Management of machine learning lifecycle artifacts: A survey

M Schlegel, KU Sattler - ACM SIGMOD Record, 2023 - dl.acm.org
The explorative and iterative nature of develo** and operating ML applications leads to a
variety of artifacts, such as datasets, features, models, hyperparameters, metrics, software …

Data management for machine learning: A survey

C Chai, J Wang, Y Luo, Z Niu… - IEEE Transactions on …, 2022 - ieeexplore.ieee.org
Machine learning (ML) has widespread applications and has revolutionized many
industries, but suffers from several challenges. First, sufficient high-quality training data is …

Workflows community summit 2024: Future trends and challenges in scientific workflows

RF Da Silva, D Bard, K Chard, S de Witt… - ar** machine learning (ML)
models, ML experiment management systems (ML EMSs), such as MLflow, are increasingly …

Guiding practitioners of road freight transport to implement machine learning for operational planning tasks

S Lechtenberg, B Hellingrath - Transportation Research Procedia, 2025 - Elsevier
Road freight transport is the most used mode, and its importance is expected to increase.
Access to any location is the main reason for its proliferated use. However, the industry faces …