During my first steps in the field I was promised that machine learning would be automated from the beginning. Unfortunately, once I’ve outsourced looking for the parameters that best matched my data to the machines, I was instead left with having to look for the hyperparameters that define the best model architecture, all by myself. This often ends up being a lengthy manual process. Is there a way to outsource this bit too? In this talk we will take a look at the automated machine learning libraries Keras Tuner and AutoKeras, which allow the user to create high level templates of deep learning models and use them in automated search for the best hyperparameters. They not only enable speedier development of better models but also make deep learning accessible to a wider pool of people thanks to the abstractions they offer. In the presentation we will go through several iterations of pretending to know progressively less and less about both our data and machine learning in general, and see how these libraries come to our help in creating highly performant deep learning models with a fraction of the effort. It is aimed at a general audience familiar with Python. Knowledge of Keras is a plus but not a requirement - that is kind of the whole point!