Tech talk

Victor Arzate a Program Manager at Microsoft, will walk you thru the process of deploying VM extensions on Windows and Linux virtual machines in Microsoft Azure Stack TP1, utilizing the Quick Start JSON templates available on GitHub.

MicrosoftML (MML) is a new machine learning package for Microsoft R Server. Microsoft R Server brings you the ability to do parallel and chunked data processing that addresses the restrictions of in-memory open source R. MML adds Microsoft's battle-tested algorithms and data transforms that are used by product teams across Microsoft. This brings new machine learning functionality with increased speed, performance and scale, especially for handling a large corpus of text data and high-dimensional categorical data. MML includes the ability to: Create text classification models for problems such as sentiment analysis and support ticket classification. Train deep neural nets in order to solve complex problems such as retail image classification and handwriting analysis. Work with high-dimensional data for scenarios like online advertising click-through prediction. Solve many other common machine learning tasks such as churn prediction, loan risk analysis, and demand forecasting ...

Microsoft R Server 9.1.0, Microsoft's R distribution with added big-data, in-database, and integration capabilities, is released in April 2017 and is available for download. The release has several exciting features, including new machine-learning capabilities to support text and image processing and improved operationalization. The update includes new functionality to MicrosoftML. This package provides state-of-the-art, fast and scalable machine learning algorithms for common data science tasks including featurization, classification and regression. Some of the new functions include: Added support for most MRS platforms including Spark, , and Linux Out of the box image featurization with several deep neural pre-trained models Easy to use sentiment analysis functionality Support for Ensembling and parallel learning Improved operationalization on web and SQL