Getting started with data science in the cloud
Learn how to manipulate data, and construct and evaluate models in Azure ML, using a complete data science example.
Sand storm (source: Wellcome Library)
Large-scale machine learning, or predictive analytics, is having a powerful impact across many industries. By using machine learning, companies, governments, and not-for-profits are replacing guesses and seat-of-the-pants estimates with valuable data-driven predictions.
Deriving value from machine learning, however, is often impeded by complex technology deployments and long model-development cycles. Fortunately, machine learning and data science are undergoing democratization. Workflow environments make tools for building and evaluating sophisticated machine learning models accessible to a wider range of users. Cloud-based environments provide secure ubiquitous access to data storage and powerful data science tools.
Learn faster. Dig deeper. See farther.
To get you started creating and evaluating your own machine learning models, O’Reilly has commissioned a new report: “Data Science in the Cloud, with Azure Machine Learning and R.” We use an in-depth data science example — predicting bicycle rental demand — to show you how to perform basic data science tasks, including data management, data transformation, machine learning, and model evaluation in the Microsoft Azure Machine Learning cloud environment. Using a free-tier Azure ML account, example R scripts, and the data provided, the report provides hands-on experience with this practical data science example.
Specifically, this report shows you how to complete the following tasks using Azure ML and R:
- Manage and transform data, using a highly scalable cloud environment
- Build and evaluate machine learning models
- Produce R graphics
- Publish your models as web services
The dataset and R code used throughout the report are available on GitHub. Free tier Azure ML accounts are now available with a Microsoft ID.
This post is part of a collaboration between O’Reilly and Microsoft. See our statement of editorial independence.