aws-solutions-library-samples
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- 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
- 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 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.
- This GenAI solution enables users to extract insights from diverse data formats (video, audio, PDFs, text) through a unified interface. Using AWS AI services, it processes and indexes content at scale, provides quick summaries, and delivers contextualized answers with direct links to source materials.
- This Guidance shows how to create a battery digital twin, a virtual representation of a physical electric vehicle battery or battery energy storage system (BESS), and overlay real-time data such as voltage, current, and temperature.
- This guidance demonstrates how to accelerate cloud adoption for VMware workloads by automating the deployment of Amazon EVS environments. Infrastructure and VMware administrators can leverage automated provisioning of essential AWS services, including VPCs, Route 53, and Transit Gateway, eliminating complex manual configurations.
- 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.
- 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 Open Source 3D Reconstruction Toolbox for Gaussian Splats provides an end-to-end, pipeline-based guidance on AWS to reconstruct 3D scenes or objects from images or video inputs.
- 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 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.
eks-saas-gitops
Public- This guidance helps game developers automate the process of creating a non-player character (NPC) for their games, and associated infrastructure, that leverage Unreal Engine MetaHuman, Large Language Models Operations (LLMOps), and generative AI to improve NPC conversational skills.
- 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.