Cool explanation of computational aspects of recommendations avoiding to "get lost in the weeds of linear algebra".
Read it!"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!A detailed analysis of the differences between MLOps and traditional DevOps.
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