Hello, welcome to Con 42 DevOps 2025. My name is Ashok John Lagata. I'm going to discuss revolutionizing data center operations using AI driven optimizations. in this topic, we are going to discover how AI and ML technologies have transformed the data center operations. And we also explore quantitative results across our four critical domains. Let's, without any delay, let's get into the topic. let's start with the predictive maintenance success. We have 47 percent reduction in critical failures, using advanced AI and ML technologies, dramatically reduce unexpected equipment downtime through real time monitoring and performance testing. Predictive analysis, and then 72 hour predictive warning time, and then 96 percent predictive prediction accuracy. Let's go to the resource optimization achievements. coming to the utilization post, AI powered systems, actually a 31 percent improvement in resource utilization rates, enabling data center to handle 40 percent more workloads, without additional hardware investment and latency reduction, advanced load balancing algorithm, delivered 38 percent decrease in Resulting in sub 10 millisecond response time significantly enhances our user experience across all applications. Coming to the energy management breakthroughs, we can say 42% Cooling efficiency gain by deploying smart thermal management systems, and then optimize airflow patterns in real time, leveraging AI to maximize cooling effectiveness across the data center floor. and then PUI reduction to less than 1. 25. The industry leading power usage, effectiveness, achievement, and performance. through AI driven load balancing and intelligent resource distribution and 23 percent cooling cost savings, direct operational cost reduction through intelligence, cooling optimization powered by deep learning models, analyzing environmental sensors. coming to the security, the enhanced security framework, by deploying AI and NMO, we can see 98. 5% Threat detection accuracy, our deep learning security framework achieved exceptionally precision in identifying cyber threats, including data, data attacks, unauthorized access attempts and anomalous data patterns across our distributed data center network. And then 45 second response time. Dramatically reduce, five minutes of response time to just a 45 seconds, enabling rapid containment of potential breaches and maintaining continuous data center operations and 0. 08 percent false positives. this is a great breakthrough through continuous model refinement and advanced pattern recognization. Our AI system maintain an industry leading low false positive rate while monitoring over 1 million security events daily across our data center infrastructure. Coming to the innovative capacity planning. So there is, there is a four different steps to approach. the whole, criteria of step one is the data collection, the aggregated real time server metrics, such as workload patterns and infrastructure utilization data across multiple data centers. The step two is advanced analysis. Process 500 million plus data points through our machine learning pipelines to identify usage pattern and growth trends. The step three is predictive insights. Generate 12 month capacity forecast with 96 percent accuracy using neural networks and time series analysis. And step four is implementation. Deploy automated infrastructure scaling AI driven recommendations. Coming to the financial impact. Impactment. Financial impact, sorry. this 2.4 millions annual savings, throughout optimized, throughout optimized cooling hardware and maintenance. and then 2 8 2 80 5%, return of investment rate within first 18 months of implementation. and then 3.2 millions, saving in large facilities, of the five megawatt plus operations. coming to the, see these topics, there is explanation for each. going to the next slide, there is, definitely there's an integration challenges, from a traditional model to the AI driven model, but AI driven, the advantage of AI driven model is, for example, dealing with legacy systems, For example, if you take a migration, there is, there's always a challenges, but using AI technologies, we can reduce 19, the maintenance time, the maintenance time and then uptime, going to increase to 99. 9%. And the data quality, the implementing rigorous data validation. protocols and standardization of frameworks to transform desperate data sources into a written farmers across 50 plus enterprise databases. And then skill gap can be reduced. and then, cultural shift, from traditional to the AI model is very fast. coming to the case study snapshot, this models are not only for the. simple or small scale data center, this is also, this can be easily implemented from small to medium to the large. coming to the future AI adoption roadmaps, there is, four steps we can take into the future. Step one is edge computing integration, implementation of distributed AI. Processing at facility edges to enable real time decision making and reduce latency and then quantum AI hybrid system, leveraging quantum computing capabilities, algorithm, traditional AI to solve complex operation, operational challenges, and then autonomous data center, developing self managing facility within a system, handling all critical operations and the maintenance sessions and the AI driven sustainability, achievement, Zero operations through advanced AI, purpose resource management, and then renewable energy optimization. the key take, key takeaways, transformative impact, a implementation as reverse reverse layers to data center operations through automated monitoring and the predictive maintenance and intelligence resource allocation, resulting in an unprecedented level of operational excellence. quantifiable results, our AI solutions have delivered exceptional ROI with a 47 percent reduction in equipment failures and 31 percent improvement in the resource utilization and 2. 4 million cents in the savings. And the continuous innovation for sure, with the emerging technologies like edge computing and quantum integration on the horizon, we are poised to unlock even greater operational efficiency and sustainability gains in the coming years. Thank you for giving me the opportunity. Thank you very much again.