If relationships involve models that are nonlinear in their parameters, you should precede with caution.
Nonlinear models are more difficult to fit than linear models.
Sometimes you need to interact with the fitting process, trying out different starting values for parameters, using loss functions, or altering the nonlinear modeling formula. In JMP you have full interactive control. You can stop the iterations at any point, examine the values, change the parameters,or restart the iterations. And the iterations run in the background. This means that you can turn to other things and continue your work while the nonlinear regression continues to run in the background.