Amazon SageMaker JumpStart

Machine learning (ML) hub with foundation models, built-in algorithms, and prebuilt ML solutions that you can deploy with just a few clicks

Foundation models from popular model providers for text and image generation that are fully customizable

Hundreds of built-in algorithms with pretrained models from popular model hubs


Fully customizable solutions for common use cases with reference architectures to accelerate your ML journey

Share ML models and notebooks across your organization to accelerate ML model building and deployment

Amazon SageMaker JumpStart is a machine learning (ML) hub that can help you accelerate your ML journey. With SageMaker JumpStart, you can access pretrained models, including foundation models, to perform tasks like article summarization and image generation. Pretrained models are fully customizable for your use case with your data, and you can easily deploy them into production with the user interface or SDK. In addition, you can access prebuilt solutions to solve common use cases, and share ML artifacts, including ML models and notebooks, within your organization to accelerate ML model building and deployment.

None of your data is used to train the underlying models. Since all data is encrypted and does not leave your Virtual Private Cloud (VPC), you can trust that your data will remain private and confidential. See FAQs for more information.

How it works

  • Foundation models
  • Built-in algorithms with pretrained models
  • Solutions
  • Solutions how it works diagram
  • ML artifact sharing
  • ML artifact sharing HIW diagram

Foundation models

SageMaker JumpStart offers a wide selection of proprietary and publicly available foundation models from various model providers. Foundation models are large-scale ML models that contain billions of parameters and are pretrained on terabytes of text and image data so you can perform a wide range of tasks such as article summarization and text, image, or video generation. Because foundation models are pretrained, they can help lower training and infrastructure costs and enable customization for your use case.

Get started with foundation models »

Foundation models available through SageMaker.

Task  Model name  Public or proprietary 
Text generation  Jurassic J2 (Large, Grande, Jumbo)  Proprietary 
Text generation Jurassic J2 Instruct (Grande, Instruct, Jumbo Instruct)  Proprietary
Text generation Cohere Command Generate  Proprietary
Text generation Lyra-Fr (French)  Proprietary
Text generation FLAN UL2, T5 XXL  Publicly available 
Text generation GPT-J, GPT Neo  Publicly available
Text generation AlexaTM  Publicly available
Image generation  Stable Diffusion 2.1  Publicly available
Image generation Stable Diffusion Upscaling  Publicly available

Built-in algorithms

SageMaker JumpStart provides hundreds of built-in algorithms with pretrained models from model hubs, including TensorFlow Hub, PyTorch Hub, HuggingFace, and MxNet GluonCV. You can also access built-in algorithms using the SageMaker Python SDK. Built-in algorithms cover common ML tasks, such as data classifications (image, text, tabular) and sentiment analysis.

Learn more about built-in algorithms »

Prebuilt solutions

Prebuilt solutions can be used for common use cases and are fully customizable.

Learn more about prebuilt solutions »


  • Tyson
  • Tyson
    “At Tyson Foods, we continue to look for new ways to use machine learning (ML) in our production process to improve productivity. We use image classification models to identify products from the production line that require package labels. However, the image classification models need to be retrained with new images from the field on a recurring basis. Amazon SageMaker JumpStart enables our data scientists to share ML models with support engineers so they can train ML models with new data without writing any code. This accelerates the time-to-market of ML solutions, promotes continuous improvements, and increases productivity.”

    Rahul Damineni, Specialist Data Scientist, Tyson Foods

  • Mission Automate
  • Mission Automate
    Mission Automate
    “Thanks to Amazon SageMaker JumpStart, we are able to launch ML solutions within days to fulfill machine learning prediction needs faster and more reliably.”

    Alex Panait, CEO – Mission Automate

  • MyCase
  • MyCase
    “Thanks to Amazon SageMaker JumpStart, we can have better starting points which makes it so that we can deploy a ML solution for our own use cases in 4-6 weeks instead of 3-4 months.”

    Gus Nguyen, Software Engineer – MyCase

  • pivotree
  • Pivotree
    “With Amazon SageMaker JumpStart, we can build ML applications such as automatic anomaly detection or object classification faster and launch solutions from proof of concept to production within days.”

    Milos Hanzel, Platform Architect – Pivotree  

Get started with Amazon SageMaker JumpStart



Incremental training with Amazon SageMaker JumpStart


Amazon SageMaker JumpStart models and algorithms available via API


New built-in Amazon SageMaker algorithms for tabular data modeling


Transfer learning for TensorFlow image classification models


Detect financial transaction fraud using a Graph Neural Network with Amazon SageMaker


Deep demand forecasting with Amazon SageMaker

Hands On Exercises


Step-by-step tutorial to get started with SageMaker JumpStart


Explore how to use SageMaker JumpStart for use cases



How to access, train, & deploy a text-to-image Stable Diffusion model ­using Amazon SageMaker JumpStart in less than 3 minutes

How to access, train, & deploy a Stable Diffusion model ­using Amazon SageMaker JumpStart

How to fine-tune & deploy a text-to-image Stable Diffusion model using Amazon SageMaker JumpStart in less than 2 minutes

How to fine-tune & deploy a Stable Diffusion model using Amazon SageMaker JumpStart

AWS Startup Showcase S3 E1: Generative AI: Hype or Reality - Opening Panel

AWS Startup Showcase S3 E1: Generative AI: Hype or Reality

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