The efficient use of current machine learning (ML) processes requires a very high level of expertise, which hinders the widespread use of ML approaches, especially by small and medium-sized enterprises. The aim of the Simple-ML project is therefore to significantly improve the usability of ML methods in order to make them more accessible to a wide range of users. As a central contribution of the project, a domain-specific language (DSL) is defined, which describes ML workflows (workflows) and their components holistically and can be specified by textual and graphical editors. Furthermore, the project contributes to the robustness of the created ML-workflows, explainability and transparency of the learned models, Efficiency and scalability of the created applications as well as the reusability of the created solutions. This is done by applying semantic technologies, further development of symbolic ML methods and building on scalable ML frameworks. The results of the Simple ML project will be validated in the application scenarios "Mobility in the city" and "Logistics" together with users from industry.