论文标题
对机器学习系统部署管道的持续学习
Continual learning on deployment pipelines for Machine Learning Systems
论文作者
论文摘要
数字化发展后，越来越多的大型原始设备制造商（OEM）在广泛的应用中调整计算机视觉或自然语言处理，例如植物中的异常检测和质量检查。这种系统的部署正成为一个极为重要的话题。我们的工作始于机器学习系统的最小自动部署技术，包括几次更新的迭代，并以自动部署技术进行比较结束。一方面，目的是比较理论和实践中各种技术的优势和缺点，以便促进后来的采用者在实施实际用例时避免犯广泛的错误，从而为自己的企业选择更好的策略。另一方面，要提高对机器学习系统部署评估框架的认识，以拥有更全面和有用的评估指标（例如表2），而不仅仅是关注一个因素（例如，公司成本）。这对于行业决策者尤其重要。
Following the development of digitization, a growing number of large Original Equipment Manufacturers (OEMs) are adapting computer vision or natural language processing in a wide range of applications such as anomaly detection and quality inspection in plants. Deployment of such a system is becoming an extremely important topic. Our work starts with the least-automated deployment technologies of machine learning systems includes several iterations of updates, and ends with a comparison of automated deployment techniques. The objective is, on the one hand, to compare the advantages and disadvantages of various technologies in theory and practice, so as to facilitate later adopters to avoid making the generalized mistakes when implementing actual use cases, and thereby choose a better strategy for their own enterprises. On the other hand, to raise awareness of the evaluation framework for the deployment of machine learning systems, to have more comprehensive and useful evaluation metrics (e.g. table 2), rather than only focusing on a single factor (e.g. company cost). This is especially important for decision-makers in the industry.
