Course Overview:

This comprehensive course provides an end-to-end learning journey into the design, implementation, and governance of generative AI systems using AWS services. Participants will explore advanced concepts such as dynamic model selection, resilient deployment patterns, and cross-region reliability strategies. You’ll learn to build vector search and retrieval systems, develop prompt engineering and agent orchestration frameworks, and enforce AI safety, security, and observability controls. The course also emphasizes performance optimization, cost management, and continuous testing methodologies, equipping learners with the skills to deliver secure, compliant, and scalable AI architectures in production environments.

After completing this course, participants will be able to:

  • Develop and deploy production-grade generative AI solutions on AWS that adhere to enterprise standards for security, scalability, and reliability.
  • Assess and select suitable foundation models for diverse business applications by benchmarking performance and implementing adaptive model selection frameworks.
  • Design and build resilient model architectures featuring circuit breakers, cross-region redundancy, and graceful degradation mechanisms to ensure high availability and fault tolerance.
  • Create robust data processing pipelines to handle multi-modal inputs, incorporating data validation workflows and optimization techniques for improved efficiency and accuracy.
  • Design and deploy advanced vector database solutions leveraging Amazon Bedrock Knowledge Bases, OpenSearch, and hybrid retrieval architectures to enhance information retrieval and contextual augmentation.
  • Establish and maintain scalable prompt engineering frameworks, incorporating chain-of-thought reasoning and enterprise-wide governance models for consistent and optimized prompt management.
  • Build and operationalize autonomous AI agents using Amazon Bedrock Agents, enabling complex reasoning workflows, multi-tool integrations, and task automation capabilities.
  • Implement robust AI safety and security frameworks, including content filtering, data privacy safeguards, and adversarial robustness testing to ensure responsible and resilient AI operations.
  • Enhance system performance and cost efficiency through token optimization strategies, batch processing, and intelligent caching mechanisms to maximize model throughput and minimize latency.
  • Develop and deploy robust monitoring and observability frameworks tailored for foundation model applications, enabling proactive insights into model behavior, performance, and reliability.
  • Establish systematic testing and validation pipelines to ensure continuous quality assurance and consistent accuracy across generative and predictive AI applications.
  • Integrate generative AI solutions seamlessly into enterprise ecosystems, applying secure, compliant, and scalable architectural principles for sustainable production deployments

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Key Features:

  • Comprehensive Coverage: The course provides an end-to-end understanding of Generative AI on AWS, from foundation model selection and data processing to enterprise integration and performance optimization.

  • Hands-On Labs: Participants will gain practical experience through immersive labs – building RAG (Retrieval-Augmented Generation) applications, developing Agentic AI frameworks, and implementing Guardrails for responsible AI using Amazon Bedrock.

  • Real-World Demonstrations: Explore live demos showcasing prompt engineering, vector search, and autonomous agent capabilities, highlighting real-world use cases and deployment best practices.

Who should Attend?

  • This course is intended for: 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

Prerequisites of Advanced Generative AI Development on AWS:

  • Generative AI Essentials or equivalent work experience
  • Foundational AWS knowledge and software development experience

    • Why choose CloudThat as your training partner?

    • Expert Instructors: CloudThat's courses are led by industry experts with extensive experience in AI and AWS, ensuring high-quality instruction.
    • Hands-On Learning: Emphasis on practical, hands-on labs and real-world demos to provide participants with valuable, applicable skills.
    • Comprehensive Curriculum: Courses cover a wide range of topics, from introductory concepts to advanced implementation, offering a well-rounded learning experience.
    • Focus on Responsible AI: Training includes responsible AI principles, ensuring ethical practices and compliance with industry standards.
    • Career Advancement: CloudThat's training programs are designed to enhance career prospects, preparing participants for various roles in AI and machine learning.
    • Post-Training Support: CloudThat offers post-training support, additional resources, and forums for ongoing learning and development.

    Course Outline: Download Course Outline

    Hands-on Lab: Develop Retrieval Augmented Generation (RAG) Applications with Amazon Bedrock Knowledge Bases

    Hands-on Lab: Develop conversation pattern with Amazon Bedrock APIs

    Hands-on Lab: Building Secure and Responsible Gen AI with Guardrails for Amazon Bedrock

    Certification Details:

      AWS Certified Generative AI Developer - Professional

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    Course ID: 26369

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    FAQs:

    This course prepares you for advanced roles such as Generative AI Engineer, AI Solutions Architect, Machine Learning Engineer, AI DevOps Specialist, and Bedrock Implementation Consultant, with expertise in building and deploying production-ready AI systems on AWS.

    You’ll develop hands-on expertise in foundation model selection, data pipeline design, vector databases, RAG architecture, prompt engineering, Agentic AI development, and AI safety and compliance. The course also enhances your skills in performance optimization, monitoring, and enterprise integration using AWS Bedrock and related services.

    Through hands-on labs and real-world demos, you’ll learn to implement and operationalize retrieval-augmented generation (RAG), autonomous agents, and secure AI architectures, equipping you to deliver scalable, reliable, and compliant generative AI solutions in enterprise environments.

    Yes. The course features multiple hands-on labs, including: a. Building RAG applications with Amazon Bedrock Knowledge Bases b. Designing conversational experiences with Bedrock APIs c. Implementing AI Guardrails for responsible AI

    Yes. This course directly supports preparation for the AWS Certified Generative AI Developer – Professional certification. It covers the core domains outlined in the exam, including foundation model selection and deployment, retrieval-augmented generation (RAG), prompt engineering, Agentic AI development, AI safety and governance, and performance optimization on AWS. Through hands-on labs with Amazon Bedrock, Knowledge Bases, and AgentCore, participants gain the practical experience and architectural understanding required to confidently pursue and succeed in this certification.

    Participants gain access to extended learning resources, community forums, and knowledge-sharing sessions to continue skill development, stay updated on AWS AI innovations, and network with professionals in the generative AI ecosystem.

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