Inference (the real time computation) and training (the computation you do when developing a model) are deeply tied by automatic differentiation.

In a way, you train by repeatedly inferring (forward), calculating a loss (how much your model sucks with its current predictions) and then improving the model by differentiating.

Take a look at Karpathy's micrograd repo to become more familiar with the process.