Amazon SageMaker JumpStart models support custom VPC and KMS settings
Amazon SageMaker JumpStart, a capability of Amazon SageMaker that accelerates your machine learning workflows with one-click access to popular model collections from TensorFlow Hub, PyTorch Hub and Hugging Face (also known as “model zoos”), and to 16 end-to-end solutions that solve common business problems such demand forecasting, fraud detection and document understanding.
Amazon SageMaker JumpStart, a capability of Amazon SageMaker that accelerates your machine learning workflows with one-click access to popular model collections from TensorFlow Hub, PyTorch Hub and Hugging Face (also known as “model zoos”), and to 16 end-to-end solutions that solve common business problems such demand forecasting, fraud detection and document understanding.
Starting today, SageMaker JumpStart lets customers pass custom VPC settings when they deploy a JumpStart model to a SageMaker endpoint or when they fine-tune a pre-trained model in a SageMaker training container. Furthermore, customers can choose to use custom KMS keys to encrypt their model data at rest in S3 and encrypt the EBS volume attached to the SageMaker hosting or training container started by JumpStart in the customers’ accounts. This new feature enables customers to use the rich security features offered by SageMaker with JumpStart models.
Amazon SageMaker JumpStart is available in all regions where Amazon SageMaker Studio is available. To get started with these new models on SageMaker JumpStart, refer to the documentation.