activity
20232026
most citedA Synthesis of Green Architectural Tactics for ML-Enabled Systems

28 citations · 56 across the 12 of their papers we have counts for

collaborators
Showing cs.SEShow all

9 papers · 1 filter

cs.SE2026

Big Questions on Software Architecture: Report of the ICSE 2026 BoF on Software Architecture

Davide Taibi, Patricia Lago, Henry Muccini

Software Architecture (SA) is more important than ever, from the need to master the pervasive impact of technology in society and planet earth, to balancing the role of humans vers…

cs.SE2026

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…

cs.SE2025

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…

cs.SE2025

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…

cs.SE2025★ 9 cited

Greening AI-enabled Systems with Software Engineering: A Research Agenda for Environmentally Sustainable AI Practices

Luís 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.…

cs.SE2025

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…

cs.SE2024★ 5 cited

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…

cs.SE2024★ 11 cited

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.…

cs.SE2023★ 28 cited

A Synthesis of Green Architectural Tactics for ML-Enabled Systems

Heli Järvenpää, Patricia Lago, Justus Bogner +3

The rapid adoption of artificial intelligence (AI) and machine learning (ML) has generated growing interest in understanding their environmental impact and the challenges associate…