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在实验室运营过程中，您是否常面临这些挑战：资源调度混乱导致设备闲置与需求冲突、测试流程依赖人工效率低下、项目进度滞后影响整体业绩？为帮助行业伙伴突破这些瓶颈，思博伦通信特举办 “自动化赋能实验室：告别低效瓶颈，效益业绩双丰收” 线上研讨会，诚邀您拨冗参与！

作为通信测试与网络性能优化领域的专业企业，思博伦通信深耕自动化实验室解决方案多年，此次我们特别邀请到自动化实验室产品专家 —— 安佰林先生，从产品特性、方案优势到实际应用，全方位为您拆解自动化技术如何为实验室提效增能，助力企业实现 “降本、提效、涨业绩” 的目标。

__会议亮点：__
- 掌握技术要点：自动化实验室管理平台、测试管理工具介绍
- 破解低效痛点：自动化实验室解决方案典型使用场景
- 获取实战参考：自动化实验室解决方案成功案例

自动化已成为实验室提升竞争力的核心趋势，我们期待与您在线上相聚，共同探索自动化赋能实验室的新路径，携手实现效益与业绩的双重增长！

作为通信测试与网络性能优化领域的专业企业，思博伦通信深耕自动化实验室解决方案多年，此次我们特别邀请到自动化实验室产品专家 —— 安佰林先生，从产品特性、方案优势到实际应用，全方位为您拆解自动化技术如何为实验室提效增能，助力企业实现 “降本、提效、涨业绩” 的目标。

网络研讨会：自动化赋能实验室 —— 告别低效瓶颈，实现效益业绩双丰收

AI demand is growing at a pace never seen before. Data centers, chipsets, power grids, and supply chains are being stretched to their limits as the race to scale accelerates. We are learning as we build—yet the cost, complexity, and environmental impact highlight the need for smarter, more sustainable approaches.

Through expert presentations, cross-industry panels, and analyst insights, the forum explores the full stack of AI infrastructure. We’ll examine strategies for upgrading legacy data centers, designing greenfield facilities, scaling edge deployments, and ensuring multi-site synchronization and operational efficiency. We’ll also tackle the political, regulatory, and societal pressures shaping how AI infrastructure is deployed worldwide.

RCR’s AI Infrastructure Forum 2025 brings together the full ecosystem to define the roadmap for building AI’s backbone and confront this defining challenge: expanding AI infrastructure sustainably, efficiently, and at scale. 

Spirent will be participating in two sessions:  

__Automating the entire network lifecycle: Leveraging AI within the infrastructure__  
A presentation by Anil Kollipara

__Topics include:__ Overcoming hybrid cloud and legacy hardware integration challenges; Using unified observability to detect threats and optimize performance; Building self-healing, event-driven networks that prevent outages; Standardization and consolidation for efficient, resilient operations; AI-driven analytics to enhance automation and resilience in data center operations; Simplifying network.     

__Stress-testing networks for AI workloads__  
A presentation by Stephen Douglas 

__Topics include:__ Why AI workloads stress networks differently: burstiness, uplink intensity, synchronization across sites; Performance and reliability: ensuring throughput, low latency, and resilience at scale; Security risks: AI-driven attacks, adversarial traffic, and data poisoning across inference pipelines; Testing approaches: traffic emulation, digital twins, large-scale stress tests, and AI-assisted monitoring.


RCR’s AI Infrastructure Forum – From training to inferencing: Scaling AI sustainably

RCR’s AI Infrastructure Forum 