Justin Basilico interview
summary
Claims that combining high-quality data with solid intuition and algorithms produces results that solve previously intractable problems; building ML systems holistically speeds real-world impact by focusing on the application, not just models. Emphasizes modular pipelines and flexible experimentation infrastructure to avoid lab/production discrepancies, enabling faster iteration and clearer outcomes. Career advice stresses broad skills—statistics, coding, communication—and learning by doing: try many algorithms on real problems and develop domain intuition.
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