Using Open- and Closed-Source LLMs in Real World Applications
Published by Pearson
Effective best practices and industry case studies
- Deep dive into open-source LLMs like FLAN-T5 and GPT-J
- Set up development environments for open- and closed-source LLMs
- Learn from real use cases of LLM applications in different industries
In this course, you'll explore both open- and closed-source Large Language Models (LLMs) and learn best practices for working with them. Over the course of four interactive hours, we'll dive deep into the world of open-source LLMs like FLAN-T5 and GPT-J, as well as closed-source LLMs such as ChatGPT and Cohere. Additionally, you'll have the opportunity to discuss and analyze real-world LLM applications in various industries.
This course is the second in a three-part series by Sinan Ozdemir designed for machine learning engineers and software developers who want to expand their skill set and learn how to work with LLMs like ChatGPT and FLAN-T5. The series provides practical instruction on prompt engineering, language modeling, moving LLM prototypes to production, and fine-tuning GPT models. The three live courses in the series are:
- LLMs, GPT, and Prompt Engineering for Developers
- Using Open- and Closed-Source LLMs in Real World Applications
- LLMs from Prototypes to Production
The book Quick Start Guide to LLMs by Sinan Ozdemir is recommended as companion material for post-class reference.
What you’ll learn and how you can apply it
By the end of the live online course, you’ll understand:
- The differences between open- and closed-source LLMs
- How to set up and configure development environments for both open- and closed-source LLMs
- The best practices for working with open- and closed-source LLMs
- How LLMs are applied across various industries and use cases
And you’ll be able to:
- Select appropriate LLMs for your projects, whether open- or closed-source
- Implement open-source LLMs in your work
- Integrate closed-source LLMs such as ChatGPT and Cohere into your applications
- Analyze and adapt LLM use cases for different industries
This live event is for you because...
- You're eager to learn about both open- and closed-source LLMs and their applications
- You want to understand how to set up development environments for LLMs
- You seek to enhance your knowledge of best practices for working with LLMs
- You're interested in discussing LLM use cases across various industries
Prerequisites
- Attendees should have prior experience with machine learning and be proficient in Python programming.
- Familiarity with natural language processing concepts and techniques is helpful but not required.
- Attendees should have a willingness to engage in hands-on exercises and apply the concepts learned in the course to real-world applications.
Course Set-up
- Jupyter notebooks can be run alongside the instructor but students can follow along without coding with pre-run notebooks on the GitHub repository
- GitHub repository with the PDFs of slides, plus code and links in a Jupyter notebook: https://github.com/sinanuozdemir/pearson-gpt-training-engineer
Recommended Preparation
- Attend: LLMs, GPT and Prompt Engineering for Developers by Sinan Ozdemir
- Read: Quick Start Guide to Large Language Models: Strategies and Best Practices for using ChatGPT and Other LLMs by Sinan Ozdemir
- Watch: Introduction to Transformer Models for NLP by Sinan Ozdemir
Recommended Follow-up
- Watch: Quick Guide to ChatGPT, Embeddings, and Other Large Language Models (LLMs) by Sinan Ozdemir
- Attend: LLMs from Prototypes to Production while Optimizing for Real-World Applications by Sinan Ozdemir
- Attend: Deploying GPT and Large Language Models by Sinan Ozdemir
- Explore: Getting Started with Data, LLMs and ChatGPT by Sinan Ozdemir
Schedule
The time frames are only estimates and may vary according to how the class is progressing.
Session 1: Open-Source LLMs (60 minutes)
- Overview of open-source LLMs like FLAN-T5 and GPT-Neo
- Setting up a development environment for open-source LLMs
- Best practices for working with open-source LLMs
Q&A (10 mins)
Break (10 minutes)
Session 2: Closed-Source LLMs (60 minutes)
- Overview of closed-source LLMs like ChatGPT and GPT 3.5
- Setting up a development environment for closed-source LLMs
- Best practices for working with closed-source LLMs
Q&A (10 mins)
Break (10 minutes)
Session 3: Use Case Discussion (60 minutes)
- Examples of LLM applications in different industries
- Group discussion and analysis of specific use cases
Introduction to Take Home Practice (5 minutes)
- These exercises are designed to solidify knowledge from the current session and prepare for subsequent sessions, should you choose to take them.
Q&A (15 mins)
Your Instructor
Sinan Ozdemir
Sinan Ozdemir is the founder of Crucible, an AI factory platform that helps teams convert existing workflows into custom models. He is a Y Combinator alum, AI & LLM Advisor at Tola Capital, and the author of multiple books on data science and machine learning including Building Agentic AI, Quick Start Guide to LLMs, and Principles of Data Science. Sinan is a former lecturer of data science at Johns Hopkins University and the founder of Kylie.ai, an enterprise-grade conversational AI platform (acquired 2014). He holds a master's degree in pure mathematics from Johns Hopkins University and is based in San Francisco, California.