In ML, "training" generally alludes to the way toward preparing up an ML model to be valuable by taking care of its information from which it can learn. "Training" may allude to the particular task of taking care of that model with the assumption that the subsequent (resulting) model will be assessed independently (e.g., on a different "test" set), or it may allude to the overall cycle of the feeding of its information with the goal of utilizing it for something.
I've seen "inference" utilized with regards to ML in two fundamental senses. In one feeling, "inference" alludes to the way toward taking a model that is as of now been trained and utilizing that trained model to make helpful forecasts (predictions). Here, training and inference are totally distinct exercises, and "inference" alludes to the way toward inferring things about the world by applying your model to new information.
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