As modeling projects grow, so grow the costs of debugging, scaling, and modifying the model pipeline. One method to minimize the costs of model maintenance is to train models in reproducible iterations. In the context of machine learning, we define a reproducible model iteration as the output of an executable script that is a pure function of three variables: code, environment, and data. Reproducible models are not an end, but a means to faster, more correct iterations. A reproducible model history implies that developers can confidently reconstruct any past model iteration. As a result, reproducibility makes it easier for developers to experiment with modifications, isolate bugs, and revert to known good iterations when problems arise.
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