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A framework for categorizing and comparing cloud platforms

bijoor.me

Agile Development. Virtual Machines : pool resources of many different servers, thereby creating a virtual layer that offers automatic load balancing and higher levels of availability than the underlying hardware can offer Examples : Amazon EC2 , OpenStack Nova , MS Windows Azure , Google Compute Engine , IBM SmartCloud. Technology.

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The Difference Between Apple & Facebook ? AGILEVC

Agile VC

Apple and Facebook are both platforms , in the sense that their software and user networks are utilized by countless software developers to build their own businesses. Windows Mobile has been a disappointment to date but Azure (an outgrowth of the.NET framework) is growing as a force on the cloud/server side. June 5, 2012.

Java 170
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The cloud landscape described, categorized, and compared

www.theenterprisearchitect.eu

The Enterprise Architect building an Agile Enterprise Home MDD PaaS Startup Presentations About The cloud landscape described, categorized, and compared October 12, 2013 Johan Den Haan 10 comments “I work for a PaaS company” I answered him. Network virtualization is often referred to as Software Defined Networking (SDN).

Cloud 48
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The Benefits Of Using Microsoft Dynamics 365

The Startup Magazine

With the built-in Predictive Analytics which are supported by Azure Machine Learning, Power BI and Cortana that are embedded natively, Dynamic 365 can deliver real-time insights and advice on how to take the next actionable and productive steps. Intelligent.

Azure 119
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Digital Analytics Simplified: The Beginner’s Guide

ConversionXL

This will ensure everyone has access to the data they need, when they need it, making teams more agile and able to tweak strategies quickly. These center on: Connections between Google Analytics data and personally identifiable information; Integrations with a wider range of ad networks; Granular, actionable data visualizations.

Analytics 106
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Artificial Intelligence and Machine Learning– Explained

Steve Blank

Neural networks make up the backbone of deep learning. The “deep” in deep learning refers to the depth of layers in a neural network.) The difference between the network’s guess and the ground truth is its error. This reduces network bandwidth issues and eliminates latency issues. Examples here , here and here.

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CEO Friday: Why we don’t hire.NET programmers

blog.expensify.com

They write everything from assembly to jQuery, on PCs to mobile phones, doing hard core computer graphics to high level social networking. Big things, like obscuring the networking stack under so many countless layers of abstraction that it’s virtually impossible to even imagine what bytes are actually going over the wire.

Java 107