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.

Describes the Regions, Availability Zones, Local Zones, Outposts, and Wavelength Zones world-wide where you can host your instances.

You can quickly remap an Elastic IP address from one instance to another to work around problems with your instance or software.

Amazon EC2 provides a variety of instance types with different compute, memory, storage, and network capabilities.

View your current quotas (also referred to as limits) for Amazon EC2 and request increases in these quotes as needed.