It is expected that the recent surge of Large Language Models (LLM) will support this development and further improve accessibility and the interaction between humans and ML-based models . While the works presented give a glimpse of what is possible with automated experimentation pipelines in combination with ML, the wide adoption of such methods is limited by the high acquisition costs of the setup, the expertise and time required to implement and maintain the hardware in the research environment and the limited versatility of the methods to a broad range of problems .