Analysis
This new open-source guide provides a comprehensive resource for data engineers working with Large Language Models, covering everything from data cleaning to Retrieval-Augmented Generation (RAG). With practical, hands-on projects, this guide is sure to accelerate your LLM development skills. The GitHub repository is a fantastic resource for anyone looking to improve their data engineering chops!
Key Takeaways
- •The guide offers a complete data engineering stack for LLMs, including multi-modal data.
- •It includes 5 end-to-end capstone projects with executable code in Jupyter Notebook format.
- •All resources, including code and data pipelines, are available on GitHub as Open Source.
Reference / Citation
View Original"The book systematically covers the complete technical stack of data engineering, from pre-training data cleaning to multimodal alignment, RAG retrieval augmentation, and synthetic data generation."