aws-solutions-library-samples
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guidance-for-training-an-aws-deepracer-model-using-amazon-sagemaker
guidance-for-training-an-aws-deepracer-model-using-amazon-sagemaker PublicDeepRacer workshop content. This Guidance demonstrates how software developers can use an Amazon SageMaker Notebook instance to directly train and evaluate AWS DeepRacer models with full control
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cloud-intelligence-dashboards-framework
cloud-intelligence-dashboards-framework PublicCommand Line Interface tool for Cloud Intelligence Dashboards deployment
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data-lakes-on-aws
data-lakes-on-aws PublicEnterprise-grade, production-hardened, serverless data lake on AWS
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fraud-detection-using-machine-learning
fraud-detection-using-machine-learning PublicSetup end to end demo architecture for predicting fraud events with Machine Learning using Amazon SageMaker
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guidance-for-personalized-ecommerce-recommendations-using-amazon-bedrock-agents
guidance-for-personalized-ecommerce-recommendations-using-amazon-bedrock-agents PublicThis Guidance demonstrates how to implement personalized ecommerce recommendations using Amazon Bedrock Agents.
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guidance-for-multi-provider-generative-ai-gateway-on-aws
guidance-for-multi-provider-generative-ai-gateway-on-aws PublicThis Guidance demonstrates how to streamline access to numerous large language models (LLMs) through a unified, industry-standard API gateway based on OpenAI API standards
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- accelerated-intelligent-document-processing-on-aws Public
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.
aws-solutions-library-samples/accelerated-intelligent-document-processing-on-aws’s past year of commit activity - guidance-for-claude-code-with-amazon-bedrock Public
This Guidance demonstrates how organizations can implement secure enterprise authentication for Amazon Bedrock using industry-standard protocols and AWS services
aws-solutions-library-samples/guidance-for-claude-code-with-amazon-bedrock’s past year of commit activity - guidance-for-media2cloud-on-aws Public
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.
aws-solutions-library-samples/guidance-for-media2cloud-on-aws’s past year of commit activity - guidance-for-battery-digital-twin-on-aws Public
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.
aws-solutions-library-samples/guidance-for-battery-digital-twin-on-aws’s past year of commit activity - cloud-intelligence-dashboards-framework Public
Command Line Interface tool for Cloud Intelligence Dashboards deployment
aws-solutions-library-samples/cloud-intelligence-dashboards-framework’s past year of commit activity - guidance-for-workforce-management-using-amazon-bedrock Public
This Guidance shows how to enhance workforce management through AI-driven automation and real-time intelligence. It demonstrates how to provide staff with quick access to relevant insights, enabling more informed decision-making
aws-solutions-library-samples/guidance-for-workforce-management-using-amazon-bedrock’s past year of commit activity - guidance-for-game-analytics-pipeline-on-aws Public
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.
aws-solutions-library-samples/guidance-for-game-analytics-pipeline-on-aws’s past year of commit activity - guidance-for-payment-systems-using-event-driven-architecture-on-aws Public
This guidance focuses on the part of payments processing systems that post payments to recieving accounts. In this phase, inbound transactions are evaluated, have accounting rules applied to them, then are posted into customer accounts.
aws-solutions-library-samples/guidance-for-payment-systems-using-event-driven-architecture-on-aws’s past year of commit activity - guidance-for-multi-agent-orchestration-langgraph-on-aws Public
This Guidance demonstrates how to effectively orchestrate multiple specialized AI agents to solve complex customer support challenges through different coordination mechanisms on AWS.
aws-solutions-library-samples/guidance-for-multi-agent-orchestration-langgraph-on-aws’s past year of commit activity - guidance-for-developing-data-and-ai-foundation-with-amazon-sagemaker Public
DAIVI is a reference solution with IAC modules to accelerate development of Data, Analytics, AI and Visualization applications on AWS using the next generation Amazon SageMaker Unified Studio. The goal of the DAIVI solution is to provide engineers with sample infrastructure-as-code modules and application modules to build their data platforms.
aws-solutions-library-samples/guidance-for-developing-data-and-ai-foundation-with-amazon-sagemaker’s past year of commit activity
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