[论文解读] Machine Learning Operations (MLOps): Overview, Definition, and Architecture

Dominik Kreuzberger, Niklas Kühl+1 more|arXiv (Cornell University)|May 4, 2022

Industrial Vision Systems and Defect Detection被引用 50

一句话总结

本论文提供了对 MLOps 的聚合性概览，基于混合方法研究给出定义、架构、原则、组件、角色、工作流以及尚待解决的挑战。

ABSTRACT

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.

研究动机与目标

• 为研究人员和从业者定义 MLOps 并明确其范围。
• 从文献、工具和专家意见中汇集原则、组件和角色。
• 描述实现自动化 ML 产品运营的架构和工作流。
• 识别尚待解决的挑战，并为采用 MLOps 技术提供指南。

提出的方法

• 进行文献综述以综合现有概念和实践。
• 进行工具评估以评估可用的 MLOps 技术和生态系统。
• 进行专家访谈以获取从业者观点。
• 提供原则、组件和架构的聚合性概览。
• 基于所收集的证据提供 MLOps 的定义。

实验结果

研究问题

• RQ1 MLOps 的组成要素是什么，应如何在研究和实践中定义？
• RQ2 MLOps 的基本原则、组件和角色是什么？
• RQ3 哪些架构和工作流能够实现 ML 产品的可靠运行？
• RQ4 在真实世界场景中应用 MLOps 仍存在哪些尚待解决的挑战？

主要发现

• MLOps 包含围绕 ML 产品部署与运营的原则、概念以及开发文化。
• 一组必要的组件、角色、架构和工作流的聚合集可以为实施提供指南。
• MLOps 的尚待解决的挑战包括标准化、工具整合，以及在生产环境中将 ML 运营化。
• 本文为研究人员和从业者在选择和应用技术以实现对 ML 产品的自动化与运营提供指导。