  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.
