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An Empirical Study of Challenges in Machine Learning Asset Management

Zhimin Zhao, Yihao Chen, Abdul Ali Bangash, Bram Adams, Ahmed E. Hassan

TL;DR

This paper delivers an empirical, mixed-methods examination of ML asset management challenges by analyzing 15,065 developer posts across Stack Overflow, tool-specific forums, and repository discussions. Using BERTopic clustering and open card sorting, it identifies 133 challenge topics grouped into 16 macro-topics, with environment management, deployment, and model development as the most prominent areas. It also uncovers 79 solution topics clustered into 18 macro-topics, revealing that environment/dependency management and feature development are the dominant solution themes and detailing how challenges and solutions map within and across forums and tools. The findings highlight a need for cohesive MLOps practices, improved tool interoperability, and targeted education and tooling enhancements to reduce friction across the ML asset lifecycle.

Abstract

In machine learning (ML), efficient asset management, including ML models, datasets, algorithms, and tools, is vital for resource optimization, consistent performance, and a streamlined development lifecycle. This enables quicker iterations, adaptability, reduced development-to-deployment time, and reliable outputs. Despite existing research, a significant knowledge gap remains in operational challenges like model versioning, data traceability, and collaboration, which are crucial for the success of ML projects. Our study aims to address this gap by analyzing 15,065 posts from developer forums and platforms, employing a mixed-method approach to classify inquiries, extract challenges using BERTopic, and identify solutions through open card sorting and BERTopic clustering. We uncover 133 topics related to asset management challenges, grouped into 16 macro-topics, with software dependency, model deployment, and model training being the most discussed. We also find 79 solution topics, categorized under 18 macro-topics, highlighting software dependency, feature development, and file management as key solutions. This research underscores the need for further exploration of identified pain points and the importance of collaborative efforts across academia, industry, and the research community.

An Empirical Study of Challenges in Machine Learning Asset Management

TL;DR

This paper delivers an empirical, mixed-methods examination of ML asset management challenges by analyzing 15,065 developer posts across Stack Overflow, tool-specific forums, and repository discussions. Using BERTopic clustering and open card sorting, it identifies 133 challenge topics grouped into 16 macro-topics, with environment management, deployment, and model development as the most prominent areas. It also uncovers 79 solution topics clustered into 18 macro-topics, revealing that environment/dependency management and feature development are the dominant solution themes and detailing how challenges and solutions map within and across forums and tools. The findings highlight a need for cohesive MLOps practices, improved tool interoperability, and targeted education and tooling enhancements to reduce friction across the ML asset lifecycle.

Abstract

In machine learning (ML), efficient asset management, including ML models, datasets, algorithms, and tools, is vital for resource optimization, consistent performance, and a streamlined development lifecycle. This enables quicker iterations, adaptability, reduced development-to-deployment time, and reliable outputs. Despite existing research, a significant knowledge gap remains in operational challenges like model versioning, data traceability, and collaboration, which are crucial for the success of ML projects. Our study aims to address this gap by analyzing 15,065 posts from developer forums and platforms, employing a mixed-method approach to classify inquiries, extract challenges using BERTopic, and identify solutions through open card sorting and BERTopic clustering. We uncover 133 topics related to asset management challenges, grouped into 16 macro-topics, with software dependency, model deployment, and model training being the most discussed. We also find 79 solution topics, categorized under 18 macro-topics, highlighting software dependency, feature development, and file management as key solutions. This research underscores the need for further exploration of identified pain points and the importance of collaborative efforts across academia, industry, and the research community.
Paper Structure (59 sections, 11 figures, 13 tables)

This paper contains 59 sections, 11 figures, 13 tables.

Table of Contents

  1. Introduction
  2. Background and Related Work
  3. Machine learning development lifecycle
  4. Machine learning asset management
  5. Methodology
  6. Goal and Research Questions
  7. Study Design
  8. Phase 1: Data collection
  9. Phase 2: Inquiry categorization
  10. Phase 3: Solution categorization
  11. RQ1 Results: Prevalence of Challenge Topics
  12. Prevalence of posts based on type of inquiry
  13. Macro-topics of the posts
  14. Prevalence of posts based on macro-topics
  15. Mapping between macro-topics and tools
  16. ...and 44 more sections

Figures (11)

  • Figure 1: Machine learning development lifecyle and asset management.
  • Figure 2: Study workflow to analyze Q&A posts related to ML asset management from developer discussion forums.
  • Figure 3: A solved knowledge inquiry from Weights&Biases discussion forum.
  • Figure 4: A closed problem inquiry in GitHub Issues.
  • Figure 5: A histogram representing the prevalence of each macro-topic in the knowledge and problem inquiries.
  • ...and 6 more figures