My EDGE AI Journey at devEco.io | Part 1

I've been wanting to make some awesome projects with machine learning at the core, but for a while I couldn't find a good resource to help me learn. Recently I discovered the EDGE AI Foundation courses at https://cert.edgeaifoundation.org/. There are five different courses covering the various aspects of edge AI, and when you're finished you receive a digital certification badge.

I'll be going through each course and writing a blog post about my journey through each one. When I'm finished I hope to have the knowledge and skills to create cool projects on the edge. This first post in the series will be about my experience in the first course, the EDGE AI FOUNDATION Fundamentals Certification.

The first thing I learned is the definition of edge AI: running artificial intelligence models directly with onsite hardware devices that perform inference on real data captured in real time, instead of sending data to machines elsewhere in the cloud. The advantages of using edge AI include lower power cost, higher security, and lower bandwidth.

I also learned about the different types of edge such as embedded, smart, and heavy. Depending on the edge you are designing for, you can choose from hardware such as microcontrollers, MPUs, FPGAs, and on-site servers. The software side of the stack includes TensorFlow, PyTorch, and LiteRT. This course also explained how one develops, deploys, and monitors EDGE AI devices at a high level.

After completing this course I now feel like I have a solid grasp on the fundamentals of edge AI and have a general understanding of the concepts and practices associated with it. In the future I hope the courses will dive deeper into the how-to aspect of creating and running a simple model on an edge AI device. I'm excited to continue learning about this exciting technology!