Tagged "machine-learning"

I’ve always been interested in how we narrate visual analytics. The hardest task in dataviz is not analysis or visualization, but figuring out what to say about it. I used to believe that a well designed chart does not need a narration. That may be valid, but over the years I’ve realized that it is the narrative that turns something that is merely pretty and insightful into something that is viral.

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ML researchers are from Mars and the ML engineers responsible for deploying models are from Venus. The two have vastly different motivations. The ML researcher’s job, given a dataset and some compute, is to find the lowest possible loss on a task. In this pursuit, no engineering cost is too high. No tech debt is too large. Worse still, if they get published, they must include their code in their paper.

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