End-To-End ML with LLMs and Semantic Data Management: Experiences from Chemistry 4.0

Machine Learning (ML) in industrial chemistry is often hindered by the complexity of preprocessing heterogeneous datasets. In this proof-of-concept study, we explore the use of semantic data management to support LLM-driven automation of end-to-end ML pipelines in a real-world Chemistry 4.0 setting. A semantic model is used to capture domain knowledge and metadata in a machine-readable form, guiding LLMs through natural language prompts to generate complete data wrangling and ML modeling code. We evaluate several state-of-the-art LLMs on their ability to autonomously produce functionally correct Python code for preprocessing and Gaussian Process modeling. Our results show that, when guided by structured semantic context, larger LLMs can reliably generate accurate pipelines, significantly reducing the need for manual intervention. These findings provide an encouraging starting point for further exploration toward leveraging the semantic model to improve the robustness of code generation by systematically integrating relevant information into the generation process, rather than relying solely on the raw intelligence of the LLM.