The job of a data scientist involves finding patterns in data, often in order to automate or augment human decision-making. Is it possible to find patterns in the way data scientists work in order to automate their own job? The concept of automatic machine learning (autoML) is compelling, and we at MFG Labs pay close attention to the development of this field, because the way we work and design our processes might be disrupted by theoretical or practical breakthroughs in this area. Furthermore it incites us to step back, deconstruct our typical workflow, and then question each part of it. At ICML (International Conference of Machine Learning), we had the chance to hear the take of the most brilliant minds on this subject. In this article we will present a brief outlook on how algorithms might replace us data scientists, or most likely assist us in doing our job better. Our intent is by no means to comprehensively survey the field of automatic machine learning, but rather to showcase a couple of specific topics that resonated particularly with our current interests. The typical data scientist workflow, when you consider it from defining the problem at hand to debugging a live production system is, in our experience, very intricate and certainly not linear. Thankfully, this process can easily be broken down into distinct parts. Rich Caruana (Microsoft Research) formulates the following pipeline, which feels very familiar for us...