Generative AI is like a groundbreaking technology, capable of like creating new content, solving problems, and also automating the processes. So today, like, we'll focus on its application in cloud native DevOps, a methodology combining the scalability of cloud native principles with the agility of like DevOps practices. So to set the expectations throughout this talk, I'll highlight like real world use cases. Key benefits and challenges along with like practical advice for integrating AI into your DevOps pipeline. And also, but first like, let's define like what a Gen AI really is. So what is like Gen AI? Like Gen AI refers to models like GPT and DAL E that can create new outputs, such as text, images, and even code by learning the patterns from existing data. Okay. So to give like real life, examples, right? So chat GPT assets with the NLP tasks, like natural language for tasks and with also with the GitHub, Copilot generates code and Dali creates like images. So these tools are not just, tools. They are like enablers of great productivity and creativity in the tech workflows. So to highlight like relevance to the topic on the DevOps, right, like in depth, like in DevOps, generally, so Genii can streamline workflows, improving the collaboration and reducing the manual effort. which leads us to why it's gaining traction in modern development. And when it comes to the transition before, like we deep dive into the applications, let's explore the challenges in the modern, software development that generates like AI that helps to address. So let me go to the next slide and let's talk about the challenges. So like what, what are the challenges in like DevOps, you know, software development, right? So let's go deep dive into this. So. So firstly, like let's say rapidly evolving technologies make it difficult for developers to stay up to date, right? So Genii can help bridge that gap by offering context, you know, assistance and also recommendations. And, also like secondly, let's talk about like increasing complexity of applications requires more sophisticated tools to manage code and infrastructure effectively. AI provides automation and error reduction and, on the next, let's talk about like shorter development cycles, place pressure on the teams to deliver faster pace. So, so AI can accelerate testing and deployment giving teams like the breaking room, right? So, and like finally security and compliance concerns are growing as we all know. Gen AI can proactively identify the vulnerabilities and ensure adherence to evolving regulations. So how do we integrate like, Gen AI into DevOps effectively? So let's, let's define a foundation of like how, native DevOps. So if I go to next slide. Okay. So let's go to the, like defining cloud native DevOps, right? So. Cloud native DevOps emphasizes applications designed for the cloud, scalable, resilient, and leveraging the microservices architecture. So if we talk about the DevOps practices, DevOps integrates It's planning, coding, deploying, testing, and also monitoring into the unified automated pipeline, fostering, the collaboration across teams. So to highlight, like, the synergy, right? So combining these principles with AI creates like an ecosystem. where applications are not only effective but also like adaptive to evolving demands. So now let's see like how AI bridges the gap into the ecosystem, right? So, okay, So let's go and talk about like see how AI can bridge the gap like integrating with DevOps, so let's break down the benefits, right? The first, firstly, AI automate the repetitive tasks, as you all know, like freeing up the developers to focus on strategic and high value content. Like, let's say if developers are, you know, writing code for the business development, they can actually focus. on, the business logic instead of, like, you know, trying to figure anything out, like DevOps stuff, like, deploying, you know, the CI CD pipelines and configuring and et cetera. And also it improves the, you know, efficiency and also by reducing errors and accepting, accelerating the workflows. And accepting like the income of, of that, right? So, and also AI provides insights from data, enhancing the application, performance, and also the security. So most importantly, it helps organizations adapt to changing technology landscapes seamlessly without no issues. And also to talk about the real world impact, many organizations already report, you know, shorter development cycles and better scalability with AI driven DevOps. DevOps. And let's dig deeper into like specific AI capabilities into DevOps cycle and, starting with like code generation. So let's talk about like how, AI can, you know, generate or help the automating the code generation. So AI powered tools, like we all know, Copilot, right? So GitHub Copilot assist. with code complexion saving developers hours of manual work and AI can generate entire code blocks or functions from simple prompts to reducing repetitive tasks over and over and code factoring tools like, analyze existing code to suggest the improvements, ensuring the better readability and also the performance. So to talk about, like, the impact, these features help, like, reduce the technical depth and also accelerate the delivery timelines. And also, let's talk about the transition. So beyond coding, AI can optimize this continuous integration and also the continuous, delivery pipelines. So let's talk about, like, how, AI can actually help in like enhancing CICD, pipelines. So the key improvements in this area, right? So automated testing generates by AI ensures broader test coverage and also the faster bug detection. This is one of the key improvements that AI can help within the CICD world. And also like AI powered deployments tools like Analyze, right? Performance data to recommend optimal like strategies for scaling and deployment and predictive maintenance is also like one of another area that we can improve with AI, like predictive maintenance, monitor systems to identify and address issues before it actually happens and to also like, let's, let's also in explore how AI role in like enhancing security. Because as we all know, like security is the key and also, for any development like nowadays, right? So let's talk about how we can improve security and vulnerability detection. So let's say, let's discuss about the, security enhancements, for example. So AI detects unusual patterns in network trafficking, right? And, enables faster responses to potential threats. So vulnerability assessment tools scan code and suggest patches, reducing the risk of breaches. And other example is like security policy, you know, and enforcement ensures compliance by automating and mitigating the risky configurations. So now let's take a look up like how AI can optimizes resource allocation and scaling. So to talk about that, let me go. Yeah. To talk about like how AI can really help in optimizing resource allocation and scaling. So one of the key areas here is the AI can predict resources based upon the historical data of the application and also optimizing cost and performance. It enables, like, dynamic scaling and also ensures applications respond to real time demands. And cost management, insights help organizations minimize the cost expenditure while maintaining the efficiency. And, also, like, like we discussed, like, AI can also help in enhancing, the observability and monitor. Let's deep dive into how AI can help in, in the monitoring, and also the observability world. So one of the couple of common features that AI monitors application performance, detecting the bottlenecks and suggesting improvements. It identifies the anomalies, alerting teams to issues like early on, like before it actually occurs, and also it can help, giving like a RCA, that root cause analysis. Speeds up the troubleshooting that using the downtime. And also one other example is like, if you want to have like an RCA for your application, like if you had like a production outage and you don't have to sit and write the whole RCA, and if you have like a tools, like AI, where it has the ability to monitor your application. It can actually provide you like insights based upon the outage. And also like lastly, not lastly, I think let's discuss about, how AI can help like, streamlining testing and also, quality insurance, right? So AI generates like test cases for ensuring comprehensive coverage. It also automates test execution, providing faster feedback and also saving time. Predictive, quality analysis, identifies potential issues before they affect production. And, and also let's talk about, like, how, like, you know, we can collaborate and also ethical consideration in AI adoption. So, overall, like, ethical and governance considerations, like, these are the key areas, right? Because the data privacy and also the fairness and the transparency is one of the key areas we have to maintain for the governance considerations. So, in talking about the data privacy, ensuring data is responsibly handled to comply with regulations. And also, we have to mitigate the AI basis to avoid discriminatory practices. And also, we have to build the AI models to foster trust and also the accountability. And also, let's talk about like, how the future of the AI powered cloud native DevOps. So, AI will continue to drive greater automation, efficiency, and innovation in DevOps. Thanks. So organizations that adopt these technologies will gain a comprehensive edge in the rapidly evolving landscape. So, and I would like to, thank you all for your attention and I'm happy to answer any questions or deep dive into like any of the topics we covered. Thank you all.