Chapter 1. Data Science in the Cloud with Microsoft Azure Machine Learning and R: 2015 Update

Introduction

This report covers the basics of manipulating data, constructing models, and evaluating models in the Microsoft Azure Machine Learning platform (Azure ML). The Azure ML platform has greatly simplified the development and deployment of machine learning models, with easy-to-use and powerful cloud-based data transformation and machine learning tools.

In this report, we’ll explore extending Azure ML with the R language. (A companion report explores extending Azure ML using the Python language.) All of the concepts we will cover are illustrated with a data science example, using a bicycle rental demand dataset. We’ll perform the required data manipulation, or data munging. Then, we will construct and evaluate regression models for the dataset.

You can follow along by downloading the code and data provided in the next section. Later in the report, we’ll discuss publishing your trained models as web services in the Azure cloud.

Before we get started, let’s review a few of the benefits Azure ML provides for machine learning solutions:

  • Solutions can be quickly and easily deployed as web services.
  • Models run in a highly scalable and secure cloud environment.
  • Azure ML is integrated with the powerful Microsoft Cortana Analytics Suite, which includes massive storage and processing capabilities. It can read data from and write data to Cortana storage at significant volume. Azure ML ...

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