Advanced Generative AI Development on AWS

Advanced Generative AI Development on AWSAGAIDAWAmazon Web ServicesAW-AGAID1.0<ul> <li>Develop production-ready generative AI solutions using AWS services that meet enterprise requirements for security, scalability, and reliability</li><li>Evaluate and select appropriate foundation models for specific business use cases, including benchmarking performance and implementing dynamic model selection architectures</li><li>Design and implement resilient foundation model systems with circuit breakers, cross-region deployment, and graceful degradation strategies</li><li>Build comprehensive data processing pipelines for multi-modal inputs, including validation workflows and optimization techniques</li><li>Implement sophisticated vector database solutions using Amazon Bedrock Knowledge Bases, OpenSearch, and hybrid approaches for effective retrieval augmentation</li><li>Create and manage advanced prompt engineering frameworks, including chain-of-thought reasoning and enterprise-wide prompt governance systems</li><li>Develop autonomous AI agents using Amazon Bedrock Agents, implementing complex reasoning patterns and tool integration capabilities</li><li>Implement comprehensive AI safety and security controls, including content filtering, privacy preservation, and adversarial testing mechanisms</li><li>Optimize performance and manage costs through token efficiency strategies, batching implementations, and intelligent caching systems</li><li>Design and implement comprehensive monitoring and observability solutions for foundation model applications</li><li>Create systematic testing and validation frameworks for continuous quality assurance of AI applications</li><li>Integrate generative AI solutions within enterprise environments using secure, compliant, and scalable architectural patterns</li></ul><ul> <li>AWS Technical Essentials</li><li>Generative AI Essentials on AWS</li><li>2 or more years of experience building production grade applications on AWS or with open-source technologies, general AI/ML or data engineering experience</li><li>1 year of hands-on experience implementing generative AI solutions</li></ul><ul> <li>Software developers</li><li>Technical Professionals</li></ul><h4>Day 1</h4> Module 1: Foundation Model Selection and Configuration <ul> <li>Enterprise foundation model evaluation framework</li><li>Dynamic model selection architecture patterns</li><li>Resilient foundation model system designs</li><li>Cost optimization and economic modeling</li></ul>Module 2: Advanced Data Processing for Foundation Models <ul> <li>Comprehensive data validation and quality assurance</li><li>Multi-modal data processing pipelines</li><li>Input optimization and performance enhancement</li></ul>Module 3: Vector Databases and Retrieval Augmentation <ul> <li>Enterprise vector database architecture</li><li>Advanced document processing and chunking strategies</li><li>Sophisticated retrieval system implementation</li><li>Hands-on Lab: Develop Retrieval Augmented Generation (RAG) Applications with Amazon</li><li>Bedrock Knowledge Bases</li></ul><h4>Day 2</h4> Module 4: Prompt Engineering and Governance <ul> <li>Advanced prompt engineering frameworks</li><li>Complex prompt orchestration systems</li><li>Enterprise prompt governance and management</li><li>Hands-on Lab: Develop conversation pattern with Amazon Bedrock APIs</li></ul>Module 5: Implementing Agentic AI Frameworks with Amazon Bedrock AgentCore <ul> <li>Agentic AI Frameworks</li><li>Amazon Bedrock AgentCore</li></ul>Module 6: AI Safety and Security <ul> <li>Comprehensive content safety implementation</li><li>Privacy-preserving AI architecture</li><li>AI governance and compliance frameworks</li></ul><h4>Day 3</h4> Module 7: Performance Optimization and Cost Management <ul> <li>Token efficiency and cost optimization</li><li>High-performance system architecture</li><li>Intelligent caching systems implementation</li><li>Hands-on Lab: Building Secure and Responsible Gen AI with Guardrails for Amazon Bedrock</li></ul>Module 8: Monitoring and Observability for Generative AI <ul> <li>Foundation model monitoring systems</li><li>Business impact and value management</li><li>AI-specific troubleshooting and diagnostics</li></ul>Module 9: Testing, Validation, and Continuous Improvement <ul> <li>Comprehensive AI evaluation frameworks</li><li>Quality assurance and continuous improvement</li><li>RAG system evaluation and optimization</li></ul>Module 10: Enterprise Integration Patterns <ul> <li>Enterprise connectivity and integration architecture</li><li>Secure access and identity management</li><li>Cross-environment and hybrid deployments</li></ul>Module 11: Course wrap-up <ul> <li>Next steps and additional resources</li><li>Course summary</li></ul>- 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- AWS Technical Essentials - Generative AI Essentials on AWS - 2 or more years of experience building production grade applications on AWS or with open-source technologies, general AI/ML or data engineering experience - 1 year of hands-on experience implementing generative AI solutions- Software developers - Technical ProfessionalsDay 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: Implementing Agentic AI Frameworks with Amazon Bedrock AgentCore - Agentic AI Frameworks - Amazon Bedrock AgentCore 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 summary3 days2025.002795.002655.001650.002600.001995.001995.001995.001995.001995.001750.001750.002395.00