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The Advanced Generative AI Development on AWS course is designed for developers who want to master the implementation of production-ready generative AI solutions on AWS. The course addresses the needs of organizations embarking on the generative AI journey and how to develop comprehensive generative AI strategies that align with overall business goals. This three-day advanced instructor-led training provides expertise on the entire generative AI stack - from basic models to enterprise integration patterns. You will also learn advanced data processing techniques, vector database implementation and extension, sophisticated prompt engineering and governance, agent-based AI systems and tool integration, AI safety and security measures, performance optimization strategies and cost management, comprehensive monitoring and observability solutions, and testing and validation frameworks. The course structure follows AWS's proven model for the introduction of generative AI and ranges from experiments to production-ready implementations.
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Course Contents
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- Selection and configuration of the base model.
- Advanced data processing for foundation models.
- Vector databases and search extension.
- Prompt engineering and governance.
- Implementation of agent-based AI frameworks with Amazon Bedrock AgentCo.
- AI security and protection.
- Performance optimization and cost management.
- Monitoring and observability for generative AI.
- Testing, validation, and continuous improvement.
- Patterns for enterprise integration
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Target Group
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- Software developers
- Technical specialists
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Knowledge Prerequisites
-
Recommended prior knowledge:
- Attend the course AWS Technical Essentials
- Attend the course Generative AI Essentials on AWS
- Two or more years of experience developing production-ready applications on AWS or with open source technologies
- General AI/ML or data engineering experience
- One year of hands-on experience in implementing generative AI solutions
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Course Objective
-
- 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 performance benchmarking and implementation of dynamic model selection architectures.
- Design and implement robust foundation model systems with circuit breakers, cross-region deployment and soft degradation strategies.
- Create comprehensive data processing pipelines for multimodal inputs, including validation workflows and optimization techniques.
- Implement sophisticated vector database solutions with Amazon Bedrock Knowledge Bases, OpenSearch and hybrid approaches to effectively improve search results.
- Create and manage advanced frameworks for prompt development, including chain-of-thought reasoning and enterprise-wide prompt governance systems.
- Develop autonomous AI agents with Amazon Bedrock Agents and implement complex reasoning structures and tool integration capabilities.
- Implement comprehensive AI safety and security controls, including content filtering, data protection, and adversarial testing mechanisms.
- Optimize performance and manage costs through token efficiency strategies, batch implementations and intelligent caching systems.
- Design and implement comprehensive monitoring and observability solutions for basic modeling applications.
- Create systematic testing and validation frameworks for continuous quality assurance of AI applications.
- Integrate generative AI solutions into enterprise environments using secure, compliant and scalable architectural patterns.
| 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 |
| Advanced Data Processing for Foundation Models |
| Comprehensive data validation and quality assurance |
| Multi-modal data processing pipelines |
| Input optimization and performance enhancement |
| 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 |
| 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 |
| Implementing Agentic AI Frameworks with Amazon Bedrock AgentCore |
| Agentic AI Frameworks |
| Amazon Bedrock AgentCore |
| AI Safety and Security |
| Comprehensive content safety implementation |
| Privacy-preserving AI architecture |
| AI governance and compliance frameworks |
| 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 |
| Monitoring and Observability for Generative AI |
| Foundation model monitoring systems |
| Business impact and value management |
| AI-specific troubleshooting and diagnostics |
| Testing, Validation, and Continuous Improvement |
| Comprehensive AI evaluation frameworks |
| Quality assurance and continuous improvement |
| RAG system evaluation and optimization |
| Enterprise Integration Patterns |
| Enterprise connectivity and integration architecture |
| Secure access and identity management |
| Cross-environment and hybrid deployments |
| Course wrap-up |
| Next steps and additional resources |
| Course summary |
-
Classroom training
- Do you prefer the classic training method? A course in one of our Training Centers, with a competent trainer and the direct exchange between all course participants? Then you should book one of our classroom training dates!
-
Online training
- You wish to attend a course in online mode? We offer you online course dates for this course topic. To attend these seminars, you need to have a PC with Internet access (minimum data rate 1Mbps), a headset when working via VoIP and optionally a camera. For further information and technical recommendations, please refer to.
-
Tailor-made courses
-
You need a special course for your team? In addition to our standard offer, we will also support you in creating your customized courses, which precisely meet your individual demands. We will be glad to consult you and create an individual offer for you.
