Deploying & Hosting Predictive Models Through Webservices

Using Azure Machine Learning Studio


This MLaaS tutorial will walk users through deploying a classification model in Azure Machine Learning. The classification model uses the Titanic dataset to predict whether a passenger will live or die, based on demographic information. We’ve already built the model for you and the front-end UI. This tutorial will show you how to customize the Titanic model that we built and deploy your own version of it.

About the Data

The Titanic dataset’s complexity scales up with feature engineering, making it one of the few datasets good for both beginners and experts. There are numerous public resources to obtain the Titanic dataset, however, the most complete (and clean) version of the data can be obtained from Kaggle, specifically their “train” data.

The “train” Titanic data ships with 891 rows, each one pertaining to a passenger on the RMS Titanic, the night of the disaster. The dataset also has 12 columns that record attributes of each passenger’s circumstances and demographics such as: passenger id, passenger class, age, gender, name, number of siblings and spouses aboard, number of parents and children aboard, fare, ticket number, cabin number, port of embarkation, and whether or not they survived.

For additional reading, a repository of biographies pertaining to everyone aboard the RMS Titanic can be found here (complete with pictures).