@article{Cardoso2025IC,	title        = {{ Reliable Cloud Operations Using Transformers }},	author       = {Cardoso, Jorge},	year         = 2025,	month        = may,	journal      = {IEEE Internet Computing},	publisher    = {IEEE Computer Society},	address      = {Los Alamitos, CA, USA},	volume       = 29,	number       = {03},	pages        = {5--12},	doi          = {10.1109/MIC.2025.3575450},	issn         = {1941-0131},	url          = {https://doi.ieeecomputersociety.org/10.1109/MIC.2025.3575450},	abstract     = {Managing large-scale public clouds presents significant challenges, particularly in analyzing the risk of commands executed by operators to guarantee that they do not cause damages to the infrastructure. Traditional rule-based systems have been used but fall short in scalability when managing operations at a global scale. Although machine learning techniques have been proposed as alternatives, they have typically been applied in small-scale environments. This article presents a novel approach to address the complexities of global-scale command risk analysis and standard operating procedure verification using large language models. Through an extensive evaluation in production, the technique shows more suitability than existing methods.},	keywords     = {Cloud computing;Transformers;Encoding;Training;Servers;Bidirectional control;Internet;Standards;Computational modeling;Structured Query Language;Large scale integration;Risk management},	webpdf       = {/Papers/JA-2025-022-IEEE_IC_Reliable_Cloud_Operations_Using_Transformers.pdf}}