Expert-Driven Monitoring of Operational ML Models
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Abstract (arXiv)
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.
Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 9 of its 27 references that are in the map. First posted on arXiv 22 Jan 2024.
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9 of 27 references.
References · 9
- 2018Failing Loudly: An Empirical Study of Methods for Detecting Dataset Shiftcited by 240
- 2022Leveraging Unlabeled Data to Predict Out-of-Distribution Performancecited by 141
- 2015Characterizing Concept Driftcited by 93
- 2021Mandoline: Model Evaluation under Distribution Shiftcited by 55
- 2017On the Reliable Detection of Concept Drift from Streaming Unlabeled Datacited by 32
- 2022Operationalizing Machine Learning: An Interview Studycited by 25
- 2021Towards Observability for Production Machine Learning Pipelinescited by 10
- 2022Understanding Unfairness in Fraud Detection through Model and Data Bias Interactionscited by 7
- 2022Identifying the Context Shift between Test Benchmarks and Production Datacited by 5
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