Re:Machine learning can run on tiny, low-power chips
by adamdrake on Monday June 11, 2018 @07:28AM
(#17283390)
Good point on the fit/predict difference. However, there are some models and techniques (e.g. logistic regression with hashing trick) where the fit and predict steps aren't all that different:
A big benefit for doing everything on-device is that a lot of privacy concerns can be mitigated. I also agree that sending data to a server for learning is an option, and the privacy problems can be addressed with something like client-side feature hashing as I mention in:
However, doing that in a very power-conscious environment dose pose difficulties with radio usage, which is comparatively power hungry. It's probably a case-by-case situation.
https://adamdrake.com/big-data-small-machine.html
A big benefit for doing everything on-device is that a lot of privacy concerns can be mitigated. I also agree that sending data to a server for learning is an option, and the privacy problems can be addressed with something like client-side feature hashing as I mention in:
https://adamdrake.com/scalable-machine-learning-with-fully-a...
However, doing that in a very power-conscious environment dose pose difficulties with radio usage, which is comparatively power hungry. It's probably a case-by-case situation.