Krauß, JonathanJonathanKraußMachado Pacheco, BrunoBrunoMachado PachecoZang, Hanno MaximilianHanno MaximilianZangSchmitt, RobertRobertSchmitt2022-03-062022-03-062020https://publica.fraunhofer.de/handle/publica/26518410.1016/j.procir.2020.04.039Applications that leverage the benefits of applying machine learning (ML) in production have been successfully realized. A fundamental hurdle to scale ML-based projects is the necessity of expertise from manufacturing and data science. One possible solution lies in automating the ML pipeline: integration, preparation, modeling and model deployment. This paper shows the possibilities and limits of applying AutoML in production, including a benchmarking of available systems. Furthermore, AutoML is compared to manual implementation in a predictive quality use case: AutoML still requires programming knowledge and is outperformed by manual implementation - but sufficient results are available in a shorter timespan.enpredictive qualitymachine learningData ScienceAutomated MLAutoMLBenchmarkingartificial intelligencedata integrationdata preprocessingHyperparameter Tuning658670journal article