  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.
