论文标题
机器学习操作（MLOPS）：概述，定义和体系结构
Machine Learning Operations (MLOps): Overview, Definition, and Architecture
论文作者
论文摘要
所有工业机器学习（ML）项目的最终目标是开发ML产品并迅速将其带入生产。但是，自动化和运营ML产品是高度挑战的，因此许多ML努力无法实现他们的期望。机器学习操作（MLOPS）的范式解决了这个问题。 MLOP包括几个方面，例如最佳实践，一组概念和发展文化。但是，MLOP仍然是一个模糊的术语，其对研究人员和专业人士的后果是模棱两可的。为了解决这一差距，我们进行了混合方法研究，包括文献综述，工具评论和专家访谈。由于这些调查，我们概述了必要的原理，组成部分和角色以及相关的架构和工作流程。 Furthermore, we furnish a definition of MLOps and highlight open challenges in the field.最后，这项工作为希望使用指定的技术集合自动化和运营其ML产品的ML研究人员和从业人员提供了指导。
The final goal of all industrial machine learning (ML) projects is to develop ML products and rapidly bring them into production. However, it is highly challenging to automate and operationalize ML products and thus many ML endeavors fail to deliver on their expectations. The paradigm of Machine Learning Operations (MLOps) addresses this issue. MLOps includes several aspects, such as best practices, sets of concepts, and development culture. However, MLOps is still a vague term and its consequences for researchers and professionals are ambiguous. To address this gap, we conduct mixed-method research, including a literature review, a tool review, and expert interviews. As a result of these investigations, we provide an aggregated overview of the necessary principles, components, and roles, as well as the associated architecture and workflows. Furthermore, we furnish a definition of MLOps and highlight open challenges in the field. Finally, this work provides guidance for ML researchers and practitioners who want to automate and operate their ML products with a designated set of technologies.
