Comparisons — AI Training Data Guide

Fundamentals Core concepts behind AI training data: what counts, what doesn't, and the vocabulary every ML team should share.

Methods How teams build AI training data in practice: the methods for generating, annotating, de-identifying, and augmenting datasets, and when to use each.

Comparisons Head-to-head evaluations of the platforms teams use to build, label, and manage AI training data.

Data Collection How to source training data — from web scraping and licensed corpora to data de-identification and synthetic generation.

Compliance Key data privacy regulations and how they apply to gathering, de-identifying, and generating data for AI model and agent training.

Agents and RL

Synthetic data vs. prompting an LLM to generate training data