Data Engineering for Large Models

Data Engineering for Large Models: Architecture, Algorithms & Projects

Introduction

“Data is the new oil, but only if you know how to refine it.”

In the era of large models, data quality determines the upper bound of model performance. Yet systematic resources on LLM data engineering remain extremely scarce — most teams are still learning by trial and error.

This book is designed to fill that gap. We systematically cover the complete technical stack from pre-training data cleaning to multimodal alignment, from RAG retrieval augmentation to synthetic data generation, including:

• 🧹 Pre-training Data Engineering: Extracting high-quality corpora from massive noisy data sources like Common Crawl
• 🖼️ Multimodal Data Processing: Collection, cleaning, and alignment of image-text pairs, video, and audio data
• 🎯 Alignment Data Construction: Automated generation of SFT instruction data, RLHF preference data, and CoT reasoning data
• 🔍 RAG Data Pipeline: Enterprise-grade document parsing, semantic chunking, and multimodal retrieval

Beyond in-depth theoretical explanations, the book includes 5 end-to-end capstone projects with runnable code and detailed architecture designs for hands-on learning.

Read Online: https://datascale-ai.github.io/data_engineering_book/en/

Links

https://github.com/datascale-ai/data_engineering_book/blob/main/README_en.md