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

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.

Select an AMI based on its Region, operating system, architecture, and root volume type.

Control access to Amazon EC2 resources by setting up security groups and using IAM.

Find or share a public AMI.

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 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.

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

Instructions and configuration information using quick setup onboarding.

The following diagram shows how you train and deploy a model with Amazon SageMaker. Your training code accesses your training data and outputs model artifacts from an S3 bucket. Then you can make requests to a model endpoint to run inference. You can store both the training and inference container images in an Amazon Elastic Container Registry (ECR).