1 citations · 3 across the 4 of their papers we have counts for
4 papers
EcoMLS: A Self-Adaptation Approach for Architecting Green ML-Enabled Systems
Meghana Tedla, Shubham Kulkarni, Karthik Vaidhyanathan
The sustainability of Machine Learning-Enabled Systems (MLS), particularly with regard to energy efficiency, is an important challenge in their development and deployment. Self-ada…
Reimagining Self-Adaptation in the Age of Large Language Models
Raghav Donakanti, Prakhar Jain, Shubham Kulkarni +1
Modern software systems are subjected to various types of uncertainties arising from context, environment, etc. To this end, self-adaptation techniques have been sought out as pote…
SWITCH: An Exemplar for Evaluating Self-Adaptive ML-Enabled Systems
Arya Marda, Shubham Kulkarni, Karthik Vaidhyanathan
Addressing runtime uncertainties in Machine Learning-Enabled Systems (MLS) is crucial for maintaining Quality of Service (QoS). The Machine Learning Model Balancer is a concept tha…
Towards Self-Adaptive Machine Learning-Enabled Systems Through QoS-Aware Model Switching
Shubham Kulkarni, Arya Marda, Karthik Vaidhyanathan
Machine Learning (ML), particularly deep learning, has seen vast advancements, leading to the rise of Machine Learning-Enabled Systems (MLS). However, numerous software engineering…