2023/01/09 by Marius Schlegel, Kai-Uwe Sattler · 3 citations
Business, Management and Accounting · Decision Sciences · #Big Data and Business Intelligence #Data Quality and Management #Scientific Computing and Data Management
paper · pdf · doi:10.1145/3582302.3582306
openalex publication_date 2023/01/09 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/31
The explorative and iterative nature of developing and operating ML applications leads to a variety of artifacts, such as datasets, features, models, hyperparameters, metrics, software, configurations, and logs. In order to enable comparability, reproducibility, and traceability of these artifacts across the ML lifecycle steps and iterations, systems and tools have been developed to support their collection, storage, and management. It is often not obvious what precise functional scope such systems offer so that the comparison and the estimation of synergy effects between candidates are quite challenging. In this paper, we aim to give an overview of systems and platforms which support the management of ML lifecycle artifacts. Based on a systematic literature review, we derive assessment criteria and apply them to a representative selection of more than 60 systems and platforms.