Table of Contents
Introduction 1
Overview of Azure ML 2
A Regression Example 7
Improving the Model and Transformations 33
Another Azure ML Model 38
Using an R Model in Azure ML 42
Some Possible Next Steps 48
Publishing a Model as a Web Service 49
Summary 52
Introduction
recently, Microsoft launched the Azure Machine Learning cloud platform—Azure ML. Azure ML provides an easy-to-use and powerful set of cloud-based data transformation and machine learning tools. This report covers the basics of manipulating data, as well as constructing and evaluating models in Azure ML, illustrated with a data science example.
Before we get started, here are a few of the benefits Azure ML provides for machine learning solutions:
• Solutions can be quickly deployed as web services.
• Models run in a highly scalable cloud environment.
• Code and data are maintained in a secure cloud environment.
• Available algorithms and data transformations are extendable using the R language for solution-specific functionality.
Throughout this report, we’ll perform the required data manipulation then construct and evaluate a regression model for a bicycle sharing demand dataset. You can follow along by downloading the code and data provided below. Afterwards, we’ll review how to publish your trained models as web services in the Azure cloud.
