Socio-Technical Anti-Patterns in Building ML-Enabled Software: Insights from Leaders on the Forefront

2026-07-03

AI Summary

This study addresses the frequent failures or delays enterprises encounter when deploying machine learning models, which often stem from non-technical factors such as organizational and managerial challenges. Drawing on 66 hours of practitioner talks from the MLOps community, the research employs qualitative methods—specifically manual coding and thematic analysis—to systematically identify 17 socio-technical anti-patterns that impede the successful operationalization of ML systems. Moving beyond a purely technical lens, the work emphasizes team collaboration and organizational mechanisms, offering targeted recommendations spanning technology, processes, and structural arrangements. These insights are further validated through cross-referencing with existing literature, thereby providing a systematic and actionable framework to guide ML engineering practices in real-world settings.

Abstract

Although machine learning (ML)-enabled software systems seem to be a success story considering their rise in economic power, there are consistent reports from companies and practitioners struggling to bring ML models into production. Many papers have focused on specific, and purely technical aspects, such as testing and pipelines, but only few on socio-technical aspects. Driven by numerous anecdotes and reports from practitioners, our goal is to collect and analyze socio-technical challenges of productionizing ML models centered around and within teams. To this end, we conducted the largest qualitative empirical study in this area, involving the manual analysis of 66 hours of talks that have been recorded by the MLOps community. By analyzing talks from practitioners for practitioners of a community with over 11,000 members in their Slack workspace, we found 17 anti-patterns, often rooted in organizational or management problems. We further list recommendations to overcome these problems, ranging from technical solutions over guidelines to organizational restructuring. Finally, we contextu-alize our findings with previous research, confirming existing results, validating our own, and highlighting new insights.

Problem

• socio-technical challenges
• ML-enabled software
• productionizing ML models
• anti-patterns
• MLOps

Innovation

• socio-technical anti-patterns
• MLOps
• qualitative empirical study
• ML deployment
• organizational challenges