
EdgeML: Algorithms for edge devices - msolujic
https://github.com/Microsoft/EdgeML
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eggy
Edge devices are what MS is calling IOT devices on cloud networks, I think:

[https://blogs.microsoft.com/iot/2017/05/10/microsoft-
azure-i...](https://blogs.microsoft.com/iot/2017/05/10/microsoft-azure-iot-
edge-extending-cloud-intelligence-to-edge-devices/)

~~~
rpeden
I used to work at a company whose software had to interact with security
cameras. In the security/surveillance industry, at least, the term 'edge
devices' has long been used to describe IP cameras and other devices like
motion sensors operating in the field, transmitting their data back to a
central server.

Interestingly, 'edge analytics' has been a thing in that industry for a
relatively long time, too. This is used to refer to cameras that do things
like motion detection, person counting, and other analytics in the camera
instead of having to post-process the video streams at a central location to
extract that data.

So in this case at least, is looks like MS is just using terminology that was
already widely used in IoT devices even before "Internet of Things" was even
really a phrase that was used to describe these devices.

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zitterbewegung
This is a neat project and having this framework could really address two
things.

1\. If we bake all the intelligence on the device then it won't have to go
outside to a network which could increase security.

2\. If the intelligence is on the device then the users could control their
data better.

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styfle
I assumed Microsoft EdgeML was related to Microsoft Edge browser...it is not.
It sounds like and “edge device” refers to an IoT device.

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m3kw9
What is an Edge device??

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tyingq
_" The trained models can be loaded onto edge devices such as IoT
devices/sensors, and used to make fast and accurate predictions completely
offline."_

Then, from: [https://www.microsoft.com/en-us/research/project/resource-
ef...](https://www.microsoft.com/en-us/research/project/resource-efficient-ml-
for-the-edge-and-endpoint-iot-devices/)

 _" Our objective is to develop a library of efficient machine learning
algorithms that can run on severely resource-constrained edge and endpoint IoT
devices ranging from the Arduino to the Raspberry Pi."_

