AI 네트워크 테스트

AI is no longer a distant promise for telecom—it’s reshaping the network from the ground up. In this insightful on-demand webinar, Spirent and leading industry experts explore how telecom operators are evolving from AI-enhanced networks to infrastructure that’s purpose is built for AI. The discussion focuses on real-world challenges and strategies for supporting AI at scale, highlighting why traditional networks aren’t enough to meet the demands of tomorrow’s intelligent services. One of the key takeaways is the recognition that AI traffic isn’t business as usual. With unpredictable throughput patterns, bursty inference spikes, high concurrency levels, and strict latency requirements, AI workloads are forcing a fundamental shift in network design. Telecom operators must rethink their role—not just as connectivity providers but as enablers of AI ecosystems. That means becoming providers of AI-ready infrastructure and delivering services like AI networking-as-a-service and infrastructure-as-a-service.

AI is transforming telecom infrastructure in ways that are both exciting and demanding. With artificial intelligence now embedded in everything from core networks to radio equipment, and AI workloads driving massive new traffic patterns, the telecom industry must rapidly adapt. But how do you assure performance, trust, and security in networks that are evolving this fast? This RCR Wireless webinar takes a high-level look at how telcos are rethinking testing and assurance for the age of AI. From validating AI-driven network functions to preparing infrastructure for AI-generated traffic, the landscape is shifting—and the tools and strategies must shift with it.

This session is dedicated to exploring the transformative impact of cutting-edge technologies on service assurance strategies within the telecommunications sector. As we delve into this topic, we will uncover how advancements in 5G infrastructure, cloud computing at the network edge, and artificial intelligence (AI) are reshaping the landscape of network operations and service delivery. The massive investments in 5G infrastructure, particularly on the RAN (Radio Access Network) side, have set the stage for an unprecedented increase in network capabilities. This expansion isn't just about speed and connectivity; it's about fundamentally changing how networks operate and how services are delivered. With carriers expanding mid-band spectrum rollouts and enhancing performance, the implications for service assurance are profound.

인공 지능과 머신 러닝은 현장부터 서비스 보증, 연료 자동화 및 효율성 향상에 이르기까지 네트워크 테스트의 여러 측면으로 이동하고 있습니다. 그렇다면 오늘날 네트워크 테스트 및 보증에서 AI/ML은 정확히 어디에 적용되고 있으며, 어떻게 그 가치를 입증하고 있으며, 현장 인력과 통신사 운영에 어떤 의미가 있을까요? 이 *RCR 무선 뉴스* 웨비나에서는 Spirent와 업계 전문가들이 향후 더욱 지능적이고 자동화된 AI 기반 네트워크를 향한 진행 상황을 공유하여 시청자들에게 AI가 네트워크 테스트 및 보증 환경을 어떻게 재편하고 있는지에 대한 통찰력을 제공합니다.

Artificial intelligence (AI) is revolutionizing the telecommunications industry. This engaging webinar explores AI's transformative potential and its future impact on telecom operations. We’ll share our predictions on how AI will drive new efficiencies in capital spending and in delighting customers with new experiences and services and creating new revenue streams. AI and machine learning are transitioning from tactical solutions to strategic tools in telecom, promising to unlock immense value, particularly in networking. This shift is essential for driving efficiencies in capital spending, optimizing network designs, and enhancing customer experiences with new services and revenue streams.

This panel discussion delves into the critical role of testing and verification in the telecom industry, particularly as it incorporates AI and machine learning. Key areas of focus include the challenges and opportunities that come with implementing AI technologies, such as data handling, cost management, people and culture, and security. Telecom operators are increasingly integrating AI to enhance various operational aspects. However, this journey involves significant hurdles. Panelists will explore the specific challenges, including handling vast amounts of data generated by networks, the costs associated with AI implementations, and the cultural shift required to embrace new technologies.