You can find the complete description of this course with dates and prices ready for download at as PDF.-
The Advanced Generative AI Development on AWS course is designed for developers who want to master the implementation of production-ready generative AI solutions on AWS. The course addresses the needs of organizations embarking on the generative AI journey and how to develop comprehensive generative AI strategies that align with overall business goals. This three-day advanced instructor-led training provides expertise on the entire generative AI stack - from basic models to enterprise integration patterns. You will also learn advanced data processing techniques, vector database implementation and extension, sophisticated prompt engineering and governance, agent-based AI systems and tool integration, AI safety and security measures, performance optimization strategies and cost management, comprehensive monitoring and observability solutions, and testing and validation frameworks. The course structure follows AWS's proven model for the introduction of generative AI and ranges from experiments to production-ready implementations.
-
Course Contents
-
- Selection and configuration of the base model.
- Advanced data processing for foundation models.
- Vector databases and search extension.
- Prompt engineering and governance.
- Implementation of agent-based AI frameworks with Amazon Bedrock AgentCo.
- AI security and protection.
- Performance optimization and cost management.
- Monitoring and observability for generative AI.
- Testing, validation, and continuous improvement.
- Patterns for enterprise integration
-
Target Group
-
- Software developers
- Technical specialists
-
Knowledge Prerequisites
-
Recommended prior knowledge:
- Attend the course AWS Technical Essentials
- Attend the course Generative AI Essentials on AWS
- Two or more years of experience developing production-ready applications on AWS or with open source technologies
- General AI/ML or data engineering experience
- One year of hands-on experience in implementing generative AI solutions
-
Course Objective
-
- 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 performance benchmarking and implementation of dynamic model selection architectures.
- Design and implement robust foundation model systems with circuit breakers, cross-region deployment and soft degradation strategies.
- Create comprehensive data processing pipelines for multimodal inputs, including validation workflows and optimization techniques.
- Implement sophisticated vector database solutions with Amazon Bedrock Knowledge Bases, OpenSearch and hybrid approaches to effectively improve search results.
- Create and manage advanced frameworks for prompt development, including chain-of-thought reasoning and enterprise-wide prompt governance systems.
- Develop autonomous AI agents with Amazon Bedrock Agents and implement complex reasoning structures and tool integration capabilities.
- Implement comprehensive AI safety and security controls, including content filtering, data protection, and adversarial testing mechanisms.
- Optimize performance and manage costs through token efficiency strategies, batch implementations and intelligent caching systems.
- Design and implement comprehensive monitoring and observability solutions for basic modeling applications.
- Create systematic testing and validation frameworks for continuous quality assurance of AI applications.
- Integrate generative AI solutions into enterprise environments using secure, compliant and scalable architectural patterns.
| 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 |
| Advanced Data Processing for Foundation Models |
| Comprehensive data validation and quality assurance |
| Multi-modal data processing pipelines |
| Input optimization and performance enhancement |
| 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 |
| 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 |
| Implementing Agentic AI Frameworks with Amazon Bedrock AgentCore |
| Agentic AI Frameworks |
| Amazon Bedrock AgentCore |
| AI Safety and Security |
| Comprehensive content safety implementation |
| Privacy-preserving AI architecture |
| AI governance and compliance frameworks |
| 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 |
| Monitoring and Observability for Generative AI |
| Foundation model monitoring systems |
| Business impact and value management |
| AI-specific troubleshooting and diagnostics |
| Testing, Validation, and Continuous Improvement |
| Comprehensive AI evaluation frameworks |
| Quality assurance and continuous improvement |
| RAG system evaluation and optimization |
| Enterprise Integration Patterns |
| Enterprise connectivity and integration architecture |
| Secure access and identity management |
| Cross-environment and hybrid deployments |
| Course wrap-up |
| Next steps and additional resources |
| Course summary |
-
Classroom training
- Do you prefer the classic training method? A course in one of our Training Centers, with a competent trainer and the direct exchange between all course participants? Then you should book one of our classroom training dates!
-
Online training
- You wish to attend a course in online mode? We offer you online course dates for this course topic. To attend these seminars, you need to have a PC with Internet access (minimum data rate 1Mbps), a headset when working via VoIP and optionally a camera. For further information and technical recommendations, please refer to.
-
Tailor-made courses
-
You need a special course for your team? In addition to our standard offer, we will also support you in creating your customized courses, which precisely meet your individual demands. We will be glad to consult you and create an individual offer for you.
You can find the complete description of this course with dates and prices ready for download at as PDF.