{"title":"生产中的机器学习系统的生命周期","authors":"V. I. Bulaev","doi":"10.3997/2214-4609.202156040","DOIUrl":null,"url":null,"abstract":"Summary The paper presents a general view of the pipeline for deploying a machine learning model to production. It is shown that today the infrastructural costs of embedding ML into the production circuit can exceed the costs of creating and training a model by almost an order of magnitude.","PeriodicalId":266953,"journal":{"name":"Data Science in Oil and Gas 2021","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2021-08-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"The Lifecycle of a Machine Learning System in Production\",\"authors\":\"V. I. Bulaev\",\"doi\":\"10.3997/2214-4609.202156040\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Summary The paper presents a general view of the pipeline for deploying a machine learning model to production. It is shown that today the infrastructural costs of embedding ML into the production circuit can exceed the costs of creating and training a model by almost an order of magnitude.\",\"PeriodicalId\":266953,\"journal\":{\"name\":\"Data Science in Oil and Gas 2021\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2021-08-04\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Data Science in Oil and Gas 2021\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.3997/2214-4609.202156040\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Data Science in Oil and Gas 2021","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.3997/2214-4609.202156040","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
The Lifecycle of a Machine Learning System in Production
Summary The paper presents a general view of the pipeline for deploying a machine learning model to production. It is shown that today the infrastructural costs of embedding ML into the production circuit can exceed the costs of creating and training a model by almost an order of magnitude.