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
Repositories
- guidance-for-scalable-model-inference-and-agentic-ai-on-amazon-eks Public
Comprehensive, scalable ML inference architecture using Amazon EKS, leveraging Graviton processors for cost-effective CPU-based inference and GPU instances for accelerated inference. Guidance provides a complete end-to-end platform for deploying LLMs with agentic AI capabilities, including RAG and MCP
aws-solutions-library-samples/guidance-for-scalable-model-inference-and-agentic-ai-on-amazon-eks’s past year of commit activity - guidance-for-integrating-sap-and-non-sap-data-using-snowflake-on-aws Public
This Guidance demonstrates how to implement a SAP Cloud Lakehouse using Snowflake on AWS, addressing the challenge of integrating SAP and non-SAP data for comprehensive analytics through step-by-step implementation instructions.
aws-solutions-library-samples/guidance-for-integrating-sap-and-non-sap-data-using-snowflake-on-aws’s past year of commit activity - guidance-for-ai-driven-robotic-simulation-and-training-on-aws Public
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
aws-solutions-library-samples/guidance-for-ai-driven-robotic-simulation-and-training-on-aws’s past year of commit activity - aws-health-events-insight Public
This Guidance demonstrates how to analyze AWS Health events across multiple AWS accounts using natural language queries and generative business intelligence. Organizations managing thousands of AWS resources can transform operations and security monitoring at scale, transitioning from manual processing to automated analysis.
aws-solutions-library-samples/aws-health-events-insight’s past year of commit activity - guidance-for-enterprise-search-and-audit-for-amazon-s3 Public
This Guidance helps customers create a single aggregation point for either an enterprise or a disparate collection of AWS accounts that host Amazon Simple Storage Service (Amazon S3) object data.
aws-solutions-library-samples/guidance-for-enterprise-search-and-audit-for-amazon-s3’s past year of commit activity - cloud-intelligence-dashboards-data-collection Public
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.
aws-solutions-library-samples/cloud-intelligence-dashboards-data-collection’s past year of commit activity - guidance-for-no-code-multi-agent-ai-orchestration-on-aws Public
This Guidance demonstrates how to overcome the complexity and lengthy development cycles of enterprise AI systems through a no-code multi-agent orchestration platform on AWS.
aws-solutions-library-samples/guidance-for-no-code-multi-agent-ai-orchestration-on-aws’s past year of commit activity - 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-realtime-3d-product-visualization-on-aws Public
This guidance demonstrates how to integrate real-time 3D product visualization capabilities into websites using Amazon GameLift Streams, enabling businesses to create immersive digital experiences where customers can explore, customize, and interact with products in real-time.
aws-solutions-library-samples/guidance-for-realtime-3d-product-visualization-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
