Why Businesses Using Machine Learning Are Ahead of the Curve

Not long ago, machine learning was only a reality in laboratory settings, think tanks, or science fiction. The idea of enterprise programs reviewing existing data to gather future insights without explicit instructions seemed grandiose. Now, it’s increasingly commonplace to see businesses using machine learning to get an analytical advantage.

Across industries, companies are beginning to incorporate programs capable of learning from data sets and making savvy predictions into their analytics strategy. Moreover, early adopters are seeing great results. Here’s a glimpse at the capabilities of machine learning developments and what it can do for your organization.

The Rise of Machine Learning In the Public Eye

The general public got its first taste of machine learning, oddly enough, through Jeopardy!. In 2011, IBM pitted their cognitive computer Watson against reigning Jeopardy! champion Ken Jennings in a match that proved man was no longer beat machines. Watson wasn’t all fun and games. It paired encyclopedic knowledge with the ability to understand and learn the nuances of natural language. From that moment onward, thought leaders and organizations saw nothing but potential for this technology.

Watson itself has already been applied to a range of challenges from accurately diagnosing patients’ symptoms to predicting weather patterns. Other businesses using machine learning tools are making their mark on the world. The Netflix movie recommendation algorithm incorporates aspects of machine learning into their service. Now, there are even opportunities to use machine learning as a service.

Machine Learning As a Service Accelerates Data Analytics

The big four cloud providers – Amazon, Google, Microsoft, and IBM – are exploring ways to make machine learning an enterprise commodity. How? The same way they went about cloud based infrastructure: by offering machine learning as a service.

The vision and scope of machine learning as a service varies between providers, but the basic concept is simple enough: input your data, allow the machine learning models to train on it, and receive a predictive model once the evaluation is made.

Powerful analytical infrastructures allow business users to detect unexpected insights and developing trends faster than before. already uses machine learning to capably make 50 billion predictions every week. Think of what a tool like that could do for your organization.

Better yet, businesses using machine learning solutions enjoy an essentially plug and play service. Though hiring for the cloud or for data analysis is still a strong strategy, companies do not need internal experts on IaaS, Hadoop, or MondoDB to extract revolutionary insights. They collect the data and the machine learning provider handles most of the rest.

Added Advantages for Businesses Using Machine Learning

With the added big data analytics power, also comes a number of added benefits for businesses using machine learning as a service. By allowing big machine learning providers to be the data workhorse, companies do not need to make investments in server infrastructure or pricey analytics programs. Hardware and software are included in the service agreement.

Earlier we mentioned faster speeds, but delivery is almost at a whole new level. Take Google’s Prediction API. Most queries take less than 200ms. That’s less time than it takes for you to look at someone nearby and interpret their emotions from their facial expressions. Blistering speeds like this can change the ways in which businesses evolve and the way entire markets move forward.

Most of these platforms have been launched within the last year, meaning that the adoption rate is still limited. Potential users are still getting their bearings about whether or not machine learning offers a worthwhile ROI. From the signs in the market, this could very well be the next infrastructure as a service.

Consider the ubiquity of those services. Gartner predicts that the cloud services will grow to a $204 billion market in 2016. It’s become the norm and before long, we expect machine learning as a service will too. Ultimately, it promises to have a positive impact on keeping operations, delivery, training and even the hiring process evolving to remain ahead of the market.

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