AI Enabled Observability Explainers - We Actually Did Something With AI!

Overview

Discover how eBay's Observability team combines algorithms with LLMs to create predictable AI "Explainers" for telemetry signals, enhancing trace, metric, and log interpretation beyond basic LLM implementations.

Syllabus

- Introduction to AI in Observability -- Overview of Observability Tools and Techniques -- Role of AI in Enhancing Observability - Fundamentals of Telemetry Signals -- Understanding Traces, Metrics, and Logs -- Challenges in Interpreting Telemetry Data - Algorithms to Enhance Observability -- Common Algorithms for Data Interpretation -- Predictive Algorithms and Their Applications - Large Language Models (LLMs) and Observability -- Basics of LLMs -- Integration of LLMs with Observability Tools - AI Explainability in Telemetry -- Need for Explainability in Observability -- Designing Explainable Models for Traces, Metrics, and Logs - eBay's AI-Driven Observability Solutions -- Case Studies and Real-World Applications -- Scalability and Efficiency of AI Solutions - Building Predictable AI Explainers -- Steps for Developing AI Explainers -- Techniques for Testing and Validating AI Models - Enhancing Interpretation Beyond Basic LLM Implementations -- Limitations of Basic LLM Applications -- Advanced Strategies for Comprehensive Data Insights - Future of AI in Observability -- Trends and Emerging Technologies -- Preparing for the Next Generation of Observability Tools - Conclusion -- Key Takeaways -- Best Practices for AI-Enhanced Observability

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