Have a personal or library account? Click to login
FAIRness Along the Machine Learning Lifecycle Using Dataverse in Combination with MLflow Cover

FAIRness Along the Machine Learning Lifecycle Using Dataverse in Combination with MLflow

Open Access
|Dec 2024

References

  1. Algorithmia (2020). 2020 state of enterprise machine learning. Report. Retrieved August 2023 from https://www.coriniumintelligence.com/2020-state-of-enterprise-machine-learning-algorithmia-whitepaper-download.
  2. Amershi, S. et al. (2019). ‘Software Engineering for Machine Learning: A Case Study’, in: IEEE/ACM 41st International Conference on Software Engineering: Software Engineering in Practice (ICSE-SEIP), pp. 291300. Available at: 10.1109/ICSE-SEIP.2019.00042
  3. Bortz, M. et al. (2023). ‘AI in Process Industries – Current Status and Future Prospects’, Chemie Ingenieur Technik, 95(7), pp. 975988. Available at: 10.1002/cite.202200247
  4. Dataverse (2023). Available at: https://dataverse.org/.
  5. Dataverse Manual (2023). Available at: https://guides.dataverse.org/en/latest/admin/index.html.
  6. Faubel, L., Schmid, K. and Eichelberger, H. (2022). ‘Is MLOps different in Industry 4.0? General and Specific Challenges’, in: Proceedings of the 3rd International Conference on Innovative Intelligent Industrial Production and Logistics – IN4PL1. INSTICC. SciTePress, pp. 161167. ISBN: 978–989-758-612–5. Available at: 10.5220/0011589600003329
  7. Fowler, M. and Beck, K. (1999). Refactoring improving the design of existing code. USA. Safari Tech Books Online
  8. Granlund, T., Stirbu, V. and Mikkonen, T. (2021). ‘Towards Regulatory-Compliant MLOps: Oravizio’s Journey from a Machine Learning Experiment to a Deployed Certified Medical Product’. Available at: 10.1007/s42979-021-00726-1
  9. ISO-22989 (2022). Information technology — Artificial intelligence — Artificial intelligence concepts and terminology. Standard. International Organization for Standardization.
  10. ISO-23053 (2022). Framework for Artificial Intelligence (AI) Systems Using Machine Learning (ML). Standard. International Organization for Standardization.
  11. ISO-23894 (2022). Information technology — Artificial intelligence — Guidance on risk management. Standard. International Organization for Standardization.
  12. ISO-24027 (2021). Information technology — Artificial intelligence (AI) — Bias in AI systems and AI aided decision making. Standard. International Organization for Standardization.
  13. ISO-24368 (2022). Information technology — Artificial intelligence — Overview of ethical and societal concerns. Standard. International Organization for Standardization.
  14. ISO-4213 (2022). Information technology — Artificial intelligence — Assessment of machine learning classification performance. Standard. International Organization for Standardization.
  15. Janardhanan, P.S. (2020). ‘Project repositories for machine learning with TensorFlow’, in: Procedia Computer Science, 171. Third International Conference on Computing and Network Communications (CoCoNet’19), pp. 188196. ISSN: 1877-0509. Available at: 10.1016/j.procs.2020.04.020
  16. Katz, D.S., Psomopoulos F.E. and Castro, L.J. (2021). ‘Working Towards Understanding the Role of FAIR for Machine Learning’, in: Workshop on Data and Research Objects Management for Linked Open Science. Available at: https://api.semanticscholar.org/CorpusID:242926042.
  17. KEEN Project (2024). Available at: https://keen-plattform.de/.
  18. Khattak, F. et al. (Mar. 2023). ‘MLHOps: Machine Learning for Healthcare Operations.’ Available at: 10.48550/arXiv.2305.02474
  19. Khaydarov, V., Becker, M.P. and Urbas, L. (2023). ‘Image-Based Flow Regime Recognition in Aerated Stirred Tanks Using Deep Transfer Learning’, in: Chemie Ingenieur Technik, cite.202200246. ISSN: 0009–286X, 1522–2640. Available at: 10.1002/cite.202200246 (visited on 05/15/2023).
  20. Kröger, C., Khaydarov, V. and Urbas, L. (2022). ‘Data-driven, Image-based Flow Regime Classification for Stirred Aerated Tanks’, in: Computer Aided Chemical Engineering. Vol. 51. Elsevier, pp. 13631368. ISBN: 978-0-323-95879-0. Available at: 10.1016/B978-0-323-95879-0.50228-9 (visited on 11/08/2022).
