An Empirical Evaluation of Flow Based Programming in the Machine Learning Deployment Context
This addresses the problem of high failure rates in ML deployment projects for software engineers and data scientists, though it is incremental in applying an existing paradigm to a new context.
The paper tackled the challenge of deploying machine learning in large software systems by proposing Flow-Based Programming as a paradigm for Data Oriented Architecture, finding that it simplifies data collection and discovery compared to Service Oriented Architecture in empirical evaluations on four typical data science applications.
As use of data driven technologies spreads, software engineers are more often faced with the task of solving a business problem using data-driven methods such as machine learning (ML) algorithms. Deployment of ML within large software systems brings new challenges that are not addressed by standard engineering practices and as a result businesses observe high rate of ML deployment project failures. Data Oriented Architecture (DOA) is an emerging approach that can support data scientists and software developers when addressing such challenges. However, there is a lack of clarity about how DOA systems should be implemented in practice. This paper proposes to consider Flow-Based Programming (FBP) as a paradigm for creating DOA applications. We empirically evaluate FBP in the context of ML deployment on four applications that represent typical data science projects. We use Service Oriented Architecture (SOA) as a baseline for comparison. Evaluation is done with respect to different application domains, ML deployment stages, and code quality metrics. Results reveal that FBP is a suitable paradigm for data collection and data science tasks, and is able to simplify data collection and discovery when compared with SOA. We discuss the advantages of FBP as well as the gaps that need to be addressed to increase FBP adoption as a standard design paradigm for DOA.