This post examines how to architect edge ML systems for flexibility, scalability, and efficiency.
In this article, Modzy Head of Product, Seth Clark, examines trends driving the intersection of edge computing and the increased need for running ML anywhere. He investigates how this makes device dependencies like power consumption and network connectivity complicated. He also explores the elements needed for an ideal edge architecture and the benefits of this approach, and wrap up with a breakdown of four edge paradigms. By the end of reading this, you’ll hopefully have a better understanding of how you can architect your ML/AI system for flexibility, scalability, and efficiency without breaking the bank.
Read the full article linked here.