Hello, welcome to con 42 Python 2025. My name is Ashok Chonlakata. I'm going to, I'm going to discuss about revolutionizing data center operations. It is a quantitative analysis of AI driven optimization. Let's, without any delay, let's get into the topic, coming to the predictive maintenance. it is about reducing equipment failures. coming to the AI powered early warning system, our machine learning algorithm process real time sensors, achieving a 47 percent reduction in unexpected The system provide critical alerts up to 72 hours before potential issues arise and enabling proactive maintenance coming to the continuous performance monitoring with a 96 percent prediction accuracy through advanced monitoring of temperature, vibration and power patterns. We prevent an average of 127 hours of downtime. Per quarter, maximizing operational efficiency and coming to the resource optimization in terms of maximizing efficiency, improved resources utilization, AI power system achieved a 31 percent improvement in resource utilization by dynamically allocating computing resources based on real time workload analysis and automated capacity management latency, advanced machine learning algorithm, Reduced server latency by 38 percent through intelligent load balancing and a predictive traffic management resulting in 42 percent faster application response time. coming to the energy management in terms of optimizing cooling systems, enhanced cooling efficiency, advanced deep learning model. Achieved a 42 percent improvement in cooling efficiency through real time temperature optimization and smart airflow management. This resulted in annual energy savings of 3. 2 million kilowatt hours across our facilities. reduced PUI, PUE. Through AI driven thermal mapping and intelligent load distribution, we reduced power usage effectiveness, PUE. from 1. 6, 1. 7 to below 1. 25 within six months. This 23 percent reduction in cooling costs translate to 2. 8 millions of savings. security, advanced threat detection and response. So 98. 5, 5 percent accuracy, detecting a security incident. Uh, our airport security framework issued 98. 5. in identifying potential threats, analyzing over 1 million security events per second while maintaining a false positive rate below 0. 1, 0. 01%. coming to the reduced response times, critical security incidents are now addressed within 45 seconds. An 85 percent improvement in security incidents. Over manual intervention. This rapid response capability has been prevented. 99.9% of attempted reaches across our global infrastructure network. coming to the capacity planning, the predictive future demand. in this, the model, we use these neural network model, our advanced deep landing framework leverage. ISTM neural network to achieve 96 percent accuracy in demand, forecasting, processing fires of historical data across 2000 performance metric to predict capacity needs up to 18 months in advance. proactive scaling by analyzing workload patterns and resource utilization trends. Our prediction system automatically initiate scaling operation 72 hours before demand spikes, resulting in finance service availability. And a 42 percent reduction in all provisioning cost while maintaining optimal performance across all systems. cost savings and return on investment, ROI, annual savings. These AI implementations have collected, collectively resulted in average annual savings of 2. 4, 2. 4 million for enterprise scale deployments. Increased efficiency by automating. Automatically process and optimization resources. AI solution reduce operation cost and improve overall efficiency. Enhanced competitive advantage. AI driven data center operations deliver a competitive edge by enabling business to adapt, change engineering needs, and deliver Superior performance coming to the intelligent coming to the integration challenges and, practical insights. in terms of data quality, implementing a robust data validation protocol and a cleansing pipelines to ensure consistent, accurate and a properly labeled data sets for a training, coming to the model training, balancing computational cost with the model. Flexicity while maintaining extensive training data set across diverse operational scenarios, coming to the deployment and monitoring, establishing a continuous integration pipelines, performance metrics and automated monitoring system to ensure reliable model performance in production environments. case studies, the real world implementations, in terms of small data centers, a regional cloud provider with, 800 kilowatt facility implemented a driven cooling optimization resulting in 35 percent energy savings, which in terms of, around 450 K annually. while improving equipment reliability by 25, 28 percent and reducing carbon emission by 420 tons per year, which is beautiful. And then coming to the large scale data centers, a fortune 500 company, more than six megawatt data center deployment, deployed AI based predictive maintenance, cutting equipment failures by 40%, achieving finance uptime and saving 2. 5, 2. 1 million annually. Roadmap for future AI adoption. we can, achieve this in three steps. step one, expand AI applications and then improve AI model accuracy. And number three is foster collaborations. conclusion, embracing AI for data center excellence. AI driven. Optimization offers significant benefits for data center operations, including increased efficiency, reduced cost, and enhanced reliability by embracing AI data center can achieve a new level of excellence and unlock the full potential of the infrastructure. And then, let me know any questions. Thank you. And thank you for giving me this opportunity.