Exploring the topology of the edge
Solutions built for the edge take on many shapes and sizes. The number of distinct devices included in a solution ranges from one to many. The network layout, compute resources, and budget allowed will drive your architectural and implementation decisions. In an edge machine learning (ML) solution, we should consider the requirements for running ML models. ML models work more accurately when they are custom built for a specific instance of a device, as opposed to one model supporting many physical instances of the same device. This means that as the number of devices supported by an edge ML workload grows, so too will the number of ML models and compute resources required at the edge. There are four topologies to consider when architecting an edge ML solution: star, bus, tree, and hybrid. Here is a description of each of them:
- Star topology: The Home Base Solutions (HBS) hub device and appliance monitoring kits represent a common pattern...
