New – Share ML Models and Notebooks More Easily Within Your Organization with Amazon SageMaker JumpStart

Amazon SageMaker JumpStart is a machine studying (ML) hub that may assist you speed up your ML journey. SageMaker JumpStart provides you entry to built-in algorithms with pre-trained fashions from common mannequin hubs, pre-trained basis fashions that will help you carry out duties equivalent to article summarization and picture technology, and end-to-end options to resolve frequent use instances.

At present, I’m glad to announce that you could now share ML artifacts, equivalent to fashions and notebooks, extra simply with different customers that share your AWS account utilizing SageMaker JumpStart.

Utilizing SageMaker JumpStart to Share ML Artifacts
Machine studying is a crew sport. You may wish to share your fashions and notebooks with different information scientists in your crew to collaborate and improve productiveness. Or, you may wish to share your fashions with operations groups to place your fashions into manufacturing. Let me present you methods to share ML artifacts utilizing SageMaker JumpStart.

In SageMaker Studio, choose Fashions within the left navigation menu. Then, choose Shared fashions and Shared by my group. Now you can uncover and search ML artifacts that different customers shared inside your AWS account. Word that you could add and share ML artifacts developed with SageMaker in addition to these developed exterior of SageMaker.

To share a mannequin or pocket book, choose Add. For fashions, present primary info, equivalent to title, description, information sort, ML process, framework, and any further metadata. This info helps different customers to seek out the proper fashions for his or her use instances. It’s also possible to allow coaching and deployment to your mannequin. This enables customers to fine-tune your shared mannequin and deploy the mannequin in just some clicks by means of SageMaker JumpStart.

To allow mannequin coaching, you possibly can choose an present SageMaker coaching job that may autopopulate all related info. This info consists of the container framework, coaching script location, mannequin artifact location, occasion sort, default coaching and validation datasets, and goal column. It’s also possible to present customized mannequin coaching info by choosing a prebuilt SageMaker Deep Studying Container or choosing a customized Docker container in Amazon ECR. It’s also possible to specify default hyperparameters and metrics for mannequin coaching.

To allow mannequin deployment, you additionally have to outline the container picture to make use of, the inference script and mannequin artifact location, and the default occasion sort. Take a look on the SageMaker Developer Information to study extra about mannequin coaching and mannequin deployment choices.

Sharing a pocket book works equally. It is advisable to present primary details about your pocket book and the Amazon S3 location of the pocket book file.

Customers that share your AWS account can now browse and choose shared fashions to fine-tune, deploy endpoints, or run notebooks instantly in SageMaker JumpStart.

In SageMaker Studio, choose Fast begin options within the left navigation menu, then choose Options, fashions, instance notebooks to entry all shared ML artifacts, along with pre-trained fashions from common mannequin hubs and end-to-end options.

Now Out there
The brand new ML artifact-sharing functionality inside Amazon SageMaker JumpStart is on the market at present in all AWS Areas the place Amazon SageMaker JumpStart is on the market. To study extra, go to Amazon SageMaker JumpStart and the SageMaker JumpStart documentation.

Begin sharing your fashions and notebooks with Amazon SageMaker JumpStart at present!

— Antje