This thesis investigates the challenges in machine learning lifecycle governance within Finnish companies. The thesis establishes the current stages and steps of the machine learning lifecycle and presents known challenges in academia. The thesis aims to recognize and discover unfound challenges when machine learning practitioners work in an enterprise environment. In this thesis, the author interviewed three (3) distinguished machine learning consultants with revered experience in machine learning. The data was collected with semi-structured interviews and analyzed with thematic analysis. The results present challenges in all of the stages and cross-cutting aspects. Furthermore, the results presented previously unfound challenges in the majority of the stages and the cross-cutting aspects of the machine learning lifecycle governance.