Advanced Generative AI Development on AWS

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Advanced Generative AI Development on AWS

Advanced Generative AI Development on AWS

Course ID: AWSAGAID

Duration: 3 Days

Training Fee: HK$18000

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.

Private in-house training

Apart from public, instructor-led classes, we also offer private in-house trainings for organizations based on their needs. Call us at +852 2116 3328 or email us at [email protected] for more details.

Why Choose Us?

The First Authorised Training Partner of AWS with full license
The Most Training Schedules delivered by AWS Authorized Instructors (AAI) and AAI Champion
Best Price Guaranteed
Trained over 50,000 talents in Asia
High Passing Rate: 90%
Appointed Exam Centre

Course Objectives

Develop production-ready generative AI solutions on AWS 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 foundation-model systems with circuit breakers, cross-region deployment, and 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
Explain components of Agentic AI frameworks and Amazon Bedrock AgentCore
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

Prerequisites

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

Intended Audience

This course is intended for those with:

Software developers
Technical Professionals

Course Outline

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

Module 4: Prompt Engineering and Governance

Advanced prompt-engineering frameworks
Complex prompt-orchestration systems
Enterprise prompt governance and management
Hands-on Lab: Develop conversation patterns 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

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

Certification

Upon successful completion of the course, participants can pursue the AWS Certified Generative AI Developer – Professional certification.

Advanced Generative AI Development on AWS