Model Provenance Management in MLOps Pipeline-Reference-Cited by-同舟云学术

Model Provenance Management in MLOps Pipeline

Published:2022-01-11 Issue: Volume: Page:
ISSN:
Container-title:2022 The 8th International Conference on Computing and Data Engineering
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Author:

Mei Songzhu¹,Liu Cong²,Wang Qinglin¹,Su Huayou¹

Affiliation:

1. Science and Technology on Parallel and Distributed Laboratory, National University of Defense Technology, China

2. Information Center, CMC Logistic Support Department, China

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3512850.3512861

Reference11 articles.

1. Sculley D. Holt G. Golovin D. Davydov E. Phillips T. Ebner D. Chaudhary V. Young M. Crespo J. & Dennison D. 2015. Hidden Technical Debt in Machine Learning Systems. NIPS. Sculley D. Holt G. Golovin D. Davydov E. Phillips T. Ebner D. Chaudhary V. Young M. Crespo J. & Dennison D. 2015. Hidden Technical Debt in Machine Learning Systems. NIPS.

2. “Everyone wants to do the model work, not the data work”: Data Cascades in High-Stakes AI

3. Building A Platform for Machine Learning Operations from Open Source Frameworks

4. Akshita Goyal. 2020. Machine Learning Operations. International Journal of Information Technology Insights & Transformations Vol 4 No 2 (2020) Akshita Goyal. 2020. Machine Learning Operations. International Journal of Information Technology Insights & Transformations Vol 4 No 2 (2020)

5. Paleyes A. Urma R. & Lawrence N.D. 2020. Challenges in Deploying Machine Learning: a Survey of Case Studies. ArXiv abs/2011.09926. Paleyes A. Urma R. & Lawrence N.D. 2020. Challenges in Deploying Machine Learning: a Survey of Case Studies. ArXiv abs/2011.09926.

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