Instructions and configuration information using quick setup onboarding.

Search for an AMI that meets your requirements.

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

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).

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.

Amazon SageMaker provides APIs, SDKs, and a command line interface that you can use to create and manage notebook instances and train and deploy models.

Learn how to attach and detach Git repo URLs to Amazon SageMaker Studio Classic with this tutorial series.

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.

Get started with Amazon EC2. Learn to launch, connect to, and use a Windows instance.