  In this study, we utilize Gaussian processes, probabilistic neural network,
natural gradient boosting, and quantile regression augmented gradient boosting
to model lead times of laser manufacturing processes. We introduce
probabilistic modelling in the domain and compare the models in terms of
different abilities. While providing a comparison between the models in
real-life data, our work has many use cases and substantial business value. Our
results indicate that all of the models beat the company estimation benchmark
that uses domain experience and have good calibration with the empirical
frequencies.
