Discover more examples at Microsoft Machine Learning Server
In these examples, we will demonstrate how to develop and deploy end-to-end advanced analytics solutions with SQL Server ML Services. The samples provided here are created in R. Samples in Python are available in the ML Server Python Samples repository. Although these templates are targeting SQL Server ML Services, they can be deployed on Microsoft ML Server as well.
Develop models in R IDE. SQL Server ML services allows Data Scientists to develop solutions in an R IDE (such as RStudio, Visual Studio R Tools) with Open Source R / Python or Microsoft ML Server, using data residing in SQL Server, and computing done in-database.
Operationalize models in SQL. Once the model development is completed, the model (data processing, feature engineering, training, saved models, and production scoring) can be deployed to SQL Server using T-SQL Stored Procedures, which can be run within SQL environment (such as SQL Server Management Studio) or called by applications to make predictions.
Templates can be easily deployed to Azure using the Deploy to Azure button on the templates' readme pages.
We have developed a number of templates for solving specific machine learning problems with SQL Server ML Services. These templates provides a higher starting point and aims to enable users to quickly build and deploy solutions. Each template includes the following components:
- Predefined data schema applicable to the specific domain
- Domain specific data processing and feature engineering steps
- Preselected *training *algorithms fit to the specific domain
- Domain specific evaluation metrics where applicable
- Prediction (scoring) in production.
The available templates are listed below.
In these templates, we show the two version of implementations:
- Development Code in R IDE
- Operationalization In SQL
The following is the directory structure for each template:
- Data This contains the provided sample data for each application.
- R This contains the R development code (Microsoft ML Server). It runs in R IDE, with computation being done in-database (by setting compute context to SQL Server).
- SQLR This contains the Stored SQL procedures from data processing to model deployment. It runs in SQL environment. A Powershell script is provided to invoke the modeling steps end-to-end.
| Template | Description |
|---|---|
| Performance Tuning | This template provides a few tips on how to improve performance of running R scripts in SQL Server compute context. |
NOTE: Please don't use "Download ZIP" to get this repository, as it will change the line endings in the data files. Use "git clone" to get a local copy of this repository.
This project has adopted the Microsoft Open Source Code of Conduct. For more information see the Code of Conduct FAQ or contact opencode@microsoft.com with any additional questions or comments.