This paper is an concentrated 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. The novelty of our approach relies on the combination of state-of-the-art topics such as AutoML, exlainability and sustain-ability in order to overcome the current challenges in MLOps identifying them not only as the answer for the incorporation of ML models in production but also as a possible tool for efficient, robust and accurate machine learning models.
%0 Conference Paper
%1 9720902
%A Symeonidis, Georgios
%A Nerantzis, Evangelos
%A Kazakis, Apostolos
%A Papakostas, George A.
%B 2022 IEEE 12th Annual Computing and Communication Workshop and Conference (CCWC)
%D 2022
%K definition seminar
%P 0453-0460
%R 10.1109/CCWC54503.2022.9720902
%T MLOps - Definitions, Tools and Challenges
%X This paper is an concentrated 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. The novelty of our approach relies on the combination of state-of-the-art topics such as AutoML, exlainability and sustain-ability in order to overcome the current challenges in MLOps identifying them not only as the answer for the incorporation of ML models in production but also as a possible tool for efficient, robust and accurate machine learning models.
@inproceedings{9720902,
abstract = {This paper is an concentrated 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. The novelty of our approach relies on the combination of state-of-the-art topics such as AutoML, exlainability and sustain-ability in order to overcome the current challenges in MLOps identifying them not only as the answer for the incorporation of ML models in production but also as a possible tool for efficient, robust and accurate machine learning models.},
added-at = {2023-05-05T17:14:25.000+0200},
author = {Symeonidis, Georgios and Nerantzis, Evangelos and Kazakis, Apostolos and Papakostas, George A.},
biburl = {https://puma.ub.uni-stuttgart.de/bibtex/27debaecf2ff53bd2f7bc870c8ca6d280/lucabennardo},
booktitle = {2022 IEEE 12th Annual Computing and Communication Workshop and Conference (CCWC)},
description = {MLOps - Definitions, Tools and Challenges | IEEE Conference Publication | IEEE Xplore},
doi = {10.1109/CCWC54503.2022.9720902},
interhash = {14d25c3c7d7b184e0be52557d739f19a},
intrahash = {7debaecf2ff53bd2f7bc870c8ca6d280},
keywords = {definition seminar},
month = {1},
pages = {0453-0460},
timestamp = {2023-05-05T17:14:25.000+0200},
title = {MLOps - Definitions, Tools and Challenges},
year = 2022
}
