The meal was fantastic, the service was friendly and professional, the setting was cozy, and the company was engaging. As the evening ended, however, there was a slight hiccup as my credit card was declined. There was more than enough money in my account to cover the cost of the (very delicious) dinner, so what was going on?  The issue, which was solved via a quick phone call to the bank, was that an algorithm dedicated to fraud detection determined that the transaction’s location was outside of my established pattern and temporarily prevented completion at the point of sale.
You can learn more about fraud detection by reading “5 Keys to Using AI and Machine Learning in Fraud Detection”.
My restaurant moment is an example of machine learning in action. My bank’s development team didn’t create software that stored a static listing of every place I might travel with an approve or deny setting; a fraud detection system ‘learned’ my spending and location patterns and dynamically intervened when I attempted an action that was outside of that pattern.
Machine learning is arguably the most successful implementation of the broad research effort known as artificial intelligence.  In popular culture, this is represented by malicious robots and computer systems that take over the world. In real life, machine learning gives software the ability to modify actions based on various types of feedback instead of strict, programmatic rules.  As simple as this may sound, it’s a very powerful innovation which increases the usefulness of software in exciting ways.
In this post, we’ll explore Microsoft Azure’s approach to machine learning which gives organizations of all sizes the ability to add dynamic capabilities to a variety of solutions. Before you continue reading this, we suggest you check out our post outlining The Benefits of Machine Learning in the Cloud.

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