2021/08/09 by Andrei Paleyes, Paleyes, Andrei, Christian Cabrera +3 · 1 citation
Computer Science · #Data Stream Mining Techniques #FOS: Computer and information sciences #Machine Learning (cs.LG) #Software Engineering (cs.SE) #Software Engineering Research #Software System Performance and Reliability #cs.LG #cs.SE
paper · pdf · doi:10.48550/arxiv.2108.04105
Extended version. Short version is accepted to Data-Centric AI Workshop, NeurIPS 2021
openalex publication_date 2021/08/09 · arxiv created 2021/10/25 · arxiv updated 2021/10/27 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Despite huge successes reported by the field of machine learning, such as voice assistants or self-driving cars, businesses still observe very high failure rate when it comes to deployment of ML in production. We argue that part of the reason is infrastructure that was not designed for data-oriented activities. This paper explores the potential of flow-based programming (FBP) for simplifying data discovery and collection in software systems. We compare FBP with the currently prevalent service-oriented paradigm to assess characteristics of each paradigm in the context of ML deployment. We develop a data processing application, formulate a subsequent ML deployment task, and measure the impact of the task implementation within both programming paradigms. Our main conclusion is that FBP shows great potential for providing data-centric infrastructural benefits for deployment of ML. Additionally, we provide an insight into the current trend that prioritizes model development over data quality management.