Working as a machine learning engineer means that I often have to simplify incredibly complex systems and concepts. My main goal is always to ensure that my audience understands me clearly, whether they are team members, stakeholders, or clients who may not have a deep understanding of machine learning algorithms.
For instance, once when working on a Neural Network-based model, I was tasked to explain its architecture to our marketing team. To make this complex system simpler to understand, I compared the neural network to a decision-making process in a corporation, where nodes were employees, and the weightings were levels of expertise in their respective fields. This analogy aided understanding significantly, as it translated the technical architecture into a context they were familiar with.