Characterizing and Detecting Mismatch in Machine-Learning-Enabled Systems

TL;DR

This paper investigates the types of mismatches that occur in the development and deployment of machine learning-enabled systems, highlighting the roles' differing priorities and proposing machine-readable descriptors to improve development processes.

Contribution

It identifies common ML mismatches across roles in ML-enabled system development and introduces machine-readable descriptors to address these issues.

Findings

Different roles prioritize mismatches differently.

Identified common mismatch categories in ML systems.

Proposed machine-readable descriptors for better development.

Abstract

Increasing availability of machine learning (ML) frameworks and tools, as well as their promise to improve solutions to data-driven decision problems, has resulted in popularity of using ML techniques in software systems. However, end-to-end development of ML-enabled systems, as well as their seamless deployment and operations, remain a challenge. One reason is that development and deployment of ML-enabled systems involves three distinct workflows, perspectives, and roles, which include data science, software engineering, and operations. These three distinct perspectives, when misaligned due to incorrect assumptions, cause ML mismatches which can result in failed systems. We conducted an interview and survey study where we collected and validated common types of mismatches that occur in end-to-end development of ML-enabled systems. Our analysis shows that how each role prioritizes the importance of relevant mismatches varies, potentially contributing to these mismatched assumptions. In addition, the mismatch categories we identified can be specified as machine readable descriptors contributing to improved ML-enabled system development. In this paper, we report our findings and their implications for improving end-to-end ML-enabled system development.