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Build Real world End-to-End AI Agents using AWS Bedrock
Rating: 4.5 out of 5(10 ratings)
170 students

Build Real world End-to-End AI Agents using AWS Bedrock

Hands-on course to build Real Agentic AI systems using AWS Bedrock, Python, Lambda, Redshift, Dynamodb and OpenSearch
Created bySid Raghunath
Last updated 6/2025
English

What you'll learn

  • Data Engineers and Architects looking to integrate LLMs into real-world cloud workflows using AWS Bedrock
  • AI/ML Engineers who want to build production-grade agentic systems with Bedrock and several other AWS Services
  • Cloud Developers interested in deploying RAG-based chatbots, tools, and automations on AWS infrastructure
  • Technical professionals seeking hands-on product-grade experience with Bedrock models like Claude, Titan, and Stable Diffusion

Course content

10 sections • 52 lectures • 5h 57m total length

  • Introduction1:43
  • Emerging AI Roles in the industry5:00
  • Course Prerequisites - Must watch1:52

    Develop Python skills and prepare an AWS account with admin access to build real-world end-to-end AI agents on AWS Bedrock, using familiar services like Lambda, Redshift, and DynamoDB.

  • Github Link
  • Discord Link For the course

Requirements

  • Prior coding experience in Python
  • Prior experience in AWS as a software or data engineer
  • AWS Account

Description

This course is designed for engineers, data professionals, and software developers who want to build production-grade and real AI applications using AWS Bedrock. You will focus on building actual workflows using AWS Bedrock, KnowledgeBase and Workflows while leverage several other AWS Cloud components such as AWS Lambda, Dynamodb, Redshift, AWS ECS and many more.

You’ll work on real-world use cases across different domains covering everything from RAG and tool invocation to full multi-agent orchestration. The course follows a code-first, deployable approach using core AWS services.

What you’ll build and learn:

  • Use Bedrock APIs to query models like Claude, Titan, and Stable Diffusion

  • Implement Retrieval-Augmented Generation (RAG) using:

    • Amazon OpenSearch serverless for vector search

    • Amazon Redshift for structured grounding

  • Design real agentic applications that:

    • Invoke tools and different application logic via AWS Lambda

    • Integrate with DynamoDB and S3

    • Fetch or write data using custom logic

  • Build and deploy chatbots using Streamlit

  • Set up multi-agent collaboration scenarios using AWS Bedrock.

  • Trigger agents via REST APIs using API Gateway

  • Deploy chatbots on AWS ECS using containerized workflows

This course is not about theoretical lectures. It’s for people who want to ship AI systems to AWS cloud infrastructure , backed by hands-on examples that work end-to-end.

Who this course is for:

  • Technical professionals who want to move beyond basic prompt usage and build real-world AI systems using AWS Bedrock
  • Data Engineers, ML/Ops Engineers, and Cloud Developers looking to integrate LLMs like Claude, Titan, or Stable Diffusion into production workflows
  • Engineers interested in orchestrating AI agents, tool invocations, and workflows using Lambda, DynamoDB, Redshift, and OpenSearch
  • Developers who want to build RAG pipelines grounded in structured and unstructured enterprise data

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