Generative AI and LLMOps: Building Blocks and Applications

Course details

As large language models are becoming increasingly prevalent in various industries, professionals need to understand how to build, fine-tune, and deploy these models effectively and responsibly. In this course, learn the foundations of building, fine-tuning, and deploying LLMs in real-world applications. Instructors Soham Chatterjee and Archana Vaidheeswaran start with an introduction to LLMs and their evolution in the AI landscape. They then dive into LLM architectures, show you how to fine tune strategies for custom tasks, explain why and how to compress LLMs, and, finally, cover important aspects of prompt engineering. Through the course, they offer a series of challenges and solutions so you can practice the lessons as you go.

Concepts

Introduction

LLMs for industry
Industry-specific LLMs

Introduction to LLMs and Their Applications

Understanding LLMs and their evolution
Real-world applications of LLMs

Diving Into LLM Architectures

Overview of LLM architectures
How LLMs process and generate text
The building blocks of LLMs
Using a simple LLM
Challenge - LLM for sentiment analysis
Solution - LLM for sentiment analysis

Fine-Tuning Strategies for Custom Tasks

Introduction to fine-tuning for LLMs
Step-by-step guide to fine-tuning LLMs
Best practices for fine-tuning LLMs
Challenge - Fine-tune a pre-trained LLM
Solution - Fine-tune a pre-trained LLM

Compression Techniques for LLMs

Why compress LLMs
Introduction to quantization and pruning
Hands-on - Implementing compression in LLMs
Challenge - Quantize a LLM
Solution - Quantize a LLM

Prompt Engineering for Effective LLM Communication

What is prompt engineering
Best practices for effective prompt engineering
Types of prompt engineering
Challenge - Prompting LLMs to generate text
Solution - Prompting LLMs to generate text

Conclusion

Next steps