Ask the Expert Webinar Series

Composite AI Using Machine Learning in Optimization Formulations

On-Demand • Cost: Complimentary

About the webinar

Predictive modeling using machine learning or deep learning can help establish the relationship between predictor and response variables in a system.

But incorporating machine learning models within optimization formulations isn’t an easy task.

This webinar will focus on how to prebuild ASTORE machine learning models that can be scored within the “runOptmodel” optimization action by the black-box solver to evaluate the objective function or constraints.

This webinar is for data scientists and business leaders who currently license SAS Optimization and/or SAS Visual Data Mining and are interested in learning how to incorporate ML models into optimization formulations to solve complex business problems in any industry.

You will learn how to:

  • Embed machine learning models in the constraints and the objective function of an optimization model.
  • Tune the black-box solver in SAS Optimization to converge to a near-optimal solution.
  • Call CASL scripts to compute values for the constraints and the objective function.
  • Understand the benefits of using machine learning versus linear models.

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About the Experts

Nabaruna Karmakar
Senior Operations Research Specialist, SAS

Nabaruna Karmakar is part of the Analytics Center of Excellence at SAS. She works with customers from various industries to develop data-driven solutions that optimize their business operations. Karmakar has a PhD in industrial engineering from North Carolina State University focusing on operations research (OR). Aside from traditional OR techniques, her areas of expertise include artificial intelligence, machine learning, computer vision and full-stack web development.

Spiros Potamitis
Senior Product Marketing Manager, SAS

Spiros Potamitis is a data scientist and a global product marketing manager of forecasting and optimization at SAS. He has extensive experience in the development and implementation of advanced analytics solutions across different industries, and he provides subject matter expertise in the areas of forecasting, machine learning and AI. Prior to joining SAS, Potamitis worked and led advanced analytics teams in various sectors such as credit risk, customer insights and CRM.