AWS Samples
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- Collection of best practices, reference architectures, model training examples and utilities to train large models on AWS.
- Sample microservice based application demonstrating observability capabilities on AWS
- Amazon SageMaker AI collection of examples, code samples and recipes.
- This repository contains a basic real-time video streaming application built with Amazon IVS, next.js, and AWS CDK. The real-time video streaming in this app is powered by Amazon IVS Real-time Streaming and the Amazon IVS Web Broadcast SDK.
- This demo application shows how to implement an audio-only web application powered by Amazon IVS Real-time, using React, the IVS Broadcast SDK, and the AWS CDK.
- This repository contains an experimental demo application that shows how you can add client-side auto-generated captions to Amazon IVS Real-time and Low-latency streams using transformers.js and WebGPU.
- A demo web application that shows how to implement a browser-based streaming tool with Nextjs and the Amazon IVS Web Broadcast SDK.
- This project demonstrates a video collaboration application powered by Amazon IVS Real-time, serving as a reference for building compelling social video experiences.
- This repository contains a social real-time application for Web. The web app leverages Amazon IVS Real-time streaming and uses the Amazon IVS Broadcast SDK for the video and audio-only experiences.
- Hands-on labs for Amazon EKS
- Run Nova Sonic on AWS Serverless
- Universal AI Agent using Amazon Bedrock, capable of customize to create/edit files, execute commands, search the web, use knowledge base, use multi-agents, generative images and more.
- This repository contains the reference architecture implementation for the AWS Serverless Developer Experience workshop in TypeScript
- Helping MD customers run workloads on Graviton 3E instances (especially hpc7g) with optimized compiler settings
- A comprehensive advanced course for building production-ready AI agents using the Strands Agents SDK. This repository contains 6 progressive labs that teach advanced capabilities including tool integration, memory persistence, Model Context Protocol (MCP), and comprehensive observability.