MLHOps: Machine Learning Health Operations

Machine Learning Health Operations (MLHOps) is the combination of processes for reliable, efficient, usable, and ethical deployment and maintenance of machine learning models in healthcare settings. This paper provides both a survey of work in this area and guidelines for developers and clinicians t...

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Bibliographic Details
Published in:IEEE access 2024-12, p.1-1
Main Authors: Khattak, Faiza Khan, Subasri, Vallijah, Krishnan, Amrit, Pou-Prom, Chloe, Akinli-Kocak, Sedef, Dolatabadi, Elham, Pandya, Deval, Seyyed-Kalantari, Laleh, Rudzicz, Frank
Format: Article
Language:English
Subjects:
Data models
Hospitals
Machine learning
Metadata
Monitoring
Pipelines
Predictive models
Production
Training
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Summary:Machine Learning Health Operations (MLHOps) is the combination of processes for reliable, efficient, usable, and ethical deployment and maintenance of machine learning models in healthcare settings. This paper provides both a survey of work in this area and guidelines for developers and clinicians to deploy and maintain their own models in clinical practice. We cover the foundational concepts of general machine learning operations and describe the initial setup of MLHOps pipelines (including data sources, preparation, engineering, and tools). We then describe long-term monitoring and updating (including data distribution shifts and model updating) and ethical considerations (including bias, fairness, interpretability, and privacy). This work therefore provides guidance across the full pipeline of MLHOps from conception to initial and ongoing deployment. We also include a checklist to ensure thorough verification of each step in the process.
ISSN:2169-3536
2169-3536
DOI:10.1109/ACCESS.2024.3521279