The need for on-device data analysis arises in cases where decisions based on data processing have to be made immediately.

For example, there may not be sufficient time for data to be transferred to back-end servers, or there is no connectivity at all.

Here’s a look at a few scenarios where this sort of localized compute will matter most.

Analyzing large amounts of data based on complex machine learning algorithms requires significant computational capabilities. Therefore, much processing of data takes place in on-premises data centers or cloud-based infrastructure. However, with the arrival of powerful, low-energy consumption Internet of Things devices, computations can now be executed on edge devices such as robots themselves. This has given rise to the era of deploying advanced machine learning methods such as convolutional neural networks, or CNNs, at the edges of the network for “edge-based” ML.

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