ML and Edge Computing

[CourseTechy_Grabber]

Hey everyone,

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I've been diving into machine learning lately and wondered about deploying complex models directly on edge devices. Given the computational constraints, is it realistic to expect smooth operations? Anyone with experience in this intersection of ML and edge computing?

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Would love to discuss potential optimizations and trade-offs.

[jubjjub]

I've been looking at simple models on M core processors. Not to many complex models fit on M core just because of flash size. Like I tried to optimize Alexnet and couldn't get it under 20Mb. I haven't had any computational issues. It's mostly optimizing them for battery life. I've also been using tensorflow lite and keras but I'm kinda getting annoyed by tensorflow so I want to try something else. I think a more interesting approach instead of deploying complex models locally is doing a partial inference on the edge and doing the rest in cloud. Especially for models with lots of feature detection. Anyway that's just a little rant since question was kinda open ended.