Training is the long, expensive process of building a model by feeding it mountains of text until it learns. Inference is what happens every time you actually use the finished model to get an answer. Training is like the years a doctor spends in medical school, while inference is a single appointment where they put that learning to work.
For example, A company might spend months training a model, then run inference millions of times a day as people chat with it.