Amazon SageMaker Canvas announces encryption support with customer managed keys for time series forecast models
Amazon SageMaker Canvas announces support for encryption at rest for datasets and machine learning (ML) models for time series forecasting use cases, on both quick and standard builds. SageMaker Canvas is a visual point-and-click interface that enables business analysts to generate accurate ML predictions on their own — without requiring any machine learning experience or having to write a single line of code.
Amazon SageMaker Canvas announces support for encryption at rest for datasets and machine learning (ML) models for time series forecasting use cases, on both quick and standard builds. SageMaker Canvas is a visual point-and-click interface that enables business analysts to generate accurate ML predictions on their own — without requiring any machine learning experience or having to write a single line of code.
Previously, SageMaker Canvas supported encryption at rest using customer managed keys (CMK) with AWS Key Management Service (KMS) for binary classification problems with 2 category predictions, multi-class classification problems with 3+ category predictions, and regression problems with numeric predictions. With this announcement, the support for encryption at rest using CMK with AWS KMS is also available for time-series forecasting, thereby covering all problem types currently supported by SageMaker Canvas.
You can enable encryption at rest for SageMaker Canvas by using your own keys to encrypt the file systems on the instances used to train models and generate insights, and the model data in your Amazon S3 bucket. You can continue to import, rotate, disable, delete, define usage policies for, and audit the use of your keys giving you full control and flexibility for your encryption policies.
Encryption with customer managed keys is supported for imported datasets, ML model artifacts, and batch predictions and is available in all AWS regions where Canvas is supported. To learn more and get started, please refer to the Amazon SageMaker Canvas product page and the FAQs page.