Good morning, good afternoon, good evening everyone. I am Cypress, the Moala. I'm working as a enterprise architect IN Focus INC, and today I will walk you through how GPU accelerated A I at hs. Not only delivering up to 15 X performance gains, but it is also solving one of the biggest challenges in enterprise computing today. Balancing performance with data sovereignty. Imagine a wind turbine in remote field. It's equipped with sensor monitoring, vibration, temperature, and blade speed in real time. Now, imagine that turbine analyzing all that data on onsite in milliseconds predicting failure before they happen without sending a single bite to the cloud. This is not a science fiction. It's HAA in action powered by GPU acceleration, and it's quietly transforming industries as diverse as manufacturing, healthcare, and logistics. We will explore real benchmark data, industry use cases and practical implementation strategies. By the end of this session, you will have actionable insights into deploying edge AI solutions in your own enterprise infrastructure. Coming to the next slide, the shifting AI deployment paradigm. Let's break this down with the simple example. Imagine a modern, smart factory. In traditional model, hundreds of missions generate sensor data, vibration level motor temperature, production rates, and all the raw data is sent straight to the cloud for analysis. But this creates a heavy button on the network, increases latency and can even become a security or compliance issue if the data is in insensitive or regulated. Now in the. Emerging paradigm, we flip the process. Each mission is a an HAI device, like A GPU enabled industrial computer. These device process, the sensor data on site, they can detect anom, analyze flag, maintenance issue, or adjust parameters in real time, all within milliseconds. Only high value insights or summary data are sent to the cloud for storage, audit, or long-term learnings had in hybrid architecture. Some models might still run in the cloud, like large scale trend forecasting while critical operations happen at the edge. This combination gives the business the best of the both worlds. Instant decision making where it is needed and strategic insight powered by the cloud. The result, faster response, lower cost, and better control over sensitive data. Coming to the next slide, GPU acceleration, performance breakthrough. GPU acceleration is driving a paradigm shift in HAI. Delivering unprecedented performance gains and energy efficiency by enabling parallel processing, GPUs unlock up to 15 X faster performance compared to traditional CPU only solutions. This immense performance boost is crucial for enterprise seeking to process complex AI workloads quickly and efficiently at the edge. Key performance gains. Let's talk about 15 X performance gain, GPU acceleration dramatically speed up to competitions, reducing the time required for deep learning models to generate a results. For instance, tasks that typically take a minute with CPUs can now be completed in seconds. Optimizing real time decision making and redundancy latency. Less than millisecond inference time. GPU enabled production grade deep learning models to achieve inference time below 10 milliseconds, even for sophisticated neural networks. This means AI application at the edge, like autonomous vehicles, industrial automation, and real time image recognition can respond instantly. Without delay, making them truly real time and responsive. Let's talk about efficiency benefit, 60% power reduction by paying GPUs with energy efficient. RM architectures companies can reduce power consumption by up to 60% compared to traditional server environments. This is not only leads to lower operational costs. But also minimize the environmental impact helping business meet sustainability goals. Coming to the next slide, real world impact, real transformation, operational cost reduction, customer experience improvement, low prevention and enhancement. The real time industries expect experiencing a digital transformation driven by a G AI technologies that are improving operational efficiency. Enhancing customer experiences and boosting security by processing data directly at the edge on devices such as cameras, sensors, and in-store kiosk retailers are gaining a real-time insights and achieving significant business outcomes. Let's talk about operational cost reduction. Operational cost reduction, 28% decreases in expenses. A GAI enable retailers to optimize inventory management by processing real time data from in the store sensors, tracking stock levels, and automating replenishment. Predict two maintenance systems, analyze sensor data from equipment to predict failure before they occur. Preventing costly downtime. As a result, overall operation expenses are reduced by 28%, improving bottom line profitability, customer experience in improvement, 65% reduction in latency by processing data locally at the edge. Retailer can achieve faster decisions making for personalized recommendation and dynamic pricing. While real time processing recommendation engines are able to provide personalized offers, targeted promotions, and product suggestions, instantaneously this leads to 65% reduction in latency, improving customer satisfaction and boosting