  21. Mcdougal, R. et al. (2017). ‘Twenty years of ModelDB and beyond: building essential modeling tools for the future of neuroscience’, Journal of Computational Neuroscience, 42. Available at: 10.1007/s10827-016-0623-7
  22. Mlflow (2023). Available at: https://mlflow.org/.
  23. Mlflow Documentation (2023). Available at: https://www.mlflow.org/docs/2.15.0/introduction/index.html.
  24. ModelDB (2023). Available at: https://modeldb.science/.
  25. Mozgova, I. et al. (2020). ‘Research Data Management System for a large Collaborative Project’, In: NordDesign. Available at: 10.35199/NORDDESIGN2020.48
  26. NNEF (2023). Available at: https://www.khronos.org/nnef.
  27. ONNX Standard (2023). Available at: https://onnx.ai/.
  28. Porter, C. (2005). ‘Developing a successful metadata schema’, Journal of Digital Asset Management. Available at: 10.1057/palgrave.dam.3640042
  29. Ravi, N. et al. (2022). ‘FAIR principles for AI models with a practical application for accelerated high energy diffraction microscopy’, Scientific Data, 9. Available at: 10.1038/s41597-022-01712-9
  30. Rijn, J. et al. (2013). ‘OpenML: A Collaborative Science Platform’, vol. 8190, pp. 645649. ISBN: 978-3-642-38708-1. Available at: 10.1007/978-3-642-40994-3_46
  31. Ruf, P. et al. (2021). ‘Demystifying MLOps and Presenting a Recipe for the Selection of Open-Source Tools’, Applied Sciences, 11(19). ISSN: 2076–3417. Available at: 10.3390/app11198861
  32. Schlegel, M. and Sattler, K. (2023). ‘Management of Machine Learning Lifecycle Artifacts: A Survey’, SIGMOD Rec. 51(4), pp. 1835. ISSN: 0163–5808. Available at: 10.1145/3582302.3582306
  33. Sculley, D. et al. (2015). ‘Hidden Technical Debt in Machine Learning Systems’, Advances in Neural Information Processing Systems. Ed. by C. Cortes et al. Vol. 28. Curran Associates, Inc. Available at: https://proceedings.neurips.cc/paper_files/paper/2015/file/86df7dcfd896fcaf2674f757a2463eba-Paper.pdf.
  34. Sherpa, L. et al. (2023). ‘ProMetaS – A Metadata Schema for Process Engineering and Industry’, Chemie Ingenieur Technik, 95(7), pp. 10411048. Available at: 10.1002/cite.202200225
  35. Shuja, J. et al. (2021). ‘Applying machine learning techniques for caching in next-generation edge networks: A comprehensive survey’, Journal of Network and Computer Applications, 181, p. 103005. ISSN: 1084–8045. Available at: 10.1016/j.jnca.2021.103005
  36. Spjuth, O., Frid, J. and Hellander, A. (2021). ‘The machine learning life cycle and the cloud: implications for drug discovery’, Expert Opinion on Drug Discovery, 16, pp. 19. Available at: 10.1080/17460441.2021.1932812
  37. Tyson, G. et al. (1995). ‘A modified approach to data cache management’, In: Proceedings of the 28th Annual International Symposium on Microarchitecture, pp. 93103. Available at: 10.1109/MICRO.1995.476816
  38. Wang, W.M., Göpfert, T. and Stark, R. (2016). ‘Data Management in Collaborative Interdisciplinary Research Projects—Conclusions from the Digitalization of Research in Sustainable Manufacturing’, ISPRS International Journal of Geo-Information, 5(4). ISSN: 2220–9964. Available at: 10.3390/ijgi5040041
  39. Wilkinson, M.D. et al. (2016). ‘The FAIR Guiding Principles for scientific data management and stewardship’, Scientific Data, 3(1), p. 160018. ISSN: 2052–4463. Available at: 10.1038/sdata.2016.18
  40. Zhao, S. et al. (2018). ‘Packaging and Sharing Machine Learning Models via the Acumos AI Open Platform’, In: CoRR abs/1810.07159. arXiv: 1810.07159. Available at: http://arxiv.org/abs/1810.07159.
Language: English
Submitted on: Mar 24, 2024
|
Accepted on: Nov 16, 2024
|
Published on: Dec 6, 2024
Published by: Ubiquity Press
In partnership with: Paradigm Publishing Services
Publication frequency: 1 issue per year

© 2024 Lincoln Sherpa, Valentin Khaydarov, Ralph Müller-Pfefferkorn, published by Ubiquity Press
This work is licensed under the Creative Commons Attribution 4.0 License.