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Amazon SageMaker JumpStart

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description Amazon SageMaker JumpStart Overview

JumpStart is a feature *within* SageMaker designed specifically to drastically reduce time-to-value. It provides immediate access to hundreds of pre-trained models (e.g., object detection, NLP) that can be deployed with minimal configuration. This is the best choice when the goal is rapid prototyping or proof-of-concept deployment using established industry models, bypassing the need to train from scratch.

help Amazon SageMaker JumpStart FAQ

What is Amazon SageMaker JumpStart used for?

JumpStart is a feature inside Amazon SageMaker that provides pretrained models, foundation models, and built-in algorithms for machine-learning projects. AWS describes it as a model hub intended to shorten the path from model selection to deployment. https://aws.amazon.com/sagemaker/ai/jumpstart/

Can SageMaker JumpStart deploy a model as an inference endpoint?

Yes, a supported JumpStart model can be deployed through SageMaker so that an endpoint handles inference requests. AWS also provides deployment choices such as cost optimized, throughput optimized, latency optimized, or balanced configurations in the Studio workflow. https://docs.aws.amazon.com/sagemaker/latest/dg/jumpstart-deploy.html

Which model ecosystems are represented in SageMaker JumpStart?

AWS says JumpStart includes pretrained models and algorithms from sources such as TensorFlow Hub, PyTorch Hub, Hugging Face, and MxNet GluonCV. The available catalog changes over time, so a particular model's license and region support should be checked before deployment.

Can I fine-tune a model from SageMaker JumpStart?

Yes, JumpStart supports fine-tuning selected foundation models in SageMaker Studio and through the SageMaker Python SDK. The required instance type, dataset format, training method, and cost depend on the specific model. https://docs.aws.amazon.com/sagemaker/latest/dg/jumpstart-foundation-models-use-python-sdk.html

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