Hi this is Cypress, the Moala. I'm working as a enterprise architect in Focus INC. Today I'm going to talk about site reliability at scale, architecting, resilient, multi-cloud infrastructure, multi-cloud reality. I'm going to talk about strategic advantage, technical resiliency. Cost optimization coming to the static advantage, eliminating single point of failure is one of the most effective way to build a resilient, capable, and reliable cloud infrastructure by embarrassing diversified cloud partnership and vendor agnostic architecture. Business can ensure continuity, reduce risk, and achieve a greater flexibility. This strategy is crucial in maintaining business operations even when certain cloud services, expensive outage or issues coming to the technical resilience. Technical resilience refers to your system ability to maintain high availability, reliability, and performance, even in the face of failures or unexpected disruptions in the context of cloud environments. This means design your infrastructure, so it can withstand yet quickly recover from any issues minimizing downtime and services disruption. In multi-cloud environment, workloads are distributed across more than one cloud provider, enabling you to benefit from each. Providers strength and mitigate potential risk. Coming to the cost optimization in today com, tive Cloud landscape, optimizing the cost is a crucial part of any cloud strategy. Each cloud provider has its unique pricing structure, services, and strengths. By strategically leveraging these differences, business can maximize the performance to cost ratio and ensure the best value for their cloud infrastructure spend. For example, one, provide provider may offer more cost effective storage while another may be. Better suited for compute heavy workloads, allowing business to allocate resources in a way that minimize expenses. Cloud native architecture, evolution, monolithic apps, microservices, containerization, Kubernetes orchestration. Monolithic apps. Monolithic apps have been the traditional approach for building software system for many years in monolithic architecture, all components of application such as user interface, database, business logic, and data access layers. Are tightly coupled and reside in a single core base. While this approach has its advantages, it also comes with significant limitations, especially as a application grow in size and complexity, coming to the microservices, decoupled, independently deployable services that communicate through. Well-defined APIs. This architecture enables targeted scaling, improved resilience, and faster delivery of individual components. Containerization, isolated, lightweight and portable runtime environment that package applications with. Their dependencies, ensuring consistency across development and testing and production environments. Coming to the Kubernetes orchestration, a production grade platform for automatic deployment, scaling and management of contention applications it use. Declarative configurations and built in fall to tolerance to ensure high availability and resiliency. AI powered incident management, MTTR reduction, 68%. Operational cost to savings, 47%. Incident response acceleration, 3.2 x. So coming to the MTTR reduction, 68% AI powered diagnostic engine utilize advanced algorithms to analyze vast amount of data in real time quickly identifying the root cause of incidents. These systems can automate process of isolating and diagnosing issues. Eliminating the need of manual intervention and reducing the take it taken to resolve an incident. This precision leads to substantial reduction in meantime to resolve by ensuring teams focus on the most accurate diagnostics first. Rather than wasting time on trial and error approaches, operational cost reduction, 47%. AI driven predictive models use historical data, mission learning algorithms and pattern recognition to detect anom analyze before they evolve into major issues By identifying. Our potential systems failures early AI can trigger preventive actions such as automated adjustments, resource scaling, or alert IT teams. This not only reduce the need for costly. Emergency fixes, but also lower downtime and the resources spent on reactive maintenance, driving operational cost reductions, independent response acceleration, that two point x neural network optimizer, trial prioritization and routing. By using rural networks, AI can intelligently. Access incoming incidents and determine their severity, urgency, and impact based on historical incidents, data system performance, and contextual clues. This optimized triage system ensure that critical incidents are routed to appropriate teams. Faster and with higher precision. Instead of wasting time on manual prioritization, the AI system speed up decision making, reducing incident response times by therefore, overall impact the integration of AI into incident management is. Clearly a game changer, reducing MTTR, lowering operational cost, and spending up response by leveraging precision. Diagnostics, predictive analytics and neural network optimization organizations cannot only solve problems faster, but also prevent them from occurring in the first place. Edge computing and 5G revolution, edge computing, distributed processing and reduced latency, complex reliability models. 