Private GPT on AWS: From Idea to Deployment

Build and deploy a secure, private AI instance using AWS infrastructure.

Note: (open to all college students and professionals )

Hosted by the AWS Cloud Club in collaboration with DevCatalyst at Matrusri Engineering College, this technical workshop provides a practical roadmap for building a Private GPT application. We will move through the essential steps of setting up a private AI environment, focusing on configuration and cloud deployment.
The session is designed to provide a direct understanding of Foundation Models within a cloud ecosystem. You will learn how to interface with models like Claude or Llama 3 using Python and AWS APIs, ensuring that your data remains secure and private within your own AWS environment.

## What You Will Learn

*** AWS Bedrock Configuration: **Setting up and managing access to high-performance Foundation Models
** *Backend Integration: **Using Python to establish secure communication with LLMs via AWS APIs
*** Prompt Engineering: **Implementing structured prompt patterns to ensure consistent and reliable model outputs
** *Cloud Deployment: **Finalizing the architecture to host a functional, private GPT-style application on the cloud

## Featured Speake

** Avinash Reddy Thipparthi **| AWS Community Builde
An expert in cloud architecture and AI implementation, Avinash brings deep industry insights into building scalable, secure cloud solutions
Connect on LinkedI

** Who Should Attend: Students and developers with a basic understanding of Python who want to learn the technical fundamentals of AI and Cloud Computing **through a straightforward, builder-focused approach