The JMP/Python ecosystem basically means an ecosystem that complementarily uses the functions of JMP and Python, and in particular, from a machine learning perspective, we have first built a JMP/Python machine learning ecosystem that complementarily uses the machine learning techniques that JMP Pro possesses as well as Python's scikit-learn, XGBoost, LightGBM, CatBoost, PyTorch, Tensor Flow, etc. to build and optimize machine learning predictive models. The roadmap of the JMP/Python ecosystem is defined by IGEO, and the IGEO roadmap is largely divided into the IG (Identify/Generate) area and the EO (Explore/Optimize) area. The IG area is an area for defining problems and generating experimental/simulation data, and the EO area, which is the core of the JMP/Python ecosystem, is an area that executes DEbO (Design Exploration based Optimization). In the EO area, design is performed by integrating MariaDB, JMP, and Python, and automation is performed by building a GUI using Python/JMP GUI as needed. In order to explore the design area, two steps of creating an experimental design/computational experimental design and building a machine learning model are repeated, and JMP and Python modules are combined according to the situation. As success stories of the JMP/Python machine learning ecosystem, the Explore area case, the ball bearing failure detection monitoring system case, and the CAE machine learning optimization case are summarized, and the direction to perform almost all engineering analyses by combining JMP and Python is presented.
