Build production-ready generative AI systems in 2026.

This advanced three-day AWS Generative AI training equips developers with hands-on skills to design, build and integrate generative AI applications using foundation models, enterprise patterns, retrieval augmentation and agentic approaches on AWS.

  • Why get trained: Gain practical expertise in building and deploying production-ready generative AI solutions using AWS services such as foundation models, vector search, agent frameworks and integration patterns.
  • Why it matters: Advanced generative AI capabilities help professionals deliver AI solutions that power innovation, automate complex tasks and support strategic business outcomes in cloud environments.
  • Who should attend: Software developers, cloud engineers and AI practitioners with development experience who want to expand their skills in advanced generative AI architectures on AWS.

Advance your AI development skills and build scalable generative AI systems – HRDC Claimable. 

Overview

Master the implementation of production-ready generative AI solutions on AWS.

The Advanced Generative AI Development on AWS 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.

Skills Covered

In this course, you will learn to:

  • 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

Target Audience

This course is intended for those with:

  • Software developers
  • Technical Professionals

Course Curriculum

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

Dates & Locations

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November 16, 2026 - November 18, 2026

Location: Kuala Lumpur
Modal: ILT
Availability: TBC
Exam:
RM 338
RM 5400 RM 4500
PROMO

November 16, 2026 - November 18, 2026

Location: Online
Modal: VILT
Availability: TBC
Exam:
RM 338
RM 5400 RM 4500
PROMO
Trainocate exam and cert

Exam & Certification

Showcase advanced skills in building and deploying production-ready AI solutions using AWS services like Amazon Bedrock.

AWS Certified Generative AI Developer – Professional (AIP-C01) showcases advanced technical expertise in building and deploying production-ready AI solutions using AWS Services like Bedrock. Perfect for developers with 2+ years of cloud experience looking to advance their careers.

The AWS Certified Generative AI Developer – Professional (AIP-C01) exam is intended for individuals who perform a GenAI developer role. The exam validates a candidate’s ability to effectively integrate foundation models (FMs) into applications and business workflows. This certification demonstrates practical knowledge of how to implement GenAI solutions into production environments by using AWS technologies.

The exam also validates a candidate’s ability to complete the following tasks:

  • Design and implement solutions by using vector stores, Retrieval Augmented Generation (RAG), knowledge bases, and other GenAI architectures.
  • Integrate FMs into applications and business workflows.
  • Apply prompt engineering and management techniques.
  • Implement agentic AI solutions.
  • Optimize GenAI applications for cost, performance, and business value.
  • Implement security, governance, and Responsible AI practices.
  • Troubleshoot, monitor, and optimize GenAI applications.
  • Evaluate FMs for quality and responsibility.

For organizations investing in AI initiatives, this certification provides a reliable way to identify and verify developers who can move beyond proofs-of-concept to build production-grade generative AI solutions that deliver tangible business results while maintaining security and cost efficiency.

Training & Certification Guide

  • Format: 75 multiple-choice or multiple-response questions
  • Duration: 180 minutes (3 hours)
  • Cost: $300 USD
  • Passing Score: Scaled score of 750 out of 1,000
  • Testing Method: Pearson VUE testing center or online proctored exam

The exam is designed for individuals with significant hands-on technical backgrounds:
  • 2+ years of experience building cloud applications on AWS or with open-source tech.
  • 1+ years of hands-on experience implementing generative AI solutions.
  • Strong familiarity with core AWS infrastructure (compute, storage, networking, security, and IaC tools).

The exam evaluates your competence across five distinct technical domains: 
  1. Foundation Model Integration, Data Management, & Compliance (31%): Selecting FMs, handling data pipelines, vector databases, and managing compliance boundaries. 
  2. Implementation and Integration (26%): Designing Retrieval-Augmented Generation (RAG) pipelines, building AI agents, orchestration, and API integrations.
  3. AI Safety, Security, and Governance (20%): Configuring Guardrails, protecting data privacy, implementing Responsible AI, and establishing IAM permissions. 
  4. Operational Efficiency & Optimization (Weighting varies): Fine-tuning cost optimization, latency management, and choosing correct model pricing models (provisioned vs. on-demand throughput). 
  5. Testing, Validation, and Troubleshooting (Weighting varies): Evaluation metrics for FMs, debugging agentic workflows, and application monitoring using AWS observability tools.

