Some parts of stats (esp. classically) do use "inference" for the whole process of going from data -> result, especially when doing descriptive rather than pred

Some parts of stats (esp. classically) do use "inference" for the whole process of going from data -> result, especially when doing descriptive rather than predictive statistics. In most of ML the process is split into two phases, training a model on a data set, followed by using the trained model to predict labels (or whatever else it's predicting) for new data. Training is an implementation of statistical induction, from data to model, while model "use" or "evaluation" is an implementation of (probabilistic) deduction, from model + query to result. In ML, "inference" is usually a synonym for the "deduction" or "evaluation" portion. It makes sense to me intuitively if you think of it as learning things vs. inferring things from learned knowledge. But you do find constructions using it as a synonym for training too, as in phrases like "model inference" (which means inferring models from data, aka model induction or training). Inferring things from other things is a pretty general concept, so it can be slippery without context...