Optimize Decisions with ML: Prescriptive & Robust Optimization for Supply Chain and Pricing | Dan Becker

How do you turn machine learning predictions into better real-world decisions—especially under uncertainty in supply chains and pricing? In this episode, Dan Becker, Founder & CEO of Decision AI and former Google data scientist and Product Director at DataRobot, walks through prescriptive analytics and decision optimization for practical business impact. With a background that includes top Kaggle performance and contributions to TensorFlow and Keras, Dan explains how to formulate optimization problems, choose objectives and constraints, and integrate ML forecasts into prescriptive and robust optimization models.

We cover robust vs. stochastic optimization, aligning loss functions with business objectives, and the solvers and tools that make this work—OR-Tools, Gurobi, Pyomo and open-source options. Dan also digs into scalability, approximation techniques, and deployment: pipelines, monitoring, and feedback loops. Use cases include supply chain optimization, resource allocation, and pricing/bidding strategies, plus operational, legal, and ethical constraints. Listeners will get practical guidance on evaluation metrics, common pitfalls like mis-specified objectives and overfitting decisions, and the cross-functional skills needed—data science, operations research, and software engineering—to get started with prescriptive optimization projects.