Microsoft Azure Machine Learning

One of the key elements of our solution is Microsoft Azure’s Machine Learning. As each athlete is different, Machine Learning is leveraged to establish a baseline against which everything else will be compared to.

For everyone interested in Machine Learning on Microsoft Azure, please check out:

• eBook – Microsoft Azure Essentials: Azure Machine Learning
  This ebook will present an overview of modern data science theory and principles, the associated workflow, and then cover some of the more common machine learning algorithms in use today. We will build a variety of predictive analytics models using real world data, evaluate several different machine learning algorithms and modeling strategies, and then deploy the finished models as machine learning web service on Azure within a matter of minutes. The book will also expand on a working Azure Machine Learning predictive model example to explore the types of client and server applications you can create to consume Azure Machine Learning web services.The scenarios and end-to-end examples in this book are intended to provide sufficient information for you to quickly begin leveraging the capabilities of Azure ML Studio and then easily extend the sample scenarios to create your own powerful predictive analytic experiments. The book wraps up by providing details on how to apply “continuous learning” techniques to programmatically “retrain” Azure ML predictive models without any human intervention.
• Webinar: Azure Machine Learning for Software Engineers
  Date: Tuesday, June 9, 2015
  Time: 10:00 A.M. – 11:00 A.M. PDT
  Get a running into machine learning with this short introductory session about Azure Machine Learning, specifically intended for engineers. For many software engineers, machine learning and data science largely remains a mystery, even as the technology becomes more and more pervasive in business models, forecasting, predictive maintenance, and more. Software engineers today must understand the concepts of machine learning in the cloud at a conceptual level along with consumption of cloud services that provide machine learning.In this session Dan Grecoe explains Azure Machine Learning through a comprehensive end-to-end example that he builds during the session and that encompasses:
  • Problem detection
  • Algorithm selection
  • Machine learning model creation and deployment as a RESTful web service
  • Consumption of the machine learning model