"Large Model Data Engineering: Architecture, Algorithms and Practical Projects" - A Comprehensive Guide to LLM Data Engineering
By
xx123122
Master baker tier. Every paragraph earns its place on the tray.
Summary
This is a data engineering book focused on large language model (LLM) data engineering, covering architecture, algorithms, and practical projects. The book addresses the scarcity of systematic resources in LLM data engineering and provides a comprehensive technical system from pre-training data cleaning to multimodal alignment, RAG retrieval enhancement, and synthetic data generation. It includes 5 end-to-end practical projects with runnable code and detailed architecture designs. The book covers modern technical stacks including distributed computing (Ray Data, Spark, Dask), data storage (Parquet, WebDataset, vector databases), text processing tools, multimodal technologies, and data versioning with DVC.
Key quotes
· 4 pulledData is the new oil, but only if you know how to refine it.
在大模型时代，数据质量决定模型上限。然而，市面上关于 LLM 数据工程的系统性资料极为稀缺——大多数团队仍在'摸着石头过河'。
本书正是为解决这一痛点而生。我们系统性地梳理了从预训练数据清洗到多模态对齐、从 RAG 检索增强到合成数据生成的完整技术体系。
本书不仅有深入的理论讲解，更包含 5 个端到端实战项目，提供可运行的代码和详细的架构设计，让你能够即学即用。
