Deciding what combination of operators to use across the Edge AI tiers to achieve specific
latency and model performance requirements is an open question for MLOps engineers …

Edge AI enables deploying models across Mobile, Edge, and Cloud (MEC) tiers using a
wide range of ML model transformation operators. Despite the wide range of operators …

To help MLOps engineers decide which operator to use in which deployment scenario, this
study aims to empirically assess the accuracy vs latency trade-off of white-box (training …

The rapid advancement of fifth-generation (5G) wireless network technology has enabled a
wide range of innovative applications with diverse requirements, including ultra-low latency …

Edge Computing (EC) provides computational and storage resources close to data-
generating devices, and reduces end-to-end latency for communications between end …

The advantages of microservices system architectures orchestrated by Kubernetes include
their capacity to adjust dynamically to meet varying demand, thereby ensuring application …