Machine learning is a powerful tool that can help businesses, the technology departments that power them, and ordinary people provide better services or create better experiences. In this post, we’ll review what machine learning is and why it’s so important to businesses.
What is machine learning?
Basically, it’s a type of artificial intelligence (AI), where computers take data and “teach” themselves through algorithms and statistical analysis to be more accurate in their analysis of that data and any pattern predictions that arise as a result. There is no programming the software to execute a certain number of commands; it does that on its own.
Machine learning is very similar to data mining, where people (or programs) search through existing data to look for patterns and adjust program actions accordingly. You are probably already familiar with one real-world example of machine learning: Relevant internet advertising based on your shopping or browsing history. On the outside, it looks pretty amazing (or creepy, depending on how you look at it) to see relevant items based on your previous interactions with a particular website, but the technology working behind the scenes is relatively simple: The software is just using statistical analysis and predictive analytics based on patterns in the user’s data.
Just because the technology is simple doesn’t mean it doesn’t have an enormous impact on the way businesses operate. Data has now become an extremely valuable commodity–the more a company knows about a user/consumer, the more they can provide relevant services or products.
What are some other examples of machine learning?
Machine learning doesn’t just pertain to advertising. When people can make predictions more quickly and with more accuracy, they can better prepare for the outcomes. For example, fraud detection on your credit card is based on machine learning. Machines are learning your spending patterns faster and reacting more quickly when you seem to deviate from the norm–potentially saving you thousands of dollars in unauthorized spending charges and the hassle to get your money back. Voice recognition is another example of machine learning–assistants like Siri and Alexa adapt to your speech patterns to better understand your questions and requests.
Online Tech has its own example of machine learning: Our latest managed Microsoft cloud offering, with SprawlGuard™ protection. We use machine analytics to monitor our clients’ monthly cloud spend, and use that data to predict whether they will have cloud sprawl in their environment and exceed their spend for a particular month.
How does machine learning tie into Big Data?
Essentially, machine learning is the backbone of Big Data. Without the ability of computers to analyze volumes of data that humans never could (and the cheaper storage capacity to keep all that data to mine), we wouldn’t have Big Data and its possibilities.
What about the cloud? Is there a connection there?
Oh yes. The cloud plays a critical role in the development of data analytics and Big Data. Where is all the data that’s being analyzed stored? The cloud. How are computers calling up that data to actually analyze it? APIs that connect to the cloud. Without the cloud and its scalability and unlimited cheap storage capacity, we wouldn’t have any of the neat features we practically take for granted today.
Cloud providers have been getting into the machine learning business, too. Amazon’s Alexa assistant? Machine learning. Microsoft is also heavily involved in machine learning, have developed its technology over the course of more than 20 years. In fact, Microsoft already uses it in way more of its numerous apps than you might think. Machine learning powers everything from Office 365 (that Outlook Clutter folder that automatically seems to know what you want to keep in your inbox and what you want to move to junk), Cortana voice assistant (what was that you said?), to the Windows keyboard on your phone (no Damn You Autocorrect here).
In 2015, Microsoft released available on Azure cloud. Developers can build learning capabilities into their own applications, such as recommendations, sentiment analysis, fraud detection, fault prediction, and more. The idea of the Azure offering is to democratize machine learning, so organizations no longer need to hire someone with a doctorate if they want to use a machine learning algorithm.
What does machine learning hold for the future?
Machine learning is already well established in many features we take for granted today, and there are many possibilities for it in the future, including opportunities in technology, science, healthcare and more. With developers able to incorporate machine learning into their own applications built in Azure and AWS, we’re sure to see a lot more examples of it in the very near future.