MLOps at the Edge in DDIL Environments

Abstract

Military operations invariably involve devices at the edge (e.g. sensors, drones, handsets of soldiers, etc.) In edge environments, good network connectivity cannot be assumed due to Denied, Degraded, Intermittent, or Low-bandwidth (DDIL) conditions. A DDIL environment poses unique challenges for deploying AI applications at the edge, particularly in the execution of Machine Learning Operations (MLOps). In this paper, we present a framework to address these challenges by considering three important dimensions: (i)the ML model lifecycle activities, (ii) specific DDIL induced challenges at the edge and (iii) the application stack. We discuss three realistic use cases in detail to explain the use of this approach to identify the underlying design patterns. We believe that use of this framework can lead to a responsive and reliable AI deployment under varying operational conditions.