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The need for systems to support machine learning inference has grown as the importance of machine learning in production systems has increased. Serving pipelines of machine learning models comes with challenges of scaling, low-latency requirements for requests, and high computation costs for different stages of the pipeline. In this paper we propose that by taking a dataflow abstraction we can simplify and increase performance of serving these machine learning pipelines. The proposed system FLOWSERVEcombines this dataflow paradigm with Cloudburst, a stateful function as a service (FaaS) system to provide a framework to deploy and serve machine learning pipelines at scale. We provide several logical and physical optimizations that make FLOWSERVEoutperform currently used research and industry systems.

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