In the field of machine learning there are an abundance of choices to make, from data acquisition, feature selection, data pre-processing, algorithm choices, compute and software requirements, model tuning, model evaluation, and choosing what and how to publish. I'll go over some guidelines on how best to tackle these choices, highlight some of the most common workflows and use cases, and give some examples of projects with different requirements.