Saurabhh G’s Post

While traditional infrastructure has served the #telecoms industry well, it's becoming increasingly clear that modernization is necessary to continue driving growth and meeting growing customer demands. However, implementing this transformation while also maintaining reliability and efficiency can be challenging. Learn how a leading global telco leveraged #AI to modernize their infrastructure, resulting in enhanced network performance and new growth opportunities: https://lnkd.in/gRx-HMZx

AI and automation, in a symbiotic manner, are revolutionizing operations for telecom tower owners. Companies are deploying AI-driven predictive maintenance, automated monitoring, and intelligent network management to enhance efficiency and reduce downtime. These technologies help in anticipating issues before they arise, while cutting costs and improving overall service quality. Staying at the forefront of AI and automation is crucial for maintaining robust and reliable tower operations.  Another way to reduce costs and streamline processes is by using G-force for telecom design, engineering, and field services. #TelecomInnovation

Platform Engineering, Standardization, and Best Practices for AI/ML in Telecom RAN In intelligent Telecom RAN networks, integrating AI/ML technologies effectively requires a robust framework of platform engineering, standardization, and best practices. These three pillars work in harmony to ensure that AI/ML models are scalable, reliable, and efficient. ◼ Platform Engineering: Platform engineering involves designing and managing the technical infrastructure and tools required to support the full lifecycle of AI/ML models in intelligent Telecom RAN. This includes: - Infrastructure Setup: Building scalable, reliable, and efficient infrastructure to support AI/ML operations. This could involve hyperscaler cloud platforms and on-premises solutions using container orchestration systems like Kubernetes. - Model Lifecycle Management: Ensuring end-to-end workflow management from data collection, preprocessing, and feature engineering to model training, deployment, and monitoring. - Tool Integration: Utilizing tools like MLflow for experiment tracking and model versioning, Kubeflow for workflow orchestration, and monitoring tools to keep track of model performance in production. - Automation and Orchestration: Automating repetitive tasks, optimizing resource management, and managing dependencies between different tasks within the AI/ML pipeline. ◼ Standardization: Standardization refers to developing and implementing industry-wide guidelines and protocols to ensure interoperability, consistency, and reliability in AI/ML integration within Telecom RAN networks. Key aspects include: - Unified Protocols: Establishing standardized protocols for AI/ML data exchange and integration to ensure seamless interoperability across different network components and platforms. - MLOps Guidelines: Creating comprehensive guidelines for MLOps tailored to network environments, facilitating the automation and management of ML models in Telecom RAN settings. - Interoperability Standards: Developing standards to enable smooth data exchange and integration between diverse AI tools and network platforms, enhancing the efficiency and innovation potential of AI/ML models. - Collaborative Initiatives: Encouraging collaboration among industry stakeholders, standards organizations (e.g., O-RAN Alliance), academia, and regulatory bodies to drive the development of universally accepted guidelines. ◼ Best Practices: Best practices are recommended methods and blueprints that have been proven effective through experience and research in implementing AI/ML in Telecom RAN networks. These practices focus on ensuring optimal performance, reliability, and security. Together, these three elements ensure that AI/ML technologies are effectively integrated and utilized in Telecom RAN networks, driving innovation and operational efficiency. #ORAN #OpenRAN #AIML #PlatformEngineering #AIMLStandardization #BestPractice #Workflow #AIMLFramework #Lifecyclemanagement #MLOps #AIOps #GenOps #NTUST

Data centers are transforming into #AI driven hubs that support innovation, sustainability, and energy efficiency. But the integration of AI into traditional data centers isn’t just about upgrading hardware – it involves a holistic approach to design and operation. Dell Technologies Brian Payne and Varun Chhabra discuss the transformation to AI data centers in a recent interview on theCUBE 📺: https://dell.to/3W7VAKT #iwork4dell

In the rapidly evolving landscape of network management, the need for constant and efficient optimisation has never been more critical. Autocon, a beacon for network automation engineers, is facilitating transformative connections across telco and enterprise sectors. This initiative is a testament to the growing importance of AI automation in modern business processes. As organisations grapple with increasing data demands, the role of AI in automation allows for more seamless, efficient, and intelligent network management. This not only elevates performance but also significantly reduces the potential for human error. With AI-driven solutions, businesses can anticipate and address challenges proactively, leading to optimised operations and enhanced decision-making. The integration of AI automation into network management represents a paradigm shift. It is about transitioning from reactive to predictive methodologies, ensuring that networks are not just functional but are performing at their peak efficiency. This proactive stance is vital for maintaining competitiveness in today’s fast-paced technological environment. Through strategic partnerships and innovative solutions, companies are beginning to harness the full potential of AI automation. This era of enhanced connectivity and integration underscores the necessity of adopting cutting-edge technologies to stay ahead. For forward-thinking businesses, this is an opportunity to revolutionise how networks are managed, ultimately leading to greater organisational success. How can your organisation leverage these advancements? Exploring AI automation is the first step towards unlocking newfound efficiencies and achieving sustained growth. www.alteredsphere.com #AIAutomation #AI #Automation #Processes #ArtificialIntelligence #NetworkManagement #EnterpriseSolutions #InnovationLeadership

