Earlier this year I wrote a post that showed how to perform sentiment analysis in Dynamics CRM using Microsoft Azure Text Analytics. Azure Text Analytics makes it incredibly easy to use sentiment analysis (with English text only), but the full Azure Machine Learning offering is much more powerful. In today's post I will show how to create a custom predictive web service in Azure ML and make predictions with it in Dynamics CRM. One of the exciting announcements about Dynamics CRM 2016 is that it includes some sort of integration with Azure ML, so what's the point of this blog post? For this demonstration I am using data from the AdventureWorks data warehouse sample database to build a model to predict whether a contact in CRM is likely to be a bicycle buyer.
It was just two years ago that Microsoft and VMware were at odds over Microsoft making a preview of VMware virtualization on Azure. When that preview was announced, VMware officials noted Microsoft's preview was developed without VMware's participation and was neither certified nor supported by VMware. What a difference a couple years make. On April 29, Microsoft and Dell Technologies (a majority shareholder in VMware) announced via an "expanded partnership" that the two would provide customers with "a native, supported, and certified VMware experience on Microsoft Azure." The newly announced Azure VMware Solutions provides a common operating framework for running, managing and securing applications across VMware and Azure.
Artificial Intelligence (AI) has emerged as one of the most disruptive forces behind the digital transformation of business. Our mission is to bring AI to every developer and every organization on the planet, and help businesses augment human ingenuity in unique and differentiated ways. Developers and data scientists are at the heart of driving this innovation force and we are committed to providing them the best tools to make them successful.
The Event Hubs team is happy to announce the general availability of our integration with Apache Spark. Now, Event Hubs users can use Spark to easily build end-to-end streaming applications. The Event Hubs connector for Spark supports Spark Core, Spark Streaming, and Structured Streaming for Spark 2.1, Spark 2.2, and Spark 2.3. For users new to Spark, Spark Streaming and Structured Streaming are scalable, fault-tolerant stream processing engines. These processing engines allow users to process huge amounts of data using complex algorithms expressed with high-level functions like map, reduce, join, and window.