Toward Standardization and Automation of Data Science Projects: MLOps and Cloud Computing as Facilitators

Christian Haertel, Christian Daase, Daniel Staegemann, Abdulrahman Nahhas, Matthias Pohl, Klaus Turowski

2023

Abstract

The significant increase in the amount of generated data provides potential for organizations to improve performance. Accordingly, Data Science (DS), which encompasses the methods to extract knowledge from data, has increased in popularity. Nevertheless, enterprises often fail to reap the benefits from data as they suffer from high failure rates in the conducted DS projects. Literature suggests that the main reason for the lack of success is shortcomings in the current pool of DS project management methodologies. Hence, new procedures for DS are required. Consequently, in this paper, the outline for a model for DS project standardization and automation is discussed. Following a summary of DS project challenges and success factors, the concept, which will incorporate MLOps and cloud technologies, and its individual components to address these issues are described on a high level. Therefore, the foundation for further research endeavors in this area is presented.

Paper Citation

in Harvard Style

Haertel C., Daase C., Staegemann D., Nahhas A., Pohl M. and Turowski K. (2023). Toward Standardization and Automation of Data Science Projects: MLOps and Cloud Computing as Facilitators. In Proceedings of the 15th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management - Volume 3: KMIS; ISBN 978-989-758-671-2, SciTePress, pages 294-302. DOI: 10.5220/0012235100003598

in Bibtex Style

@conference{kmis23,
author={Christian Haertel and Christian Daase and Daniel Staegemann and Abdulrahman Nahhas and Matthias Pohl and Klaus Turowski},
title={Toward Standardization and Automation of Data Science Projects: MLOps and Cloud Computing as Facilitators},
booktitle={Proceedings of the 15th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management - Volume 3: KMIS},
year={2023},
pages={294-302},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012235100003598},
isbn={978-989-758-671-2},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 15th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management - Volume 3: KMIS
TI - Toward Standardization and Automation of Data Science Projects: MLOps and Cloud Computing as Facilitators
SN - 978-989-758-671-2
AU - Haertel C.
AU - Daase C.
AU - Staegemann D.
AU - Nahhas A.
AU - Pohl M.
AU - Turowski K.
PY - 2023
SP - 294
EP - 302
DO - 10.5220/0012235100003598
PB - SciTePress