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Creators/Authors contains: "Ahmed, Adnan"

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  1. ; ; (, IEEE)
    We present POPPER, a dataflow system for building Machine Learning (ML) workflows. A novel aspect of POPPER is its built-in support for in-flight error handling, which is crucial in developing effective ML workflows. POPPER provides a convenient API that allows users to create and execute complex workflows comprising traditional data processing operations (such as map, filter, and join) and user-defined error handlers. The latter enables inflight detection and correction of errors introduced by ML models in the workflows. Inside POPPER, we model the workflow as a reactive dataflow, a directed cyclic graph, to achieve efficient execution through pipeline parallelization. We demonstrate the in-flight error-handling capabilities of POPPER, for which we have built a graphical interface, allowing users to specify workflows, visualize and interact with its reactive dataflow, and delve into the internals of POPPER. 
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    Free, publicly-accessible full text available May 19, 2026
  2. ; ; (, Passive and Active Measurement Conference (PAM), 2020)
    Full Text Available