An Exploratory Study on Machine Learning Model Management
Bibliographic record
Abstract
Effective model management is crucial for ensuring performance and reliability in Machine Learning (ML) systems, given the dynamic nature of data and operational environments. However, standard practices are lacking, often resulting in ad hoc approaches. To address this, our research provides a clear definition of ML model management activities, processes, and techniques. Analyzing 227 ML repositories, we propose a taxonomy of 16 model management activities and identify 12 unique challenges. We find that 57.9% of the identified activities belong to the maintenance category, with activities like refactoring (20.5%) and documentation (18.3%) dominating. Our findings also reveal significant challenges in documentation maintenance (15.3%) and bug management (14.9%), emphasizing the need for robust versioning tools and practices in the ML pipeline. Additionally, we conducted a survey that underscores a shift toward automation, particularly in data, model, and documentation versioning, as key to managing ML models effectively. Our contributions include a detailed taxonomy of model management activities, a mapping of challenges to these activities, practitioner-informed solutions for challenge mitigation, and a publicly available dataset of model management activities and challenges. This work aims to equip ML developers with knowledge and best practices essential for the robust management of ML models.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.023 | 0.130 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.009 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".