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What you need to know about Azure Machine Learning

January 25, 2017

Machine Learning is a key component of building intelligent applications. Hence, Machine Learning helps developers in building smart, intelligent and predictive applications. Machine Learning has been available for awhile in the market, but the rise of Machine Learning in the last few years has been impressive due to the democratizing of Machine Learning through the cloud. Using cloud platforms is an incredible force for data scientists, developers, and data geeks to utilize what Machine Learning offers in the cloud era.

 

Microsoft introduced the Azure Machine learning (AML) tool in early 2015. Prior that, Microsoft used this platform in its products before releasing it to the public. AML was used in Xbox, Bing and others to provide new intelligent features to Microsoft’s existing products.

 

Azure Machine Learning is a web tool. This tool is designed not just for data scientists, but for every person who contributes on building a predictive model from planning, designing, evaluating and deploying a model into production.

 

Using AML, 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 as first class citizens in addition to our world-class Microsoft algorithms.

 

 

 

AML overcomes one of the primary issues facing data scientists today – putting finished work into production in a way others can use. 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 AML, 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.

 

 

AML provides RESTful APIs for all deployed models through predictive experiments where you can use any client programming language to integrate and predict values through the deployed endpoints. 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. The below figure shows an end to end process to develop AML solution using AML studio.

 

 

 

In addition, Microsoft has released lots of experiments to get started through learning by example. You can find all these experiments in Cortana Intelligence Suite here. Using Cortana Intelligence Suite, You can copy any experiment into your workspace by clicking on "Open In Studio" button that allows you looking and later on customizing any experiment as per your needs.

 

You can start working with Azure Machine learning for free, sign up now here: https://studio.azureml.net Got questions? Feel free to connect with me and I will be more than happy to answer any question you may need assistance with.

 

Mostafa Elzoghbi

Twitter: @MostafaElzoghbi

http://mostafa.rocks

 

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