Is Moning from AWS, and first off, I wanna thank conference 42 for the opportunity to be here today. In this talk, we are going to explore how AI first platforms are reshaping lops, moving from manual reactive operations to autonomous self-healing, and continuously improving systems. By the end of this session, you'll have a clear understanding of the building blocks behind self wheeling infrastructure, intelligent scaling, and auto autonomous code modernization. Let's take a step back and look at how MOPS has evolved. In the early days, it was very manual. Engineers were literally on call at 2:00 AM trying to put out fires. Whenever pipeline broke. It was stressful. Reactive work then came automation. It helped a lot. Workflows were faster, errors were reduced, but it still needed constant human supervision. Now we are inferring something new. Autonomous m lops systems that learn, adapt, and fix themselves. No more. 2:00 AM wake up calls, no more firefighting. That's the real game changer. So the question is how do we get there? I like to break it down into three pillars, which we'll explore next, the three pillars of autonomous m lops. First pillar is self-healing infrastructure systems that can detect issues and fix themselves. The second is intelligent scaling, anticipating demand, and scaling proactively. And the third is automated code modernization systems that consciously evolve through AI driven refactoring and patching. Think of this like a human immune system, detect, respond, and adapt new threats. Let's try deeper into the first page. Self-healing infrastructure, a self-healing system that has four key points. Monitoring that uses ml to spot nominals diagnosis that pinpoints root causes across logs and signals resolution with automated playbooks and a feedbacking that makes the system smarter at every incident. It's like an autopilot in airplanes. It doesn't just alert the pilot, it stabilizes the system immediately. Real world use cases. Observability driven AI that detects certain deviations, cross service intelligence that understand dependencies and automated patch where systems fixed vulnerabilities themselves. These speak themselves for themselves. NR reduced by 90% and up to 80% of routine operations eliminated once infrastructure heals itself. The next step is scaling intelligent. So how do we actually implement self hearing systems? It starts with observability driven AI tools that don't just monitor, but actively detect subtle deviations before to become incidents. Next, cross service intelligence systems that understand dependencies across services, not just with inbox. That means failures are diagnosed in context, not in isolation. And finally, automated patching infrastructure that can find and fix vulnerabilities on its own without waiting for human intervention. The results are powerful and we see recovery times reduced by almost 60% and as much as 55% of routine operation tasks linear. Once your infrastructure can heal itself, the natural step is to make it scale intelligent. Traditional auto-scaling is reactive. It waits until demand spikes before responding. That's like s slamming on the brakes after you've already run the red line. It works, but it's late and inefficient. With predictive resource management, we flip the model using analytics and machine learning system forecast demand hours or even days. This allows resources to be provisioned proactively and just as importantly scaled down when they're no longer needed. The impact is clear, better performance, lower costs and resources aligned with business priorities, not just technical triggers. So how does predictive scaling actually work? It follows five steps. Collect telemetry, not CPU or memory, but also request patterns and business matrix. Analyze with ML or AI time series models, uncover S circles, correlations, even seasonality. Forecast the demand. Produce predictions from the next few minutes to several days out. Plan resources turning forecast into precise allocation strategies, automation, or automate the execution using infrastructure and scope and feedback loops to scale in real time. It's a closed loop system that gets smarter with every itration. Ensuring resources are always aligned with the impact. Now let's shift to the third pillar, AI driven code evolution. Think of your code base, not as something static, but as a living system. With AI code can now self optimize, self-cure and self adapt. This means automat refactoring or performance proactive patching before vulnerabilities are exploited or continuous adoption of emerging risk practices. All without waiting for manual intervention. This is powered by large language models. Advanced code analysis and reinforcement learning. Instead of developers constantly chasing technical data, the system reduce reduces on its own, allowing teams to focus on innovation. At AWS my team used to spend around 30% of the time in cleaning, migration or migration detected itself. So how do we actually modernize code? There are three main approaches. First, performance optimization. AI detect bottlenecks, applies, fixes, and even validates improvements through AB testing. Second, dependency management, autonomous systems assess risk and libraries flag issues and perform safe upgrades without waiting for path cycles. And third architecture revolution. ML power tools recommended and sometimes implement structural improvements as systems grow. The payoff is huge. Like we have seen 90% fewer vulnerabilities, 30 to 50% performance gains, and a 30% dramatic reduction in maintenance overhead for just my team. So what's the real business impact of auto autonomous lops organizations? Adopting the systems has seen operational overhead cut by 75%, recovery times reduced by almost 90%. Infrastructure costs lowered by now 35%. These aren't small, incremental games gains. They their step function improvements, unlocking efficiencies, bilities, and agility all at once. That's why autonomous lops isn't just a technical evolution, it's a business transformation. The journey to AutonoMe doesn't happen overnight. It's phased progression usually over 18 to 36 months. Phase one is foundation get observability in place and standardized infrastructure as code. Without good telemetry, autonomic cannot work. Phase two is augmentation layer in AI powered monitoring, anomaly detection, and early predictive scaling, often with human percent. Phase three is autonomy systems not handling remediation and predictive scaling with minimal intervention plus early code optimization. Phase four is evolution. Self-improving systems that adapt architecture through reinforcement learning. The key point is that each phase delivers measurable value on its own, so the benefits start well before full autonomy is reached. Now looking ahead, several trends will push autonomous lops even further. First, multi-agent systems. Instead of one AI making decisions, multiple agents collaborate to manage infrastructure dynamic. Second, explainable AI operations or x AI ops, bringing transparency and accountability to autonomous decisions. Third cross-platform optimization. AI that can seamlessly shift and optimize workloads across hybrid and multi-cloud environments. Finally, continuous learning infrastructure systems that don't just learn from local incidents, but from global patterns across industries. The takeaway are self-healing, intelligent scaling and code modernization are only the be what's coming as fully autonomous cloud ecosystems. To wrap up, thank you all for joining me on this journey into autonomous mops. I hope the talks gives you the vision of what's possible and a roadmap to start your own journey towards Autonom. I'd love to hear the conversation, so please feel free to reach out to me. Thank you.