In numerous scientific disciplines, ML methods offers significant potential for advancing research in novel ways. However, a considerable challenge for domain experts lies in the necessity for extensive ML experience to effectively apply these methods. Similarly, the Centre for Solar Energy and Hydrogen Research Baden-Württemberg (ZSW) encountered this obstacle when attempting to integrate ML into the work of their researchers. This talk will demonstrate how the ZSW applied AutoML to create new opportunities for renewable energy domain experts who lacked ML expertise. The session will also illustrate the use of AutoML through a variety of examples in this domain and present the ZSW's self-developed no-code AutoML tool, KI-Lab.EE.