Hello everyone, myself, Ian. I'm here to present how we can use generative AI to optimize hybrid cloud workloads and infrastructure. The in integration of generative AI into hybrid cloud environments has revolutionized. Enterprise infrastructure management, transforming how organizations handle complex computing workloads across distributor systems. This presentation explores how AI driven optimization. Creates more efficient, secure, and resilient work. Hybrid architectures enabling enterprise to move from reactive to proactive infrastructure management while realizing significant cost benefits and performance. Improvements. Let's understand the complexity of hybrid cloud. You have the data centers, the on-prem data centers with high control, but limited scalability. The public cloud resources. Like AWS Google Cloud Azure, which are highly scalable with consumption based pricing model. You need a complex network topology to connect both the on-prem data centers and the cloud resources with careful optimization. Keeping the security and compliances in mind. Cross environment protection require requires consistent policies. Keeping the security in mind. The traditional management approach falls short, manual provisioning, time consuming processes that can't keep pace with the changing. Workload demands leading to resource under utilization or performance bottleneck, like e-commerce website during the, during the black Friday, there's a, the huge traffic inflow during the black Friday. So accordingly the resources has to scale up as per the demand and scale down when the demand goes down. Secondly, the static resource allocation. Fixed resource assignments that fail to adopt to fluctuating workload patterns, resulting of excess capacity or performance constraints. Reactive troubleshooting traditionally. Addressing issues after they impact users instead of preventing them, increases downtime and degrading the user experience. The conventional approach react the conventional approach. The conventional approaches create significant inefficiencies in hybrid environments where workloads must move seamlessly between the. On-prem and cloud infrastructure to maintain optimal performance and cost effectiveness. AI powered predictive workload. Generative AI examines the historical workload, workload patterns like various metrics, logs, historical logs to identify usage trends and predict. Future demands machine learning models, forecast, resources need before demand. Spikes occur after pre, after predicting. It optimizes the allocation. Resources automatically scale up and down across the hybrid environments. Lastly, it reduces the cost. Eliminating the lower provisioning we to significant infrastructure savings. By implementing AI driven predictive scaling enterprises can achieve up to 35% of reduction in the infrastructure costs while ensuring application have the resources that need precisely when they need them. The AI in inte, the AI's intelligent Security framework identifies anomalies and pattern anomaly patterns that indicate potential threats using behavioral analysis, continuous verification across hybrid environments that makes a zero trust architecture that verifies each and every connections coming inside. The infrastructure, immediate threat containment without human intervention with your automated responses. The generative AI enhances security by creating synthetic attack scenarios to. Test differences. Identify vulnerabilities before they could be. Explore exploited these intelligent frameworks continuously learn from new threats. Adopting protection strategies across both on-prem and cloud infrastructure in real time. The year adoptive network optimization, the real time monitoring of network flows and application demands by analyzing the traffic, the network traffic that flows between the on-prem and cloud infrastructure. Dynamic path selection based on current condition routing optimization. And intelligent distribution of traffic across variable resources, which are situated in various regions or zones. AI driven network optimization can reduce latency by up to 40% in hybrid environment by continuously analyzing the traffic patterns and adjusting routing configuration. This adaptive approach ensure. Optimal application performance, even as the network conditions change throughout the day. Automated decis decision making. The uses infrastructure as code AI generates and optimizes the infrastructure templates based on the workload requirement using various tools like Ansible. Kubernetes is terraform for infrastructure as code automatic detection and remedi of infrastructure issues. Self-healing systems like the AI looks into the logs and try to understand if there is any issue, if there is any errors. It self-heal. It detects those errors and remediates immediately for faster resolution. Continuous optimization ongoing refinement of resources, location, and performance tuning if there is any. Any long running queries that is using huge resources like memory. It does a continuous refinement of resources allocation. And does the performance tuning? Yeah. Recommends architectural improvements based on evolving patterns that gives a proactive suggestions. By automating routine infrastructure decisions, IT teams can reduce manual intervention by up to 80%. Freeing, valuable time for innovation and strategic innovative initiatives. While ensuring more consistent system performance across hybrid environments, what are the business impacts when we are using generative ai One. Cost reduction, lower infrastructure expenses through optimized resource utilization, up to 35%, reduction in cost, faster recovery, improved disaster recovery timeframes with yay orchestrator processes. And 60% performance stability, reduction in response time, variably across applications, 45% of team productivity. The team. Concentrate more on, on the innovation rather than on the maintenance of the systems, that gives 45% of team productivity when using ai, generative ai, let's look into the real world success stories. A leading bank, reduced infrastructure cost by 30%. While improving transaction processing speeds up by implementing AI driven workload optimization across their hybrid workload, saving over 15 million annually a hospital network improved patients data access speeds by 45% while maintaining stringent compliance requirement through intelligent data placement and access control across hybrid infrastructure. An automotive part manufacturer achieved 99.99 production system uptime by implementing AI powered productive maintenance and workload balance balancing across factory floor and cloud analytic performance. What are the challenges and the solution implementation challenges and solutions? Integration complexity. Implementing incremental AI adoption with focused use case could in expanding to enterprise wide deployment. Start with clearly defined workload that offers measurable optimization potential. Skill gaps. Develop internal expertise through targeted training with leveraging managed services for specialized AI capabilities. Create cross-functional teams that combine cloud. Skills, build confidence through transparent AI decision processing with human oversight. During initial phase, document and communicate successes to build organization, buy-in, data quality issues, establish comprehensive data. Governance and cleansing processes to ensure AI model receive high quality inputs implementing in continuous data validation pipeline. What is the future of generally ai? With hybrid cloud and edge, the convergence of edge computing with yaka optimized hybrid cloud creates unprecedented opportunities for real time processing of all the data source. While maintaining centralized management and analytic capabilities, organizations embracing the this integrated approach with benefit for more resilient, responsive, and efficient infrastructure as AI capabilities continue to evolve. We will see increasing automation of complex infrastructure decision making, eventually leading to self designing systems that continually optimize themselves based on the business requirements and technology landscapes. The convergence of edge computing with AI optimized hybrid cloud creates unprecedented opportunities for real time processing of the data source. Of the data source. While maintaining centralized management and analytic capabilities, organization organizations, embracing this integrated approach will benefit from more resilient, responsive, and effective infrastructure. As AI capabilities continue to evolve, we will see increasingly automate automation of complex infrastructure decisions. Eventually le leading to self designing system that continuously optimize themself based on changing business requirement and technology landscape. Thank you.