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Toward an Open Source MLOps Architecture

delete2025-01-01
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PRE
AI
A
A. Burgueño *
A
Antonio Benítez‐Hidalgo
C
Cristóbal Barba-González
J
José F. Aldana‐Montes
DOI:10.1109/MS.2024.3421675delete
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Abstract

Abstract

En 中文
We present a Kubernetes-based, open source MLOps framework to streamline the lifecycle management of machine learning models in production environments. We compared state-of-the-art MLOps tools and frameworks, demonstrating that ours meets the same features as proprietary options, such as Amazon SageMaker.
Keywords:
Pipelines
Computer architecture
Monitoring
Production
Testing
Python
Predictive models

Journal

IEEE Software cover
IEEE Software
IF:
3
Papers:
3.1K
Citations:
3.6K

Organization

U
universidad de malaga
Scholars:
1.2W
Papers: 9.2K
Citations: 6