Hello everyone. Welcome and thank you for joining today's session. My name is Rahul Amte and I work as a cloud architect with expertise in AI infrastructure, DevOps, and security. Today we are going to explore how AI and machine learning revolutionizing cloud automation. Transforming how we manage and operate cloud infrastructure. This isn't just about automation. It's about intelligent automation that anticipates, adapts and optimizes in real time. Basically, cloud computing, as we all know, has become the backbone of modern IT infrastructure, but as environments become larger and more complex, manual management is no longer feasible. That's where AI and ML come into the picture. They offer intelligent automation solutions that not only reduce human innovation, but also improve efficiency and decision making across the board. With that said, we also look at like, why do we need automation? What is the need for automation? Cloud operations often involve repetitive time and tasks such as provisioning, scaling, and monitoring. As organizations adopt multi-cloud or hybrid cloud strategies, the operational burden is increasing significantly. So what can we do and how AI can handle this and help us? AI can handle this complexity with ease, reduce human error, and enable continuous service availability. With that side, let's look at what is AI powered cloud automation? AI powered cloud automation refers to the use of artificial intelligence and machine learning algorithms to manage, optimize, and troubleshoot cloud infrastructure like traditional rule based automation. We have currently, which of course was not in the traditional world, but in the current cloud world, AI learns from historical and real time data. To make predictive and adapt decisions automatically as you speak. We have some cool machine learning based security tools already doing the real time data, predictive and adaptive decisions. But just imagine spreading this into the infrastructure. It's definitely going to be a powered cloud automation. Let's look at the core capabilities of AI in cloud ops. Let's talk about what AI can actually do in the cloud. It detects anize like certain traffic surges or failures. Predicts future needs, as a resource, needs optimizes resource placement, and triggers automated responses. These intelligent futures can create a self-managing, self-healing environment with minimum manual effort. Isn't it? Cool? Let's look at some use cases now. Predictive resource scaling is one of the use case I wanna talk about today. With predictive scaling. AI monitors the historical usage patterns to forecast upcoming demands. For example during an e-commerce flash sale, like a, like during Thanksgiving or Christmas, AI can predict traffic spikes and scale up resources in advance. I. This ensures performance while minimizing over provisioning and cost. Cost is one of the important factors. At the same time, making sure that consumer has no issues while there is traffic surges, especially during the holiday season. So AI is definitely, a helping factor there. And the other use case is, as I was mentioning, cost optimization. Cloud costs can spiral quickly. AI helps by analyzing resource usage and suggesting optimizations like right sizing instances, shutting down ideal resources or switching to reserved instances. It's proactive, not reactive, and helps maintain financial efficiency across cloud deployments as it underlines. It's proactive. What we do today for cost optimization is reactive. We have to set up things, we'll have to make a decision, we'll have to execute to make it work, but AI can definitely help us with the proactive nature and help us in cost optimization, which has been a major issue in most of the organizations in this couple of years because of the spinning of infrastructure left and right in the last decade. We look at other use case where AI can help us. Downtime is expensive. AI can significantly reduce mean time to repair, which is by detecting failures, correct? Correct. Correlating locks and metrics, and automatically applying fixes or rerouting traffic. For example, if a note fails, a can trigger a script to restart the service of provision. New instance. In the world of incident management and self feeling, AI can act as the remediation as a service. In the current setup we have in the current world of infrastructure, we have incident response. We have monitoring as service. We have alerting a service. Just imagine incidents response as an automatic service would be awesome. That is what AI is going to do for us. What are some key tools and platforms? Let's look at some providers. Each major cloud provider is investing heavily in AI power tools. As we already know, AWS offers DevOps, guru and CloudWatch and MLA detection. Azure has AI integrated with the monitoring and monitor and advisor, as in GCP. Also provides auto ML and cloud operations. There are also some third party apart from these three major cloud providers native services. The third party platforms like Dynatrace and Splunk using AIOps are actually used for the advanced automation in the cloud infrastructure world. But there are other tools, a lot of companies investing in ai. So there are a lot of other third party tools as well, which are really helpful, but we can only choose few. Next let's look at benefits of AI driven cloud automation. The benefits are multifold, as you already know. You get higher uptime through proactive management, cost savings through intelligent resource allocation and enhance security through annually detection. It also frees up DevOps teams to focus on innovation rather than the fight fighting. Innovation as in making things better and faster than actually trying to fix things, troubleshoot things. AI is one of our team members. Of course, every. Future comes with some challenges and some considerations. Let's look at some here. However, as I said, there are challenges to consider. AI systems are only as good as the data they are trained on. Poor data quality can lead to incorrect decisions. Integration with legacy systems can be complex, and there's always the concern of blindly trusting automated decisions. So especially in the regulatory industries. We'll have to be very careful because some decisions made based on the data, poor data quality can lead to some big problems. So definitely the quality of the data of the training being given for the models that's considered by AI is very important. With that said, I would like to conclude by talking about the future outlook of AI in cloud automation. From what I said altogether, to sum it up, AI and ML are pushing the boundaries of what's possible in cloud operations. Today, we are moving towards future of autonomous cloud management, where AI handles provisioning, optimization, and even security in real time. This evolution is not just inevitable, it's already underway. It's going to get better and amazing. Once again, thank you for the opportunity. I really enjoyed it and really appreciate your time. Thank you.