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
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Foundation models
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Built-in algorithms with pretrained models
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Solutions
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ML artifact sharing
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Foundation models
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Built-in algorithms with pretrained models
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Solutions
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ML artifact sharing
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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.
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.
Prebuilt solutions
Prebuilt solutions can be used for common use cases and are fully customizable.
Customers
“At Canva, we’re on a mission to empower the world to design and make it easy for anyone to create something beautiful on any device. With generative AI, we’re helping users bring their ideas to life with as little friction as possible. Thanks to SageMaker JumpStart, we’re able to empower our teams to get started with generative AI and test various foundation models. In our global hackathon, Canvanauts were able to easily deploy a wide variety of foundation models and get their projects up and running. It was a key part of our hackathon’s success.”
Nic Wittison, Engineering Lead for AI Products, Canva
“At Dovetail, we’re helping organizations improve the quality of their products and services through the power of better understanding their customers. With Amazon SageMaker JumpStart, we’re able to easily access, test, and deploy cutting-edge foundation models. We used AI21 Jurassic-2 Mid to enable enhanced summarization and were able to integrate it into our SaaS application within weeks, instead of taking months to implement. Our customers can now efficiently distill and understand insights from their data while maintaining data privacy and security assurance.”
Chris Manouvrier, Enterprise Architect Manager, Dovetail
“Our clients have thousands of legal documents and the process of parsing through these documents is tedious and time consuming. Often times, there isn’t a way to quickly get answers, such as understanding who asked a question in a deposition. Now with Amazon SageMaker JumpStart, we can access state of the art foundation models to power our products so customers can address a variety of use cases, such as contradiction detection and semantic searching, through thousands of documents at once. Attorneys can now leverage past transcripts to prepare for future events, while maintaining strict security and compliance needs.”
Jason Primuth, Chief Innovation Officer, Lexitas
“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
“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
“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
“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