Most discussions about AI model training focus on architecture choices, compute budgets, and evaluation benchmarks. The data pipeline that feeds those models? It gets a paragraph, maybe two. Maybe a diagram with an arrow labeled “data ingestion.”
That gap is a real problem. In practice, data engineering is where most AI projects quietly fall apart. Not at the model level. Not at inference. At the pipeline.