一句话：提出工业级AI软件持续实验的研究议程，归纳五大挑战并提出七个研究问题。
工业级 AI 软件开发不能只关注孤立的 AI/ML 模型属性，而需在大规模软件系统中集成并运维模型；同时业务需求与模型持续演化，形成“未知的未知”问题。本文主张将先进软件工程范式引入 AI 开发，使开发工作与业务战略保持精益、快速的协同。作者给出一个基于业务目标、需求与最小可行产品的迭代、持续开发整体视角，并围绕实验展开讨论。在此基础上，文章归纳出五个挑战领域，并提出面向未来研究的七个问题，形成持续实验研究议程。
Moving from experiments to industrial level AI software development requires a shift from understanding AI/ ML model attributes as a standalone experiment to know-how integrating and operating AI models in a large-scale software system. It is a growing demand for adopting state-of-the-art software engineering paradigms into AI development, so that the development efforts can be aligned with business strategies in a lean and fast-paced manner. We describe AI development as an “unknown unknown” problem where both business needs and AI models evolve over time. We describe a holistic view of an iterative, continuous approach to develop industrial AI software basing on business goals, requirements and Minimum Viable Products. From this, five areas of challenges are presented with the focus on experimentation. In the end, we propose a research agenda with seven questions for future studies.
DOI 原文 ·
@inproceedings{paperbot5763,
title = {Continuous experimentation on artificial intelligence software: a research agenda},
author = {Anh Nguyen-Duc and Pekka Abrahamsson},
booktitle = {FSE},
year = {2020},
doi = {10.1145/3368089.3417039}
}