Hands-on Tutorials

Learn how to utilize Amazon Bedrock and Amazon Textract to extract and process information from unstructured documents.

Learn how to use Amazon SageMaker Canvas to build machine learning (ML) models and generate accurate predictions without writing a single line of code.

In this tutorial, you learn and experiment with machine learning using Amazon SageMaker Studio Lab, a no-setup, free development environment.

In this tutorial, you’ll learn how to use Amazon SageMaker to train, a machine learning (ML) model using the AWS Trainium instances.

Learn how to use Amazon SageMaker geospatial capabilities to access readily available geospatial data, make ML predictions, and visualize the results.

Learn how to prepare data for machine learning (ML) using Amazon SageMaker Data Wrangler. 

Learn how to use the face recognition features in Amazon Rekognition using the AWS console.

Learn how to create an Audio transcript using Amazon Transcribe.

Larn how to fast-track your machine learning (ML) project using pretrained models and prebuilt solutions offered by Amazon SageMaker JumpStart.

Learn how to use Amazon Comprehend to analyze and derive insights from text.

Learn how to use the translation features of Amazon Translate using the AWS console.

Learn how to use Amazon CodeGuru Profiler machine learning capabilities to evaluate running applications in 10 minutes.

Learn how to use the automatic content redaction feature of Amazon Transcribe to add privacy to your transcriptions in 10 minutes.

Learn how to use Amazon SageMaker Ground Truth to build a highly accurate training dataset for an image classification use case.

Learn how to use Amazon SageMaker Ground Truth to build a highly accurate training dataset for an image classification use case.

Learn how to use Amazon Kendra create a new index and query the index for results.

Learn how to create a machine learning model automatically with full visibility and control using Amazon SageMaker Autopilot and Amazon SageMaker Studio in 10 minutes.