In recent years, serverless computing has received significant attention due to its innovative
approach to cloud computing. In this novel approach, a new payment model is presented …

With the digitization of modern cities, large data volumes and powerful computational
resources facilitate the rapid update of intelligent models deployed in smart cities. Continual …

Data drift is a thorny challenge when deploying person re-identification (ReID) models into
real-world devices, where the data distribution is significantly different from that of the …

The Industry 4.0 revolution has been made possible via AI-based applications (eg, for
automation and maintenance) deployed on the serverless edge (aka fog) computing …

As machine learning models are increasingly deployed in production, robust monitoring and
detection of concept and covariate drift become critical. This paper addresses the gap in the …

Continuous learning (CL) has recently been adopted into edge video analytics, gaining
huge success in maintaining high accuracy without constantly retraining DNN models by …

The edge-cloud collaboration architecture can support Deep Neural Network-based (DNN)
video analytics with low inference delays and high accuracy. However, the video analytics …

Catastrophic forgetting is a thorny challenge when updating keyword spotting (KWS) models
after deployment. To tackle such challenges, we propose a progressive continual learning …

Continuously learning new classes without catastrophic forgetting is a challenging problem
for on-device environmental sound classification given the restrictions on computation …

MLOps is about taking experimental ML models to production, ie, serving the models to
actual users. Unfortunately, existing ML serving systems do not adequately handle the …