| title | R tutorials |
|---|---|
| description | This article describes the R tutorials and quickstarts for SQL Server Machine Learning Services. |
| ms.prod | sql |
| ms.technology | machine-learning |
| ms.date | 04/13/2020 |
| ms.topic | tutorial |
| author | dphansen |
| ms.author | davidph |
| ms.custom | seo-lt-2019 |
| monikerRange | >=sql-server-2016||>=sql-server-linux-ver15||=sqlallproducts-allversions |
[!INCLUDEappliesto-ss-xxxx-xxxx-xxx-md]
This article describes the R tutorials and quickstarts for SQL Server Machine Learning Services.
- Learn how to run R scripts.
- Build, train, and deploy R models to SQL Server.
- Learn about remote and local compute contexts.
- Explore the Microsoft R packages for data science and machine learning.
| Link | Description |
|---|---|
| Quickstart: Create and run simple R scripts | First of several quickstarts, with this one illustrating the basic syntax for calling an R function using a T-SQL query editor such as SQL Server Management Studio. |
| Tutorial: Learn in-database R analytics for data scientists | For R developers new to SQL Server, this tutorial explains how to perform common data science tasks in SQL Server. Load and visualize data, train and save a model to SQL Server, and use the model for predictive analytics. |
| Tutorial: Learn in-database R analytics for SQL developers | Build and deploy a complete R solution, using only [!INCLUDEtsql] tools. Focuses on moving a solution into production. You'll learn how to wrap R code in a stored procedure, save an R model to a [!INCLUDEssNoVersion] database, and make parameterized calls to the R model for prediction. |
| Tutorial: RevoScaleR deep dive | Learn how to use the functions in the RevoScaleR packages. Move data between R and SQL Server, and switch compute contexts to suit a particular task. Create models and plots, and move them between your development environment and the database server. |
| Link | Description |
|---|---|
| Build a predictive model using R and SQL Server | Learn how a ski rental business might use machine learning to predict future rentals, which helps the business plan and staff to meet future demand. |
| Perform customer clustering using R and SQL Server | Use unsupervised learning to segment customers based on sales data. |