Abhishek Paul, Roderick Y. Son, Shiv A. Balodi, K. Crooks
{"title":"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","authors":"Abhishek Paul, Roderick Y. Son, Shiv A. Balodi, K. Crooks","doi":"10.1109/RAMS51492.2024.10457600","DOIUrl":null,"url":null,"abstract":"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.","PeriodicalId":518362,"journal":{"name":"2024 Annual Reliability and Maintainability Symposium (RAMS)","volume":"2 9","pages":"1-7"},"PeriodicalIF":0.0000,"publicationDate":"2024-01-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"MLOps FMEA: A Proactive & Structured Approach to Mitigate Failures and Ensure Success for Machine Learning Operations\",\"authors\":\"Abhishek Paul, Roderick Y. Son, Shiv A. Balodi, K. Crooks\",\"doi\":\"10.1109/RAMS51492.2024.10457600\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"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.\",\"PeriodicalId\":518362,\"journal\":{\"name\":\"2024 Annual Reliability and Maintainability Symposium (RAMS)\",\"volume\":\"2 9\",\"pages\":\"1-7\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2024-01-22\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2024 Annual Reliability and Maintainability Symposium (RAMS)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/RAMS51492.2024.10457600\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2024 Annual Reliability and Maintainability Symposium (RAMS)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/RAMS51492.2024.10457600","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
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.