Learn about analytics, predictive modeling, and optimization from top trainers and business solution providers!

Access free online learning from the world’s largest association of operations research, analytics, and data science professionals. Learn from leaders of innovation how optimization, predictive analytics, modeling, machine learning, and other critical areas impact success. INFORMS Sponsored Webinar Series provides insight from top educators and cutting-edge businesses that are delivering real-world applications, and research-based solutions.

Don’t miss these free webinars and opportunity to build your knowledge. 

  • Learn new skills
  • Find solutions to problems
  • Grow your career

Register below for each individual webinar and check back for upcoming webinars and recordings.  

Register for SAS JMP webinar

The Predictive Modeling Workflow

October 8, 2020
3pm EDT

The webinar will move through the full life cycle of a predictive analytics project. Steps shown includes data cleaning and preparation, initial exploration and analyses, model fitting, evaluating and comparing model performance, final model selection, and deployment into a production environment. Modeling techniques shown will include Multiple Linear Regression, Regression trees, Neural Nets, and K-Nearest Neighbors.

In this webinar you’ll learn:

  1. Best practices in how to approach a predictive modeling effort.
  2. Some of the common mistakes analyst make in a predictive modeling effort.
  3. Why using a point-and-click interactive software environment can be more efficient.


Kevin Potcner


How Python Modelers Can Increase ROI and Business User Engagement with FICO Xpress Insight

November 2, 2020
1:00pm EST
  1. How to deploy a Python model in FICO Xpress Insight
  2. Customization options for Python models
  3. Practical demonstrations of several common use cases


Oliver Bastert, Johannes Mueller, and Vladimir Roitch

Register for FICO webinar



Adding Optimization to Your Applications: AMPL's Model-Based Approach to Fast Development and Reliable Deployment

In this webinar, you’ll learn:

  1. An understanding of the optimization modeling lifecycle and the components of the modern optimization toolchain.
  2. An appreciation for the benefits of model-based optimization, including the advantages of modeling and modeling languages over programming and programming languages for optimization.
  3. Case studies of successful model-based optimization applications across a variety of business types.


Robert Fourer
Northwestern University
Website with CV

Robert Fourer is co-founder and President of AMPL Optimization Inc. and Professor Emeritus of Industrial Engineering and Management Sciences at Northwestern University. In collaboration with colleagues in Computing Science Research at Bell Laboratories, he initiated the design and development of AMPL, a widely used optimization language and system, and wrote a popular book on optimization modeling in AMPL. Additionally, he has been a key contributor to the NEOS Server project to make optimization software available over the Internet, and has supported development of open-source software for operations research through his service on the board of the COIN-OR Foundation. He shared the Beale-Orchard-Hays Prize for NEOS, and the INFORMS Impact Prize in recognition of the influence of algebraic modeling languages for optimization.


The Princeton 20 for AI Projects: A Framework to Manage Project Risks and Successfully Deploy Solutions

Princeton Consultants has had the privilege of working with innovators across a wide range of industries to improve their decision-making using advanced analytics. Over 40 years, this work has worn many labels. What has historically been called expert systems, decision support, and operations research is today often simply called Artificial Intelligence (AI). During this time, the core techniques have vastly expanded – from bases in statistics, simulation, and mathematical optimization, to include machine learning and new classes of hybrid algorithms. These advances have been multiplied by the exponential improvements in supporting hardware.

We have found consistent and repeatable factors that govern whether a given analytics project will make it into successful production. These factors also apply to the latest AI projects. We have codified these learnings into the “Princeton 20” – a list of 10 environmental and 10 technical risk factors that will help you anticipate issues and increase the likelihood of a successful transition from promising research to production deployment and wide usage.

Join Princeton Consultants Founder and CEO Steve Sashihara for a webinar that is ideal for practitioners and team leaders who deploy solutions into production. In this webinar, you’ll learn:

  1. 10 business risk factors and 10 technical risk factors to monitor in AI projects.
  2. How to score project risk factors.
  3. Tips and techniques to overcome obstacles and deploy solutions.


Steve Sashihara, Founder and CEO

Princeton Consultants

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