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.
How it works
Foundation models
Built-in algorithms with pretrained models
Solutions
ML artifact sharing
Benefits of SageMaker JumpStart
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.
Prebuilt solutions for common use cases
SageMaker JumpStart provides one-click, end-to-end solutions for many common machine learning use cases such as demand forecasting, credit rate prediction, fraud detection and computer vision.