arXiv:2201.00162
MLOps -- Definitions, Tools and Challenges
원문 초록
This paper is an overview of the Machine Learning Operations (MLOps) area. Our aim is to define the operation and the components of such systems by highlighting the current problems and trends. In this context, we present the different tools and their usefulness in order to provide the corresponding guidelines. Moreover, the connection between MLOps and AutoML (Automated Machine Learning) is identified and how this combination could work is proposed.
함께 살펴볼 논문
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Neuraxle - A Python Framework for Neat Machine Learning Pipelines
ATM: A distributed, collaborative, scalable system for automated machine learning
Overtuning in Hyperparameter Optimization
From Tiny Machine Learning to Tiny Deep Learning: A Survey
VirnyFlow: A Design Space for Responsible Model Development
OptiMindTune: A Multi-Agent Framework for Intelligent Hyperparameter Optimization
토론
이 논문과 연결된 포럼 글이 아직 없습니다.