Hello everyone. I'm Vij Bajo. Today I'm going to talk about something I deal with all the time, how we can use machine learning to make log analysis faster and easier. If you have ever spent hours digging through logs trying to figure out what went wrong, this talk is for you. I'll walk you through how ML can help detect patterns spot tissue early, and made debugging a lot less frustrating. Let's talk about the reality we all face. Traditional debugging is still largely slow and reactive. We wait for alerts, dig through logs, and hope we can connect the dots fast enough. On top of that, we are dealing with sheer volume, millions of log lines, most of which are noise. It's like trying to find a sentence in Australia dictionary. This leads to one painful metric. MTTR mean time to resolution. It keeps rising, and with that comes stress pager fatigue and delays that ripple into customer impact. What's missing? Intelligence? Something that can help us make sense of the mess quickly. Not just search faster, but think smarter. Now let's flip the perspective. What if instead of treating logs like noise, we saw them as a gold mine. Logs are packed with patterns, repetitive behavior, known error trails and hidden signals. They're often hiding in plain sight, just waiting to be uncovered. More importantly, logs are real. They reflect exactly what happened. User activity, system behavior failure traces. There's no modeling guesswork. It's raw, authentic telemetry. And here's the thing, manual analysis just can't keep up. Humans are great at spotting obvious behaviors, but ML can uncover subtle correlations and patterns that would otherwise be invisible, especially at scale. Even though logs are rich in value, there are major hurdles that can make analyzing them a real challenge. First, the noise signal ratio is brutal. Most locks are noise. Finding something meaningful feels like digging through sand for a needle. Second, formal inconsistency. Format, inconsistency. Different teams, different systems. Every log looks different. Some are JSON, some are plain text and some are semi-structured. Passing becomes a nightmare. Then there are temporal gaps. Logs don't always line up neatly in time. You miss cause effect, relationships and important, closer or lost in between. And finally, rare event detection. The critical stuff, it often shows up once quietly, not in a burst, and those are the things that ML can help us actually surface. So how do we actually extract meaningful patterns from messy log data? Let's look at a few core ML techniques that make this possible. First one is clustering helps us group similar log entries. It's like letting the algorithm say, Hey, these events looks related. Embeddings convert law raw data into a format that models can actually understand. Think of it like turning unstructured text into numbers that carry meaning. Third is dimensionality reduction like PCA. Help us remove the noise and focus on the most important features. The next is sequence model. Sequence. Models like L SDMs can identify patterns across time. This is the key for catching time-based anomalies or repetitive behavior. All of these sequences or techniques work together to make patterns, recognition, more stakes, more scalable and intelligent. So when it comes to finding anomalies in logs, there are a few main schools of thought. Each with its own strengths. First, we have statistical models. These are great for known patterns and baselines. Things like zco analysis or moving averages can quickly flex spikes or drops. But for more complex, hidden issues, we need machine learning models, techniques like isolation, forest auto encoders, and one class SBMs can detect patterns that humans or simple rules just miss. And in many production systems, the most practical solution is a hybrid approach. Combining rule-based filters with ML bag verification. With feedback loops, the system gets smarter over time. The key is to choose the right mix based on scale, noise level, and what kind of anomalies you're after. Here is a high level look at how everything fits together in a real world. ML powered log analysis system. It starts with log collection. We gather logs from multiple sources, services, apps, infrastructure layers. The goal is to cast a wide net so we don't miss any signal. Then comes pre-processing. This is a critical step. We clean, normalize and structure the raw logs into a machine readable format. Think of this as a translating chaos in a mess. Sorry, into order. After that, we feed into the ML pipeline where models perform pattern recognition and anomaly detection. This is where all those technologies we talked about earlier, kick in. Finally, the insight engine takes those signals and turns it into something useful. Alerts, probably dashboards, visualizations, so team can, teams can act, not just observe. So this full pipeline ensures that we go beyond just logging and actually deliver operational intelligence in real time. So let's see what all of this looks like in practice. A real world example, right? Incorporating machine learning into observability and incident management isn't just theoretical, it has tangible benefits. Take the H Estonia's Health and Welfare Information System Center, for example, by implementing Elastic's observability platform. They streamline their incident management processes, achieving a 40% reduction in MTTR. Similarly, a major e-commerce platform reported a 40% decrease in MTTR after deploying an AI driven root cause analysis tool. These cases underscored the substantial impact ML can have on improving system reliability and reducing downtime. Even with all the power of m. L, debugging is never a fully hands off process. Machine learning can surface patterns. But it lacks context. It doesn't know if a spike is normal for Black Friday or truly unusual. That's where human come in. Engineers play a vital role in validating what the models find. They help fine tune alerts, provide a edge case context, and correct the system when needed. And over time this becomes a feedback loop. The system learns from human responses and gets smarter. The more you interact with it and more, the more useful it becomes. So this isn't just an AI tool, it's a collaboration between machine learning and human insight that builds trust and reliability. As much as I believe in the power of ml, it's important to recognize its limitations and build and responsibly build responsibility. To say first transparency is a challenge. Many advanced models work like black boxes, which can hurt trust. Engineers need to know why an alert is fired. If we can't explain it, then we don't trust it. Second model maintenance ML models aren't set and forget. They drift over time as system behavior changes without regular retraining, their accuracy drops and that creates risk. And the third, ethical concerns, not all systems get equal. Attention bias can creep in. For example, if alerting prioritizes revenue critical services and ignores others, we need to design with fairness in mind. The takeaway here. ML isn't magic. It's a tool. It works best when paid with human responsibility and continue scale. To wrap up, here are the four key takeaways. I hope you'll leave with first, scale and speed. ML brings scale. We simply can't achieve normal. It spots patterns faster across more data with less noise. Second root cause detection, pattern recognition helps us debug smarter. We get to the why faster, not just the what. That's where real time savings happen. Third, reduce noise. Smarter. Anomaly detection means fewer false rhythms. It improves focus, less alert, fat fatigue, more clarity, and finally, incremental adoption. You don't have to go all in one day on day one. Start small. Prove value, build trust. That's how we evolve debugging one smarter layer at a time. I truly believe ML is not just a trend here, it's a path forward to building calm, more intelligent systems. So that's it from my side. Thank you so much for joining this session. I really hope this gave you some insights useful into how ML can help streamline debugging and reduce noise. If it did spark any ideas, questions, or if you're working on similar challenges, I would love to know what you think about this and discuss this more on LinkedIn. If you want to connect to me, please feel free to connect me on LinkedIn. Thank you guys.