From Science to Implementation

During my summer internship, I closely observed how scientific models are combined with real-time data to generate tailored information for each farm to guide daily agricultural operations. I realize one big difference between the data science in industry settings and the work that we do in academics, is how this company’s science does not stop at the point when the model was proved to be working. Instead, the model is continuously being validated, updated, and improved for better performance in daily operations when new data or tools are coming in.

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