CoCo Challenges in ML Engineering Teams: How to Collaboratively Build ML-Enabled Systems

Collaboration and Communication (CoCo) among team members while building ML-enabled systems are crucial for integrating diverse expertise. Building an ML-enabled system by practitioners from interdisciplinary fields led to the need for CoCo between experts. This collaborative environment fosters innovation and ensures that the system works as intended. Many papers have recently identified challenges and recommendations for building ML-enabled systems among different team members, but there is a lack of implementation of these recommendations to assess their real effectiveness. Therefore, we will address these problems by implementing our approaches and assisting teams in choosing the most effective techniques for their context.

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Publication Details

Published
2025-04-27
DOI
https://doi.org/10.1109/cain66642.2025.00036
Primary Topic
Distributed and Parallel Computing Systems
Type
article
Field-Weighted Citation Impact
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article

CoCo Challenges in ML Engineering Teams: How to Collaboratively Build ML-Enabled Systems

Aidin Azamnouri
Distributed and Parallel Computing Systems
article

CoCo Challenges in ML Engineering Teams: How to Collaboratively Build ML-Enabled Systems

Aidin Azamnouri
article en

Abstract

Collaboration and Communication (CoCo) among team members while building ML-enabled systems are crucial for integrating diverse expertise. Building an ML-enabled system by practitioners from interdisciplinary fields led to the need for CoCo between experts. This collaborative environment fosters innovation and ensures that the system works as intended. Many papers have recently identified challenges and recommendations for building ML-enabled systems among different team members, but there is a lack of implementation of these recommendations to assess their real effectiveness. Therefore, we will address these problems by implementing our approaches and assisting teams in choosing the most effective techniques for their context.

Technical University of Munich (DE)
Industry, innovation and infrastructure
Openalex Percentile: Top 86%
Distributed and Parallel Computing Systems
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