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Machine learning is an established and frequently used technique in industryand academia but a standard process model to improve success and efficiency ofmachine learning applications is still missing. Project organizations andmachine learning practitioners have a need for guidance throughout the lifecycle of a machine learning application to meet business expectations. Wetherefore propose a process model for the development of machine learningapplications, that covers six phases from defining the scope to maintaining thedeployed machine learning application. The first phase combines business anddata understanding as data availability oftentimes affects the feasibility ofthe project. The sixth phase covers state-of-the-art approaches for monitoringand maintenance of a machine learning applications, as the risk of modeldegradation in a changing environment is eminent. With each task of theprocess, we propose quality assurance methodology that is suitable to adresschallenges in machine learning development that we identify in form of risks.The methodology is drawn from practical experience and scientific literatureand 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 toaddress machine learning specific tasks. Our work proposes an industry andapplication neutral process model tailored for machine learning applicationswith focus on technical tasks for quality assurance.

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