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We conduct an empirical study of machine learning functionalities provided bymajor cloud service providers, which we call machine learning clouds. Machinelearning clouds hold the promise of hiding all the sophistication of runninglarge-scale machine learning: Instead of specifying how to run a machinelearning task, users only specify what machine learning task to run and thecloud figures out the rest. Raising the level of abstraction, however, rarelycomes free - a performance penalty is possible. How good, then, are currentmachine learning clouds on real-world machine learning workloads?  We study this question with a focus on binary classication problems. Wepresent mlbench, a novel benchmark constructed by harvesting datasets fromKaggle competitions. We then compare the performance of the top winning codeavailable from Kaggle with that of running machine learning clouds from bothAzure and Amazon on mlbench. Our comparative study reveals the strength andweakness of existing machine learning clouds and points out potential futuredirections for improvement.

MLBench: How Good Are Machine Learning Clouds for Binary Classification Tasks on Structured Data?