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A detailed guide on designing effective ML experiments, emphasizing starting with a clear research question, developing research taste, and scaling results. Based on the author's experience running ~100 experiments weekly at Poolside.
A researcher expresses the desire for a simpler, more reproducible ML experiment tracking system than existing tools like Weights & Biases, advocating for one-command launching and plotting.