10 papers
Attention to Detail: Evaluating Energy, Performance, and Accuracy Trade-offs Across vLLM Configurations
Nada Zine, Tristan Coignion, Vincenzo Stoico +4
Large Language Models are reshaping how software is developed and maintained. They are typically deployed in production using inference engines such as vLLM, which can efficiently…
Monitoring and Observability of Machine Learning Systems: Current Practices and Gaps
Joran Leest, Ilias Gerostathopoulos, Patricia Lago +1
Production machine learning (ML) systems fail silently -- not with crashes, but through wrong decisions. While observability is recognized as critical for ML operations, there is a…
Tracing Distribution Shifts with Causal System Maps
Joran Leest, Ilias Gerostathopoulos, Patricia Lago +1
Monitoring machine learning (ML) systems is hard, with standard practice focusing on detecting distribution shifts rather than their causes. Root-cause analysis often relies on man…
From Tea Leaves to System Maps: A Survey and Framework on Context-aware Machine Learning Monitoring
Joran Leest, Claudia Raibulet, Patricia Lago +1
Machine learning (ML) models in production fail when their broader systems -- from data pipelines to deployment environments -- deviate from training assumptions, not merely due to…
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…
Greening AI-enabled Systems with Software Engineering: A Research Agenda for Environmentally Sustainable AI Practices
LuÃs Cruz, João Paulo Fernandes, Maja H. Kirkeby +22
The environmental impact of Artificial Intelligence (AI)-enabled systems is increasing rapidly, and software engineering plays a critical role in developing sustainable solutions.…