Data Engineering for Large Models: Architecture, Algorithms & Projects

Purpose

Comprehensive technical resource addressing the scarcity of systematic LLM data engineering knowledge. Covers the complete pipeline from pre-training data cleaning through multimodal alignment, RAG systems, synthetic data generation, and enterprise DataOps platforms.

Key Quote

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

Core Coverage Areas

Pre-training & Text Processing: - Data extraction from massive sources like Common Crawl - Cleaning, deduplication, and decontamination techniques - Tokenization and efficient loading mechanisms

Multimodal Engineering: - Image-text pair collection and alignment - Video and audio data processing - Cross-modal fusion strategies

Fine-tuning & Alignment: - Supervised fine-tuning (SFT) instruction design - Preference data and reward signal construction - Chain-of-thought and reasoning data engineering - Tool-use and agent interaction data

Synthetic Data: - Factory approaches from seed generation to validation - Knowledge distillation methods - Quality control against model collapse

Application Layer: - RAG pipeline architecture - Multimodal retrieval systems - Online feedback loops

Enterprise Systems: - DataOps flywheel and team organization - Data versioning and experiment tracking - Privacy-preserving techniques and compliance frameworks

Structure

28 chapters across 10 thematic parts, 10 capstone projects with runnable code. Modern tech stack (Ray, Spark, DVC, Airflow, Milvus, Federated Learning). Supports Chinese, English, and Japanese.