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

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.

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

Learn how to evaluate a text-based foundation model by using SageMaker Clarify

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.

Learn more about Amazon SageMaker Model Building Pipelines.

How to connect to your Windows instance.

Use a key pair to connect to your Amazon EC2 instance.

Launch an instance from an AMI using the launch instance wizard in the Amazon EC2 console.