Machine Learning at the Edge

Machine Learning at the Edge

This IDC Perspective provides an overview of machine learning (ML) at the edge and the challenges associated with ML software and infrastructure deployment at the edge. This document also provides recommendations to the end user to overcome such challenges and leverage ML at the edge successfully."Edge-based deployments pose unique challenges to deploying ML software at the edge. A recent IDC study shows that less than 10% of ML software currently deployed is at edge locations," said Sriram Subramanian, research director, AI and Automation Software research group at IDC. "With increasing adoption of ML techniques, expanding use cases, and increased investments in edge deployments, edge-based deployment of ML software is expected to increase."

Please Note: Extended description available upon request.