sales. Coming to the last prevention enhancement, real-time security monitoring. A G AI powered video analytics system provide real-time security monitoring, analyzing camera feed for suspicious activity with sub-second identification of potential theft events. Security teams can respond instantly reducing shrinkage and increasing store safely. Coming to the next slide, manufacturing excellence through Edge AI A G AI, revaluation manufacturing by providing real-time insights, enhance operational visibility and predictive maintenance by processing the data directly at the source edge computing ensure that manufacturers can react faster. Optimize the production lines and reduce downtime, leading to significant cost saving and improved quality. Let's talk about increased operational visibility, predictive maintenance, quality control, automation coming to the increased operational visibility. Real time monitoring of production line. Using edge devices such as sensors and cameras allow manufacturers to gain continuous visibility into their operations. Instant data processing helps identify bottlenecks, inefficiencies, and equipment failures at the occur, allowing for immediate corrective action. Predictive maintenance, 44% decrease in unplanned downtime. A G AI power predictive maintenance system. Monitor the health of missionary in real time analyzing sensor data for early sign of wear or malfunction. The proactive approach reduce unplanned downtime by 43%, translating to millions in saved production cost by preventing equipment failure and ensuring the continuous operations. Quality control automation sub millisecond. Second defective detection with the computer vision at the edge. Manufacturing systems can analyze products in real time deducting defective with sub milliseconds latency. Immediate corrective action can take into address issue before product move further down to the production line, preventing costly waste and rework. The quality control automation help ensure product meet regress due to the combination of high volume sensor data and the need for real-time processing. By implementing edge based solutions, manufacturing can achieve real time operational insight that enhance visibility. And decision making use predictive maintenance to dramatically reduce unplanned downtime and optimize asset utilization. These edges based systems drive operational efficiency, cost savings, and higher quality output making edge AI critical component in modern manufacturing excellence. Coming to the next slide, telecom enabling 5G application innovation. The rollout of 5G Network is unlocking new possibilities for telecom providers and their customers with application that require ultra low latency and high performance computing. Traditional cloud-based infrastructures however, struggle to meet these demanding requirements. Due to inherent network latency. Let's talk about legacy infrastructure, age deployment, next generation application. Let's talk about L Leg legacy infrastructure. Five to between five to a hundred millisecond milliseconds. Application response times traditional telecom systems, relay on cloud. Dependent architectures where data is set to centralize the cloud servers for processing and analysis. This setup introduces latency in the range of between 50 to hundred milliseconds, which is insufficient for realtime rep responsiveness required by advanced applications like autonomous vehicles or industrial automation coming to the edge deployment. Moving process to cell sites and aggregation points To address this challenge, telecom providers are deploying GPU accelerated compute at cell sites and aggregation points to closer to the network edge. This edge deployment allows to be. Process locally reducing the need for long distance data transmission to centralized data centers coming to the next generation application, single digit millisecond response times. Edge computing enables real time single digit millisecond response times, which is essential for next generation applications like. Autonomous vehicle coordination, which requires near instantaneous communication between vehicles, infrastructure, and the cloud industrial automation where factory robots must operate with minimal delay to maintain high efficiency and safety automated reality. Where. Imperceptible processing delay are crucial for providing seamless immersive user experience. The deployment of GPU accelerated edge computing at the edge of network is making it possible to meet the strike sign latency requirement of 5G application. Coming to the data sovety challenges. As AI technologies continues to scale globally, data Sovety has emerged as one of the most significant non-technical challenges, regulations around data privacy and cross border transfer data complexities that require careful ion to ensure compliance and mitigate risks coming to the cross border data transfer restrictions. Let's talk about this cross border data transfer restrictions, different jurisdictions. How, where very varying rules regarding how and where data can be stored, processed, and transferred. For example, the European Unions, GDPR, mandates that personal data must not to be transferred outside the US unless certain conditions are met. Creating hurdles of global AI deployment