Every day, EF Education First receives thousands of expressions of interest by prospective students to attend an education program delivered in any of the 150+ locations around the world. How is all this information processed promptly in order to provide a swift and effective response to applicants? We rank leads and interests based on program, location , past history, and hundreds, literally, of other criteria. We cannot do this manually clearly. We use the power of outcome prediction algorithms in Azure Machine Learning.
Targeted at software architects, developers and product owners, this session explores the foundation of Azure Machine Learning for building outcome prediction services, describing how data is collected and defined into a model; how the model is trained and then scored; and finally how the evaluation of the model is processed to generate the ranked outcome.
Custom decision-tree algorithms are presented in the programming language R, along with RESTful Web Services consumed by our CRM application. This session completes also with the illustration of best practices and guidelines for maintaining and deploying large-scale datasets in the Cloud and optimisation of computing time of ML experiments.