Getting started with Amazon SageMaker AI

Machine Learning Tutorials

Follow along the hands-on tutorials to learn how to use Amazon SageMaker to accomplish various machine learning lifecycle tasks, including data preparation, training, deployment, and MLOps.

Data Scientists: Develop ML models using code

Tutorial

Data Scientists: Low-code ML

Tutorial

Prepare training data for ML with minimal code

Get started

Tutorial

Get started quickly using SageMaker JumpStart

Get started

Tutorial

Automatically create an ML model

Get started

ML Engineers: MLOps

Tutorial

Deploy a trained model to production for inference

Get started

Tutorial

Automate end-to-end ML workflows

Get started

Video

Dive deep into automating MLOps

Watch demo

Business Analysts: No-code ML

Tutorial

Generate predictions without writing code using SageMaker Canvas

Get started

Tutorial

Practical Decision-Making Using No-code ML on AWS

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Next steps

Pricing

Learn more about Amazon SageMaker pricing

Visit the pricing page

Console

Getting started with Amazon SageMaker AI

Machine Learning Tutorials

Data Scientists: Develop ML models using code

Label training data for ML

Build an ML model locally

Train an ML model

Data Scientists: Low-code ML

Prepare training data for ML with minimal code

Get started quickly using SageMaker JumpStart

Automatically create an ML model

ML Engineers: MLOps

Deploy a trained model to production for inference

Automate end-to-end ML workflows

Dive deep into automating MLOps

Business Analysts: No-code ML

Generate predictions without writing code using SageMaker Canvas

Practical Decision-Making Using No-code ML on AWS

Next steps

Learn more about Amazon SageMaker pricing

Get started building with Amazon SageMaker in the AWS Management Console

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