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

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.

Learn more about Amazon SageMaker Model Building Pipelines.

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

EBS volumes are exposed as NVMe block devices on instances built on the Nitro System . The device names are /dev/nvme0n1 , /dev/nvme1n1 , and so on. The device names that you specify in a block device mapping are renamed using NVMe device names (

Amazon EBS provides the following data services.

Use Amazon Data Lifecycle Manager to create, retain, and delete Amazon EBS snapshots and EBS-backed AMIs.

Accelerated computing instances are intended for graphics and general purpose GPU compute applications.

Use compute optimized instances to deliver high performance for compute-bound applications.

Before you terminate an instance, review the following.