Embedding Open Cognitive Analytics at the IoT’s Edge

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Cognitive computing is penetrating more aspects of the IoT as algorithms enable edge devices and applications. Understand how unstructured data captured by IoT edge devices with the help of cognitive algorithms distilled into actionable insights.

Driving the IoT into ubiquity is the smartphone. Device-embedded analytics of various sorts are a pervasive feature of more IoT applications for consumer, business, science, government, and other applications. When IoT edge devices capture unstructured data–such as video, audio, and environmental data feeds—cognitive algorithms can continuously distill it all down to actionable insights..

For example, the intelligent digital advisors embedded in IoT-enabled products leverage more sophisticated machine-learning algorithms, analyze myriad sources of device-acquired sensor data, and adjust their responses rapidly within dynamically shifting environmental contexts. And such up-and-coming product segments as autonomous vehicles would be impossible without embedded cognitive analytics that act on streaming sensor data.

A new generation of data scientists is emerging to build cognitive IoT applications and deploy them into every conceivable type of device and application. By 2020, new products in every sector of the economy will have been rearchitected as “cognitive IoT” endpoints. As this trend intensifies, embedded cognitive IoT will become a key focus of the next generation of cloud application developers. What will these skilled professionals need to be successful?

Read the source article at Analytics, Data Mining, and Data Science