Build and Deploy a GenAI App with RAG on AWS Cloud

  • IntermediateLevel

  • 133+Students Enrolled

  • 1 HrDuration

  • 4.8Average Rating

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About this Course

  • Learn how to build and deploy a complete GenAI chatbot using Retrieval Augmented Generation on AWS cloud services including Bedrock, OpenSearch, and App Runner.
  • Understand the full RAG pipeline on AWS including document ingestion, embeddings generation, vector search, and intelligent response generation.
  • Gain hands-on experience building a GenAI application with Python, LlamaIndex agents, Gradio UI, and containerized deployment using Docker.
  • Deploy a production-ready RAG chatbot on AWS by integrating S3 storage, knowledge bases, IAM roles, and secure API configuration.

Course Benefits

  • Build a complete RAG chatbot on AWS using Bedrock, OpenSearch, Gradio, and Docker in one end-to-end deployment workflow.
  • Learn how to move from local experimentation to cloud deployment by packaging, hosting, and testing a GenAI app on AWS services.
  • Gain hands-on experience with vector search, document ingestion, and retrieval pipelines used in real-world GenAI applications.
  • Understand how to securely deploy AI applications using IAM roles, Parameter Store, App Runner, and scalable cloud architecture.

Learning Outcomes

Build RAG Apps

Build AWS RAG chatbots with retrieval, search, and LLMs.

Deploy GenAI Systems

Deploy Dockerized GenAI apps on AWS with App Runner.

Implement Vector Search

Use Bedrock and OpenSearch for scalable retrieval flows.

Who Should Enroll

  • Machine learning engineers who want to deploy RAG chatbots on AWS and build scalable GenAI applications.
  • Developers interested in building GenAI apps with RAG pipelines, vector databases, and cloud deployment workflows.
  • Data scientists who want to move from experimentation to deploying production-ready AI applications on AWS.
  • Professionals exploring AWS Bedrock and vector search technologies for building intelligent document analysis systems.

Course Curriculum

Learn how to build a complete RAG chatbot on AWS from scratch. The curriculum covers document ingestion, vector database creation, LLM integration, application development, Docker containerization, and full cloud deployment.

tools

Understand the architecture of a GenAI RAG application on AWS. Learn about the core services including S3 storage, Bedrock Knowledge Bases, vector databases, embeddings models, and the overall system design required to build scalable RAG systems.

  1. 1. Introduction, AWS Services & Architecture

Set up AWS services, configure credentials, and build a RAG pipeline using AWS Bedrock Knowledge Bases. Learn how documents are ingested, chunked, converted to embeddings, and stored in an OpenSearch vector database.

  1. 1. Account Setup: AWS, OpenAI Keys & .env

  2. 2. Building the RAG Pipeline: Knowledge Base & Vector DB

Explore the full application code including frontend and backend components. Understand how the Gradio UI interacts with the backend agent, how LlamaIndex integrates with Bedrock, and how queries retrieve relevant document chunks.

  1. 1. Project Structure, Local Setup & Live Demo

  2. 2. Frontend Deep Dive: app.py & Gradio UI

  3. 3. Backend Deep Dive: agent.py & ReAct Agent

Learn how to containerize the GenAI application using Docker and deploy it on AWS using ECR and App Runner. Configure IAM roles, environment variables, and monitoring to run the chatbot securely in the cloud.

  1. 1. Containerization: Dockerfile, ECR & Parameter Store

  2. 2. Deploying on App Runner: Config, IAM Role & Launch

  3. 3. Live Cloud Demo, Verification & Resource Cleanup

Meet the instructor

Our instructor and mentors carry years of experience in data industry

company logo
Kartik Nighania

MLOps Engineer, Typewise

Kartik Nighania, MLOps Engineer at Typewise, has 7+ years of AI experience in CV, NLP & DevOps. Former Head of Engineering at Pibit.ai, he led AI automation & scaling. He honed CI/CD expertise at HSBC & worked on ML-driven crop health detection

Get this Course Now

With this course you’ll get

  • 1 Hour

    Duration

  • Kartik Nighania

    Instructor

  • Intermediate

    Level

Certificate of completion

Earn a professional certificate upon course completion

  • Industry-Recognized Credential
  • Career Advancement Credential
  • Shareable Achievement
certificate

Frequently Asked Questions

Looking for answers to other questions?

A RAG chatbot combines retrieval systems with large language models to generate more accurate responses. Instead of relying only on the model's training data, it retrieves relevant documents from a vector database and uses them to generate contextual answers.

You will build a complete GenAI application that performs document analysis using a RAG pipeline. The application allows users to upload documents, ask questions, retrieve relevant information from them, and generate intelligent answers using an LLM.

The course uses several AWS services including S3 for document storage, AWS Bedrock Knowledge Bases for RAG pipelines, OpenSearch as a vector database, ECR for container registry, and App Runner for hosting the application.

Basic familiarity with cloud computing is helpful but not mandatory. The course walks through the setup process step-by-step including account setup, credentials configuration, and deploying applications on AWS.

The application is built primarily using Python. Libraries such as LlamaIndex are used for agent orchestration, while Gradio is used to build the user interface. Docker is used for packaging the application before deployment.

AWS Bedrock Knowledge Bases simplify the RAG workflow by automatically handling document parsing, chunking, embedding generation, and vector storage. This allows developers to focus on building applications rather than managing complex infrastructure.

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