Deep Learning on the Edge

Scalable Deep Learning services are contingent on several constraints. Depending on your target application, you may require low latency, enhanced security or long-term cost effectiveness. Hosting your Deep Learning model on the cloud may not be the best solution in such cases.
Computing on the edge alleviates the above issues, and provides other benefits. Edge here refers to the computation that is performed locally on the consumer’s products. This blog explores the benefits of using edge computing for Deep Learning, and the problems associated with it.

This is by far the best post I've seen that breaks down the advantages and disadvantages of deploying deep learning on the edge (close to the user).


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