On Continuous Integration / Continuous Delivery for Automated Deployment of Machine Learning Models using MLOps-Reference-Cited by-同舟云学术

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On Continuous Integration / Continuous Delivery for Automated Deployment of Machine Learning Models using MLOps

Published:2021-12 Issue: Volume: Page:
ISSN:
Container-title:2021 IEEE Fourth International Conference on Artificial Intelligence and Knowledge Engineering (AIKE)
language:
Short-container-title:

Author:

Garg Satvik¹,Pundir Pradyumn¹,Rathee Geetanjali²,Gupta P.K.¹,Garg Somya³,Ahlawat Saransh³

Affiliation:

1. Jaypee University of Information Technology,Solan,India

2. Netaji Subhas University of Technology,New Delhi,India

3. Deloitte LLC,New York,NY,USA

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/9723599/9723658/09723793.pdf?arnumber=9723793

Reference23 articles.

1. Amazon sagemaker – machine learning – amazon web services,0

2. Introduction to kubeflow — kubeflow,0

3. Accelerating the machine learning lifecycle with mlflow;zaharia;IEEE Data Eng Bull,2018

4. Gitops — gitops is continuous deployment for cloud native applications,0

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3. "We Have No Idea How Models will Behave in Production until Production": How Engineers Operationalize Machine Learning;Proceedings of the ACM on Human-Computer Interaction;2024-04-17

4. MLOps critical success factors - A systematic literature review;VFAST Transactions on Software Engineering;2024-03-31

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