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 numerous 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.

Explore available foundation models »

Foundation models available through SageMaker.

Meta

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 »

Customers

  • Tyson
  • 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
    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
    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 SageMaker JumpStart

Blogs

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Incremental training with Amazon SageMaker JumpStart

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Amazon SageMaker JumpStart models and algorithms available via API

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New built-in Amazon SageMaker algorithms for tabular data modeling

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Transfer learning for TensorFlow image classification models

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

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Deep demand forecasting with Amazon SageMaker

Hands-on exercises

Tutorial

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

WORKSHOPS

Explore how to use SageMaker JumpStart for use cases

Videos

VIDEO

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

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

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

How to fine-tune and deploy a text-to-image Stable Diffusion model using SageMaker JumpStart
VIDEO

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

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

What's new

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