Expert-Driven Monitoring of Operational ML Models
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arXiv
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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
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| _version_ | 1866914647703027712 |
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| author | Leest, Joran Raibulet, Claudia Gerostathopoulos, Ilias Lago, Patricia |
| author_facet | Leest, Joran Raibulet, Claudia Gerostathopoulos, Ilias Lago, Patricia |
| contents | We propose Expert Monitoring, an approach that leverages domain expertise to enhance the detection and mitigation of concept drift in machine learning (ML) models. Our approach supports practitioners by consolidating domain expertise related to concept drift-inducing events, making this expertise accessible to on-call personnel, and enabling automatic adaptability with expert oversight. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2401_11993 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Expert-Driven Monitoring of Operational ML Models Leest, Joran Raibulet, Claudia Gerostathopoulos, Ilias Lago, Patricia Machine Learning Software Engineering We propose Expert Monitoring, an approach that leverages domain expertise to enhance the detection and mitigation of concept drift in machine learning (ML) models. Our approach supports practitioners by consolidating domain expertise related to concept drift-inducing events, making this expertise accessible to on-call personnel, and enabling automatic adaptability with expert oversight. |
| title | Expert-Driven Monitoring of Operational ML Models |
| topic | Machine Learning Software Engineering |
| url | https://arxiv.org/abs/2401.11993 |