Machine learning has revolutionized the way we approach data analysis and decision making across various industries. In this presentation, we specifically focus on its utility when integrated with JMP Pro, a powerful statistical and data visualization tool. We highlight various real study cases (with anonymized and modified data just to give the context) to show how we can unravel complex relationships within data sets, leading to insightful clustering, segmentation, and predictions. Machine learning techniques in JMP and JMP Pro also empower users to identify and optimise the most relevant variables, enhancing model performance and interpretability.  The main methods presented in this paper are:

• Neural networks for prediction on several responses and desirability optimisation.
• Regression trees, called Partition in JMP, to understand complex process issues.
• Some hybrid models using both standard statistical methods and machine learning to blend the different methods.
• Functional Data Explorer on sensor data from the high-tech industry for prediction.

We also detail several JMP Pro features, including the model comparison.