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:
- Integrated, because you can run R or Python in SQL Server. This lets you easily merge enterprise processes for ETL and reporting with data science tasks such as feature engineering, model creation, and scoring.
- Scalable, because the data scientist can develop and test a solution on a laptop, and then deploy it to multiple platforms for distributed or parallel processing of key operations such as model training and scoring. Supported platforms include SQL Server on Windows, Hadoop, and Spark.
This article provides links to resources for each product in the Microsoft Machine Learning platform.
Machine learning in SQL Server
SQL Server 2017
Beginning with SQL Server 2017, you can now use Python code in SQL Server. To reflect the broader support for solutions in multiple languages (with more to come!), and the name was changed to Machine Learning Services (In-Database). Now you can automate machine learning tasks by using SQL tools to run either R or Python code. Or, use the SQL Server computer as the compute context for jobs launched from a remote development environment.
SQL Server 2016
SQL Server 2016 supports running R code in SQL Server using stored procedures. This makes it easy to automate machine learning tasks by using SQL tools. Or, you can run R code from a remote laptop or R development environment, while using the SQL Server computer as the compute context.
This integration provides security for your data and lets you manage and balance resources used by R.
Microsoft Machine Learning Server (Microsoft R Server)
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.
In SQL Server 2016, use the option to install Machine Learning Server.
You can also install Microsoft R Server or Machine Learning Server through platform-specific installers:
If you want to run Python using R Server, be sure to install the latest version, Machine Learning Server, which is available only through SQL Server 2017 setup:
If you already installed one of the machine learning server products, this article provides information about how to set up a separate computer for development and testing of solutions, including tools and required libraries.
Jump-start your entry into machine learning by getting this complete machine learning solution from the Azure Marketplace. The data science virtual machine (frequently shortened to "DSVM") includes SQL Server, Microsoft Machine Learning Server, and all development tools.
The latest version of the Data Science Virtual Machine (DSVM) runs on Windows 2016 Preview Edition, to provide the clean customizable look of Windows 10. It comes pre-configured with NVIDIA drivers, CUDA Toolkit 8.0, and the NVIDIA cuDNN library for GPU workloads.
Resources for learning
Start here to find a list of all resources for learning about machine learning solutions using SQL Server 2017 or SQL Server 2016.
Learn how to run R in SQL Server, create and use remote compute contexts, or perform simulations in R using SQL Server.
Includes "all code provided" tutorials so that you can run a complete R solution from SQL Server Management Studio, without ever opening an R IDE!
New to R? Wondering how Microsoft R (or RevoScaleR) compares to standard R? See these quick-starts for R Server.
Learn how to run Python in SQL Server. Build a model using Python and use it to score SQL Server data.
An end-to-end solution for SQL developers provides all the code you need to run Python from SQL Server Management Studio.
This walkthrough comes with all code needed to deploy a model to a web service using Machine Learning Server.
Product samples with code
These solutions from the SQL Server development team run in either R or Python, and demonstrate common scenarios for integrating machine learning with business applications.
Data science solution templates
The solution templates from the Microsoft Data Science team represent complete sample solutions tailored for specific industries or scenarios. They are intended to serve as building blocks to help you implement a solution fast, or to demonstrate best practices. Each template includes sample data and customizable code.