MLHOps: Machine Learning for Healthcare Operations

TL;DR

This paper surveys the field of MLHOps, detailing processes, guidelines, and ethical considerations for deploying and maintaining machine learning models in healthcare settings throughout their lifecycle.

Contribution

It provides a comprehensive overview of MLHOps in healthcare, including setup, monitoring, updating, and ethical issues, serving as a practical guide for clinicians and developers.

Findings

Guidelines for deploying ML models in healthcare environments

Strategies for monitoring and updating models over time

Considerations for ethical issues like bias and privacy

Abstract

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.