Advanced Generative AI Development on AWS
Field | Description / Template |
|---|---|
Purpose | To empower developers to transition from experimentation to production-ready generative AI implementations by mastering advanced data processing, agentic systems, and enterprise-grade governance on AWS. |
Audience | Experienced technical professionals and developers tasked with scaling AI solutions within an enterprise environment. |
Role | Senior Software Engineers, AI/ML Engineers, Data Engineers, and Solutions Architects. |
Domain | Advanced AI/ML / Enterprise Architecture / Data Engineering. |
Skill Level | Advanced |
Style | Comprehensive instructor-led training covering the full generative AI stack, focusing on resilient design patterns, autonomous agents, and production observability. |
Duration | 3 Days |
Related Technologies | Amazon Bedrock (Knowledge Bases & Agents), Amazon OpenSearch Service, Vector Databases, RAG, Chain-of-Thought Reasoning. |
Course Description
Advanced Generative AI Development on AWS is designed for developers seeking to master the implementation of production-ready generative AI solutions. The course addresses the needs of organizations building comprehensive AI strategies that align with broader business objectives. This intensive training builds expertise across the entire stack—from foundation models to enterprise integration. You will explore advanced data processing, vector database implementation, agentic AI systems, and rigorous security measures. The curriculum follows AWS's proven adoption model, ensuring you can move projects from initial concept to a scalable, secure, and cost-effective production reality.
Who is this course for
This course is intended for Software Developers and Technical Professionals who are already familiar with the basics of GenAI but need to solve complex enterprise challenges like performance optimization, cost management, and multi-region resilience.
Course Objectives
Enterprise Implementation: Develop production-ready solutions that meet strict requirements for security, scalability, and reliability.
Resilient Architecture: Design systems with circuit breakers, cross-region deployment, and graceful degradation strategies.
Advanced Retrieval: Build sophisticated vector solutions using Amazon Bedrock Knowledge Bases and OpenSearch for hybrid RAG.
Autonomous Agents: Create AI agents using Amazon Bedrock Agents with complex reasoning patterns and tool integration.
Governance & Safety: Implement enterprise-wide prompt governance, content filtering, and adversarial testing mechanisms.
Optimization: Manage costs and performance through token efficiency, intelligent caching, and comprehensive observability.
Prerequisites
AWS Foundational Knowledge: Completed AWS Technical Essentials and Generative AI Essentials on AWS.
Experience: 2+ years of building production-grade applications on AWS (or with open-source equivalents).
Specialized Experience: 1 year of hands-on experience specifically implementing generative AI solutions.
Course outline
Section 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
Section 2: Advanced Data Processing for Foundation Models
Comprehensive data validation and quality assurance
Multi-modal data processing pipelines
Input optimization and performance enhancement
Section 3: Vector Databases and Retrieval Augmentation
Enterprise vector database architecture
Advanced document processing and chunking strategies
Sophisticated retrieval system implementation
Hands-on Lab: Develop RAG Applications with Amazon Bedrock Knowledge Bases
Section 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
Section 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
Section 6: AI Safety and Security
Comprehensive content safety implementation
Privacy-preserving AI architecture
AI governance and compliance frameworks
Section 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
Section 8: Monitoring and Observability for Generative AI
Foundation model monitoring systems
Business impact and value management
AI-specific troubleshooting and diagnostics
Section 9: Testing, Validation, and Continuous Improvement
Comprehensive AI evaluation frameworks
Quality assurance and continuous improvement
RAG system evaluation and optimization
Section 10: Enterprise Integration Patterns
Enterprise connectivity and integration architecture
Secure access and identity management
Cross-environment and hybrid deployments
Section 11: Course wrap-up
Next steps and additional resources
Course summary