# Training Data Engineering

> Cleaning, dedup, JSONL tasks, memorization tests.

Canonical: https://learn.significanthobbies.com/curriculum/concepts/ml-data-engineering

Cleaning, dedup, JSONL tasks, memorization tests.

- Difficulty: core
- Tracks: AI Systems

## Mental model

In ML, the dataset is the product. The biggest quality wins usually come from cleaning, deduping, and filtering the data — not from changing the model. Pipelines need versioning and provenance the same way code does.



## Primary sources

- [Stanford CS336 — Language Modeling from Scratch (course)](https://cs336.stanford.edu/spring2025/) (course)
- [CS336 Assignment 4 — Common Crawl → pretraining data](https://github.com/stanford-cs336/assignment4-data/tree/main) (doc)
- [Rules of Machine Learning: Best Practices for ML Engineering (Google)](https://developers.google.com/machine-learning/guides/rules-of-ml) (doc)
- [Made With ML — Data engineering](https://madewithml.com/courses/mlops/data-stack/) (doc)
- [Hidden Technical Debt in Machine Learning Systems (NeurIPS 2015)](https://proceedings.neurips.cc/paper/2015/hash/86df7dcfd896fcaf2674f757a2463eba-Abstract.html) (paper)

## Practice

### Estimate training token budget

Corpus: 1M docs × 800 tokens each. 3 epochs. Total tokens seen? If Chinchilla says ~20 tokens/param for 1B model, rough token budget?

**Expected evidence:** 2.4B tokens for corpus; 20B tokens suggested for 1B params.

## Review prompts

- Why does near-duplicate removal matter more than exact deduplication for a training corpus?

## Build evidence

- **Synthesize: AI Models & Training** — Move from transformer foundations through pre-training, fine-tuning, post-training, compression, and evaluation. Produce one working system, benchmark, or evidence-backed design that integrates the path.
- **Synthesize: Inference & Serving** — Build a production mental model for inference engines, memory, kernels, routing, hardware, and serving economics. Produce one working system, benchmark, or evidence-backed design that integrates the path.
- **Synthesize: Multimodal & Spatial Computing** — Connect vision, audio, generation, on-device intelligence, robotics, spatial interfaces, and HCI. Produce one working system, benchmark, or evidence-backed design that integrates the path.

## Prerequisites

- [LoRA & PEFT](https://learn.significanthobbies.com/curriculum/concepts/ml-lora)

## Related concepts

- [Model Evaluation](https://learn.significanthobbies.com/curriculum/concepts/ml-evaluation)

## Learning paths

- [12-Week AI Models & Training](https://learn.significanthobbies.com/curriculum/roadmaps/ai-models-training-12w)
- [12-Week Inference & Serving](https://learn.significanthobbies.com/curriculum/roadmaps/inference-serving-12w)
- [12-Week Multimodal & Spatial Computing](https://learn.significanthobbies.com/curriculum/roadmaps/multimodal-spatial-12w)