#Machine Learning Templates with SQL Server 2016 R Services

In these examples, we will demonstrate how to develop and deploy end-to-end advanced analytics solutions with SQL Server 2016 R Services.

Develop models in R IDE. SQL Server 2016 R services allows Data Scientists to develop solutions in an R IDE (such as RStudio, Visual Studio R Tools) with Open Source R or Microsoft R 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.

Machine Learning Templates. We have developed a number of templates for solving specific machine learning problems with SQL Server R 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. Please check back often, as there will be new templates added periodically.

• Predictive Maintenance. Predict machine failures.
• Customer Churn Prediction. Predict when a customer churn happens.
• Online Purchase Fraud Detection. Predict if an online purchase transactions is fraudulent.
• Energy Demand Forecasting. Forecast electricity demand of multiple regions.
• Retail Forecasting Forecast the product sales for a retail store.
• Campaign Management Predict when and how to contact potential customers.

Templates with SQL Server R Services. 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 R 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.

Performance Turning. This folds provide a few tips on how to improve performance of running R script in SQL Server compute context.

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