Master Large Language Models from basics to advanced deployment, including prompt engineering, fine-tuning, and RAG.
Overview
This comprehensive LLM Engineers Handbook equips developers and AI engineers with the knowledge and skills to master Large Language Models. Students will learn core concepts, advanced techniques like prompt engineering, fine-tuning (PEFT, LoRA, RLHF), and Retrieval Augmented Generation (RAG), as well as practical deployment strategies. The course uses a hands-on approach with Jupyter notebooks, covering topics from LLM architecture and tokenization to agents, responsible AI, and scalable deployment. It's ideal for those seeking to build and deploy real-world LLM applications.
Instructor
Maxime Labonne
Senior Data Scientist & LLM Expert
Maxime Labonne is a Senior Data Scientist, prolific open-source contributor, and author known for his practical expertise in Large Language Models and generative AI.
Learning Outcomes
Understand the core architecture and working principles of Large Language Models.
Apply effective prompt engineering techniques for various LLM applications.
Master fine-tuning methods like PEFT, LoRA, and RLHF to customize LLMs.
Implement Retrieval Augmented Generation (RAG) for factual and context-aware LLMs.
Develop and integrate AI agents using frameworks like LangChain with custom tools.
Deploy LLM applications using frameworks like Streamlit and FastAPI.
Evaluate and address ethical considerations and biases in LLM development.
Optimize LLM inference for efficiency and performance.