FAIRness Along the Machine Learning Lifecycle Using Dataverse in Combination with MLflow
Typical Machine Learning (ML) approaches are characterized by their iterative and exploratory nature: continuously refining and adapting not only code but also ML models to optimize the results and the performance on new data. This poses novel challenges related to keeping the trained model Findable...
Saved in:
| Main Authors: | Lincoln Sherpa, Valentin Khaydarov, Ralph Müller-Pfefferkorn |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Ubiquity Press
2024-12-01
|
| Series: | Data Science Journal |
| Subjects: | |
| Online Access: | https://account.datascience.codata.org/index.php/up-j-dsj/article/view/1731 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
Correction: How FAIR Is Bioarchaeological Data: With a Particular Emphasis on Making Archaeological Science Data Reusable
by: Alphaeus Lien-Talks
Published: (2024-12-01) -
Learning Materials as FAIR Digital Objects: A Shared Methodology for Training in H2IOSC
by: Giulia Pedonese, et al.
Published: (2025-07-01) -
Why isn’t FAIR enough? Bringing together methods and values for Open Science uptake
by: Francesca Di Donato, et al.
Published: (2025-05-01) -
The FAIR data point populator: collaborative FAIRification and population of FAIR data points
by: Daphne Wijnbergen, et al.
Published: (2025-06-01) -
The role of FAIR principles in high-quality research data documentation: Looking at national election studies
by: Wolfgang Zenk-Möltgen
Published: (2025-06-01)