Applying Deep Learning at Cloud Scale, with Microsoft R Server & Azure Data Lake


This post is by Max Kaznady, Data Scientist, Miguel Fierro, Data Scientist, Richin Jain, Solution Architect, T. J. Hazen, Principal Data Scientist Manager, and Tao Wu, Principal Data Scientist Manager, all at Microsoft. Today's businesses collect vast volumes of images, video, text and other types of data – data which can provide tremendous business value if efficiently processed at scale and using sophisticated machine learning algorithms. Example applications include real-time labeling and monitoring of sentiment in tweets, itemization of equipment and materials at construction sites through video surveillance, and real-time fraud detection in the financial domain, to name a few. In a previous blog post, we described how to set up DNNs in the cloud using a high performance GPU VM and MXNet. In this sequel, we outline a pipeline process for training and scoring with DNNs in a large-scale production environment.