Advanced Generative AI Development on AWS

In this course, you will learn to:

• Develop production-ready generative AI solutions using AWS services that meet enterprise requirements for security, scalability, and reliability.
• Evaluate and select appropriate foundation models for specific business use cases, including benchmarking performance and implementing dynamic model selection architectures.
• Design and implement resilient foundation model systems with circuit breakers, cross-region deployment, and graceful degradation strategies.
• Build comprehensive data processing pipelines for multi-modal inputs, including validation workflows and optimization techniques.
• Implement sophisticated vector database solutions using Amazon Bedrock Knowledge

Bases, OpenSearch, and hybrid approaches for effective retrieval augmentation.

• Create and manage advanced prompt engineering frameworks, including chain-of-thought reasoning and enterprise-wide prompt governance systems.
• Develop autonomous AI agents using Amazon Bedrock Agents, implementing complex

reasoning patterns and tool integration capabilities.

• Implement comprehensive AI safety and security controls, including content filtering, privacy preservation, and adversarial testing mechanisms.
• Optimize performance and manage costs through token efficiency strategies, batching implementations, and intelligent caching systems.
• Design and implement comprehensive monitoring and observability solutions for foundation model applications.
• Create systematic testing and validation frameworks for continuous quality assurance of AI applications.
• Integrate generative AI solutions within enterprise environments using secure, compliant, and scalable architectural patterns.

This course is intended for those with:

• Software developers
• Technical Professionals

We recommend that attendees of this course have:
• AWS Technical Essentials
• Generative AI Essentials on AWS
• 2 or more years of experience building production grade applications on AWS or with opensource
technologies, general AI/ML or data engineering experience
• 1 year of hands-on experience implementing generative AI solutions

Day 1 Module 1: Foundation Model Selection and Configuration

• Enterprise foundation model evaluation framework
• Dynamic model selection architecture patterns
• Resilient foundation model system designs
• Cost optimization and economic modeling

Module 2: Advanced Data Processing for Foundation Models

• Comprehensive data validation and quality assurance
• Multi-modal data processing pipelines
• Input optimization and performance enhancement

Module 3: Vector Databases and Retrieval Augmentation

• Enterprise vector database architecture
• Advanced document processing and chunking strategies
• Sophisticated retrieval system implementation
• Hands-on Lab: Develop Retrieval Augmented Generation (RAG) Applications with Amazon Bedrock Knowledge Bases

Day 2 Module 4: Prompt Engineering and Governance

• Advanced prompt engineering frameworks
• Complex prompt orchestration systems
• Enterprise prompt governance and management
• Hands-on Lab: Develop conversation pattern with Amazon Bedrock APIs

Module 5: Agentic AI and Tool Integration

• Agentic AI architecture and evolution
• Amazon Bedrock Agents implementation
• AWS Agentic AI service ecosystem
• Tool integration and production observability

Module 6: AI Safety and Security

• Comprehensive content safety implementation
• Privacy-preserving AI architecture
• AI governance and compliance frameworks

Day 3 Module 7: Performance Optimization and Cost Management

• Token efficiency and cost optimization
• High-performance system architecture
• Intelligent caching systems implementation
• Hands-on Lab: Building Secure and Responsible Gen AI with Guardrails for Amazon Bedrock

Module 8: Monitoring and Observability for Generative AI

• Foundation model monitoring systems
• Business impact and value management
• AI-specific troubleshooting and diagnostics

Module 9: Testing, Validation, and Continuous Improvement

• Comprehensive AI evaluation frameworks
• Quality assurance and continuous improvement
• RAG system evaluation and optimization

Module 10: Enterprise Integration Patterns

• Enterprise connectivity and integration architecture
• Secure access and identity management
• Cross-environment and hybrid deployments

Module 11: Course wrap-up

• Next steps and additional resources
• Course summary