Machine learning models — artificial intelligence (AI) that identifies relationships among hundreds, thousands, or even millions of data points — are rarely easy to architect. Data scientists spend weeks and months not only preprocessing the data on which the models are to be trained, but extracting useful features (i.e., the data types) from that data, narrowing down algorithms, and ultimately building (or attempting to build) a system that performs well not just within the confines of a lab, but in the real world.
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