Uploaded May 2026 | Updated September 2026, 2 weeks ago

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SageMaker AI Inference delivers dedicated endpoints via Managed Inference and inference stacks via HyperPod Inference for Traditional ML, Generative AI, and BYO Custom Model workloads. SageMaker Managed Inference serves customers seeking fully managed inference experiences with configurability and control over instance types, model architecture, and cost-latency-throughput tradeoffs. Through a hands-on example, let's dive deep on deploying SageMaker JumpStart, Inference Recommender benchmarks for your model optimizations across different instance types for your cost, throughput, and latency targets — shrinking your time from weeks to hours. Whether you're deploying your first model or orchestrating hundreds at global scale, walk away with the architecture patterns and best practices to run production inference at the right performance, the right cost, and the right operational posture.

SageMaker AI code samples - github.com/aws-samples/sagemaker-genai-hosting-examples
Workshop: https://catalog.workshops.aws/fm-inference/en-US