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).

Learn more about Amazon SageMaker Model Building Pipelines.

Learn how to use the @step decorator to convert local code to pipeline steps.

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

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.

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

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.

Automatically build, train, tune, and deploy models using Autopilot.

Using SageMaker for large model inference.