Edge AI is the process of running artificial intelligence (AI) and machine learning (ML) algorithms on computing devices at the periphery of a network, rather than on large cloud servers. By doing this, we enable a wide range of possibilities to solve unique problems. The benefits of edge AI are the same as those found in edge computing: decreased network bandwidth usage, reduced network latency, less energy consumption, improved reliability, and enhanced data privacy.
We are announcing the release of an Edge AI course to help you become familiar with edge computing and machine learning. The course is entirely free and hosted on our site with embedded YouTube videos. At the end of each section, you are encouraged to take a quiz to test your knowledge. Once you complete all 10 episodes, you can take a comprehensive test and earn a free digital certificate.
Start the course here: Introduction to Edge AI
The videos are also accessible as a playlist on YouTube:
We divide the course into 10 parts plus an introduction page:
- Introduction to edge AI
- What is edge computing?
- What is machine learning (ML)?
- What is edge AI?
- How to choose an edge AI device
- Edge AI lifecycle
- What is edge MLOps?
- What is Edge Impulse?
- Case study: Izoelektro smart grid monitoring
- Test and certification
The course is high-level to help you become familiar with the concepts and vocabulary around edge AI. There are no hands-on exercises or programming required. If you would like to dive into code and hardware, check out our Introduction to Embedded Machine Learning course on Coursera.
Edge AI is a fast-growing field enabling new technologies, especially in healthcare and industrial settings. For example, Izoelektro created a power grid monitoring system, and the Oura Ring tracks sleep with incredible precision.
We are excited to see what you will build with edge AI!