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Take 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 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 Python. Elston uses downloadable Python 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 report, you’ll learn how to:
- Navigate Azure ML Studio
- Use the Python Script module
- Load Python modules from a zip file
- Use the Sweep Parameters module
- Apply a SQL transformation
- Use the Cross Validate Model module
- Publish a scoring model as a web service to Excel
- Use Jupyter Notebooks with Azure ML
Stephen F. Elston, Managing Director of Quantia Analytics, LLC is a big-data geek, data scientist, instructor, and O’Reilly author. He has over two decades of experience in predictive analytics and machine learning with R, S/SPLUS, and Python. Elston has developed, sold, and supported multiple analytics solutions. He creates solutions for financial market and credit risk, trading analytics, wireless telecom, and fraud prevention, and is an Azure Advisor to Microsoft.
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