Thanks to Russ Wolfinger and JMP Super-Users like @SDF1, there are now add-ins that allow easy tuning of hyperparameters for XGBoost, neural nets, bootstrap forests, and others, but I would really like to see these integrated into JMP. One thing I would like to do is test out thousands of neural networks with multiple Y's, which cannot yet be done with the add-in (and is not possible at all for XGBoost, as far as I can tell).
Sometimes, we need to predict more than one thing. For instance, maybe you want to optimize a process while minimizing costs. With several JMP modeling platforms, you can create a two-Y model, then use the desirability analysis under the profiler to do this, but you cannot build thousands of dual-Y models using neural networks, bootstrap forests, or even standard least squares. I think this would really increase the capacity of JMP Pro to do some hardcore predictive modeling.