SageMaker JumpStart

Amazon SageMaker JumpStart is a key service for the AWS Certified Machine Learning Engineer – Associate exam, particularly under the model selection and cost considerations objectives. It sits at the intersection of the modeling and deployment stages of the ML life cycle, giving engineers a fast path from model discovery to production inference. Understanding how JumpStart works, what it deploys, and how it compares to Amazon Bedrock directly affects your ability to answer scenario-based exam questions about foundation model selection, infrastructure trade-offs, and data privacy requirements.

SageMaker JumpStart functions as a curated model hub embedded within Amazon SageMaker StudioThe integrated development environment (IDE) for machine learning on AWS, providing a web-based visual interface to build, train, and deploy models.. It provides access to hundreds of pretrained foundation models from providers such as Hugging Face, Meta (Llama), Stability AI, and Amazon’s own model families. Engineers can browse the catalog, select a model, and deploy it to a SageMaker endpoint with minimal configuration, often with a single click. JumpStart also ships example notebooks and solution templates that accelerate common ML workflows. Within the broader AWS ML ecosystem, JumpStart occupies a distinct position from Amazon Bedrock. Bedrock offers a fully managed, serverless API for foundation model inference, while JumpStart deploys models onto infrastructure that you own and control inside your AWS account. This lesson focuses specifically on JumpStart’s capabilities, deployment mechanics, and the decision criteria that separate it from Bedrock.

The following diagram illustrates how SageMaker JumpStart connects the model catalog to ...