DBTB INT Jeremey Corbett r
summary
Built a production pipeline that ingests and processes a highly structured corpus and trains multiple specialized ML algorithms to solve real tasks, with emphasis on treating machine learning as software: use profiling, avoid premature optimization and don’t overfit. Focus is on engineering practices that let models reach production and produce measurable results, plus combining theoretical knowledge with business sense and communication to turn experiments into deployed systems.
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