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Dataset Engineering: Build the Data That Makes Models Great
The discipline behind every great AI system. Collection, cleaning, deduplication at scale, synthetic data, instruction tuning, preference data, evals, contamination defense. Full GitHub repo with production code.
16 chaptersFirst chapter free to preview
Full syllabus
2
Anatomy of a Dataset
3
Data Collection
4
Cleaning and Quality Filtering
5
Deduplication at Scale
6
Synthetic Generation Fundamentals
7
Domain Generation: SQL
8
Instruction-Tuning Datasets
9
Preference Data
10
LLM-as-Labeler
11
Quality Evaluation
12
Contamination and Leakage
13
Augmentation
14
Versioning and Lineage
15
Production Pipelines
16
Capstone: End-to-End Dataset
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