Microsoft Azure Technical Overview

Editor's Notes

• #4: At the core of Azure is its global infrastructure that spans 38 regions worldwide. We have the largest footprint of any cloud provider – 2X that of AWS. https://azure.microsoft.com/en-us/regions/
• #67: Debug and Optimize your Big Data programs with ease:Debugging failures in cloud distributed programs are now as easy as debugging a program in your personal environment. Our execution environment actively analyzes your programs as they run and offers recommendations to improve performance and reduce cost. For example, if you requested 1000 AUs for your program and only 50 AUs were needed, the system would recommend that you only use 50 AUs resulting in a 20x cost savings.
• #70: The elegance of the solution is in its simplicity – something that has been lacking in the machine learning space The first issue many enterprises face is data ingestion. With the cloud, you can bring in data sources with the ease of a drop down or drop your on-premises data set into the built in storage space. Users can then model in our development environment – Machine Learning Studio where we’re offering R, Python and SQLite as first class citizens in addition to our world-class Microsoft algorithms. The second issue – and often the primary one – is putting finished work into production in a way others can use. Client devices, websites, mobile applications, excel spreadsheets you name it anything that supports HTTP requests. We’ve heard from many data scientists that they model in R on a Linux stack but then have to hand over their work to developers who need to translate that into another language to actually make it work. This time consuming and unnecessary process has been eliminated with our system, as the model is with a click transformed into a web service end-point that can run over any data, anywhere and connect to any solution or client. Next, not only can this model be put into production for your company, it can be made available for the world on our Machine Learning Marketplace. Microsoft hosts your solution and markets it for you, while you have the freedom to brand and monetize as you see fit. 
• #71: Microsoft built our machine learning solution as part of Azure so it comes with all the benefits of the cloud. Scalable, no new hardware investments, pay as you go, and as you see value. Let’s review a snapshot of the key benefits here. First and foremost, it’s comes fully managed. No software to install, no hardware to manage, and no updates to worry about. and one portal to view and update. And it’s all based in Azure, so there is one portal to manage, monitor and update the solution. No moving back and forth between technologies or struggling with integrations. It’s Integrated. This is what customers expect from Microsoft and here we deliver in a space where ease of use is lacking today. The visual interface provides simple, drag, drop and connect features. Users can easily access it from anywhere, and share across geographies. Azure Machine Learning is the result of years of innovation, so it comes packed with best in class algorithms like the ones that power Bing and Xbox. There are sample experiments to help customers quickly get started, support for custom R solutions so customers don’t lose investments they’ve already made in machine learning—easily port over their current solutions – no wasted work. And it supports over 350 open source R packages. And uniquely in the market customers can deploy solutions in minutes.
• #72: SVM = Support vector machines