Distributed data processing systems have been widely adopted to robustly scale out computations on massive data sets to many compute nodes in recent years. These systems are also popular choices to scale out the training of machine learning models. However, there is a lack of benchmarks to assess how efficiently data processing systems actually perform at executing machine learning algorithms at scale. For example, the learning algorithms chosen in the corresponding systems papers tend to be those that fit well onto the system's paradigm rather than state of the art methods. Furthermore, experiments in those papers often neglect important aspects such as addressing all aspects of scalability. In this paper, we share our experience in evaluating novel data processing systems and present a core set of experiments of a benchmark for distributed data processing systems for machine learning workloads, a rationale for their necessity as well as an experimental evaluation.
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