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, 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.

本文介绍机器学习在医疗保健领域中部署和维护的可靠、高效、可用和道德化的过程，包括基础概念、数据准备、数据工程和工具等，在从策划到落地实现全过程提供指导。