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FRCAWS-20AWS

Advanced Generative AI Development on AWS

Duration · 3 daysVirtual + In-PersonInstructor-Led

$2,025

Course Description

This advanced three-day instructor-led course equips experienced developers, architects, and AI engineers with the skills required to design, develop, secure, optimize, and operationalize enterprise-grade generative AI solutions using services from Amazon Web Services. Participants explore advanced application architectures, foundation model integration, Retrieval-Augmented Generation (RAG), AI agents, orchestration frameworks, model customization, observability, and production deployment strategies using Amazon Bedrock and related AWS services.

The course combines technical instruction, architecture reviews, demonstrations, design workshops, and extensive hands-on labs.

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Audience Profile

This course is intended for:

  • Senior software developers
  • AI engineers
  • Cloud architects
  • Machine learning engineers
  • DevOps engineers
  • Technical leads
  • Innovation teams

Prerequisites

Before enrolling, you should have:

  • Strong AWS Cloud experience
  • Experience developing cloud-native applications
  • Familiarity with APIs, Python, or SDK integration
  • Understanding of generative AI and large language models
  • Completion of foundational generative AI training recommended

— What You'll Learn —

Learning Objectives

In this course, you will learn to:

  • 1Design advanced generative AI application architectures
  • 2Build scalable AI-powered applications using Amazon Bedrock
  • 3Implement advanced prompt engineering techniques
  • 4Develop Retrieval-Augmented Generation (RAG) pipelines
  • 5Build and orchestrate AI agents and workflows
  • 6Integrate generative AI with enterprise systems and APIs
  • 7Apply security, governance, and responsible AI controls
  • 8Monitor, optimize, and operationalize AI workloads
  • 9Deploy production-ready generative AI solutions on AWS

— Day-by-Day —

Course Outline

Day 1 – Advanced Foundation Models and Application Design

Module 1

Advanced Generative AI Architecture Concepts

Topics

  • Evolution of enterprise generative AI systems
  • Advanced foundation model capabilities
  • Multi-model application strategies
  • Model selection criteria
  • Latency, throughput, and scalability considerations
  • Enterprise generative AI reference architectures
  • Demonstration
  • Reviewing enterprise AI architecture patterns

Module 2

Deep Dive into Amazon Bedrock

Topics

  • Amazon Bedrock advanced capabilities
  • Foundation model providers and specialization
  • Model inference optimization
  • Streaming responses and asynchronous processing
  • Security architecture and IAM integration
  • Cost management strategies

Hands-On Lab

  • Configuring advanced Amazon Bedrock model interactions

Module 3

Advanced Prompt Engineering

Topics

  • System prompts and orchestration prompts
  • Chain-of-thought prompting concepts
  • Structured output generation
  • Prompt templating frameworks
  • Function calling and tool invocation
  • Prompt evaluation methodologies

Hands-On Lab

  • Building reusable prompt workflows for enterprise scenarios

Module 4

Designing Generative AI Applications

Topics

  • Stateless vs. stateful AI architectures
  • Conversation memory management
  • Context window optimization
  • Session persistence strategies
  • AI middleware and orchestration layers
  • Error handling and fallback mechanisms
  • Architecture Workshop
  • Designing scalable AI application workflows

Module 5

Advanced Retrieval-Augmented Generation (RAG)

Topics

  • Advanced RAG architecture patterns
  • Embeddings and semantic similarity
  • Vector database integration concepts
  • Chunking and indexing optimization
  • Hybrid retrieval approaches
  • Reducing hallucinations with contextual grounding

Hands-On Lab

  • Building an enterprise-grade RAG pipeline

Day 2 – AI Agents, Integration, and Security

Module 6

Building AI Agents with Amazon Bedrock

Topics

  • Agentic AI architecture concepts
  • Amazon Bedrock Agents deep dive
  • Task planning and orchestration
  • Action groups and API integrations
  • Multi-step reasoning workflows
  • Human approval workflows

Hands-On Lab

  • Building and testing autonomous AI agents

Module 7

Workflow Orchestration and Automation

Topics

  • Event-driven AI architectures
  • Workflow orchestration patterns
  • Integrating AI with business processes
  • Long-running AI workflows
  • Serverless orchestration concepts
  • Multi-agent collaboration models
  • Demonstration
  • Enterprise workflow automation example

Module 8

Integrating Enterprise Data and Systems

Topics

  • Connecting AI applications to enterprise systems
  • API integration strategies
  • Secure enterprise data access
  • Knowledge bases and document ingestion
  • Real-time data integration
  • Data lifecycle management

