What synthetic data is, and where it actually helps ML teams
A practical guide to how synthetic data is generated across data types, where it earns its place in an ML workflow, and where it doesn't.
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Notes on training data.
Practical writing on synthetic data, evaluation, and building better datasets for AI and ML.
How to evaluate synthetic data before you train on it
Fidelity, utility, and privacy checks that tell you whether a synthetic dataset is fit for your task, before it reaches a training run.
Read articleTraining for the long tail: synthetic data for rare events
Reweighting, SMOTE, conditional generation, and simulation: how to give models enough examples of the events that matter most.
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Put these ideas to work on your data.
If you're weighing synthetic data for a model you're building, we're happy to talk through the approach.