Summarizing the key lessons
In this section, we will group and summarize the key lessons from throughout this book as a quick reference to ensure that the most important lessons were not missed. There is a loose chronology to the groupings based on the material from Chapters 1 to 9, but some lessons may appear in a group outside the order in which they appeared in this book.
Defining edge ML solutions
The following key lessons capture the definition, value proposition, and shape of an edge ML solution:
- Definition of an edge ML solution: Bringing intelligent workloads to the edge means applying ML technology that's been incorporated into cyber-physical solutions that interoperate the analog and digital spaces. An edge ML solution uses devices that have sufficient compute power to run ML workloads and either directly interface with physical components such as sensors and actuators, or indirectly interface with end devices over a local network or serial protocol. ...