Hands-On Lab

  • Integrating AI applications with enterprise APIs and data sources

Module 9

Responsible AI and Governance

Topics

  • Responsible AI implementation strategies
  • Bias detection and mitigation
  • Guardrails and policy enforcement
  • Content moderation and filtering
  • Data privacy and regulatory considerations
  • Governance frameworks for enterprise AI
  • Group Activity
  • Evaluating governance and compliance scenarios

Module 10

Security for Generative AI Applications

Topics

  • IAM and least-privilege access
  • Encryption and data protection
  • Secure prompt handling
  • Threat modeling for AI systems
  • Monitoring and anomaly detection
  • Secure deployment best practices
  • Hands-On Exercise
  • Applying security controls to AI application architectures

Day 3 – Production Deployment, Operations, and Optimization

Module 11

Operationalizing Generative AI Applications

Topics

  • Production deployment strategies
  • CI/CD pipelines for AI applications
  • Infrastructure as code concepts
  • Environment management
  • Release management for AI systems
  • Rollback and recovery planning
  • Demonstration
  • Deploying a production-ready AI solution architecture

Module 12

Monitoring, Evaluation, and Observability

Topics

  • AI application monitoring strategies
  • Logging and tracing AI interactions
  • Evaluating model performance
  • Latency and throughput analysis
  • User feedback integration
  • Observability best practices

Hands-On Lab

  • Monitoring and analyzing AI application performance

Module 13

Cost Optimization and Scalability

Topics

  • Managing inference costs
  • Throughput optimization techniques
  • Caching and response reuse
  • Scaling AI workloads
  • Capacity planning
  • Performance benchmarking
  • Workshop
  • Optimizing enterprise AI deployment architectures

Module 14

Advanced AI Application Patterns

Topics

  • Multi-modal AI application concepts
  • AI copilots and assistants
  • Autonomous workflow systems
  • Conversational enterprise applications
  • AI-enhanced analytics workflows
  • Emerging generative AI trends
  • Demonstration
  • Advanced enterprise generative AI use cases

Module 15

Capstone Design Exercise

  • Activity
  • Participants design an enterprise generative AI solution incorporating:
  • Amazon Bedrock foundation models
  • AI agents and orchestration
  • Retrieval-Augmented Generation
  • Enterprise data integration
  • Security and governance controls
  • Monitoring and operational considerations
  • Presentation
  • Team architecture presentations and instructor review
  • Course Wrap-Up
  • Final Review and Q&A
  • Review of advanced concepts
  • Architecture and operational best practices
  • Lessons learned from labs and workshops
  • Additional AWS learning pathways
  • Final Q&A session
  • Suggested Hands-On Labs
  • Advanced prompt engineering workflows
  • Building scalable Amazon Bedrock applications
  • Enterprise RAG implementation
  • AI agent orchestration and automation
  • Knowledge base integration
  • Secure AI application deployment
  • Monitoring and observability setup
  • Cost optimization analysis
  • Production deployment simulations

— Next Steps —

Recommended Follow-On Courses

Continue your learning path with courses that build on what you just covered.

— Additional Details —

What else is included

Workshop Activity

Optimizing enterprise AI deployment architectures Module 14: Advanced AI Application Patterns Topics Multi-modal AI application concepts AI copilots and assistants Autonomous workflow systems Conversational enterprise applications AI-enhanced analytics workflows Emerging generative AI trends Demonstration Advanced enterprise generative AI use cases Module 15: Capstone Design Exercise Activity Participants design an enterprise generative AI solution incorporating: Amazon Bedrock foundation models AI agents and orchestration Retrieval-Augmented Generation Enterprise data integration Security and governance controls Monitoring and operational considerations Presentation Team architecture presentations and instructor review Course Wrap-Up Final Review and Q&A Review of advanced concepts Architecture and operational best practices Lessons learned from labs and workshops Additional AWS learning pathways Final Q&A session Suggested Hands-On Labs Advanced prompt engineering workflows Building scalable Amazon Bedrock applications Enterprise RAG implementation AI agent orchestration and automation Knowledge base integration Secure AI application deployment Monitoring and observability setup Cost optimization analysis Production deployment simulations Recommended Follow-On Training Machine Learning Engineering on AWS Advanced Architecting on AWS Security Engineering on AWS DevOps Engineering on AWS Data Engineering on AWS Advanced AI Operations and MLOps Workshops

Note: Course outlines are provided as a general guide. Content, pacing, labs, and instructional emphasis may vary based on instructor expertise, student experience levels, and customer-specific learning objectives.

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