One UX Practitioner's Journal
In the field of data science or more specifically Machine Learning (ML), you might expect that certain foundational concepts and terms are well established and commonly understood. After working in this domain for years, I can point to many examples where this is not the case.
When I moved to ML from another technical domain, I was surprised with the amount of new concepts and especially new meanings for common terms, like “training” a model. The typical ML workflow involves collecting data, training and evaluating the model from that data, deploying a model to produce sound predictions and monitoring the model’s performance in the production state. One particular topic that confused me as I was starting out was trying to figure out what people meant by model evaluation and model