标题： 面向CRISP-ML(Q)：一种具有质量保证方法的机器学习过程模型 显示英文标题

标题： Towards CRISP-ML(Q): A Machine Learning Process Model with Quality Assurance Methodology

摘要： 机器学习是工业界和学术界广泛使用的一项成熟技术，但目前仍缺乏一种标准的流程模型来提高机器学习应用的成功率和效率。 项目组织和机器学习从业者在整个机器学习应用生命周期中需要指导以满足业务期望。 因此，我们提出了一个用于开发机器学习应用的流程模型，该模型涵盖了从定义范围到维护已部署的机器学习应用的六个阶段。 第一阶段结合了业务理解和数据理解，因为数据的可用性往往会影响项目的可行性。 第六阶段涵盖了机器学习应用监控和维护的最新方法，因为在变化的环境中模型退化风险十分明显。 对于流程中的每个任务，我们提出了适合解决我们在机器学习开发中识别出的风险的质保方法。 该方法来源于实践经验和科学文献，并已被证明具有普遍性和稳定性。 该流程模型扩展了CRISP-DM，这是一个享有强大行业支持的数据挖掘流程模型，但未能解决特定于机器学习的任务。 我们的工作提出了一个针对机器学习应用的行业和应用中立的流程模型，重点关注质量保证的技术任务。 显示英文摘要

摘要： Machine learning is an established and frequently used technique in industry and academia but a standard process model to improve success and efficiency of machine learning applications is still missing. Project organizations and machine learning practitioners have a need for guidance throughout the life cycle of a machine learning application to meet business expectations. We therefore propose a process model for the development of machine learning applications, that covers six phases from defining the scope to maintaining the deployed machine learning application. The first phase combines business and data understanding as data availability oftentimes affects the feasibility of the project. The sixth phase covers state-of-the-art approaches for monitoring and maintenance of a machine learning applications, as the risk of model degradation in a changing environment is eminent. With each task of the process, we propose quality assurance methodology that is suitable to adress challenges in machine learning development that we identify in form of risks. The methodology is drawn from practical experience and scientific literature and has proven to be general and stable. The process model expands on CRISP-DM, a data mining process model that enjoys strong industry support but lacks to address machine learning specific tasks. Our work proposes an industry and application neutral process model tailored for machine learning applications with focus on technical tasks for quality assurance.