5G integration, distributed processing. Computing resources strategically positioned at Network Edge two. Latency reduce bandwidth consumption, and enable real time data processing closer to the source. Reduce latency, ultra responsive sub millisecond performance, enabling crucial operations where. Time timing is paramount from autonomous vehicles to industrial automation and tele mite applications, complex reality models, sophisticated resilience frameworks, incorporating operating echo, geo distributed redundancy, productive maintenance, and intelligent traffic routing. To ensure continuous operations despite node network or regional failures. 5G integration revolution, revolutionary connectivity, delivering up to 20 gigabytes throughput with 99.99% reliability. Creating new possibilities for argumented reality 4K and eight K video streaming and Ivo T devices. Portal federation. Distributed observability challenges, comprehensive metric collect collection, scalable log management, cross servicing, tracing, intelligent anomaly detection. Comprehensive metric Cion, implementing uniform telemetry protocols across heterogeneous infrastructure components. Scalable log management, orchestrating real time aggregation from thousands of distributed edge points. Cross servicing, tracing, maintaining request context. Propagation through complex multi-cloud service meshes intelligent anomaly detection, leveraging mission learning algorithms to identify SubT performance deviations before cascade security, reliability, integration. There are two approaches. Traditional approach, integrated DevSecOps coming to the traditional approach, security teams operate in isolated silos, detached from core operations. Fragmented monitoring creates critical visibility gaps between security and reliability. Conflicting security and performance objectives, force unnecessary trade-offs, complex approval workflows and testing cycles, delay, vulnerability remediation. Coming to the integrated dev SecOps, automated security control embedded directly through infrastructure as a code. Unified tool chains provide seamless visibility across all operation domains. Continuous security validation through automated compliance checks in CICD pipelines, shared metric, cross-functional accountability drive, collaborative incident responses. Breach impact analysis. Security breaches carry significant financial consequence that extend for beyond immediate losses. Our analysis reveals that comprehensive cost to breakdown across key impact areas, demonstrating how indirect cost often out outweigh direct financial damages. Recovery operations, legal penalties, customer compensation, brand damage. As illustrated brand damage represents the most significant financial impact at milli dollars. 8000008.3 millions AC accounting for over 35% of total breach costs. Legal potential follow at $12.4 million. Highlighting the growing regulatory consequences of security failures. Organization must implement proactive security, reliability, integration to mitigate these substantial financial risks, balancing innovation and. Stability. Gradual feature rollouts, cement systematic deployments strategy with intelligent automated rollback mechanism to mitigate risks, experimentation, frameworks, regressively. Controlled A and B testing in production environments to validate changes with the real world data. So before moving to the production data needs to test done, test and dev environment. Once the testing is done and everything is looks good, then move to the production reliability guard trails. Implementing robust service level objectives as crucial quality gates for deployment authorization infrastructure as a code. Fully versioned. Systematically testable infrastructure definitions enable consistent repeatable environments implementation framework. He access current state, define service level. Object to build. Observability platform, automate remediation, practice, chose engineering. He access current state, conduct comprehensive infrastructure audit, and identify critical reliability vulnerabilities across your multi-cloud environment. Define service level objectives. Establish quantifiable reliability metrics aligned with business outcomes and customer experience requirements. Build observability platform, deploy unified monitoring solution with cross cloud visibility and. Contextual alerting capabilities, automate remediation, implement self-healing infrastructure with ML driven prediction and autonomous recovery workflows. Practice shows engineering systematically introduce. Controlled failures to validate resilience, mechanism and uncover hidden dependencies. Key takeaways, multi-cloud standardization, AI driven incident response, edge computing, architecture, security, reliability, integration. So multi-cloud standardization, implement consistently observability frameworks and shared metric across cloud providers while maintaining, provide specific optimization, AI driven incident response. Deploy ML powered prediction models with autonomous recovery workforce to reduce MTTR by. To by up to 70% edge computing architecture, extend reliability practices to accommodate 5G enabled edge deployments with distributed observability solutions. Security, reliability integration establish cross-functional accountability to mitigate financial impacts exceeding. $30 million from brand damage and regulatory penalties, and thank you. Thank you. This opportunity.