Abstract: Machine learning operations (MLOps) achieves faster model development, deliver higher machine learning models quality, and faster deployment cycle. Unfortunately, MLOps is still an uncertain concept with ambiguous research implications. Professionals and academics have focused only on creating machine learning models, rather than using sophisticated machine learning systems in practical situations. Furthermore, the monitoring system must have a comprehensive view over the system interactions. The need for a strong efficient monitoring system increases when it comes to use the multi container services. Therefore, this research provides a new proposed model called Multi Containers Monitoring (MCM) Model, based on multi container service and machine learning approaches which are bidirectional long short-term memory (BI-LSTM) and state-action-reward-state-action (SARSA). The proposed MCM model enables MLOps systems to be scaled and monitored efficiently. The proposed MCM model realizes and interprets the interactions between the containers. The proposed MCM model enhances the performance of the software release and increases the number of software deployments across different types of environments. Moreover, this research proposes four routines for each layer of the proposed MCM model that illustrates how each layer is going to be developed. This research also illustrates how the proposed MCM model achieves improvements ratio in software deployment cycles by using MLOps up to 24.55% and in build duration cycle up to 13%.

Zeinab Shoieb Elgamal, Laila Elfangary and Hanan Fahmy, “A Machine Learning Operations (MLOps) Monitoring Model Using BI-LSTM and SARSA Algorithms” International Journal of Advanced Computer Science and Applications(IJACSA), 15(10), 2024. http://dx.doi.org/10.14569/IJACSA.2024.0151060

@article{Elgamal2024,
title = {A Machine Learning Operations (MLOps) Monitoring Model Using BI-LSTM and SARSA Algorithms},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2024.0151060},
url = {http://dx.doi.org/10.14569/IJACSA.2024.0151060},
year = {2024},
publisher = {The Science and Information Organization},
volume = {15},
number = {10},
author = {Zeinab Shoieb Elgamal and Laila Elfangary and Hanan Fahmy}
}