Frequently Asked Questions

AWS-AGAID teaches experienced developers how to design and implement production-ready generative AI systems that meet enterprise requirements for security, scalability, reliability, performance, and cost.

The course covers the complete production generative AI stack, progressing from foundation-model selection and advanced data processing into RAG, vector databases, prompt governance, agentic AI, security, performance optimization, observability, testing, and enterprise integration.

Unlike introductory generative AI training, AWS-AGAID focuses on the engineering decisions required when AI applications move beyond proofs of concept and into enterprise production environments.

Pro Tip: Bring an existing generative AI use case to mind when attending. Evaluating how you would make that application more secure, reliable, observable, and cost-efficient will help connect the course concepts to real production challenges.

AWS-AGAID is intended for experienced software developers and technical professionals who already have hands-on generative AI experience and want to build production-grade solutions.

The course recommends substantially more experience than foundational AWS AI training. Learners should ideally have completed AWS Technical Essentials and Generative AI Essentials on AWS, and have:

  • 2+ years 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

This makes AWS-AGAID most appropriate for developers who already understand generative AI fundamentals and need deeper production engineering expertise.

Pro Tip: If RAG, foundation models, AWS architecture, and generative AI application development are still new to you, build those foundations first rather than starting directly with this advanced course.

You will develop the skills needed to engineer generative AI systems across the full production lifecycle, from model selection and retrieval architecture to security, optimization, monitoring, and enterprise integration.

The course develops capabilities including:

  • Evaluating and dynamically selecting foundation models
  • Designing resilient foundation-model architectures
  • Processing multimodal data
  • Implementing vector databases and advanced RAG
  • Establishing enterprise prompt governance
  • Implementing agentic AI with Amazon Bedrock AgentCore
  • Applying AI safety and privacy controls
  • Optimizing token usage, caching, performance, and costs
  • Monitoring and troubleshooting AI applications
  • Evaluating and continuously improving RAG systems
  • Integrating AI securely into enterprise environments

Pro Tip: Focus on the relationships between these capabilities. Production AI engineering is less about mastering one service and more about making model, data, security, performance, and operational decisions work together.

The target candidate should have 2 or more years of experience building production grade applications on AWS or with open-source technologies, general AI/ML or data engineering experience, and 1 year of hands-on experience implementing generative AI solutions. The target candidate should have the following AWS knowledge:

  • Experience with AWS compute, storage, and networking services
  • Understanding of AWS security best practices and identity management
  • Experience with AWS deployment and infrastructure as code tools
  • Familiarity with AWS monitoring and observability services
  • Understanding of AWS cost optimization principles

The AWS Certified Generative AI Developer – Professional positions candidates for significant career growth in a rapidly expanding market where 1 in 4 technology jobs now require AI skills according to the Wall Street Journal.

As companies shift toward integrating AI into existing roles rather than creating entirely new AI-focused positions, this certification demonstrates you can deliver practical AI solutions that drive business value.

You are not required to earn any specific certifications prior to preparing for this certification.

However, candidates could benefit from earning the following AWS certifications:

Malaysia’s demand for AI skills is increasing, strengthening the case for developers who can progress from experimenting with AI to engineering reliable production solutions.

PwC’s July 2026 Malaysia AI Jobs Barometer reports that Malaysian job postings requiring AI-related skills have risen 7.4%, indicating growing AI skills demand across the labour market.

More specifically, PwC Malaysia reported in July 2026 that the share of Malaysian job postings requiring AI skills increased from 1.9% in 2024 to 4.0% in 2025, more than doubling its share of advertised roles.

AWS-AGAID addresses a particularly advanced part of this skills requirement: professionals capable of taking generative AI beyond experimentation and engineering systems around RAG, agents, security, governance, observability, testing, cost management, and enterprise integration.

Pro Tip: Differentiate yourself by learning the operational side of generative AI. Being able to build a prototype is useful; being able to make it secure, reliable, measurable, and production-ready demonstrates a much deeper engineering capability.

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