Large Language Models (LLM Foundation)
Overview
Bring the power of Large Language Models (LLMs) to life in LLM Foundation, a 2-day course designed for data professionals. This introductory course covers topics such as LLM solutions architecture, families of state-of-the-art LLMs, as well as utilization of LLM APIs and self-hosted LLMs. Gain hands-on experience with LLM tools such as LangChain, LlamaIndex and vLLM. Prerequisites include intermediate programming skills, mathematics foundation, basic familiarity with machine learning, and a willingness to review pre-course materials. This course will catapult you from understanding the foundational concepts to mastering the art of building solutions using these behemoths of the NLP world.
Duration
2 Days
Who Should Take This Course
Audience
Data Scientists, Data Engineers, Machine Learning Engineers, MLOps professionals.
Prerequisites
Participants should have intermediate programming skills (preferably in Python), a strong understanding of mathematics and statistics, basic familiarity with machine learning / neural networks, a grasp of computer science fundamentals, familiarity with common operating systems and basic command-line operations, and a willingness to review pre-course materials.
These prerequisites will help participants engage effectively with the course material and hands-on labs, making the learning experience more rewarding. Attendees will also need to be able to ssh into a supplied cloud instance during the course to complete the lab work.
Why You Should Take This Course
Upon completing this course, participants will be able to:
- Understand the fundamental concepts behind Large Language Models (LLMs), from training to deployment
- Acquire hands-on experience deploying and evaluating LLMs
- Gain insight into the current state-of-the-art in language modeling
- Build the skills and understanding to develop LLM solutions
Course Outline
Large Language Models (LLM Foundation)
Day 1: Understanding LLMs
- 1: LLM Overview
- 2: Families of LLMs
- 3: Datasets and Benchmarks
- 4: LLM Tools
Day 2: Working with LLMs
- 5: Applying LLMs
- 6: Using LLM API Providers
- 7: Self Hosting LLMs
- 8: Testing and Debugging LLM Processes