Good morning, afternoon, everyone. Thank you for joining me. Today we are going to explore a key shift in how we approach system monitoring and incident management. As distributed systems grow in complexity, traditional monitoring methods are simply not enough. Today we will see how a powered observability, specifically with Splunk, can help us. Beyond reactive troubleshooting and into the realm of predictive sub minute incident resolution. By the end of the session, you'll have a deeper understanding of how to improve system reliability, reduce downtime, and take control of incidents before they affect your users. Let's dive in. The observability challenge. Today's distributed architecture have triggered an observability crisis. Traditional siloed monitoring tools produce floods of uncorrelated alerts, leaving IT teams were and struggling to respond effectively. Modern systems generate vast volumes of metrics, logs and traces, but capturing the right data in context is the first hurdle. Detecting meaningful patterns across diverse signals is increasingly difficult, and without intelligent correlation, alerts remain noise rather than insect. The real challenge is turning the data into actionable insights, ensuring observability translates into better performance and resilience. The evolution of observability in the early 20 century monitoring was about basic metrics like CPU usage, memory, or the space. It was reactive, just an alert. When thresholds were crossed in 2000 tens, locks became central to debugging with tools like ELK and Splunk. Aggregating them for better visibility, but locks were still siloed. Then in 2010, sorry, we saw the shift. To full observability, not just about what's happening but why it's happening. Bringing metrics, logs, and traces together, providing richer insights today. Yay. Powered observability takes it even further. Analyzing trends, predictive failures, and even initiating automated responses. Moving us from reactive to proactive incident management. Observability as a strategic imperative. Most 94% of enterprises now consider digital services critical to their core business, reflecting how essential software and infrastructure are to delivering customer value. Eight 54% of organizations still lack. Full stack observability. Without comprehensive visibility across infrastructure, applications, and user experiences, teams struggle to detect, diagnose, and fix problems quickly. This lead to downtime, lost revenue, and damages damaged customer impact. The good news organizations embracing a powered observability have reduced their mean time to resolution by 45%, and those with mature practices are seeing a 3.5 times return on investment. Observability has evolved beyond it. It's now a strategic capability driving business performance. Observability platform. Splunk uses machine learning to detect anomalies across different data layers in the metrics layer. It learns normal behavior over time, like memory usage or request rates, and adjust thresholds automatically. Whereas in the log layer, it analyzes patterns using NLP. To spot rare or unexpected log entries like authentication failures. And in the traces layer, it detects abnormal latencies or service hubs in microservices, which helps to spot issues before they escalate. The real power comes when these anomalies are correlated across layers. For example, a spike in response time. Tied to an error log burst and traced back to the slow backend microservice instead of just more alerts coming from all the systems, Splunk gives you cross layer visibility, reducing investigation time, and increasing understanding. Once the pattern is detected, Splunk can automatically execute remediation actions like tree starting service or scaling resources. Automatic remediation with yay. Power observability isn't just about directing problems, it's about taking action. When a pattern is detected, automated remediation can kick in. This is where playbooks and intelligent decision making comes into play. For example, if there is a spike in CPU usage, the system might scale resources automatically. Start service if it detects degraded performance. DA doesn't just stop there. It learns from past incidents improving its ability to respond to future issues. In complex environments like Kubernetes or hybrid clouds, this proactive approach is crucial in ensuring minimal downtime as and faster recovery times. Let's dive into technical architecture data collection. We started the source data collection, Splunk universal forwarders and lightweight agents designed for reliable log forwarding from on-prem systems. For metrics and tracers, we leverage open telemetry and open source vendor neutral standard that allows deep instrumentation across apps and infrastructure. Splunk also supports out of the box integration with cloud services like AWS, Azure and GCP, making it seamless to ingest data from cloud native sources. As the data is gathered, now we need to process the pipeline process using pipeline, sorry. Once the data is ingested, it enters Splunk processing pipeline. Here we perform field extraction and enrichment. Adding context like geolocation, device info and mode normalization ensures that regardless of the source, your data is structured. And