Companies are beginning to realize that they can accelerate IoT analytics and decision-making capabilities by adding machine learning to edge networks. By applying machine learning at the edge, the network can perform many of the tasks previously performed only by cloud servers. This approach can increase operational functionality since the data it utilizes is stored locally and can be processed more rapidly for intelligent decision-making. The same concept holds true for real-time predictions. An edge computing IoT network can achieve significantly faster results when running on-device machine learning models than a typical cloud computing environment. Finally, an edge network, when paired with machine learning can process sensor collected data locally, thereby addressing many of the privacy and compliance issues companies face.

Machine learning can become a robust analytical tool for vast volumes of data. The combination of machine learning and edge computing can filter most of the noise collected by IoT devices and leave the relevant data to be analyzed by the edge and cloud analytic engines.

The advances in Artificial Intelligence have allowed us to see self-driving cars, speech recognition, active web search, and facial and image recognition. Machine learning is the foundation of those systems. It  is so pervasive today that we probably use it dozens of times a day without knowing it.

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