Summary of Expert-driven Monitoring Of Operational Ml Models, by Joran Leest et al.
Expert-Driven Monitoring of Operational ML Models
by Joran Leest, Claudia Raibulet, Ilias Gerostathopoulos, Patricia Lago
First submitted to arxiv on: 22 Jan 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 In this paper, researchers introduce Expert Monitoring, a novel method that utilizes domain-specific knowledge to improve the detection and handling of concept drift in machine learning models. By consolidating domain expertise related to concept drift-inducing events, this approach makes it easier for practitioners to adapt ML models to changing data distributions. The proposed method also enables automatic adaptability with expert oversight, allowing on-call personnel to respond effectively to concept drift. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Machine learning models can struggle when data distributions change over time. This problem is called concept drift. To help solve this issue, researchers have developed a new approach called Expert Monitoring. It uses knowledge from experts in specific areas to make machine learning models more adaptable to changing data. This means that people working with the models will be better able to handle changes and keep the models accurate. |
Keywords
* Artificial intelligence * Machine learning
