Data Insight
ON-DEMAND WEBINAR
Model Behavior: Scaling Predictive Model Deployment with JMP Pro
Building a strong model is only the first step – delivering value requires effective deployment. This session presents a practical, end-to-end framework for operationalizing predictive models in JMP Pro, ensuring they remain stable, interpretable, and ready for real-world use.
Discover how to assess model stability, compare candidate models using Model Screening, and manage formulas efficiently with the Formula Depot. Profilers help visualize model behavior and support confident decision making before deployment.
Learn how to deploy validated models across platforms – including Python, SQL, SAS, and JMP – while enabling broader access through interactive HTML5 outputs and secure sharing via JMP Live.
Learn how to:
- Evaluate and select the most robust model for deployment.
- Streamline model management and visualization to support decision making.
- Deploy and share models across platforms for scalable use throughout the organization.
By the end of the session, participants gain practical workflows and tools for moving from development to reliable, production-ready model deployment.
About the presenters
Jed Campbell is a Senior Systems Engineer at JMP Statistical Discovery, which creates interactive and highly visual statistical discovery software for scientists and engineers. An educator by training, Campbell helps people see how they can get better and faster insights to their data by using JMP.
A Master Black Belt in Six Sigma, he has a vast experience as a problem solver, ranging from process improvement in manufacturing to designing and coaching experiments in rocket science. Campbell led a successful application process for which he won the enterprise-level Shingo Prize in 2011. He has a strong business acumen, sharpened by a nationally ranked MBA, focusing on entrepreneurship. Campbell also understands quality systems and how they can be incorporated into existing businesses without undue bureaucracy.
Kemal Oflus is a Principal Systems Engineer for JMP Statistical Discovery, which creates interactive and highly visual statistical discovery software designed for scientists and engineers. He supports sales and customer development in the Southwest region of the U.S. as technical lead.
An applied physicist by training, Oflus previously worked as a rocket scientist using statistical methods to support risk assessment on the design and simulation of several projects for NASA and U.S. Air Force. He is a member of the American Society of Quality and the American Statistical Association. Oflus regularly delivers talks and keynotes on data mining and predictive modeling; he also teaches data science at the University of California Riverside as a part-time lecturer.