Reflecting on a Year of Making Machine Learning Actually Useful
Read OriginalAn ML engineer shares lessons from a year at a startup, contrasting academic research with industry realities. The article argues that data quality and feature engineering are more crucial than model complexity for production success, exploring why most data science projects fail to deploy.
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