strategies, the complexity of these regulations across countries can make it difficult for companies to build consistent globally. AI models that require large scale cross border data exchange, GDPR, and similar regulation frameworks, regulations such as GDPR impose strength. Requirements on how data is handled, including the need to ensure data is processed securely, has the appropriate consent, and is stored within specific geographical boundaries. Non-compliance with these regulations can result in hefty fines, millions of dollars in penalties. That pose serious financial and reputational risks to organization using centralized cloud-based data processing architectures coming to the governance consistency with the distribution of edge computing across various locations. Maintaining consistent governance and security policies become increasingly challenging. Ensuring compliance at each edge, node, or deployment point, which could span several countries or even continents, requires a robust framework to enforce uniform policies and maintaining data integrity data. Ty challenges are increasingly complex in a globalized world, especially for AI driven solutions. While Edge Computing offers a potential solution by keeping data within its jurisdiction of origin, it introduces new challenges in maintaining consistent governance across distributed edge environments. Developing a robust governance model that balance data severity, compliance, and security across all edge node is critical for successful. Scalable AI developments. Coming to the next slide, severe security in distributed edge, AI edge deployment, grow and evolve. Securing distributed system become a top priority. The expanded attack surface in distributed environments calls for rethink in traditional security models, focusing on defense in depth. Strategies and zero trust approach to safeguard data and workloads across edge nodes. Let's talk about zero trust architecture, quantum resistant encryption, continuous vulnerability scanning, secure AR hardware enclaves. Coming to the zero trust architecture, zero trust architecture, assume that threats can exist both inside and outside the network. Requiring identity verification for every system interaction. Whether it's a user, device or service, this never trust, always verify ensures that only authenticated users and systems can interact with since two data and applications, minimize the risk of unauthorized access coming to the quantum resistant encryption. With the rise of quantum computing on the horizon, traditional encryption methods may no longer be sufficient to protect sensitive data. To prepare for forced quantum threat landscape, forward thinking, organizations are already implementing quantum resistance encryption protocols, which are designed to secure data against the future capabilities of quantum computers. Coming to the continuous vulnerability, scanning the dynamic nature of distributed edge environments necessary states continuous monitoring and proactive security measure. Automated vulnerability scanning helps detect and remediate security flaws in real time across all edge nos. This continuous assessment ensure that vulnerabilities are addressed before they can be exploited. Keeping secure posture up to date and reducing response times to potential threats. Coming to the secure Hardware enclaves offers isolated exception environments designed especially for running since two workloads securely. These enclaves protect the critical data by preventing unauthorized access or. Tampering, even if surrounding system is compromised, ensuring that high stake operations like AI model training or financial transactions remains secure. Coming to the next slide at autonomous edge infrastructure, the operational complexities of managing distributed edge infrastructure. Especially in remote and geographical environments are driving the development of autonomous system. These systems are designed to optimize, heal, and defend the edge infrastructure without human intervention, ensuring continuous, efficient and secure operations, self-optimizing, dynamic workload balancing and resource allocation, self-optimizing. Platforms automatically adjust resource allocation based on the demands of incoming workloads, ensuring that computational power is used efficiently. This dynamic workloads balancing optimizes performances while simultaneously minimizing energy consumption, making edge infrastructure both cost effective and sustainable as workload. These platforms can scale resource up or down as needed without manual intervention. Coming to the self-healing, automatic fault detection and recovery. Self-healing capabilities are crucial for ensuring the reliability of distributed edge system where physical access to hardware may be limited. These systems can automatically detect the hardware or software failures and quickly initiate recovery procedures such as a rerouting traffic or reconfiguration nodes to maintain functionality. This fall. Detection and recovery reduces downtime and help ensure high vis availability in environments where immediate hormone intervention is not feasible. Let's talk about self defending. Prior to threat mitigation without human intervention, the most advanced edge systems now incorporate self