Low or no code machine learning platforms, whereof tools such as KNIME, DataRobot or WEKA are among the best-known, have facilitated the implementation of machine learning applications in industrial environments in recent years by transferring programming tasks to an assistance system instead of demanding users to provide the respective skills. Despite the high number of innovations, to the best of the authors’ knowledge, there is no comprehensive classification scheme to assess the autonomy of those tools. Hence, this paper demonstrates a maturity model that classifies the assistance level of existing digital machine learning tools with respect to the requirements of manufacturing environments. It is based on the levels of driving automation and concretized by the so-called CRISP-ML(Q) procedure model. The model allows researchers to rate newly developed tools against existing ones and aims to serve as a baseline for future research. To evaluate the added value to the research landscape, semi-structured interviews with four ML experts were conducted. Finally, five commercial tools were categorized in the model to show its applicability.