aws-samples

SVS402 - Examples from re:Invent 2020 presentation by James Beswick (@jbesw).

Sample serverless architecture showing an AWS Lambda function based on AWS Lambda Layers using the Serverless Framework (https://www.serverless.com) on AWS.

The AWS Biotech Blueprint Multi Account is a landing zone for life sciences startups looking to build well architected research environments in the cloud. This CDK based solution creates the infrastructure as code to manage security, identity, and networking across 10s or 100s of accounts.

This demo is designed to educate people who want to build live streaming platform with chatting feature. This demo is implemented using Amplify with Amazon IVS, ChimeSDK Messaging.

This project creates two regional WAF IP sets and automatically updates them with AWS service's IP ranges from the ip-ranges.json file. The ranges are configurable as well as the regions for EC2 ranges. Use cases include allowing CloudFront requests, Route53 health checker and EC2 IP range (which includes AWS Lambda and CloudWatch Synthetics).

Referece code for protecting your Amazon CloudFront media distributions with AWS Cognito JWT Token, Lambda@Edge and Video.js

Demonstrates an approach to invoking AWS Lambda from messages in an Amazon MQ queue.

This repository shows how you can build a compelling social feed experience of auto-player live streams with Amazon IVS.

Employee Productivity GenAI Assistant Example is an innovative code sample and architecture pattern designed to enhance writing tasks efficiency using AWS serverless technologies and Amazon Bedrock's generative AI models.

Build Train and Deploy your own custom container using AWS StepFunctions Data Science SDK

This repository shows how you can build a compelling eCommerce experience with Amazon IVS.

This is a Angular starter for building a fullstack app with AWS Amplify.

Safe Deployments with AWS Lambda

This project contains source code and supporting files for a serverless application which can be used for Computer Vision inferencing using Amazon Rekognition.

With this CDK application, you can easily build a simple and customizable game studio on AWS. The default setting covers a build farm, a source code repository, CI tool, workstation, and backups. If long build time, build farm management, and slow response from CI tool suffers you, this project could help to save your time.

Personal Protective Equipment detection demo showed at AWS re:Inforce 2019: source code and materials.

Previewing environments using containerized AWS Lambda functions

Rules Engine for Amazon Connect aims to deliver an engine sitting on top of Amazon Connect which has the capability to build a hyper-personalised IVR experience for customers.

An example project showing how to enable tiered compilation on a Java AWS Lambda function.

A pipeline to convert contextual knowledge stored in documents and databases into text embeddings, and store them in a vector store

AWS CloudFormation and SAM templates for machine learning inference with AWS Lambda.

Unit testing AWS interactions with pytest and moto. The examples demonstrate how to structure, setup, teardown, mock, and conduct unit testing. These concepts can be applied to general code and are not restricted to AWS interactions. The source code is only intended to demonstrate unit testing.

This repository contains source code for the AWS Database Blog Post Reduce data archiving costs for compliance by automating RDS snapshot exports to Amazon S3

Infrastructure as Code framework for automating Amazon Connect deployments using GitLab CI/CD and Terraform

A demo iPhone application intended as an educational tool for demonstrating how Amazon IVS can be used to build a compelling customer experience for eCommerce use-cases.

This repository shows how you can build a compelling user-generated content (UGC) live streaming webapp with Amazon IVS.

This project contains the webapp sample integrated with AWS HealthOmics, which allows users such as admin and bioinformaticians to operate Amazon Omics workflow easily and check the run command status with charts and tables.

With Amazon Rekognition Custom Labels, you can easily build and deploy Machine Learning (ML) models to identify custom objects which are specific to your business domain in images without requiring advanced ML knowledge. When combined with Amazon Augmented AI (A2I), you can quickly integrate a ML workflow to capture and label images with a human workforce for model training. As ML lifecycle is an iterative and repetitive process, you need to implement an effective workflow that can provide for continuous model training with new data and automated deployment. Your workflow also needs to be flexible enough to allow for changes without requiring development rework as your business objectives change. Operationalizing an effective and flexible workflow can be resource intensive, especially for customers who have limited machine learning capabilities. In this post, we will use AWS Step Functions, AWS Lambda, and AWS System Manager Parameter Store to automate a configurable ML workflow for Rekognition Custom Labels and A2I. We will provide an overview of the solution and instructions to deploy it with AWS CloudFormation.