Use Amazon Elastic Inference (EI) to speed up the throughput and decrease latency for inference in Amazon SageMaker

Add rules to your security group for specific kinds of access.

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

Learn how the AWS shared responsibility model applies to data protection in Amazon Elastic Compute Cloud.

Using SageMaker for large model inference.

View a list of existing Amazon SageMaker domains and edit domain settings from that list.

Learn more about Amazon SageMaker Model Building Pipelines.

Describes how you can track the lineage of machine learning workflows.

To train a machine learning model, you need a large, high-quality, labeled dataset. Ground Truth helps you build high-quality training datasets for your machine learning models. With Ground Truth, you can use workers from either Amazon Mechanical Turk, a vendor company that you choose, or an internal, private workforce along with machine learning to enable you to create a labeled dataset. You can use the labeled dataset output from Ground Truth to train your own models. You can also use the output as a training dataset for an Amazon SageMaker model.

Use reinforcement learning in Amazon SageMaker to solve complex machine learning problems that optimize objectives in interactive environments.