Summary of How to Sustainably Monitor Ml-enabled Systems? Accuracy and Energy Efficiency Tradeoffs in Concept Drift Detection, by Rafiullah Omar et al.
How to Sustainably Monitor ML-Enabled Systems? Accuracy and Energy Efficiency Tradeoffs in Concept Drift Detection
by Rafiullah Omar, Justus Bogner, Joran Leest, Vincenzo Stoico, Patricia Lago, Henry Muccini
First submitted to arxiv on: 30 Apr 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Software Engineering (cs.SE)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed paper investigates ways to mitigate the degradation of machine learning (ML) model prediction quality in production environments due to concept drift. By periodically retraining ML models, energy consumption increases, making it essential to optimize this process. The study explores various monitoring methods to detect concept drift and evaluates their impact on the tradeoff between accuracy and energy efficiency. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper explains how to improve the performance of machine learning models in real-world environments by detecting when they need to be retrained. This helps reduce energy consumption, which is an important issue as many systems are deployed in production environments where this is a concern. |
Keywords
» Artificial intelligence » Machine learning
