In this position paper we propose what we here call a CodeNoCode (CNC) approach to the creation and evaluation of predictive models. NoCode tools are increasingly becoming more powerful, but there is a lack of understanding about the practical/industry adoption of predictive modeling NoCode tools such as KNIME or Orange. To speed-up adoption-in particular for non-technical users- we find this a good time to investigate the LLM-supported co-development of code and GUI (NoCode) for Machine Learning (ML) tasks. ML code follows a rather sequential structure which limits the complexity of the automatic creation of modeling code and as a consequence also the representations and integrations of the modeling process and analysis within a graphical interface. CNC avoids intermediate model formats (transparency), and, in support of LLMs, can be made programming language agnostic while opening up for collaboration between coders and non-coders, in support of LLM assistants (inclusivity).