MLOps FMEA:减轻故障并确保机器学习运营成功的积极主动的结构化方法

Abhishek Paul, Roderick Y. Son, Shiv A. Balodi, K. Crooks
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引用次数: 0

摘要

机器学习应用在医疗保健、银行、制造和国防等多个行业的普及率呈指数级增长。虽然机器学习应用潜力巨大,但并不能确保成功开发和生产。为了防止失败并确保成功,我们提出了机器学习操作(MLOps)故障模式和影响分析(FMEA),作为一种积极主动的结构化风险识别和缓解方法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
MLOps FMEA: A Proactive & Structured Approach to Mitigate Failures and Ensure Success for Machine Learning Operations
Machine learning applications have seen an exponential rise in prevalence across many different industries including healthcare, banking, manufacturing, and defense. While there is a lot of potential for machine learning applications, successful development and productionization is not assured. To prevent failures and ensure success, a Machine Learning Operations (MLOps) Failure Modes and Effects Analysis (FMEA) is proposed as a proactive structured approach for risk identification and mitigation.
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