The user experience (UX) design practice (c.m.p interaction design practice) has started to make profound changes in designing intelligent digital services using Machine Learning (ML) to enhance the UX. ML has the capability to enhance the user’s experience, for example, facilitating more accurate decisions or improving efficiency in achieving one's goals. However, research suggests that ML is a challenging design material in design practice, such as not envisioning the best-suited solution because of not comprehending data dependency when prototyping or the lack of tools and methods for evaluating the solution. Without a doubt, ML opens new doors for UX designers to be creative in their practice. However, research indicates that lack of knowledge transfer into UX design practice may hamper this potential. This paper explores how UX designers experience ML. The findings resulted in 5 experiences: 1) Absence of competence, 2) Lack of incentive for competence development, 3) Challenging articulating design criteria, 4) Mature vs. Immature customers, 5) Lack of support for ethical concerns. I discuss the implications of these findings and propose how we can understand UX design practice and opportunities for additional design research to support designers working with ML.