Machine Learning (ML) technologies are becoming increasingly popular and have various applications, ranging from smartphones and computers to large-scale enterprise infrastructure that serves billions of requests per day. Building ML tools, however, remains difficult today because there are no industry-wide standardised approaches to development. Many engineering students studying ML and Data Science must re-learn once they begin their careers. In this article, I've compiled a list of the top five problems that every ML specialist faces only on the job, highlighting the gap between university curriculum and real-world practice.