How to Build a Data Science Pipeline

The more time we at Fishtown Analytics are spending on data science, the more interested I get in all of the non-algorithmic parts of the process. This just-released post summarizes it incredibly well:

Building and optimizing the predictor is easy. What is hard is finding the business problem and the KPI that it will improve, hunting and transforming the data into digestible instances, defining the steps of the workflow, putting it into production, and organizing the model maintenance and regular update.


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