Evolution of Machine Learning, from Server to the Edge

Traditionally, Machine Learning (ML) and Deep Learning (DL) models were implemented within an application in a server-client fashion way. The server provided training and inference capabilities by exposing web APIs of any sort (REST, web-socket, PUB/SUB, etc…) while the client was used mainly to exchange data with the server and present the inference result to the user. Whilst this approach has been proven to work well in many cases, it involves a lot of Input/Output (I/O) and a subsequent slowdown of the application.

However, in recent years, the concept of moving DL models to the client-side has emerged, which is, in most cases, referred to as the EDGE of the system. This approach has been made possible by the newest advancement in GPU/mobile technologies, like the NVIDIA Jetson microcomputer, and by the introduction and support of ML frameworks for EDGE devices, like TensorFlow Lite and TensorFlow.js.

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Due to this exciting new development in machine learning and deep learning, we figured it would be interesting to show you how you can use Tensorflow.js and a pre-trained model called PoseNet to create new possibilities for real-time, human-computer interaction that takes on a Kinect-like style. So, in this article, you’ll find a simple tutorial for applying ML to your project, (even if for the first time) and some use cases for it so you can gain a better understanding of why you would want to apply this technology.

Keep reading on the original blog post on the evolution of Machine Learning here!

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