Hello everyone. This is Uluru. Today's focus is on building a resilient, automated monitoring platform for voice or IT systems and how this approach reflects a broader shift in platform engineering. Clear, reliable voice calls matter, whether from business or personal chats. Keeping those systems humming is no small fe, and this presentation shows how it's done. The platform predicts issues before they disrupt calls. Streamlining work for developers. Let's start with the big picture. Platform engineering has evolved from reactor maintenance to intelligent predictive operations. Modern infrastructure demands, monitoring systems that detect and resolve issues before end users are affected. The resolution presented here. The solution presented here is a centralized platform that emphasizes automation, scalability, and seamless integration with the development workflows. This is not just another dashboard producing endless alerts. It is an intelligent system capable of rapidly identifying genuine issues, minimizing operational burden on reducing costs. The emphasis on secure service interrupt disruptions. Faster innovation. Picture, a monitoring system that doesn't just flag problems, it stops them before they start. First, let's explore the challenges this platform tackles monitoring distributed system often feels like chaos, disconnected tools, also alarms and time consuming manual fixes. IP Systems introduce complex heterogeneous environments with fragmented visibility. Traditional monitoring causes alert fatigue due to false positives while manual remediation, slow recovery. The platform was designed to address all these problems simultaneously providing accuracy, speed, and self-op optimization. This platform uses machine learning to solve real issues, predict trends and learns from past patterns, all the while cutting through the marks. How does this happen? It starts with platform engineering principles. A strong platform should be invisible, meaning, reliable, stable, and low maintenance. Platform engineering builds infrastructure. The developers' log monitoring infrastructure must be treated as a product with user experience, reliability, and continuous improvement as core priorities In real time communications such as voice or ip, there is no tolerance for delays. Reliability must be absolute. The platform hangs routine tasks. Screening developers to focus on creating with clear insights when needed. And let's look at architecture that powers this. Imagine a system built for flexibility, speed, and handling any challenging thrown at it. Microservices provide modularity and independent scaling. Apache Kafka forms the backbone enabling high throughput fault tolerance telemetry streaming. A plugin based ingestion layer collects data from diverse voiceover IT and environment. Prometheus and Elastic Stack deliver deeper metrics, logging and visualization. These tools are selected because they are proven and they're highly scalable. They're open source. The architecture ensures scalability and fault tolerance without redesign. It is like a toolbox for voiceover. IP monitoring works with any setup, no hassle. Let's zoom in on how these pieces come together. Data flows from call quality metrics. Network indicators and system performance that will diverse data into realtime pipelines. Machine learning, analyzes telemetry, continuously dashboards, alerts, and automated remediation are produced from this unified flow. This is not just data collection, it is continuous learning and adaptation. This diagram maps the journey from raw wise or IP data to actionable insights. Solve metrics, network stacks, flow through Kafka, get processes instantly with fine tuned machine learning models, and appear as dashboards or alerts or automatic fixes. Developers access these insights, writing their tools, APIs ID plugins, and so on. Machine learning plays a huge role here. Let's dive into that. Machine learning algorithms, establish baseline performance patterns and detect subtle degradations early. False positives are reduced by distinguishing normal variation from real issues, quality prediction models, forecast feature performance trends, enabling proactive intervention before users are affected. This represents a shift from reactive monitoring to intelligent production. If call quality drops, the system knows. If it's a blip or a problem, avoiding pointless alert, your false alarms and proactive fixes keep users happy without the chaos. This predicts, this predictive power needs a fast processing system. Let's check that out. Using a Lambda architecture, the system combines. Millisecond level streaming and it's alerts with batch analytics for deeper insights. This platform uses a dual approach, instant alerts for argent tissue, and deeper analysis for print builtin back pressure mechanism prove prevent overload during the traffic spikes event Correlation identifies root causes. Minimizing cascading failures and unnecessary alerts. Features like circuit breakers and event correlation keeps things steady even during traffic spikes and pinpoint root causes. Parting issues is one thing. Fixing them automatically is where the magic happens. Issue detection, trigger automated response. Validated remediation actions are selected based on severity and historical success. When a glitch appears, the platform doesn't just send an alert. It takes the best fix checks if it's safe, and replace it. Procedures execute safely with the drive canary deployments and rollback mechanisms. Every action improves feature responses. The system continuously learns from outcomes, delivering self-healing infrastructure with guardrails. This automation lets teams focus on building rather than firefighting. Developers benefit big time from this setup. Let's see how. Monitoring must enhance, not hinder developer workflows. The platform is designed to make developers' lives easier, not add complexity. And API first designs offers programmatic access to the configuration and data. CICD integration deploys monitoring upgrades alongside application code. Issues can be caught locally before code ever hits production. Local monitoring and ID plugins provide realtime insights without extra overhead, and it's not just user-friendly. It saves money too. There's no point in developing intelligent monitoring systems if the benefits do not outweigh the costs. It is important to keep the cost low. Smart data management and streamlined workflows deliver top tier monitoring without a hefty price tag. Intelligent data lifecycle management reduces storage costs by 65%. Stream processing optimization delivers 40% higher efficiency. Right sizing and workload elimination achieve 30% reduce resource savings. This makes the monitoring both comprehensive and cost efficient. This platform doesn't just save costs. It's built to grow the platform processes, massive telemetry volumes with millisecond alerting latency. Oriental Scaling supports future growth while predictive scaling. Anticipates. Demand reliability is validated through load testing, chaos engineering, and the meta monitoring of the platform itself. Sophisticated caching and benchmarking maintain exceptional performance under stress. So how does this get rolled out? Let's talk best practices. Key success factors include infrastructure as code for repeatable, auditable deployments. Comprehensive integration testing for realistic validation, team training and knowledge transfer for operational independence, incremental adoption to deliver quick wins and build confidence. Start small, see quick wins, and scale up as confidence works. This platform is not standing stake. Let's look at what's next. Plan enhancements include advanced AI models with a deeper understanding and exploding smarter AI for trickier predictions to keep the number of false possible. Insignificant Native Cloud integration with service measures, serverless and eeb. P. S observability, edge computing support for constrained and environments immersive visualization in Q2 navigation for complex systems, the future of monitoring is about staying ahead, and this platform is leading the charge. Let's wrap up with what this means for everyone. This monitoring platform demonstrates how modern platform engineering can transform operations. This platform transforms monitoring into something smart, scalably and developer friendly spliting infrastructure as a product enables continuous improvement, scalability, and reliability. It's more than tech. It's a way to build infrastructure that teams love, balancing reliability and innovation. Intelligent automation turns reactive monitoring into predictive self-optimizing infrastructure. Consider how this approach can elevate systems and drive progress. Thank you.