aws-solutions-library-samples
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- This Guidance demonstrates a scalable, serverless approach for automated document processing and information extraction using AWS services, such as Amazon Bedrock Data Automation and Amazon Bedrock foundational models. It combines generative AI and optical character recognition (OCR) to process documents at scale.
- This Guidance demonstrates how to deploy Cloud Intelligence Dashboards in your AWS environment using AWS CloudFormation templates or command line tools. These pre-built dashboards enable you to drive financial accountability, optimize costs, and track usage goals across their AWS infrastructure.
- This Guidance shows how to automate non-conformance review (NCR) disposition recommendations using generative AI and image analysis to reduce manufacturing delays. It demonstrates a multimodal recommender system that integrates with existing quality ticketing systems to accelerate quality engineering decisions.
- Seamless User Interface for replicating data into AWS.
- The Game Analytics Pipeline solution helps game developers to apply a flexible, and scalable DataOps methodology to their games. Allowing them to continuously integrate, and continuously deploy a scalable serverless data pipeline for ingesting, storing, and analyzing telemetry data generated from games, and services.
- The AWS DeepRacer Event Manager (DREM) is used to run and manage all aspects of in-person events for AWS DeepRacer, an autonomous 1/18th scale race car designed to test reinforcement learning (RL) models by racing on a physical track.
- This guidance demonstrates how to deploy a comprehensive agentic application for advertising workflows using Amazon Bedrock AgentCore. The solution showcases advanced multi-agent collaboration across the entire advertising value chain - from strategic media planning and audience targeting to real-time bid optimization and publisher revenue manageme
- This Guidance demonstrates how to effectively orchestrate multiple specialized AI agents to solve complex customer support challenges through different coordination mechanisms on AWS. Modern customer service environments demand sophisticated handling of multi-step interactions, personalized responses, and seamless access to various data sources.
- Guidance for Media2Cloud on AWS solution (formerly known as AWS Media2Cloud Solution) is designed to demonstrate a serverless ingest framework that can quickly setup a baseline ingest workflow for placing video assets and associated metadata under management control of an AWS customer.
- This project demonstrates how to build a cost-effective Retrieval-Augmented Generation (RAG) solution using Amazon DynamoDB as a vector store for small use cases, enabling small businesses to implement AI personalization without the high costs typically associated with specialized vector databases.
- This Guidance demonstrates how to securely run Model Context Protocol (MCP) servers on the AWS Cloud using containerized architecture. It helps organizations implement industry-standard OAuth 2.0 authentication while protecting server deployments with multiple security layers, including content delivery networks and web application firewalls.
- This Guidance demonstrates how to utilize advanced artificial intelligence (AI) capabilities within your existing Salesforce environment to gain more valuable insights about your customers. It allows you to seamlessly integrate the user-friendly interface of Salesforce with the powerful data storage and processing capabilities of AWS.
- This Guidance demonstrates how to build an AI-assisted robot training and fleet management system using Amazon Bedrock foundation models and AWS Trainium. It helps organizations overcome the complexity of training robots for precise tasks and managing fleets at scale
eks-saas-gitops
Public- This Guidance demonstrates how to enhance database resiliency using a Maximum Data Availability Architecture (MD2A). It introduces MD2A, a data platform that uses APIs and SDKs to deliver full-stack resiliency from the user interface to the database layers.