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Generative AI on AWS: From Foundations to Enterprise-Scale Implementation

Design, Build, Govern, and Scale Production-Ready GenAI & Agentic Systems Using Amazon Bedrock and AWS AI Services

Duration

3 Day

Level

Basic to Advanced Level

Design and Tailor this course

As per your team needs

Enable Your Workforce

Overview

This 3-day intensive program provides a comprehensive journey from Generative AI fundamentals to advanced enterprise-scale architectures using AWS. Participants will explore Amazon Bedrock, foundation models, RAG pipelines, agentic AI systems, evaluation frameworks, governance, and cost optimization strategies.

Designed for practitioners and engineers, the training balances conceptual clarity, architectural thinking, and hands-on implementation to enable participants to build secure, scalable, and production-ready GenAI applications on AWS.

Audience

  • Generative AI Engineers
  • AI/ML Engineers
  • ML Platform Engineers
  • Data Scientists
  • Cloud AI Practitioners
  • Solution Architects

Prerequisites

  • Working knowledge of Python
  • Understanding of ML fundamentals
  • Familiarity with REST APIs
  • Basic AWS knowledge (IAM, S3, Lambda helpful)
  • No formal prerequisites required

Curriculum

Generative AI & Foundation Model Fundamentals

  • Evolution from machine learning to foundation models

  • Transformer architecture overview

  • Tokens, embeddings, and context windows

  • Decoding strategies (temperature, top-p)

  • Prompt engineering fundamentals

  • Fine-tuning vs prompting vs RAG

AWS Generative AI Ecosystem Overview

  • Overview of AWS AI/ML services

  • Architecture of Amazon Bedrock

  • Foundation model providers (Anthropic, AI21, Cohere, etc.)

  • Model selection strategy

  • Serverless model access patterns

Working with Amazon Bedrock

  • Accessing foundation models

  • Prompt templates and structured outputs

  • Guardrails configuration

  • Content filtering and moderation

  • SDK and API integration

Security & Governance Foundations

  • IAM roles and policies

  • VPC endpoints for Bedrock

  • Data privacy considerations

  • Encryption and key management

  • Shared responsibility model

Hands-on Labs

  • Access and invoke Bedrock models

  • Build prompt-driven GenAI application

  • Implement structured JSON output

  • Configure guardrails

  • Monitor token usage and latency

Retrieval-Augmented Generation (RAG) on AWS

  • RAG architecture patterns

  • Embeddings generation

  • Amazon OpenSearch Service vector search

  • Chunking strategies and metadata tagging

  • Hybrid retrieval techniques

  • RAG vs fine-tuning trade-offs

Data Ingestion & Knowledge Pipelines

  • S3-based document ingestion

  • Event-driven pipelines (Lambda + S3)

  • Preprocessing and chunking workflows

  • Index lifecycle management

  • Cost optimization strategies

Agentic AI & Tool Use with Bedrock Agents

  • Introduction to AI agents

  • Architecture of Amazon Bedrock Agents

  • Tool invocation and API integration

  • Memory and context handling

  • Multi-step reasoning loops

Orchestration & Serverless Integration

  • Lambda orchestration patterns

  • Step Functions for AI workflows

  • Asynchronous processing

  • Observability with Amazon CloudWatch

  • Failure handling and retries

Hands-on Labs

  • Build end-to-end RAG chatbot

  • Integrate OpenSearch vector store

  • Develop Bedrock Agent with tool use

  • Implement Lambda orchestration

  • Add logging and monitoring

Production-Grade GenAI Architecture

  • Reference architecture for GenAI on Amazon Web Services

  • API Gateway integration

  • Microservices patterns

  • Stateless vs stateful designs

  • Scaling with auto-scaling groups

Performance & Cost Optimization

  • Model selection for cost efficiency

  • Caching strategies

  • Token optimization

  • Throughput provisioning

  • Concurrency and quota management

Responsible AI & Risk Management

  • Bias and fairness considerations

  • Hallucination mitigation strategies

  • Guardrail enforcement

  • AI risk governance framework

  • Compliance considerations

LLM Evaluation & Monitoring

  • Automated evaluation pipelines

  • Human-in-the-loop workflows

  • Drift detection

  • Continuous improvement strategies

  • Observability dashboards

Capstone Case Study

  • Design enterprise GenAI solution architecture

  • Define governance and security controls

  • Implement RAG plus agent workflow

  • Optimize cost and scalability

  • Present architecture decisions and trade-offs

  • Design secure and scalable Generative AI architectures on Amazon Web Services

  • Build RAG and agentic AI systems using Amazon Bedrock

  • Optimize performance and manage operational costs

  • Implement enterprise governance and Responsible AI controls

  • Deploy production-ready GenAI applications

  • Lead cloud-native AI initiatives with architectural confidence

Duration

3 Day

Level

Basic to Advanced Level

Design and Tailor this course

As per your team needs

Enable Your Workforce

Strategic Capability Areas

Artificial Intelligence

Generative AI

Agentic AI

Data

Cloud

Cyber Security

blockchain

Blockchain

Agile

DevOps

RPA

RPA

QA and Testing

Soft skills

Strategic Capability Areas

Artificial Intelligence

Generative AI

Agentic AI

Data

Cloud

Cyber Security

blockchain

Blockchain

Agile

DevOps

RPA

RPA

QA and Testing

Soft skills

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