| _version_ | 1866901871185100800 |
|---|---|
| author | Vamsi Krishna Gadireddy |
| author_facet | Vamsi Krishna Gadireddy |
| contents | <p><span>Network engineering has undergone a fundamental transformation with the integration of artificial intelligence technologies, shifting from traditional reactive management practices to proactive, data-driven network optimization paradigms. This technical review explores how modern network engineering professionals must master an intricate blend of theoretical knowledge and practical implementation skills across networking protocols, hardware architectures, and software systems while simultaneously developing competencies in machine learning algorithms and automated decision-making systems. The transformation involves multiple organizational scenarios, including enterprise networks focused on reliability and security compliance, service provider scenarios that deal with a level of routing complexity at scale, and cloud environments that require virtualization and elastic scalability. AI-driven transformation will allow network engineers to analyze large quantities of telemetry data through advanced behavioral recognition, implement predictive maintenance, and perform automated optimization of their networks for improved performance. In the future, the work of network engineers will involve operating and overseeing AI systems, making educated decisions for interpreting algorithmic recommendations, and continuing to research and learn new technologies. The transformation is not taking away expertise, but enhancing it for professionals to approach more intricate network problems and have oversight into autonomous intelligent network infrastructure that is able to self-optimize, self-heal, and adapt dynamically to changes in demand.</span></p> |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_5281_zenodo_17055742 |
| institution | Zenodo |
| language | |
| publishDate | 2025 |
| publisher | Zenodo |
| record_format | zenodo |
| spellingShingle | Network Engineering in the Era of AI: A Technical Review Vamsi Krishna Gadireddy <p><span>Network engineering has undergone a fundamental transformation with the integration of artificial intelligence technologies, shifting from traditional reactive management practices to proactive, data-driven network optimization paradigms. This technical review explores how modern network engineering professionals must master an intricate blend of theoretical knowledge and practical implementation skills across networking protocols, hardware architectures, and software systems while simultaneously developing competencies in machine learning algorithms and automated decision-making systems. The transformation involves multiple organizational scenarios, including enterprise networks focused on reliability and security compliance, service provider scenarios that deal with a level of routing complexity at scale, and cloud environments that require virtualization and elastic scalability. AI-driven transformation will allow network engineers to analyze large quantities of telemetry data through advanced behavioral recognition, implement predictive maintenance, and perform automated optimization of their networks for improved performance. In the future, the work of network engineers will involve operating and overseeing AI systems, making educated decisions for interpreting algorithmic recommendations, and continuing to research and learn new technologies. The transformation is not taking away expertise, but enhancing it for professionals to approach more intricate network problems and have oversight into autonomous intelligent network infrastructure that is able to self-optimize, self-heal, and adapt dynamically to changes in demand.</span></p> |
| title | Network Engineering in the Era of AI: A Technical Review |
| url | https://doi.org/10.5281/zenodo.17055742 |