Amazon SageMaker smart sifting is a capability of SageMaker Training that helps improve the efficiency of training datasets and reduce total training time and cost.

Amazon SageMaker Model Monitor continuously monitors the quality of Amazon SageMaker machine learning models in production.

Create, store, and share features with Amazon SageMaker Feature Store.

Instructions and configuration information using quick setup onboarding.

For the AWS Regions supported by Amazon SageMaker and the Amazon Elastic Compute Cloud (Amazon EC2) instance types that are available in each Region, see Amazon SageMaker Pricing .

Use Amazon SageMaker Model Card to document critical details about your machine learning (ML) models for governance and reporting.

Learn how to use shared spaces for collaboration between all users in a SageMaker domain.

Learn about Amazon Augmented AI (A2I), a service for augmenting AI application labeling with human review loops to boost confidence in datasets.

Attach an Amazon EBS volume that you have created to an EC2 instance.

EBS volumes are exposed as NVMe block devices on instances built on the Nitro System . The device names are /dev/nvme0n1 , /dev/nvme1n1 , and so on. The device names that you specify in a block device mapping are renamed using NVMe device names (