As Machine Learning (ML) is becoming an important part of every industry, the demand for Machine Learning Engineers (MLE) has grown dramatically. MLEs combine machine learning skills with software engineering knowhow to find high-performing models for a given application and handle the implementation challenges that come up -- from building out training infrastructure to preparing models for deployment. New online resources have sprouted in parallel to train engineers to build ML models and solve the various software challenges encountered. However, one of the most common hurdles with new ML teams is maintaining the same level of forward progress that engineers are accustomed to with traditional software engineering. The most pressing reason for this challenge is that the process of developing new ML models is highly uncertain at the outset.