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Observability 2.0 - More Than Just Logs, Metrics & Traces
37 minutes Posted Feb 1, 2026 at 4:34 pm.
Welcome & Introduction
Neel's Background & Community Work
The Evolution of Observability
The 2 AM Production Incident Scenario
OpenTelemetry's Role in Modern Observability
Dynamic Sampling Techniques
ML & AI in Anomaly Detection
LLM Observability Explained
Cost Optimization Strategies
Context Windows & Token Management
Self-Healing Systems Discussion
Edge Cases: When Dynamic Sampling Doesn't Work
Wrap-up & Resources
0:00
37:36
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Show notes
Join us as Neel explores how observability is evolving beyond traditional logs, metrics, and traces into a predictive, AI-powered discipline.
Neel walks through the evolution of Observability, demonstrating how OpenTelemetry, machine learning, and LLMs are transforming how we monitor and maintain modern applications. You'll learn about dynamic sampling techniques that reduce costs while maintaining visibility, how ML algorithms detect anomalies before they cause outages, and practical implementations using tools like the OpenTelemetry Collector. This episode covers real-world scenarios from reducing massive log volumes to predicting system failures before they impact customers.
Timestamps
How to find Neel:
https://www.linkedin.com/in/neelcshah/
https://bento.me/neelshah
Links from the show:
https://neelshah.dev/blogs/observability-2
https://opentelemetry.io/
https://middleware.io/blog/observability-2-0/