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(This article was first published on <strong><a href="http://blog.revolutionanalytics.com/2017/10/two-upcoming-webinars.html"> Revolutions</a></strong>, and kindly contributed to <a rel="nofollow" href="https://www.r-bloggers.com/">R-bloggers)</a> </div>
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<p>Two new Microsoft webinars are taking place over the next week that may be of interest:</p>
<p><strong><a rel="nofollow" href="https://info.microsoft.com/ai-development-with-dsvm.html" target="_blank">AI Development in Azure using Data Science Virtual Machines</a></strong></p>
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<p>The <a rel="nofollow" href="http://aka.ms/DSVM" target="_blank">Azure Data Science Virtual Machine</a> (DSVM) provides a comprehensive development and production environment to Data Scientists and AI-savvy developers. DSVMs are specialized virtual machine images that have been curated, configured, tested and heavily used by Microsoft engineers and data scientists. DSVM is an integral part of the Microsoft AI Platform and is available for customers to use through the Microsoft Azure cloud. In this session, we will first introduce DSVM, familiarize attendees with the product, including our newest offering, namely Deep Learning Virtual Machines (DLVMs). That will be followed by technical deep-dives into samples of end-to-end AI development and deployment scenarios that involve deep learning. We will also cover scenarios involving cloud based scale-out and parallelization.</p>
<p>This webinar runs from <strong>10-11AM Pacific Time on Thursday October 26</strong>, and is presented by Gopi Kumar, Principal Program Manager, Paul Shealy, Senior Software Engineer, and Barnam Bora, Program Manager, at Microsoft. <a rel="nofollow" href="https://info.microsoft.com/ai-development-with-dsvm.html" target="_blank">Register here</a>.</p>
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<p><strong><a rel="nofollow" href="https://info.microsoft.com/document-collection-analysis-ondemand.html" target="_blank">Document Collection Analysis</a></strong></p>
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<p>With the extremely large volumes of data, especially unstructured text data, that are being collected every day, a huge challenge facing customers is the need for tools and techniques to organize, search, and understand this vast quantity of text. This webinar demonstrates an efficient and automated end-to-end workflow around analyzing large document collections and serving your downstream NLP tasks. We’ll demonstrate how to summarize and analyze a large collection of documents, including techniques such as phrase learning, topic modeling, and topic model analysis using the <a rel="nofollow" href="http://azureml.azureedge.net/content/apphome/index.html" target="_blank">Azure Machine Learning Workbench</a>.</p>
<p>This webinar runs from <strong>10-11AM Pacific Time on Tuesday October 31</strong>, and will be presented by Ke Huang, Data Scientist at Microsoft. <a rel="nofollow" href="https://info.microsoft.com/document-collection-analysis-ondemand.html" target="_blank">Register here</a>.</p>
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