In two previous posts, I covered the emerging industry of cloud-based machine learning solutions. First, I covered Microsoft's Azure Machine Learning and IBM's Watson Analytics. Microsoft's Azure ML provides a graphical drag-and-drop interface for connecting preprogrammed components of a data science pipeline together. The service is similar to KNIME and seemed targeted for users who knew just enough to know what to do, but not so much that they would want to code up fresh algorithms. One value added for Microsoft's product is a smooth integration for companies which already have their data stored in Microsoft's Azure compute cloud.

Amazon recently announced Amazon Machine Learning, a cloud machine learning solution for Amazon Web Services. Able to pull data effortlessly from RDS, S3 and Redshift, the product could pose a significant threat to Microsoft Azure ML and IBM Watson Analytics. Upon selecting a model, the service asks whether the user would like to holdout data for validation from the training set or to provide holdout data from a different source. Once these selections are made, Amazon ML trains the model on the given dataset. Using the sample dataset of dummy bank customers (5MB in size), training takes roughly 10 minutes. When evaluating the evaluation metric for a binary classification task, Amazon ML reports the area under the ROC curve (AUC).

In two previous posts, I covered the emerging industry of cloud-based machine learning solutions. First, I covered Microsoft's Azure Machine Learning and IBM's Watson Analytics. Microsoft's Azure ML provides a graphical drag-and-drop interface for connecting preprogrammed components of a data science pipeline together. The service is similar to KNIME and seemed targeted for users who knew just enough to know what to do, but not so much that they would want to code up fresh algorithms. One value added for Microsoft's product is a smooth integration for companies which already have their data stored in Microsoft's Azure compute cloud.