Hello everyone. I'm Var, the lead full stack engineer company agency with more than something years of experie in the tech industry. I'm passionate about creating seamless and secure solutions. I may work focus on key areas like full stack development, cloud technologies, application security, risk analysis, and a integration. With that, let's get started with the today's topic. Automating infrastructure to scale for enhanced developer experience. With the advent of cloud technology, multi applications are divided into multiple microservices, which of which runs multiple instances. Managing such a huge number of instances creates a administrative overhead platform. Teams are struggling to manage increasingly complex cloud native infrastructure while supporting thousands of developers. And applications across distributed environments. The recent research across one point, some proper engineering team shows that almost seven three percentage report difficulties in balancing infrastructure complexity with the developer experience traditional manual approach, proving inadequate for the modern scale requirements and growing technical depth from quick fix solutions. The impact of infrastructure complexity, we can categorize mainly into three groups. First one is developer productivity, and secondly, operational burden. And finally, business agility. Other developer productivity developers often spend the valuable time in infrastructure, resources, and navigating complex system and platform tools on an average of almost 4.7 hours per week lasting to infrastructure release. And almost 42% of feature data are attributed to platform bottlenecks and platform disor, made by manual processes and support tickets. Almost 60 big percentage of platform engineer Spain and react to troubleshooting, growing backlog of platform improvements. Slow infrastructure provisioning and scaling directly impacts time to market. Infrastructure delays contribute towards 38% longer recycles and competitive disadvantage in fast moving markets. Traditional platform approaches cannot scale to meet the demand of modern cloud native development. At enterprise scale, the AA port platform engineering opportunity, 58% of faster development is achieved by reducing the deployment cycle time. Through a power automation and intelligent orchestration, 67 percentage of provisioning speeds achieved by decrease in infrastructure provisioning time with the intelligent resource allocation and predictive scaling, and 70 on percentage of system liability improvement in overall system reliability through predictive maintenance and automated remediation analysis of AI enhance platform implementation shows. Remarkable improvements in operational efficiency compared to traditional platform management approaches. AA driven developer experience transformation platform teams implementing a power self-service capabilities, report dramatic improvements and developer experience metrics. Almost 64% of production in the developer rate times for infrastructure resources, and 52% decreasing support tickets and platform related questions. And 78% of improvement in developer satisfaction scores across EA implementation. These improvements directly correlate with the increased development velocity and reduce time to market for any new features. Addressing the scalability challenge with the EAA manual scaling in initial cloud data is manually adjust the resources based on the anticipated need. And now leading towards that, all provisioning are performance issues. This approach has the hours of response time on inefficient research utilization. Let's talk about inefficient research utilization. When an instance is finding with the zero percentage use of CPU and memory multiple instance of same surfaces running with the low ation in this case, are, these resources can reuse for. Any the services that needs additional insurance to be spinned up. Let's about the manual scaling. Let's talk about rule based automation. This is a very common approach in the current market time, and this actually, this is actually providing basic automation with fixed thresholds and predetermined scaling rules, and it provides very good implements, but lack, adaptability, rule-based approach. Resulted in moderate improvement, but still records significant human intervention. Then finally, let's talk about a port orchestration. This is an advanced approach with intelligent system that can learn from the usage patterns, predict needs and autonomously optimize infrastructure in real time, a orchestration results in minutes instead of spending hours of scaling, that's providing almost 99.9 availability. During peak months case studies from high growth organization, RAM said that a platform engineering team dramatically reduces infras scaling response time while maintaining exceptional reliability. Kubernetes optimization through EA Kubernetes orchestration enhance with EA driven resource management, delivers significant operational and cast benefits. We can get intelligent pod placement based on historical performance data, automated node scaling that anticipate workload changes, self feeling capability that reduce mean time to respond by almost 61 percentage. And finally, proactive detection of potential cluster level issues. So these are some of the operation improvement that we can achieve. And 40, so 43 percentage of. Better reduction in the resource utilization