Amazon SageMaker JumpStart is a machine learning (ML) hub that can help you accelerate your ML journey. SageMaker JumpStart gives you access to built-in algorithms with pre-trained models from popular model hubs, pre-trained foundation models to help you perform tasks such as article summarization and image generation, and end-to-end solutions to solve common use cases.
Today, I’m happy to announce that you can now share ML artifacts, such as models and notebooks, more easily with other users that share your AWS account using SageMaker JumpStart.
Using SageMaker JumpStart to Share ML Artifacts
Machine learning is a team sport. You might want to share your models and notebooks with other data scientists in your team to collaborate and increase productivity. Or, you might want to share your models with operations teams to put your models into production. Let me show you how to share ML artifacts using SageMaker JumpStart.
In SageMaker Studio, select Models in the left navigation menu. Then, select Shared models and Shared by my organization. You can now discover and search ML artifacts that other users shared within your AWS account. Note that you can add and share ML artifacts developed with SageMaker as well as those developed outside of SageMaker.
To share a model or notebook, select Add. For models, provide basic information, such as title, description, data type, ML task, framework, and any additional metadata. This information helps other users to find the right models for their use cases. You can also enable training and deployment for your model. This allows users to fine-tune your shared model and deploy the model in just a few clicks through SageMaker JumpStart.
To enable model training, you can select an existing SageMaker training job that will autopopulate all relevant information. This information includes the container framework, training script location, model artifact location, instance type, default training and validation datasets, and target column. You can also provide custom model training information by selecting a prebuilt SageMaker Deep Learning Container or selecting a custom Docker container in Amazon ECR. You can also specify default hyperparameters and metrics for model training.
To enable model deployment, you also need to define the container image to use, the inference script and model artifact location, and the default instance type. Have a look at the SageMaker Developer Guide to learn more about model training and model deployment options.
Sharing a notebook works similarly. You need to provide basic information about your notebook and the Amazon S3 location of the notebook file.
Users that share your AWS account can now browse and select shared models to fine-tune, deploy endpoints, or run notebooks directly in SageMaker JumpStart.
In SageMaker Studio, select Quick start solutions in the left navigation menu, then select Solutions, models, example notebooks to access all shared ML artifacts, together with pre-trained models from popular model hubs and end-to-end solutions.
The new ML artifact-sharing capability within Amazon SageMaker JumpStart is available today in all AWS Regions where Amazon SageMaker JumpStart is available. To learn more, visit Amazon SageMaker JumpStart and the SageMaker JumpStart documentation.
Originally posted on AWS News Blog
Author: Antje Barth