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The Microsoft Azure ML team recently announced the availability of 3 ML templates on the Azure ML Studio – for online fraud detection, retail forecasting and text classification. These templates demonstrate industry best practices and common building blocks used in an ML solution for a specific domain, starting from data preparation, data processing, feature engineering, model training to model deployment (as a web service) . The goal for Azure ML templates is to make data scientists more productive and faster in building and deploying their custom ML solutions on the cloud. Templates include a collection of pre-configured Azure ML modules as well as custom R scripts in the Execute R Script modules to enable an end-to-end solution. We'll walk through these templates in detail in this and future webinars. In this webinar, we focus on the Text Classification template. There are broad applications of text classification: categorizing newspaper articles and news wire contents into topics, organizing web pages into hierarchical categories, filtering spam email, sentiment analysis, predicting user intent from search queries, routing support tickets, and analyzing customer feedback. The goal of text classification is to assign some piece of text to one or more predefined classes or categories. The piece of text could be a document, news article, search query, email, tweet, support tickets, customer feedback, user product review etc. This template demonstrates how to do text processing, feature engineering, training a sentiment classification model, and publishing it as a web service using twitter sentiment dataset.