Machine Learning Systems: A Survey from a Data-Oriented Perspective

TL;DR

This survey examines how data-oriented architecture (DOA) is implicitly adopted in deploying ML systems, highlighting design decisions, practices, and benefits for handling big data, latency, and security challenges.

Contribution

It explicitly analyzes the implicit adoption of DOA in ML system deployment, providing insights and practical advice to improve implementation practices.

Findings

Most ML systems partially adopt DOA principles.

Adoption of DOA improves handling of big data and low latency requirements.

Practical guidelines are proposed for deploying ML-based systems.

Abstract

Engineers are deploying ML models as parts of real-world systems with the upsurge of AI technologies. Real-world environments challenge the deployment of such systems because these environments produce large amounts of heterogeneous data, and users require increasingly efficient responses. These requirements push prevalent software architectures to the limit when deploying ML-based systems. Data-oriented Architecture (DOA) is an emerging style that equips systems better for integrating ML models. Even though papers on deployed ML systems do not mention DOA, their authors made design decisions that implicitly follow DOA. Implicit decisions create a knowledge gap, limiting the practitioners' ability to implement ML-based systems. \hlb{This paper surveys why, how, and to what extent practitioners have adopted DOA to implement and deploy ML-based systems.} We overcome the knowledge gap by answering these questions and explicitly showing the design decisions and practices behind these systems. The survey follows a well-known systematic and semi-automated methodology for reviewing papers in software engineering. The majority of reviewed works partially adopt DOA. Such an adoption enables systems to address requirements such as Big Data management, low latency processing, resource management, security and privacy. Based on these findings, we formulate practical advice to facilitate the deployment of ML-based systems.