By the sound of it, the Internet of Things (IoT) might be one of the biggest technological breakthroughs since the invention of the internet itself. The basic concept revolves around sensors embedded in nearly anything (mobile devices, clothing, manufacturing equipment, appliances, etc.) being able to communicate with each other, providing for a more convenient interconnected life with the cloud. While the IoT is still in its infancy, it has become clear that it will soon come to be part of everyone’s life, if it isn’t already. According to a study from Gartner, the number of units connected to the Internet of Things is expected to reach 26 billion by the year 2020. But as companies begin to see the revolutionary potential of the IoT, they’re also beginning to find a number of obstacles they need to address. With this in mind, many businesses and industries are starting to utilize machine learning, and more specifically machine-learning-as-a-service (MLaaS), to grasp the IoT’s potential.
The real key to making the Internet of Things work is the data being generated by the billions of sensors located in various items. But it’s not enough to simply collect that data; organizations need to analyze and make sense of it, recognizing specific patterns that can be utilized. That’s where machine learning can play such a powerful role. Machine learning basically means having specialized algorithms that help computers learn without actually having specific programming. Without it, the IoT would be severely limited in what it can do.
With machine learning, organizations can tackle many of the biggest obstacles that make utilizing the Internet of Things difficult. One such challenge is the amount of data being generated. This amount can be enormous, sometimes getting into the petabyte range, and as more sensors become connected to the IoT, the volume of data is only expected to get bigger (think exabytes of data). Trying to track and find noticeable patterns to act on within so much data is virtually impossible using traditional data gathering and analysis methods. The large variety of data can be a big problem as well. When embedded sensors are involved, data can come from pretty much anything, from health statistics to traffic patterns to social media status updates. Machine learning takes the variety of data and learns from it, figuring out trends and patterns for businesses to use.
While these obstacles can be overcome by machine learning, they still represent almost insurmountable challenges for small businesses. Not only is it difficult for smaller companies to handle large data amounts, but the cost of investing in technology that eases that workload is often too high. Companies also need to spend resources on hiring data scientists to interpret the analyses that results from machine learning, which again is out of the question for smaller organizations. The potential for innovative solutions from the Internet of Things is large, and while machine learning may hold the key to unlocking it, that technology is still out of reach for many companies. Luckily, machine-learning-as-a-service has slowly been developing in the background. Much like how companies outsource certain functions and services to cloud vendors, companies can hire MLaaS vendors to handle their machine learning needs. Instead of spending large sums on technological investments and new hirings, small businesses can spend a comparatively small amount to get a vendor to do analysis work for them.
The benefits of this approach go beyond simply saving money. Small businesses can save on the time-consuming process of machine learning. Vendors can also provide greater experience and expertise since machine learning is the one specific thing they do. MLaaS vendors can also conduct more queries more quickly, which also provides more types of analyses to get more actionable information from vast caches of data. This partnership between organizations and MLaaS vendors can pay off in big ways. In one example, a MLaaS company named Prelert was hired by a city to use machine learning to improve traffic congestion. By using their detection technology from sensors all over the city, Prelert was able to create statistical models that helped the city identify where traffic problems were occurring, when they happened, and how best to solve the problem. MLaaS vendors are only expected to increase in number and more services are sure to be made available to small businesses, like Microsoft’s Azure ML.
Without machine learning, the Internet of Things would likely be dead in the water. And now that small businesses can take advantage of machine learning due to MLaaS, more innovations and creative enterprises will likely result. Advances in other technologies like data storage will also help small businesses compete, like the larger adoption of flash storage vs hard drive storage that was traditionally used. As more enterprises use these technologies, expect the true potential of the Internet of Things to be tapped.