
Core ML vs. ML Kit: Which mobile machine learning platform do you prefer? - austin_kodra
https://heartbeat.fritz.ai/core-ml-vs-ml-kit-which-mobile-machine-learning-framework-is-right-for-you-e25c5d34c765
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mtgx
> The on-device version of ML Kit offers low accuracy compared to the cloud
> version, but at the same time it offers more security to user data.

I understand why that could be true, but I also wonder if Google did that on
purpose a little bit, for the same reason they make you configure their Wi-Fi
routers and other devices over the internet.

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abdinoor
(Full disclosure: I work at Fritz, and we publish Heartbeat).

There are a couple of legitimate reasons that the on-device model is lower
accuracy: compute and size. Models are often reduced in complexity when
running on edge devices, but cloud-based GPU (TPU in Google's case) models can
require far more calculations in order to squeeze that bit of accuracy. As for
model size, a 100MB model in the cloud is not a big deal, but having to
download a 100MB model on every edge device is expensive in both time and
bandwidth.

Outside of these reasons, Google/Firebase may want on-device capabilities
limited in order push adoption of their cloud ML services.

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Razengan
> compute and size ... having to download a 100MB model on every edge device
> is expensive in both time and bandwidth.

The WWDC 2018 ML sessions showed some solutions to address these issues. (I
don't exactly recall which as I'm not using ML yet, so I skimmed through
them.)

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upgoat
Haven’t used ML Kit but CoreML has been very simple to setup (few lines of
python + drag n drop into Xcode) and very fast on device. You can do video
stream classification live

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amelius
I'm hoping developers choose the long-term sensible option, which is the
multi-platform one.

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Razengan
Multi-platform solutions don't always seem to be the most optimal/efficient
solution on each specific platform.

DirectX vs. OpenGL for example, or Adobe RGB vs. DCI-P3 on recent wide-color
devices, or the convenient but clunky abomination that is Electron.

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amelius
Yes, but the point is that if the open, portable solution gets a sufficiently
large userbase, then the platform owners will be highly motivated to
provide/implement efficient support for the open standard.

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gok
Turi Create is a nice alternative to Create ML for building Core ML models.

