Take a look at the benefits of EdgeMl and how it might put an end to the cloud computing solutions
Edge ML is a technique by which Smart Devices can process data locally (either using local servers or at the device level) using machine learning and deep learning algorithms, reducing reliance on Cloud networks. The term edge refers to processing that occurs at the device or local-level (and closest to the components collecting the data) by deep and machine-learning algorithms.
Edge devices do still send data to the Cloud when needed, but the ability to process some data locally allows for screening of the data sent to the Cloud while also making real-time data processing (and response) possible.
The EdgeML library provides a suite of efficient machine learning algorithms designed to work off the grid in severely resource-constrained scenarios. The library allows the training, evaluation, and deployment of these algorithms onto various target devices and platforms. EdgeML is written in Python using Tensorflow. We also provide experimental PyTorch support and highly efficient C++ implementations for certain algorithms.
With EdgeML, classical machine learning tasks such as activity recognition, gesture recognition, regression, and so forth can be efficiently performed on tiny devices like the Arduino Uno, with as low as 2kb of RAM. It is the fast, accurate, and compressed deep learning solution to solve complex time-series tasks, for instance, audio keyword detection and wake-word detection on processors as small as a Cortex M4.
This library is a product of the Intelligent Devices Expedition from Microsoft Research India. As part of this expedition, we strive to push the state of the art in machine learning to enable privacy-preserving, energy-efficient, off-the-grid intelligence on low-resource computing devices. The EdgeML library is open-sourced under the MIT License.
The Benefits of Edge Machine Learning
Higher security
When the device can offer instant feedback without being connected to the internet, autonomous cars and manufacturing robots can recognize and avoid dangerous situations as they happen. At high speeds of driving or production, the situation could have escalated beyond control before the inference made it back from the cloud.
Lower costs
Cloud computing can be expensive. With the enterprise’s major focus on cutting costs, edge machine learning is an obvious choice. When machine learning is done on the individual device or machine, the expenses for cloud computing and bandwidth are reduced considerably.
Easier access to ML
As bandwidth needs and costs are lowered, the benefits of machine learning are made available to a larger group of the population of our planet.
Increased privacy
Edge machine learning can process video and audio data in or close to real-time. Therefore, the source data can be deleted as soon as the process is complete. It further increases privacy and decreases the need for storage and bandwidth.
Lower bandwidth needs
Edge ML saves massive amounts of bandwidth. Cars, planes, and other machine run machine learning on the collected data by themselves and only send off what they need more power to process – or what feedback their manufacturer needs to improve all endpoints.
Better user experience
Nobody wants to wait around for a joke or witty comeback from their voice assistant. Likewise, most of us would like our cars and planes to be able to function optimally even when they are out of range of a proper connection to the internet. Aside from our safety the user experience is also significantly improved with the immediate feedback in harmless situations.
What’s There in the Future?
In the future, there is talk about developing EdgeML-based systems in hospitals and assisted living facilities to monitor things like patient heart rate, glucose levels, and falls (using cameras and motion sensors). These technologies could be life-saving and, if the data is processed locally at the edge, staff would be notified in real-time when a quick response would be essential for saving lives.
Source: analyticsinsight.net
