Hello everyone. Welcome to my talk. In this video I'm gonna talk about revolutionizing the data management the role of AI optimized block storage in the exponential enterprise data growth. So traditionally, the block storage systems have operated with static configurations and manual management approaches. However the recent growth in the data volume and velocity has exposed the limitations in these conventional approaches. The artificial intelligence optimized block storage represents a transf shift. Leveraging the machine learning algorithms and advanced analytics to create an intelligent and self-optimizing systems that can handle the exponential growth of enterprise data, which is projected to go up to once of unstructured structured and block storage data by 2025. Let's see more about the data growth challenge that the IT industry is currently facing. So the modern storage infrastructure is facing and very complex challenge as the volumes expanded unprecedented rates. So the enterprise data centers are undergoing. A big transformation both in scale and complexity with the global data sphere. Not merely growing, but exploring exponentially creating an immediate need for and adaptive solutions. Three, three key sectors be it healthcare industry and manufacturing industry With iot generated telemetry and financial institutions with transaction records and compliance documentations are at the top most forefront for this data. Critical applications are stringent. Their medic, their mission critical applications are stringent and regulatory requirement. Their mission critical applications and regulatory requirements, demand storage solutions that go beyond traditional approaches, caring for intelligence driven architecture that can anticipate growth patterns and optimize the resources dynamically. So this, the, one of the solutions for this data growth challenges are artificial intelligence driven, storage management, evolution integrating artificial intelligence into storage architectures. I. Brings a fundamental shift in the enterprise data management strategy. The modern artificial intelligence enhance the storage systems continuously monitor, analyze and performance. Analyze the performance metrics so significantly reduces the time required for management, operational, and troubleshooting tasks. Let's talk about how artificial intelligence and its predictive analytics framework bring us like, the extra benefits the artificial intelligence optimized block storage systems. I. Employ sophisticated they have a sophisticated predictive analytics that revolutionize the enterprise data management by analy. By analyzing this predictive analytics, what it does is it analyzes the historical. Usage patterns and workload characteristics and infrastructure telemetry. These intelligent systems generate actionable insights in real time applications. This predictive analytics capability dramatically improves the storage utilization rates from an industry average of 48% to 72% while reducing the procurement cost up to 35%. Through precision capacity planning and intelligence resource allocation, intelligent data tiering architecture. A driven storage systems implemented sophisticated multi-tiered architectures that automatically optimizes optimize data placement based upon the access patterns and also the performance requirements. This intelligent approach to data tiering has delivered substantial benefits. In both cost efficiency and performance with the organizations achieving storage cost reduction between most of the organization has achieved a cost reduction between 19 to up to 30, 37 percentage. This intelligent data tiering is applied on a hybrid storage where the storage system has 15 to have five to 15% of high performance storage disks. Rest of them with a mix of standard sssts and, high capacity storage drives and also in archival storage with cheaper cost. So what we, what this intelligent data tiering does is it combines the whole storage as a single package, and then these intelligent all algorithms will move the data between different tiers based upon the usage and based upon the performance requirements. Performance optimization engine. So the modern artificial intelligence driven storage system employ a sophisticated optimization engines that continuously monitor and adapt to changing the workloads. These autonomous systems represent a significant evolution in storage management, simultaneously analyzing and adjusting multiple configuration parameters in the real time. Let's see more about let's see. What are the implementation challenges implementation challenges in achieving this AI driven storage solution? Multi-layered MO in multi-layered monitoring, combining the infrastructure level metrics with application performance data to create a holistic view of the system behavior, enhancing the accuracy of predictive models. And optimization decisions and higher in high resolution performance tracking, capturing detailed metrics through agent based and agentless collection methods. Enabling precision correlation of events across the technology stack of early detection and potential issues. Block level meta data collection. Gathering a detailed operational data includes the timestamps, operation types performance metrics through distributed collection mechanisms to identify subtle patterns and anomalies and multi-stage data processing. Employing a local aggregation and adaptive sampling techniques with edge processing capabilities to optimize a bandwidth utilization. While preserving analytical capabilities. Let's see more about implementing strategies, using machine learning techniques under supervised learning particularly effective in predictive failure analysis. These models analyze patterns in device health metrics to identify potential hardware failures before they impact operations. The accuracy of these predictions show continuous improvements. As the models incorporate more historical failure data and refine their future selection. Unsupervised learning excel set identifying and adapting to evolving workload patterns by analyzing iwo characteristics, including request size read and write ratios and temporal patterns this capability proves especially valuable. In dynamic environments where new applications are frequently deployed with changing requirements, reinforcement learning enables continuous optimization of complex storage parameters by treating storage management as decision process. Evaluation, evaluating actions based on their impact on system performance metrics. This approach has shown particular success in environments with changing workload characteristics, often discovering optimization strategies that surpasses traditional approaches. Let's see how an enterprise environment strategize in, integrate in, in, in the machine learning integration. So the storage virtualization layer creates an abstract creating an abstraction layer that gradually introduces artificial inter intelligence optimization capabilities alongside existing storage systems, allowing organizations to target specific workloads for optimization. While maintaining an existing operations for less critical systems. We can also have an API based integration. Leveraging API Connections to coordinate storage optimization with broader IT automation frameworks, ensuring storage optimization storage optimizations align with overall operational objectives. While enabling automated responses to changing application requirements, a phased approach, also a phased approach which is like beginning with monitoring an analytics functions before enabling automated actions, allowing organizations to validate model accuracy and build operational confidence with maintaining a clear fallback procedures. Let's see how organizations benefit economically and operat operationally. A optimized block storage deployments has brought in a remarkable advantage in resource utilization and total cost of ownership in enterprise environments. Modern AI driven storage systems excel at optimizing. Capacity allocation through continuous monitoring and analysis of usage patterns. The operational benefits extend well beyond cost reduction and encompassing significant improvements in reliability and performance consistency. Advance in monitoring systems track hundreds of performance metrics simultaneously enabling early detection of any potential issues before. They impact a critical service. I. With the current trend seeing industry seeing the challenges with the data growth and data management the future of the enterprise storage will be. Should be based out of a transform, should have a transformation transformative approach like a optimize artificial intelligence optimized storage. With the fundamental shift in managing the increasing complexity of enterprise data environments, embedding intelligence throughout the storage infrastructure. With proactive operations where organizations can shift from reactive to proactive operational models, significantly enhancing resource utilization system reliability, and staff productivity. And this has to be like like a virtuous cycle, a synergistic relationship where storage system systems support AI workloads. While being enhanced through artificial intelligence enabling organizations to manage a growing data complexity while improving business outcomes. Thank you. I hope everyone has enjoyed this talk and thank you so much.