What you'll learn

Teams building on foundation models face a deceptively hard question early: how much customization does this workload actually need? In this course, Inference and Model Customization Patterns for AWS, you'll gain the ability to architect and defend a model-customization strategy for an AI workload. First, you'll explore the customization spectrum — base model, prompting, retrieval-adjacency, and fine-tuning — and the trade-offs each carries in cost, latency, data residency, and maintenance burden. Next, you'll discover how to resolve that decision into AWS's actual inference, model-access, and managed-tuning capabilities, and reason through the real cost relationship between per-token inference and hosting a customized model. Finally, you'll learn how to stress-test your architecture against a workload's cost and lifecycle requirements and present that strategy convincingly in a design review. When you're finished with this course, you'll have the skills and knowledge to make and defend model-customization decisions that hold up under real workload constraints.