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. Imagine one of Hans Rosling’s talks without him in the picture.

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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. Reproducible research only means that everyone be able to reproduce benchmarks and charts from the original paper. It usually has very little to do with production.

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