Technical Debt Management in Industrial ML - State of Practice and Management Model Proposal

Abstract

With the increasing application of artificial intelligence (AI) and machine learning (ML), the topic of technical debt management for machine learning systems is gaining more attention. Additionally, industrial systems including manufacturing or logistics processes are also supposed to benefit from AI and ML, which is reported in many publications related to ML application models. However, fewer studies on “how is technical debt managed in context of ML systems” are being published. This contribution fills this gap by reporting findings from 15 semi-structured and in-depth interviews conducted with industrial practitioners. Based on the interview results, suggestions for an initial technical debt management process and two document artifacts that facilitate the process are addressed.