Optimizing AI Workflows and Deploying Edge Models

Modern AI systems require efficient training workflows, scalable data pipelines, and deployment strategies that meet real-world performance constraints. In this course, you'll learn how to optimize machine learning workflows and deploy AI models in production environments, including edge devices. You'll begin by working with PyTorch to implement neural network components using tensor operations and automatic differentiation. You'll analyze GPU utilization and training performance to identify computational bottlenecks and improve throughput. Next, you'll explore tools and techniques used to v…