28 citations · 56 across the 12 of their papers we have counts for
5 papers · 1 filter
Insights into resource utilization of code small language models serving with runtime engines and execution providers
Francisco Durán, Matias Martinez, Patricia Lago +1
The rapid growth of language models, particularly in code generation, requires substantial computational resources, raising concerns about energy consumption and environmental impa…
The More the Merrier? Navigating Accuracy vs. Energy Efficiency Design Trade-Offs in Ensemble Learning Systems
Rafiullah Omar, Justus Bogner, Henry Muccini +3
Background: Machine learning (ML) model composition is a popular technique to mitigate shortcomings of a single ML model and to design more effective ML-enabled systems. While ense…
An Analysis of MLOps Architectures: A Systematic Mapping Study
Faezeh Amou Najafabadi, Justus Bogner, Ilias Gerostathopoulos +1
Context. Despite the increasing adoption of Machine Learning Operations (MLOps), teams still encounter challenges in effectively applying this paradigm to their specific projects.…
How to Sustainably Monitor ML-Enabled Systems? Accuracy and Energy Efficiency Tradeoffs in Concept Drift Detection
Rafiullah Omar, Justus Bogner, Joran Leest +3
ML-enabled systems that are deployed in a production environment typically suffer from decaying model prediction quality through concept drift, i.e., a gradual change in the statis…
Expert-Driven Monitoring of Operational ML Models
Joran Leest, Claudia Raibulet, Ilias Gerostathopoulos +1
We propose Expert Monitoring, an approach that leverages domain expertise to enhance the detection and mitigation of concept drift in machine learning (ML) models. Our approach sup…