This lab explores unsupervised learning in Azure Machine Learning and how to deploy a predictive model as a web service. The lab will walk through copying an experiment from the Azure Machine Learning Gallery into the ML Studio, creating a scoring experiment, deploying a model as a web service, and interacting with the API using the included web interface.
“Where should I open my next restaurant location?” This question is often very difficult to answer. The right choice could lead to increased revenue and profit, but the wrong choice could lead to losing a major investment. Trying to make this decision by manually sifting through hundreds or even thousands of possible cities or neighborhoods can be almost impossible. Machine learning can help with this task by analyzing large volumes of data about different locations, finding common characteristics among locations, and grouping those like-attributed locations together. These groups can then be compared to previously successful restaurant locations to help narrow the choices for where to open next. In this lab, you will work with a dataset that includes geographic, economic, and demographic data about different US cities. The model you will explore uses a K-Means algorithm to cluster cities into distinctive buckets.