Amazon SageMaker JumpStart now enables you to more easily share ML artifacts within your organization
Amazon SageMaker JumpStart now enables you to more easily share machine learning (ML) artifacts, including notebooks and models, across your organization to accelerate model building and deployment. Amazon SageMaker JumpStart is an ML hub that accelerates your ML journey with built-in algorithms and pretrained models from popular model hubs, such as Hugging Face, and end-to-end solutions that solve common use cases.
Amazon SageMaker JumpStart now enables you to more easily share machine learning (ML) artifacts, including notebooks and models, across your organization to accelerate model building and deployment. Amazon SageMaker JumpStart is an ML hub that accelerates your ML journey with built-in algorithms and pretrained models from popular model hubs, such as Hugging Face, and end-to-end solutions that solve common use cases.
Many enterprises have multiple data science teams who build ML models and Jupyter notebooks and many artifacts could be leveraged by other science and operations teams to increase productivity; however, it is often challenging to share ML artifacts internally or setup the execution environment to take the models and notebooks into production. Starting today, Amazon SageMaker JumpStart helps you to more easily share ML artifacts, including notebooks and models, within your enterprise. You can add ML artifacts developed from SageMaker as well as those developed outside of SageMaker. Users within your organization can browse and select shared models to fine-tune, deploy endpoints, or execute notebooks directly in SageMaker JumpStart.
The new ML artifact sharing capability within Amazon SageMaker JumpStart is available in all AWS regions where SageMaker JumpStart is supported. To learn more, refer to the AWS News Blog and SageMaker JumpStart product documentation.