Towards better data discovery and collection with flow-based programming

TL;DR

This paper investigates how flow-based programming can improve data discovery and collection for machine learning deployment, highlighting its potential to enhance data-centric infrastructure compared to service-oriented paradigms.

Contribution

It introduces the application of flow-based programming to data management in ML deployment and compares its benefits with traditional service-oriented approaches.

Findings

FBP offers significant data-centric benefits for ML deployment.

Compared to service-oriented paradigms, FBP simplifies data discovery and collection.

The study highlights a trend prioritizing model development over data quality.

Abstract

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.