searchable in a unified format. Then we inject correlation IDs, which are essential for tying logs, metrics, and traces together across services. This allows true end-to-end observability. The AI L layer, this is where the intelligence kicks in. The A ML layer starts with feature extraction. Identifying signals from noise. Splunk then applies pattern recognition models to baseline system behavior Over time when deviations occur, anomaly detection engine alerts you often before users even notice something wrong. Visualization and response, whatever we have process now becomes actionable. Splunk offers rich real-time dashboards that display KPI, error rates, service latency, and more automated alerting routes, insights to the right teams via Slack or PagerDuty, or email or any alerting mechanism. And with remediation workflows, you can go a step further regarding scripts or runbooks to fix common issues automatically. Here the Splunk strength we should consider. Splunk is built for massive scalable to handle millions of events per second without compromising query performance. The system is distributed for all tolerant and optimized for high ingestion environments, which makes it strong fit for enterprise grade observability needs. Feature selection and model tuning. When building AI models for observability, what we feed into the model is just as important as how we tune it. We go beyond basic metrics like CPU and memory. We combine signals from infrastructure, application, behavior, user interactions, and business performance to get the complete picture of system health. A good model needs careful tuning. If it's too sensitive, you get noise. If it's too specific, you miss out real problems. We refine our models using real world failures, improving accuracy and reducing false alarms over time. Let's look at a case study of an e-commerce company during their annual sales event. Traffic spikes to 10 times. Its regular volume. Critical services like payment processing comes under immense pressure. Using Yay powered observability. The team trained machine learning models on normal operating behavior and potential failure patterns as traffic client yay detected a bottleneck in payment processing, which detected 15 minutes earlier than traditional alerting system would've triggered Autoscaling actions this earlier. Helped prevent a slow checkout process that could have cost 2.3 million in lost sales. This shows how a driven observability can turn data into real time actionable insights to prevent issues before impacting the business building autonomous remediation workflows. Now let's explore autonomous remediation. It starts with anomaly detection, using machine learning algorithms to spot deviations in system behavior. Next, the system performs root cause analysis, leveraging correlation engines to identify the cost of issues. Once the cause is determined, the system selects the appropriate remediation response based on the historical data. For example, if scaling resources worked in the past, it might trigger that action again. The system then executes these actions automatically. Finally, after the remediation, the system verifies the results, gathering feedback to continuously improve its responses. This learning loop. Allows the system to handle more complex incidents automatically over time. Implementation framework To implement AI powered observability, we start in identifying critical services and defining instrumentation standards. This ensures that data collection is consistent and comprehensive. We also set data retention policies to balance cost and compliance. On the technical side, we deploy correlation agents, configuring data pipelines, and build service map to visualize dependencies. We implement machine learning models to detect anomalies and align our observability data with incident management process. This framework is designed to provide incremental value at each stage. From initial data collection to full maturity, which takes about four to six months. Next steps on your observability journey. Start by assessing your current maturity using an online tool to identify gaps in your observability practices. With this assessment, you can develop a tailored strategy to enhance your observability. Next. We recommend running a pilot project in the targeted area demonstrating quick wins while minimizing risk as you scale. Building internal capabilities through training ensures long-term success. Our team is here to help guide you through these steps, ensuring you maximize the value of a powered observability and make your system more resilient. I should talk about the challenges. Like any technology, yay, power observability has its own challenge. Implementing this solution requires solid data strategy integration with existing systems and continuous tuning to ensure accuracy. Additionally, there may be an initial learning curve as team adjusts to the predictive nature of AI power tools. A power observability with Splunk can revolutionize how we manage monitor modern distributed systems. By moving from reactive monitoring to predictive management, we can not only reduce instant resolution time, but also improve the overall reliability and performance of water systems. Thank you for your time today.