"The difficulty is that machine learning is a fundamentally hard debugging problem. Debugging for machine learning happens in two cases: 1) your algorithm doesn't work or 2) your algorithm doesn't work well enough."
Read it!While implementing retraining on a set cadence is easier, dynamic retraining can prevent models from becoming outdated and optimize computational costs.
Read it!A compilation of several examples that illustrate the motivation behind open sourcing a machine learning model.
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