Towards better data discovery and collection with flow-based programming
This addresses infrastructure challenges for businesses deploying ML, but it is incremental as it compares FBP with existing service-oriented paradigms.
The paper tackles the high failure rate of ML deployment in production by exploring flow-based programming (FBP) for data discovery and collection, concluding that FBP shows great potential for providing data-centric infrastructural benefits.
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.