.

If you installed SQL Server in your own instance, you are usually executing scripts as an administrator, or at least as a database owner, and thus have implicit permissions for various operations, all data in the database, and the ability to install new packages as needed.

The user account that is used to run R might need to read data from other databases, create new tables to store results, and write data into tables.

If you create an R solution on a data science client computer and need to run code by using the SQL Server computer as the compute context, you can use either a SQL login or integrated Windows authentication.

If you think you might use R heavily, or if you expect many users to be running scripts concurrently, you can increase the number of worker accounts that are assigned to the Launchpad service.

The default settings for SQL Server setup are intended to optimize the balance of the server for a variety of services that are supported by the database engine, which might include extract, transform, and load (ETL) processes, reporting, auditing, and applications that use SQL Server data.

The R solutions you create for SQL Server can call basic R functions, functions from the properietary packes installed with SQL Server, and third-party R packages compatible with the version of open-source R installed by SQL Server.

In SQL Server 2017, you can set up user groups to share packages on a per-database level, or configure database roles to enable users to install their own packages.

For answers to common questions and known issues, see the following article: To check the installation status of the instance and fix common issues, try these custom reports.

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