The models you train in SageMaker are stored in S3 as .tar.gz files that you can use to deploy to an endpoint, or even test locally (extracting the model file from the tar file). If you are using a built-in algorithm, you can share the .tar.gz file to the second account and deploy the model in the second account, since built-in algorithm containers can be accessed from any AWS account.
If you are using a custom training image (docs here), you can push this image to ECR and allow a second account to pull the image and then use the image with the model that you have trained. However, note that Studio at this time does not support building Docker images out of the box. You can use SageMaker Notebook Instances instead.
I would recommend keeping the model (.tar.gz) and the image (Docker) separate, since you can easily retrain and deploy the newer versions of models without updating the image every single time.
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