LLM Deployment Course

A comprehensive course on deploying Large Language Models (LLMs) efficiently and cost-effectively.

Course Objectives

• Load and fine-tune pre-trained transformer models
• Apply optimization techniques: distillation, pruning, quantization
• Deploy models using FastAPI, Gradio, Docker, and AWS ECS
• Implement production best practices

Getting Started

Option 1: Google Colab (Recommended)

Open any notebook directly in Colab:

https://colab.research.google.com/github/[your-repo]/blob/main/[notebook-path]

Tip: An easy way to convert a Jupyter Notebook from GitHub to Google Colab is by changing https://github.com/... to https://githubtocolab.com/... in the URL.

Option 2: Local Setup

pip install -r requirements.txt
jupyter lab

Course Structure

Module	Topic	Notebooks
00	Course Intro	1
01	Foundations	2
02	Fine-Tuning	3
03	Optimization	5
04	Deployment	4
05	Capstone	1

Prerequisites

• Python 3.8+
• Basic understanding of machine learning
• Familiarity with PyTorch (helpful but not required)

Key Dependencies

• transformers - Hugging Face Transformers
• torch - PyTorch
• datasets - Hugging Face Datasets
• gradio - Web UI framework
• fastapi - REST API framework