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Core concepts behind AI training data: what counts, what doesn't, and the vocabulary every ML team should share.
How much training data you need depends on your task, model type, and data quality.
Learn what synthetic training data is, how it's generated from scratch or from existing data, and when it can replace real data for training AI models.
What makes a training dataset good — and how to measure and improve label quality, coverage, and bias for better model performance.
Learn what AI training data is, the types models need, and how teams source it — from real-world data or synthetic generation.