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.

我们提出了一个适用于机器学习应用程序的过程模型，该模型涵盖从定义范围到维护部署的机器学习应用程序的六个阶段，其中每个阶段都包括适合解决风险挑战的质量保障方法，是一个具有普适性和稳定性的质量保障技术任务的行业和应用程序中性过程模型，该模型扩展了数据挖掘过程模型 CRISP-DM，但缺乏解决机器学习特定任务的能力。