In this post, I want to share how Python can be used to automate the documentation of machine-learning (ML) experiments using AsciiDoc. The search for the best-performing ML model is an empirical process, which involves fitting models with differing parameters and evaluating their predictive performance. Only after a multitude (e.g. hundreds or thousands) of models have been evaluated, is it possible confidently proclaim that a suitable model has been identified. The major challenge of running vast numbers of experiments is that they are time- and compute-intensive because results usually have to be delivered within a certain time frame (e.