Comprehensive repository for AWS Generative AI and Agentic AI solutions, architectures, and learning resources
This repository serves as a comprehensive resource hub for AWS Generative AI and Agentic AI solutions, designed by certified AWS GenAI Solutions Architects. It provides industry-specific solution architectures, best practices, learning materials, and complete project-based implementations.
Comprehensive learning materials and reference documentation for AWS GenAI services:
resources/
├── aws-services/ # Detailed AWS GenAI service documentation
├── learning-paths/ # Structured learning curricula
├── best-practices/ # Industry best practices and guidelines
├── architecture-patterns/ # Reusable solution patterns
├── tools-and-sdks/ # SDKs, tools, and utilities
└── certification-prep/ # AWS certification preparation materials
Real-world, production-ready solutions across various industries:
genAI-labs/
├── healthcare/ # Healthcare AI solutions
├── financial-services/ # FinTech and banking solutions
├── retail-ecommerce/ # E-commerce and retail AI
├── media-entertainment/ # Media and content solutions
├── manufacturing/ # Industrial AI applications
├── education/ # EdTech and learning solutions
├── legal-compliance/ # Legal tech and compliance
└── customer-service/ # Customer experience solutions
- Foundation Models: Amazon Bedrock integrations with Claude, Llama, Titan
- Custom Models: Amazon SageMaker for fine-tuning and deployment
- Multimodal AI: Text, image, audio, and video processing capabilities
- RAG Systems: Advanced Retrieval-Augmented Generation implementations
- Amazon Bedrock Agents: Autonomous AI agents with tool integration
- Multi-Agent Systems: Coordinated agent workflows
- Function Calling: Dynamic tool and API integrations
- Memory Systems: Persistent conversation and context management
- Serverless Patterns: AWS Lambda and event-driven architectures
- Containerized Solutions: ECS/EKS deployments with auto-scaling
- Real-time Processing: Kinesis and streaming analytics
- Security & Compliance: End-to-end security and governance
- AWS Account with appropriate permissions
- AWS CLI configured
- Python 3.9+ / Node.js 18+
- Docker (for containerized solutions)
# Clone the repository
git clone https://github.com/pxkundu/aws-genai-labs-builder.git
cd aws-genai-labs-builder
# Explore learning resources
cd resources/learning-paths
# Try industry solutions
cd genAI-labs/[industry-of-choice]- AWS GenAI Fundamentals → Start with basic concepts
- Amazon Bedrock Basics → Foundation model usage
- Simple RAG Implementation → First hands-on project
- Advanced Bedrock Features → Agents and function calling
- SageMaker Integration → Custom model deployment
- Multi-Modal Solutions → Text, image, and audio processing
- Agentic AI Systems → Complex agent orchestration
- Production Deployment → Scalable, secure architectures
- Industry Specialization → Domain-specific solutions
- Clinical Decision Support: AI-powered diagnostic assistance
- Medical Document Processing: Automated clinical note analysis
- Drug Discovery: Molecular generation and optimization
- Patient Care Automation: Intelligent triage and monitoring
- Fraud Detection: Real-time transaction monitoring
- Investment Research: Automated financial analysis
- Risk Assessment: Predictive risk modeling
- Customer Advisory: Personalized financial guidance
- Product Recommendations: Personalized shopping experiences
- Inventory Optimization: Demand forecasting and planning
- Customer Service: Intelligent chatbots and support
- Content Generation: Product descriptions and marketing
- Amazon Bedrock: Foundation models and agents
- Amazon SageMaker: ML model development and deployment
- Amazon Textract: Document and form processing
- Amazon Comprehend: Natural language processing
- Amazon Rekognition: Computer vision and image analysis
- Amazon Polly: Text-to-speech synthesis
- Amazon Transcribe: Speech-to-text conversion
- AWS Lambda: Serverless compute
- Amazon API Gateway: API management
- Amazon DynamoDB: NoSQL database
- Amazon S3: Object storage
- AWS Step Functions: Workflow orchestration
- Amazon EventBridge: Event-driven architectures
- AWS CloudFormation: Infrastructure as Code
- Boto3: AWS SDK for Python
- AWS CDK: Cloud Development Kit
- LangChain: LLM application framework
- Streamlit: Rapid prototyping and demos
- FastAPI: High-performance API development
User Input → API Gateway → Lambda → Bedrock → Response
↓
DynamoDB ← EventBridge ← S3 (Logs/Artifacts)
Documents → Textract → Embeddings → Vector DB
↓
User Query → Bedrock Agent → Retrieval → Generation
Orchestrator Agent
├── Research Agent
├── Analysis Agent
└── Report Agent
- IAM Best Practices: Least privilege access patterns
- Data Encryption: At-rest and in-transit encryption
- VPC Integration: Private network deployments
- Audit Logging: Comprehensive activity tracking
- Compliance Frameworks: HIPAA, SOC 2, GDPR ready
- Auto Scaling: Dynamic resource allocation
- Caching Strategies: Redis/ElastiCache integration
- Load Balancing: High availability patterns
- Monitoring: CloudWatch and X-Ray integration
- Cost Optimization: Reserved capacity and spot instances
We welcome contributions from the AWS GenAI community! Please see our Contributing Guidelines for details on:
- Code standards and practices
- Documentation requirements
- Testing protocols
- Review process
Each industry solution includes:
- 🏗️ Architecture Diagrams: Visual solution blueprints
- 📋 CloudFormation Templates: Infrastructure as Code
- 🐍 Python Implementation: Complete source code
- 📚 Documentation: Setup and deployment guides
- 🧪 Testing Suite: Unit and integration tests
- 📊 Monitoring: Observability and metrics
This repository aligns with AWS certification paths:
- AWS Certified Machine Learning - Specialty
- AWS Certified Solutions Architect - Professional
- AWS Certified AI Practitioner (Beta)
- 🐛 Issues: Report bugs and request features
- 💬 Discussions: Community Q&A and knowledge sharing
- 📧 Contact: [inboxpartha@outlook.com]
- 🔗 LinkedIn: https://www.linkedin.com/in/pxkundu/
This project is licensed under the MIT License - see the LICENSE file for details.
Built with ❤️ by AWS GenAI Solutions Architects
"Empowering the next generation of AI-driven business solutions with AWS"