Configure Amazon EC2 to meet your security and compliance objectives, and learn how to use other AWS services that help you to secure your Amazon EC2 resources.

Attach a network interface to your instance to specify its private IP addresses, Elastic IP addresses, and security groups.

Your AWS account automatically has a default security group for the default VPC in each Region. If you don't specify a security group when you launch an instance, the instance is automatically associated with the default security group for the VPC. If you don't want your instances to use the default security group, you can create your own custom security groups and specify them when you launch your instances.

Find an Amazon EC2 instance to match the features that you need.

Learn how to evaluate a text-based foundation model by using SageMaker Clarify

Amazon SageMaker Studio Classic is an integrated machine learning environment where you can build, train, deploy, and analyze your models all in the same application.

Fine-tune foundation models on your data to get responses that are customized to your use case.

Learn more about how to get inferences from your Amazon SageMaker models and deploy your models for serving inference.

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).

SageMaker Distribution is a collection of Docker images, which includes popular libraries and packages for machine learning, data science, and data analytics visualization. The Docker images include deep learning frameworks such as the following: