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Machine Learning Templates with SQL Server 2016 R Services

Machine Learning Templates with SQL Server 2016 R Services

Microsoft recently launched SQL Server 2016, which, in addition to many other great features, offers in-database advanced analytics with R Services, allowing users to combine the power of SQL Server and Microsoft R Server (or Open Source R), without data leaving the database.

With SQL Server R Services, users can develop analytic models in a local R IDE (e.g., R Tools for Visual Studio or RStudio), while data resides in SQL Server, and computation happens on SQL Server (by setting the compute context to SQL Server).

Once the model is ready for production, it can be operationalized via SQL stored procedures (where R code is encapsulated inside), which can be run within SQL Server Management Studio or called by outside applications to make predictions.

To jump-start users on building advanced analytics applications with SQL Server R Services, Microsoft provides a few data science templates that address real-world scenarios, including: online fraud detection,predictive maintenance, and customer churn prediction.  These templates are sample advanced analytics solutions that demonstrate best practices and provide building blocks to help users implement a solution quickly. Each template is designed to solve a specific problem, and includes sample data, R code (which uses the highly scalable Microsoft R Server ScaleR APIs) and SQL stored procedure code that extends from data preparation and feature engineering to model training and scoring. The code runs in R IDE (with computation done in SQL Server) or SQL Server client (SQL Server Management Studio) respectively. A Windows PowerShell script is provided to run SQL stored procedures end-to-end. The collection of templates can be found here and more information on each is provided below.

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Online Fraud Detection Template (SQL Server R Services) One of the important tasks of online businesses is to detect fraudulent transactions and identify transactions made by stolen payment instruments or credentials in order to reduce charge back losses. When fraudulent transactions are discovered, online store businesses typically take measures to block related accounts as soon as possible, to prevent further losses. In this scenario, you’ll learn how to use data from online purchase transactions to identify likely fraud.;



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