GPU-Accelerated Operations Research: Production-Grade Optimization with NVIDIA cuOpt on Databricks

Enterprises face a surge of optimization challenges—workforce scheduling, supply chain planning, resource allocation, etc. that are computationally explosive as scale and constraints grow. Learn to solve these problems with Databricks using NVIDIA cuOpt on serverless GPUs, with large-scale linear, mixed-integer, and routing models in minutes instead of hours. We will walk through end-to-end architectures on the Databricks Lakehouse—parameterizing and submitting optimization jobs to serverless GPU clusters, managing data with Unity Catalog and Delta tables, and orchestrating workloads alongside existing ML pipelines. Learn from real-world examples I.e. shift scheduling, production allocation, and route optimization with performance benchmarks and cost benefits compared to traditional CPU solvers. Leave with practical patterns for modeling constraints, validating feasibility, and knowing when GPU-accelerated optimization can drive measurable gains in efficiency and delivery performance.