Context: Machine Learning Operations (MLOps) has emerged as a set of practices that combines development, testing, and operations to deploy and maintain machine learning applications. Objective: In this paper, we assess the benefits and limitations of using the MLOps principles in online supervised learning. Method: We conducted two focus group sessions on the benefits and limitations of applying MLOps principles for online machine learning applications with six experienced machine learning developers. Results: The focus group revealed that machine learning developers see many benefits of using MLOps principles but also that these do not apply to all the projects they worked on. According to experts, this investment tends to pay off for larger applications with continuous deployment that require well-prepared automated processes. However, for initial versions of machine learning applications, the effort taken to implement the principles could enlarge the project's scope and increase the time needed to deploy a first version to production. The discussion brought up that most of the benefits are related to avoiding error-prone manual steps, enabling to restore the application to a previous state, and having a robust continuous automated deployment pipeline. Conclusions: It is important to balance the trade-offs of investing time and effort in implementing the MLOps principles considering the scope and needs of the project, favoring such investments for larger applications with continuous model deployment requirements.

翻译：背景：机器学习运维（MLOps）作为融合开发、测试与运维的实践体系，旨在部署和维护机器学习应用程序。目标：本文评估在线监督学习中应用MLOps原则的效益与局限性。方法：我们组织了两场焦点小组讨论，邀请六位资深机器学习开发者探讨将MLOps原则应用于在线机器学习应用的利弊。结果：焦点小组讨论表明，机器学习开发者虽认可MLOps原则的诸多优势，但指出这些原则并非适用于其参与的所有项目。专家认为，对于需要持续部署且依赖完善自动化流程的大型应用程序，此类投入往往能产生回报。然而，对于机器学习应用的初始版本而言，实施这些原则所投入的精力可能扩大项目范围，并延长首个版本部署至生产环境的时间。讨论指出，大多数效益体现在规避易出错的人工步骤、支持应用回滚至历史状态，以及构建稳健的持续自动化部署流水线。结论：需根据项目范围与需求，权衡投入时间与精力实施MLOps原则的得失，建议优先为需持续模型部署的大型应用程序进行此类投入。