Excited to share the 3rd installment that I wrote for The Fast Mode on AI in the RAN with a focus on going beyond automation, embracing openness, and having the right workforce. Here is a summary of the article: 1. RAN Automation's Limitations: Traditional RAN automation, though well-intentioned, often struggles to keep pace with the dynamic demands of modern communication networks. While it aims to optimize network performance and troubleshoot issues, its rigid nature limits adaptability to rapidly evolving conditions. This results in frequent shortcomings, leaving network operators seeking a more intelligent and responsive solution. 2. AI's Data-Driven Intelligence: AI thrives on data, leveraging vast amounts of network logs, user behavior patterns, and performance metrics to make informed decisions. By dynamically adjusting parameters and predicting potential issues, AI optimizes network performance, ensuring seamless connectivity even during peak usage. 3. AI's Self-Awareness and Adaptability: A significant advantage of AI lies in its self-awareness and adaptability. Unlike traditional automation, AI can autonomously diagnose issues, apply corrective measures, and optimize network parameters in real-time. This self-healing capability ensures uninterrupted connectivity without human intervention, positioning RAN as an agile and reliable entity in the digital landscape. 4. Collaboration and Ecosystem: The success of AI implementation in RAN hinges on collaboration and cooperation among various stakeholders within the telecommunications industry to open up the RAN. From Communication Service Providers (CSPs) and vendors to regulators and researchers, building a collaborative ecosystem is essential to unlocking the full potential of AI in RAN management. 5. Transformation Beyond Automation: While traditional automation focuses on optimizing existing processes, AI represents a paradigm shift towards transformational change in RAN management. Beyond mere efficiency gains, AI has the potential to revolutionize how networks are managed, transforming them into intelligent, adaptive, and self-healing entities. AI represents a paradigm shift in RAN management, offering unprecedented levels of intelligence, adaptability, and reliability. By harnessing AI's power, network operators can overcome the limitations of traditional automation, ensuring optimal performance and seamless connectivity in today's dynamic communication networks. That can only be done through access to data, the right workforce, and openness of all the components. #ai #telco #openran #data

Telcos forecasted to invest $20B in AI network orchestration by 2028 According to Juniper Research, telecom companies are set to significantly boost spending on AI for network automation, projecting a 240% increase to $20 billion by 2028 from $6 billion in 2024. With the expansion of 5G and future 6G networks, AI software will be crucial for optimizing network performance and security, accounting for over 50% of operator spending. As enterprises rely more on cellular connectivity for bandwidth-intensive applications, AI orchestration becomes vital for telcos to enhance efficiency, reduce costs, and deliver superior service quality. Failure to embrace AI could hinder telcos in meeting customer demands for performance and security. Read More: https://lnkd.in/eft8WryE #AI #DigitalTransformation #Automation #News #IndustryArticle

The future of network automation is here, and it’s transforming how telecoms operate. On November 13th at #FYUZ, industry leaders, including Amdocs' Neil Coleman will explore how RAN Intelligent Controller (RIC) technology is streamlining Open #RAN deployments worldwide. This session will highlight practical advancements in AI and machine learning that are enhancing network performance, efficiency, and security. Discover how a collaborative, multi-vendor approach is setting the stage for fully autonomous #networks. Don’t miss out on these insights that are shaping telecom’s next era > https://lnkd.in/eQ65XHpW #Amdocs #NetworkAutomation #OpenRAN #TelecomInnovation Telecom Infra Project

As networks become more automated and complex, Service Assurance is evolving from a support function to a critical enabler of network performance and business outcomes. With AI, automation, and cloud-native architectures, the next generation of Service Assurance will actively shape networks, ensuring agility, precision, and enhanced customer experiences. Discover how telecom operators can stay ahead of the curve with intelligent, data-aware systems designed to streamline operations and unlock new revenue streams. https://buff.ly/3ZrQ3kr #ServiceAssurance #Telecom #AI #Automation #CloudNative #NetworkTransformation #DigitalTransformation

Come join us for a live webinar tomorrow as we discuss the challenges facing network operators today and how you can address them with AI, automation, and data. Whether you’re dealing with ongoing network procurement, a complete network transformation, or a network cost-cutting initiative, Stephen Collins and Dennis Thankachan from Lightyear will give you valuable insights to modernize your telecom and network operations. It's free to register here: https://hubs.li/Q02HgTw80 #AI #Enterprise