The difference between training and inference is similar to that of a student being taught something like algebra at school and then applying it in the real world. In school, the student is given numerous exercises; for each exercise, the student attempts the question and hands his/her answer over to the teacher, who provides feedback indicating whether it is correct or not. Initially, this feedback is likely to be skewed toward the student being wrong more often than right, but after many attempts, as the student starts building his/her understanding of the concepts, the feedback shifts towards mostly being right. At this point, the student is considered to have sufficiently learned algebra and is able to apply it to unseen problems in the real world, where he/she can be confident of the answer based on his/her exposure to the exercises provided during the lessons at school.
ML models are no different; the initial phase of building the model is...