cluster and 56 percentage of reduction in the infrastructure cost by maximum resource utilization and 37 percentage of increase in application performance, and finally able to handle almost three times of workloads on the same hardware. These are some of the business outcomes that we can achieve a power self service platform capabilities. Intelligence infrastructure provision, automated configuration management, adaptive developer portals, proactive security compliance. These are some of the major capabilities of a power cell service platform. We can use a natural language infrastructure code, which are again translated to properly configured resources with the AI systems that detect any configuration drips and such as optimizations. And automatically remediating new issues before they impact even the production environments. Personal interfaces that learn from the downer behavior to surface relevant resources, documentations, and optimization suggestions based on project context and continuous scanning of infrastructure as a code for any security vulnerabilities. It'll provide remediation solutions. So these capabilities starts of the developer experience from frustration to friction productivity, while maintaining the anthrop grade security and reliability case study, global financial servicing firm, almost supporting 28 hundreds of developers across three for the application teams with a critical ability requirement and very strict regulatory compliance needed. Very big challenge. NA platform solution was implemented by, started with implementing in self-service infrastructure provisioning, followed by applying NA based anomaly direction for performance issues, and finally creating automatic compliance validation for all the deployments that result almost eight to 3% reduction in the infrastructure promotion time, and 43 percentage of, I'm sorry, almost 47% of decrease in production incidents. And our four millions in annual savings per cloud infrastructure cost. Let's talk about implementation. Implementation has four phased. First starts with assessment and opportunity identification phase and foundation building phase, targeted a implementation phase, and finally scaling optimization phase. How to assess and identify opportunities. Start with the evaluate current platform capabilities and bottlenecks and analyst developer experience pain points followed by identify high impact automating opportunities. This is something we will actually start with and finally define clear success metrics and KPIs. This is to progress. This is to track the progress for the process. How to build a foundation, implement comprehensive observability for ai, turning data, standardize infrastructure as code practice, develop AP first approach for all platform services, and create platform team AI capability development plan, and how to implement target daily. Start with deploy intelligent resource provisioning capabilities, then implement predictive scaling for critical workloads. Create a power developer self service portal, and finally establish feedback loops for continuous improvements. Next, how to scale and optimize. First, extend a capabilities across all the platform services, then implement advance predict to maintenance. Integrate with continuous integration and continuous deployment for intelligent deployment pipelines and file develop. Organization specific GA models these phase approach and choose measurable progress while building the foundation for comprehensive AI platform engineering. Let's talk about what are the common implementing challenges and how, what are the mitigation strategies? Insufficient quality data for AI training skills gap in platform engineering team. Assistance to automation from operations. Team integration, complexity with legacy systems. Concerns about ai decision transparency. These are some of the common implementation challenges. Let's talk about how we can mitigate these. Begin with enhanced observ implementation. Use synthetic data and simulations while building real data set. Establish a education program partner with specialized consultants. Manage a services as a transaction strategy. Start with human loop approach. Demonstrate the value through metrics, create clear upskilling pathways, then create abstraction layers with the well different APAs. Implement incremental monetization use of AI to generate integration adapters, implement explainable area approaches. Maintain comprehensive logging. And create capabilities for any critical systems. Key takeaways, the future of a driven platform. Engineering organizations implementing AI platforms, seeing dramatic I improvements and developer productivity, operation efficiency, and business agility begin with targeted AI capabilities that address your most stream pain point by building the foundation for comprehensive automation. AI adapter, a power platform engineering are achieving almost two to three times of better developer productivity and significant no operation cost. The international AI into platform engineering is not just optimization. It's a funda, fun, fundamental shift. How do we deliver and scale infrastructure for the. Thank you so much for joining for my session. I truly enjoyed sharing my insights on Adrian Quantum Engineering, automating infrastructure at Scale for enhanced developer experience. Thank you again for the invitation and for organizing such a well done event. Thank you.