The Toy Dataset Training Gap
AI education focuses on clean, pre-existing datasets. AI practice requires creating messy data pipelines from scratch.
Courses teach with UCI Census, Kaggle datasets, and other curated benchmarks where the hard decisions, what to collect, how to label, what counts as ground truth, have already been made. Students learn to build models, not to create the data those models need.
In practice, especially in high-stakes domains, practitioners must define ground truth in subjective situations, design data collection protocols, work with domain experts, handle live data drift, and document decisions for others. None of this appears in the curriculum.
The gap leaves practitioners under-prepared for the most consequential decisions they’ll make: the data decisions that determine whether their systems work at all.
Related: 04-atom—data-cascades-definition, 05-atom—model-valorization