Bridging the Gap: Embedded Deployment of PyTorch Deep Learning Models in Complex Engineered Systems - Ashvant Ram Selvam & Sarah Mohamed, MathWorks Embedded deployment of deep learning algorithms in autonomous vehicles, industrial machines, and other complex engineered systems requires much more than training a PyTorch model. This talk will discuss integration of PyTorch-trained AI models into complex engineered systems with mechanical, electrical, and software components, where practitioners often need to simulate before moving to hardware, and deploy without writing code. We will walk through a practical workflow for training deep learning models in PyTorch and integrating them into Simulink for model compression, simulation, verification, and code generation. Attendees will learn how this workflow facilitates system-level design, enables testing, and helps with cross-platform embedded deployment of PyTorch models, helping engineers to accelerate their journey from research to production in safety-critical applications.