DevOps for AI – Challenges in Development of AI-enabled Applications
DevOps for AI – Challenges in Development of AI-enabled Applications
复制标题
AI 的 DevOps – 开发支持 AI 的应用程序面临的挑战
DOI：
10.23919/softcom50211.2020.9238323
复制
发表时间：
2020
期刊：
影响因子：
--
通讯作者：
J. Bosch
中科院分区：
文献类型：
--
作者：
Lucy Ellen Lwakatare;I. Crnkovic;J. Bosch
当开发包含基于机器学习 (ML) 的组件的软件系统时，开发过程会变得更加复杂。机器学习过程的核心部分是训练迭代以找到最佳的预测模型。现代软件开发流程（例如 DevOps）已被广泛采用，通常强调频繁的开发迭代和软件变更的持续交付。尽管现代方法能够解决构建基于机器学习的软件系统时面临的一些问题，但目前还没有关于如何将它们与实践中的机器学习工作流程中的流程相结合的既定程序。本文指出了开发包含 ML 组件的复杂系统所面临的挑战，并讨论了由 DevOps 和 ML 工作流程相结合驱动的可能解决方案。提出工业案例来说明这些挑战和可能的解决方案。
When developing software systems that contain Machine Learning (ML) based components, the development process become significantly more complex. The central part of the ML process is training iterations to find the best possible prediction model. Modern software development processes, such as DevOps, have widely been adopted and typically emphasise frequent development iterations and continuous delivery of software changes. Despite the ability of modern approaches in solving some of the problems faced when building ML-based software systems, there are no established procedures on how to combine them with processes in ML workflow in practice today. This paper points out the challenges in development of complex systems that include ML components, and discuss possible solutions driven by the combination of DevOps and ML workflow processes. Industrial cases are presented to illustrate these challenges and the possible solutions.
影响因子：
7.4
作者：
Kim, Miryung;Zimmermann, Thomas;Begel, Andrew
通讯作者：
Begel, Andrew