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

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.

Use Docker containers with SageMaker for build and runtime tasks, including running scripts, training algorithms, and deploying models.

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).

Search for an AMI that meets your requirements.

Automatically build, train, tune, and deploy models using Autopilot.

You can describe the public keys that are stored in Amazon EC2. You can also retrieve the public key material and identify the public key that was specified at launch.

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

Because your instance metadata is available from your running instance, you do not need to use the Amazon EC2 console or the AWS CLI. This can be helpful when you're writing scripts to run from your instance. For example, you can access the local IP address of your instance from instance metadata to manage a connection to an external application.

Amazon SageMaker provides containers for its built-in algorithms and pre-built Docker images for some of the most common machine learning frameworks, such as Apache MXNet, TensorFlow, PyTorch, and Chainer. It also supports machine learning libraries such as scikit-learn and SparkML.