--- title: Machine Learning In Observability Systems: From Insights... description: A conference talk by Nike's Observability Platform Engineering Director covering practical ML applications in observability systems at massive scale. Key... canonical: https://daily.dev/posts/machine-learning-in-observability-systems-from-insights-to-action-by-panos-tsilopoulos-eitck2dde twitter:card: summary_large_image twitter:site: @dailydotdev og:type: website og:site_name: daily.dev og:title: Machine Learning In Observability Systems: From Insights To Action by Panos Tsilopoulos | daily.dev og:description: A conference talk by Nike's Observability Platform Engineering Director covering practical ML applications in observability systems at massive scale. Key... og:url: https://daily.dev/posts/machine-learning-in-observability-systems-from-insights-to-action-by-panos-tsilopoulos-eitck2dde og:image: https://api.daily.dev/og/posts/EItCk2DDe.png og:image:alt: Machine Learning In Observability Systems: From Insights To Action by Panos Tsilopoulos og:image:width: 1200 og:image:height: 630 og:locale: en --- > ## Documentation Index > Fetch the complete documentation index at: https://daily.dev/llms.txt > Use this file to discover all available pages before exploring further. # Machine Learning In Observability Systems: From Insights To Action by Panos Tsilopoulos **[Devoxx](https://daily.dev/sources/devoxx)** · 41 min read · 0 upvotes · 0 comments ## Summary A conference talk by Nike's Observability Platform Engineering Director covering practical ML applications in observability systems at massive scale. Key topics include: the challenge of data volume and cardinality in telemetry (metrics, logs, traces), using ML for intelligent anomaly detection, building event-to-incident funnels to reduce alert noise (Nike processes ~2M alerts/day with a 15:1 noise-to-incident ratio), predictive pre-scaling for traffic spikes (shoe drops, Black Friday, Singles Day), using LLMs for natural language querying of observability data instead of proprietary DSLs like PromQL or SPL, AI-generated dynamic dashboards, automated postmortem generation, and AI-assisted runbook creation. The speaker emphasizes that organizational maturity and cultural readiness are prerequisites for AI-driven automation, and encourages experimenting with open-source ML tools immediately. ## Full article daily.dev links to this article rather than hosting it. Read it at the original source: ## Similar posts on daily.dev - [Observability 2.0: Why DevOps Teams Are Moving From Monitoring to Intelligent System Understanding](https://daily.dev/posts/observability-2-0-why-devops-teams-are-moving-from-monitoring-to-intelligent-system-understanding-uveczef2f) · DevOps.com · 9 upvotes · 1 comments - [The AI layer of observability: Making telemetry human-readable](https://daily.dev/posts/the-ai-layer-of-observability-making-telemetry-human-readable-lnaud7orj) · Red Hat Developer · 0 upvotes · 0 comments - [AI Raises the Stakes for Observability Engineering](https://daily.dev/posts/ai-raises-the-stakes-for-observability-engineering-i4tkn4oti) · DevOps.com · 0 upvotes · 0 comments - [Monitoring and Observability in MLOps Lifecycle](https://daily.dev/posts/monitoring-and-observability-in-mlops-lifecycle-9r6wubss1) · Daily Dose of Data Science \| Avi Chawla \| Substack · 4 upvotes · 0 comments --- Tags: [#machine-learning](https://daily.dev/tags/machine-learning), [#observability](https://daily.dev/tags/observability), [#opentelemetry](https://daily.dev/tags/opentelemetry), [#aiops](https://daily.dev/tags/aiops) [View this post on daily.dev](https://daily.dev/posts/machine-learning-in-observability-systems-from-insights-to-action-by-panos-tsilopoulos-eitck2dde) ```json {"@context":"https://schema.org","@graph":[{"@type":"Organization","@id":"https://daily.dev/#organization","name":"daily.dev","url":"https://daily.dev","logo":{"@type":"ImageObject","url":"https://daily.dev/apple-touch-icon.png","width":180,"height":180},"sameAs":["https://twitter.com/dailydotdev","https://github.com/dailydotdev","https://www.linkedin.com/company/daily-dev-ltd"]},{"@type":"WebSite","@id":"https://daily.dev/#website","url":"https://daily.dev","name":"daily.dev","publisher":{"@id":"https://daily.dev/#organization"},"potentialAction":{"@type":"SearchAction","target":{"@type":"EntryPoint","urlTemplate":"https://daily.dev/search?q={search_term_string}"},"query-input":"required name=search_term_string"}}]} {"@context":"https://schema.org","@type":"TechArticle","headline":"Machine Learning In Observability Systems: From Insights To Action by Panos Tsilopoulos","url":"https://daily.dev/posts/machine-learning-in-observability-systems-from-insights-to-action-by-panos-tsilopoulos-eitck2dde","mainEntityOfPage":{"@type":"WebPage","@id":"https://daily.dev/posts/machine-learning-in-observability-systems-from-insights-to-action-by-panos-tsilopoulos-eitck2dde"},"datePublished":"2026-03-16T08:13:52.269Z","dateModified":"2026-03-16T08:21:54.534Z","description":"A conference talk by Nike's Observability Platform Engineering Director covering practical ML applications in observability systems at massive scale. 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