AI Systems · core

Training Data Engineering

Cleaning, dedup, JSONL tasks, memorization tests.

ai-systemstraining

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.

How to study Training Data Engineering

Begin by restating the mental model in your own words, then connect it to a concrete system you have built or operated. Name the mechanism, the constraint it addresses, and the trade-off it introduces. Use Stanford CS336 — Language Modeling from Scratch (course), CS336 Assignment 4 — Common Crawl → pretraining data, Rules of Machine Learning: Best Practices for ML Engineering (Google) to check details, but close the source before writing your explanation. Retrieval is the learning step; rereading is only preparation.

Next, compare Training Data Engineering with Model Evaluation. Ask what changes in correctness, latency, resource use, operability, and failure recovery. Complete Estimate training token budget and preserve the command, input, output, and one failed attempt as evidence. Finish by explaining the idea without jargon to someone who has not studied the track.

Proof of understanding

Explain the mechanism from first principles and identify the state it reads or changes.
Give one situation where the concept is the right choice and one where it is not.
Predict a realistic failure mode before running the drill, then compare the prediction with evidence.
Connect the result to a roadmap or build artifact instead of treating the concept as isolated trivia.

Learn from primary sources

Practice and explain it back

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.

Open the interactive drill →

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.

Implements or precisely models the core mechanisms from all three milestones
Includes at least one injected failure or adversarial case and demonstrates recovery
Reports quality, latency, resource, reliability, or usability measurements relevant to the domain
Ships a concise architecture note explaining decisions, trade-offs, and remaining risks

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.

Implements or precisely models the core mechanisms from all three milestones
Includes at least one injected failure or adversarial case and demonstrates recovery
Reports quality, latency, resource, reliability, or usability measurements relevant to the domain
Ships a concise architecture note explaining decisions, trade-offs, and remaining risks

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.

Implements or precisely models the core mechanisms from all three milestones
Includes at least one injected failure or adversarial case and demonstrates recovery
Reports quality, latency, resource, reliability, or usability measurements relevant to the domain
Ships a concise architecture note explaining decisions, trade-offs, and remaining risks