Amazon SageMaker JumpStart provides pretrained models for a wide range of problem types to help you get started with AI workloads. SageMaker JumpStart offers access to solutions for top use cases that can be deployed to SageMaker AI Managed Inference endpoints or SageMaker HyperPod clusters. Through pre-set deployment options, customers can quickly move from model selection to model deployment.
Model deployments through SageMaker JumpStart are fast and straightforward. Customers could select options based on expected concurrent users, with visibility into P50 latency, time-to-first token (TTFT), and throughput (token/second/user). While concurrent user configuration options are helpful for general-purpose scenarios, they aren’t task-aware, and we recognize that customers use SageMaker JumpStart for diverse, specific use cases like content generation, content summarization, or Q&A. Each use case might require specific configurations to improve performance. Moreover,…
https://aws.amazon.com/blogs/machine-learning/use-case-based-deployments-on-sagemaker-jumpstart/
