MLOps -- Definitions, Tools and Challenges

G. Symeonidis; E. Nerantzis; A. Kazakis; G. A. Papakostas

MLOps -- Definitions, Tools and Challenges

Machine Learning 2022-01-04 v1 Software Engineering

Authors: G. Symeonidis , E. Nerantzis , A. Kazakis , G. A. Papakostas

Abstract

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.

Keywords

unified modeling language information modeling machine learning

Cite

@article{arxiv.2201.00162,
  title  = {MLOps -- Definitions, Tools and Challenges},
  author = {G. Symeonidis and E. Nerantzis and A. Kazakis and G. A. Papakostas},
  journal= {arXiv preprint arXiv:2201.00162},
  year   = {2022}
}

Comments

8 pages, figure 5, table 4

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