Generative AI and LLMOps: Building Blocks and Applications

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Soham Chatterjee and Archana Vaidheeswaran

1:21:23

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01 - Introduction

01 - LLMs for industry.mp4

00:52

02 - Industry-specific LLMs.mp4

02:59

02 - 1. Introduction to LLMs and Their Applications

01 - Understanding LLMs and their evolution.mp4

04:28

02 - Real-world applications of LLMs.mp4

02:37

03 - 2. Diving Into LLM Architectures

01 - Overview of LLM architectures.mp4

03:15

02 - How LLMs process and generate text.mp4

02:47

03 - The building blocks of LLMs.mp4

03:18

04 - Using a simple LLM.mp4

03:19

05 - Challenge LLM for sentiment analysis.mp4

01:43

06 - Solution LLM for sentiment analysis.mp4

01:27

04 - 3. Fine-Tuning Strategies for Custom Tasks

01 - Introduction to fine-tuning for LLMs.mp4

03:19

02 - Step-by-step guide to fine-tuning LLMs.mp4

03:57

03 - Best practices for fine-tuning LLMs.mp4

02:22

04 - Challenge Fine-tune a pre-trained LLM.mp4

02:07

05 - Solution Fine-tune a pre-trained LLM.mp4

02:44

05 - 4. Compression Techniques for LLMs

01 - Why compress LLMs.mp4

02:57

02 - Introduction to quantization and pruning.mp4

07:04

03 - Hands-on Implementing compression in LLMs.mp4

02:39

04 - Challenge Quantize a LLM.mp4

01:38

05 - Solution Quantize a LLM.mp4

01:49

06 - 5. Prompt Engineering for Effective LLM Communication

01 - What is prompt engineering.mp4

03:42

02 - Best practices for effective prompt engineering.mp4

06:12

03 - Types of prompt engineering.mp4

06:45

04 - Challenge Prompting LLMs to generate text.mp4

01:31

05 - Solution Prompting LLMs to generate text.mp4

05:08

07 - Conclusion

01 - Next steps.mp4

00:44

Description

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.

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Machine Learning

Artificial Intelligence

Soham Chatterjee and Archana Vaidheeswaran

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Linkedin Learning

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LinkedIn Learning is an American online learning provider. It provides video courses taught by industry experts in software, creative, and business skills. It is a subsidiary of LinkedIn. All the courses on LinkedIn fall into four categories: Business, Creative, Technology and Certifications. It was founded in 1995 by Lynda Weinman as Lynda.com before being acquired by LinkedIn in 2015. Microsoft acquired LinkedIn in December 2016.