The development of machine learning and deep learning solutions typically follows a workflow that starts from the problem definition and goes through the crucial steps of collecting and exploring useful data, training and evaluating candidate models, deploying a solution, and finally documenting and maintaining the system once it is running in the wild (Figure 1). Despite its predictable structure, some steps of this process are iterative by nature and usually require multiple rounds of adjustments, fine-tuning, and optimizations. In this blog post we look at the process of running multiple machine learning experiments while searching for the best solution for a given problem and discuss the need to document the process in a structured way. We will discuss the Why, What and How of managing experiments and then we'll walk through an example of what this looks like in a real-world example. Machine learning (ML) and deep learning (DL) involve a fair amount of "trial and error" regardless of the task (regression, classification, prediction, segmentation), choice of model architecture, and size or complexity of the associated data set.