Use Amazon SageMaker Assets to provide controlled and regulated access to assets , models or data tables, belonging to your organization. Within SageMaker Assets, users from different AWS accounts can create and share assets related to specific business problems without additional administrator overhead. Instead of having permissions being statically tied to their identity, users can provide permissions to assets that they’re using for their active workflows.

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.

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 .

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

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

Select the JupyterLab version to use for an Amazon SageMaker Studio instance.

Your security groups use connection tracking to track information about traffic to and from the instance. Rules are applied based on the connection state of the traffic to determine if the traffic is allowed or denied. With this approach, security groups are stateful. This means that responses to inbound traffic are allowed to flow out of the instance regardless of outbound security group rules, and vice versa.

Add or remove users on an Linux instance.

Hibernate an Amazon EC2 On-Demand Instance or Spot Instance with an encrypted EBS volume as its root device.

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