AWS SageMaker Inference Optimization for Cloud and Edge Devices

Deploying machine learning models efficiently requires balancing speed, cost, and hardware constraints. This text-only course guides you through the essential strategies for optimizing your models for both cloud endpoints and resource-constrained edge environments. You will transition from simply training models to deploying highly optimized, cost-effective inference pipelines. Through clear explanations and written walkthroughs, you will learn how to analyze performance bottlenecks and compile models for diverse hardware targets. What you'll learn: 1. Understand foundational concepts of machine learning inference, latency, and throughput. 2. Configure SageMaker Inference Recommender to select the ideal cloud instance sizes for your workloads. 3. Compile and optimize models for edge devices using SageMaker Neo. 4. Apply quantization and model pruning techniques to reduce memory footprints. 5. Deploy containerized inference endpoints using modern MLOps best practices. 6. Practice troubleshooting performance bottlenecks through written step-by-step guides and code analyses. The course starts with key terminology and foundational concepts of model compilation before moving into practical configuration guides for cloud and edge scenarios. It is designed for beginning machine learning engineers, developers, and cloud enthusiasts, with no advanced deployment prerequisites required. Start optimizing your machine learning deployment pipelines today.