Amazon_EFS_3
AWS SageMaker removes the barriers that typically slow down data developers who want to use machine learning in the cloud. A SageMaker notebook instance is a fully managed Machine Learning (ML) instance based on the Jupyter Notebook web application. It is recommended that your SageMaker notebook instance storage volumes are encrypted with Amazon KMS Customer Master Keys (CMKs) instead of AWS managed-keys in order to have a more granular control over the data-at-rest encryption/decryption process and meet compliance requirements. SageMaker is a fully-managed AWS service that enables data scientists and developers to build, train and deploy machine learning models at any scale. A SageMaker notebook instance is a fully managed Machine Learning (ML) instance based on the Jupyter Notebook web application.
You can enable encryption of data at rest when creating an Amazon EFS file system. 1. Open the Amazon Elastic File System console at https://console.aws.amazon.com/efs/.
2. Choose Create file system to open the file system creation wizard.
2a. For Step 1: Configure file system access, choose your VPC, create your mount targets, and then choose Next Step.
2b. For Step 2: Configure optional settings, add any tags, choose your performance mode, check the box to encrypt your file system, and then choose Next Step.
2c. For Step 3: Review and create, review your settings, and choose Create File System.
3. Check Enable encryption checkbox and choose the name of the AWS KMS Customer Master Key from Select KMS master key dropdown list to enable encryption using your own KMS CMK key. You can use CMK key from another AWS account by choosing a KMS key ARN from another account and providing the ARN of the foreign KMS key.
4. The following guidance provides you instructions on how to enable At-Rest encryption for your Amazon EFS: https://docs.aws.amazon.com/efs/latest/ug/encryption.html
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