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Machine learning workflow development is anecdotally regarded to be aniterative process of trial-and-error with humans-in-the-loop. However, we arenot aware of quantitative evidence corroborating this popular belief. Aquantitative characterization of iteration can serve as a benchmark for machinelearning workflow development in practice, and can aid the development ofhuman-in-the-loop machine learning systems. To this end, we conduct asmall-scale survey of the applied machine learning literature from fivedistinct application domains. We collect and distill statistics on the role ofiteration within machine learning workflow development, and report preliminarytrends and insights from our investigation, as a starting point towards thisbenchmark. Based on our findings, we finally describe desiderata for effectiveand versatile human-in-the-loop machine learning systems that can cater tousers in diverse domains.

How Developers Iterate on Machine Learning Workflows -- A Survey of the Applied Machine Learning Literature