Data Science in the Cloud with Microsoft Azure Machine Learning and R: 2015 Update

Take some time to explore Microsoft’s Azure machine learning platform, Azure ML—a production environment that simplifies the development and deployment of machine learning models. In this updated and expanded O’Reilly report, Stephen Elston from Quantia Analytics uses a complete data science example (forecasting hourly demand for a bicycle rental system) to show you how to manipulate data, construct models, and evaluate models with Azure ML.

The report walks you through key steps in the data science process from problem definition, data understanding, and feature engineering, through construction of a regression model and presentation of results. You’ll also learn how to extend Azure ML with R. Elston uses downloadable sample R code and data to demonstrate how to perform data munging, data visualization, and in-depth evaluation of model performance. At the end, you’ll learn how to publish your trained models as web services in the Azure cloud.

With this 2015 Update, you’ll learn how to:

• Navigate the Azure ML Gallery
• Use the R Model module
• Load R packages from a zip file
• Use the Metadata Editor
• Publish a scoring model as a web service
• Use the Cross Validate model module
• Publish a web service to Excel
• Apply a SQL transformation
• Use the new Sweep Parameters module