What is Edge AI?

Edge AI is the development of artificial intelligence (AI) algorithms and programs that run on edge computing devices. It is an extension of edge computing where data is analyzed and processed near the point of data acquisition. Edge AI offers decreased network latency and bandwidth over cloud-based AI along with improved energy efficiency, reliability, and data privacy.

In most modern machine learning (ML) applications, models are trained on powerful servers before being deployed to end devices. In cloud-based AI, these models perform inference in “prediction serving” programs that respond to requests from clients. In edge AI, such models are deployed to edge computing devices, like smartphones, watches, and embedded systems, to perform inference on gathered data. Examples of edge AI include automated optical inspection (AOI) systems, robotics, self-driving cars, smart speakers, and fitness trackers.

Chapters:
0:00 Introduction
0:31 ML model training and deployment
2:31 Drawbacks of cloud-based AI
3:52 Benefits of edge AI
5:42 Drawbacks of edge AI
6:31 Examples of edge AI
8:41 Edge AI market growth

[piatek]

You have a great way of explaining things simply. Thank you!

[Khansworld-m]

Your explanation of edge AI is excellent. I found your video by chance, but it made the concept very easy to understand.

[AhmedReda-ig]

Very powerful and spiring. waiting for more videos keep going