This article breaks down what's needed to build flexible, scalable, efficient ML-enabled solutions running at the edge.
"Current approaches to running ML models center around the type of compute you’ll be using to run your models — in the cloud, on-prem, hybrid, air gap, or at the edge. At Modzy we saw all of this happening and flipped the problem on its head. We’ve started to approach running and managing models from a location-centric mindset, which reduces some of the complexity that arises with only worrying about the compute. In this framework, each new environment becomes an “edge location,” and your ML models could be running wherever you want. Be it on-prem, private cloud, public cloud, these are just “edge” locations with significantly greater compute capacity than those we usually think of when talking about the edge, like a Jetson Nano, Raspberry Pi, or Intel Up board. Although there’s a lot of variety in all these environments, the main factors impacting how your ML models will run are power consumption and network connectivity."
Read the full article linked here.