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Microsoft's Prediction Power : Azure Machine Learning - A Quick Overview

Microsoft recently announced the general availability of its cloud-based Azure Machine Learning service for Predictive Analytics. This post is a quick read about Azure Machine Learning's features, what it means to Data Scientists & Developers and some important Microsoft market news surrounding the topic.

Microsoft has unleashed its Azure Machine Learning as a general availability from 18-02-2015 onwards. Microsoft’s driving factor behind Azure Machine Learning is “to offer a fully-managed cloud service that will enable data scientists and developers to efficiently embed predictive analytics into their applications, helping organizations use massive amounts of data and bring all the benefits of the cloud to machine learning”. 

A 7 Point Features Overview :

  1. Azure Machine Learning is a  fully managed service – which means you do not need to buy any hardware nor manage VMs manually
  2. A browser-based machine learning IDE that enables quick creation and automation of  machine learning workflows
  3. Built-in ML libraries that can be drag/dropped to jump-start your predictive analytics solutions
  4. Features a library of sample experiments, R and Python packages and best-in-class algorithms from Microsoft businesses
  5. Supports R and Python custom code with shared workspaces
  6. Creation and discovery of web services, train/retrain your models through APIs, scale web services on demand and configure diagnostics for service monitoring and debugging
  7. Publish, consume, monetize and brand Machine Learning web services that expose trained models directly using the Azure Machine Learning Marketplace 

4 Advantages for Data Scientists?

  • No setup time, ready to start and no more computing resource limitations
  • Azure marketplace to utilize existing models or publish/monetize your new models
  • Supports familiar R, Python languages and reuse of existing scripts
  • Simple and easy configurations for modeling and deployment 

4 What’s in it for IT Developers?

  • Brings prediction capabilities to the masses and available to non-experts
  • Opens up the possibility to utilize ML models in day-to-day IT  applications and infrastructure
  • Predictive models would be a big plus to interpret the huge data that would result from the Internet Of Things(IOT)
  • Ready to use ML APIs , sample experiments to learn from, Machine Learning blogs and forums to support 

4 Initial Challenges :

  • IT Pros: Support for familiar languages like C# would make it more quickly explorable
  • IT Pros: “Drag/Drop” and “Quick Start” examples might help in designing basic models but the selection of algorithms, methods and tuning the models for real-world examples would require statistics, science and mathematical knowledge
  • Data Scientists : Spreading the word to non-Microsoft line of scientists/researchers, adapting and shifting to the new working model from familiar environments like Matlab
  • Data Scientists : Most of the algorithms and methods are available considering its initial launch but still lacks some methods, algorithms that the data scientists might need in their experiments (feedback from a Data Scientist) 

A 2 point recent Microsoft’s Analytics Market Facts: 

  • Microsoft acquired Revolution Analytics in January 2015, a leading commercial provider of software and services using R language for statistical computing and predictive analytics to power its analytics vision
  • Gartner’s Magic Quadrant for Advanced Analytics Platforms for 2015 states Azure Machine Learning(preview during the evaluation) as one of Microsoft’s strength placing it as a visionary in 2015 from a niche player in 2014.
    Gartner Report 2015

A 1 point action :

Go and try it!  Microsoft Azure Machine Learning free trial. 

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