defending mission mechanism to address security threat in real time. These systems continuously monitor for anomaly and vulnerabilities within the Edge network and can proactively take action to mitigate potential threats. Such as isolating compromise node or blocking malicious traffic without human involvement. This prior to threat mitigation ensures that edge environments remain secure even when operating in remote unmonitored locations. Coming to the next slide, implementation strategy framework. Successful A G AI implementations required a methodo methodical structure approach that spans from the initial workload assessment to continuous optimization. It is comprehensive framework that guides the deployment of AI application at the edge. Ensuring both efficiency and scalability. Let's talk about work workload assessment, infrastructure deployment, application migration, continuous optimization. Let's talk about workload assessment. The first opinion, any IT deployment is to thoroughly evaluate the application characteristics to determine if the edge is right environment for the workloads. This includes accessing. Latency requirements, data volumes, processing needs. This assessment help prioritize which application will benefit the most of the edge computing, ensuring optimal performance and resource allocation. Let's talk about infrastructure deployment. The next phase is the physical deployment of edge infrastructure with a focus on standardization and modularity. 80 noes should be implemented with the GPU acceleration to handle the compute intensive task of AI applications, ensuring high performance at the edge. The infrastructure should also emphasize reliability and security, especially in remote or distributed environments. Standardized edge node allow for eraser. Easier scaling and maintenance across diverse deployment locations. Improving consistency in management and performance security protocol must be implemented to safeguards, data and operations across distributed edge notes. Application migration. Once the infrastructure is in place, application needs to be migrated to the edge environments. This typically involves refactoring existing application to make them compatible with edge environments. Containerization technologies are used to package application into portable containers, making them easier to deploy, manage and scale across distributed edge nodes. Orchestration system help manage containers across multiple edge nodes. Ensuring that the application operates consistently across different environments. The use of con containerization and orchestration also enhances flexibility and portability, allowing applications to be moved between different edge environment with easy, continuous optimization. Finally, after deployment, it is essential to continuously monitor performance metrics to ensure that a GA AI system operates optimally. The, this includes tracking metrics like latency, data throughput, and resource utilization based on the insight organization can interactively improve the edge deployment. Adjusting configuration, scaling resources, or refining application as needed to address real world challenges. The future of intelligent edge computing, the governance of GPU accelerated AI and edge computing is poised to rephrase enterprise technology landscape in profound ways as the next wave of distributed compounding unfolds. These technologies will seamlessly integrate into every FT of our physical infrastructure. From smart buildings and connected vehicles to industrial systems and urban environments, embedding intelligent everywhere. In the coming years, edge computing will become a core component of many physical environments. Buildings will become smarter, vehicles will have autonomous capabilities. And entire industry will leverage real-time AI insights at the edge to drive efficiency and innovation. This ing of inte at the edge means that enterprise will not only process data closer to its source, but will also see applications evolve into self aware systems capable of making real-time decisions without relaying on centralized cloud infrastructure. Leading the future with a GI. Organizations that successfully implement edge intelligence strategies today are poisoning the cells as leaders in future of distributed computing. By adapting GPU Accelerated Edge AI early on, they are setting themselves up to take advantage of future innovations in field like autonomous systems, industrial automation, and smart cities. However, this journey is not without its challenge addressing the technical security and operational huddles we have discussed such as data sovereignty, security at the edge and workload optimization is crucial to ensure a successful, scalable deployments unlocking transformative potential. The power of GPU Accelerated Edge AI license, its ability to transform business operations, enabling real time decisions, making predictive analytics and enhanced user experience, all while reducing reliance on centralized cloud systems. By overcoming these challenges, organization can unlock the full transformative potential of. These technologies gaining a competitive edge while maintaining the control and sovereignty needed in rapidly involving landscape. And thank you. Thank you for this opportunity.