3 papers
cs.SE2026
Making Sense of AI Agents Hype: Adoption, Architectures, and Takeaways from Practitioners
Ruoyu Su, Matteo Esposito, Roberta Capuano +4
To support practitioners in understanding how agentic systems are designed in real-world industrial practice, we present a review of practitioner conference talks on AI agents. We…
cs.LG2024
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…
cs.LG2024
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…