Amazon SageMaker JumpStart
Machine learning (ML) hub with foundation models, and built-in algorithms that you can deploy with just a few clicks
Why SageMaker JumpStart?
Amazon SageMaker JumpStart is a machine learning (ML) hub that can help you accelerate your ML journey. With SageMaker JumpStart, you can evaluate, compare, and select FMs quickly based on predefined quality and responsibility metrics 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. You can also share artifacts, including models and notebooks, within your organization to accelerate model building and deployment, and admins can control which models are visible to users within their organization.
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.
Foundation models
Built-in algorithms with pretrained models
Benefits of SageMaker JumpStart
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Foundation models from popular model providers for text and image generation that are fully customizable
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Hundreds of built-in algorithms with pretrained models from popular model hubs
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Share ML models and notebooks across your organization to accelerate ML model building and deployment
SageMaker HyperPod now supports deploying open-weights foundation models from SageMaker JumpStart directly to your SageMaker HyperPod clusters in just a few easy steps.
Amazon SageMaker JumpStart features
Foundation models
Explore numerous proprietary and publicly available foundation models from model providers like AI21 Labs, Cohere, Databricks, Hugging Face, Meta, Mistral AI, Stability AI, and Alexa to perform a wide range of tasks such as article summarization and text, image, or video generation.
Access hundreds of built-in algorithms
SageMaker JumpStart provides hundreds of built-in algorithms with pretrained models from model hubs, including TensorFlow Hub, PyTorch Hub, Hugging Face, 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.
How customers use SageMaker JumpStart
With Amazon SageMaker JumpStart, Slack can access state-of-the-art foundation models to power Slack AI, while prioritizing security and privacy. Slack customers can now search smarter, summarize conversations instantly, and be at their most productive
Jackie Rocca, VP Product, AI at Slack
With Amazon SageMaker JumpStart, we were able to experiment with several foundation models, select the ones that best fit our needs in healthcare, and quickly launch ML applications using SageMaker’s HIPAA-compliant model deployment. This has allowed us to improve the speed and scale of the data entry process for prescriptions and of customer care.
Alexandre Alves, Sr. Principal Engineer, Amazon Pharmacy
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
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