The purpose of the ROCKFlows project is to lay the foundations of a Software Product Line (SPL) that helps the construction of machine learning workflows. Based on her data and objectives, the end user, who is not necessarily an expert, should be presented with workflows that address her needs in the " best possible way ". To make such a platform durable, data scientists should be able to integrate new algorithms that can be compared to existing ones in the system, thus allowing to grow the space of available solutions. While comparing the algorithms is challenging in itself, Machine Learning, as a constantly evolving, extremely complex and broad domain, requires the definition of specific and flexible evolution mechanisms. In this paper, we focus on mechanisms based on meta-modelling techniques to automatically enrich a SPL while ensuring its consistency.