Name: Advanced Generative AI Development on AWS
Availability: InStock
Rating: 4.6 (5878 reviews)

Duration:

24.00 hours

19.5 CPD hours

Overview:

The Advanced Generative AI Development on AWS is designed for developers seeking to master the implementation of production-ready generative AI solutions on AWS. The course addresses the needs of organizations embarking on their generative AI journey and how to build comprehensive generative AI strategies that align with broader business objectives. This advanced 3-day instructor-led training builds expertise across the entire generative AI stack - from foundation models to enterprise integration patterns. In addition, you will learn about advanced data processing techniques, vector database implementation and retrieval augmentation, sophisticated prompt engineering and governance, agentic AI systems and tool integration, AI safety and security measures, performance optimization and cost management strategies, comprehensive monitoring and observability solutions, testing and validation frameworks. The course structure follows AWS's proven model for generative AI adoption, progressing from experimentation to production-ready implementations.

Description:

The Advanced Generative AI Development on AWS is designed for developers seeking to master the implementation of production-ready generative AI solutions on AWS. The course addresses the needs of organizations embarking on their generative AI journey and how to build comprehensive generative AI strategies that align with broader business objectives. This advanced 3-day instructor-led training builds expertise across the entire generative AI stack - from foundation models to enterprise integration patterns. In addition, you will learn about advanced data processing techniques, vector database implementation and retrieval augmentation, sophisticated prompt engineering and governance, agentic AI systems and tool integration, AI safety and security measures, performance optimization and cost management strategies, comprehensive monitoring and observability solutions, testing and validation frameworks. The course structure follows AWS's proven model for generative AI adoption, progressing from experimentation to production-ready implementations.

Course Outline:

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: 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 summary, 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

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

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, 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

Skills Taught:

- 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

Available Delivery Options for Advanced Generative AI Development on AWS

Live Instructor Led Classroom Online (Live Online)
Traditional Instructor Led Classroom training (TILT/ILT)
Delivery at your offices in United Kingdom
Private dedicated course for your staff

How many CPD hours does Advanced Generative AI Development on AWS provide?

The 3 day Advanced Generative AI Development on AWS course gives you up to 19.5 CPD hours/structured learning hours.

Who is the correct audience for Advanced Generative AI Development on AWS?

- Software developers
- Technical Professionals

Do you provide training for Advanced Generative AI Development on AWS?

Yes, we provide corporate training, dedicated training and closed classes for Advanced Generative AI Development on AWS. This can take place anywhere in United Kingdom including London, Manchester, Birmingham, Edinburgh or live online.

What is the duration of Advanced Generative AI Development on AWS?

The Advanced Generative AI Development on AWS training takes place over 3 day(s), with each day lasting approximately 24.00 hours including breaks.

What other terms do people search for?

Popular related searches include: Advanced Generative AI Development on AWS, Machine Learning, AGAID, Amazon Web Services, vILT

Why are Nexus Human the best provider for Advanced Generative AI Development on AWS?

Nexus Human are recognised as one of the best training companies. Our trainers have won awards including Small Firms Best Trainer, national training partner of the year for Ireland, and global top 30 instructor awards in 2012, 2019 and 2021.

Is there a discount code for Advanced Generative AI Development on AWS?

Yes, the discount code PENPAL5 is currently available for Advanced Generative AI Development on AWS training. Only one discount code can be used per booking.

Advanced Generative AI Development on AWS