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

Overview

Syllabus

• Introduction to AI in Observability
Overview of Observability in modern systems
The role of AI in enhancing observability
Introduction to eBay's Observability team and their approach
• Fundamentals of Telemetry Signals
Understanding traces, metrics, and logs
Challenges in telemetry signal interpretation
• Algorithms for Signal Interpretation
Review of foundational algorithms used in telemetry
Advanced techniques for signal processing
Case studies of algorithm applications in the industry
• Introduction to Large Language Models (LLMs)
What are LLMs and how do they work?
Capabilities and limitations of current LLMs
• Integrating LLMs with Observability Systems
Strategies for combining algorithms and LLMs
How LLMs can enhance trace, metric, and log interpretation
Real-world examples and use cases from eBay
• Building Predictable Explainers
Concept of "Explainers" in AI
Steps to develop explainers for telemetry data
Predictability and reliability in AI explanations
• Evaluation and Improvement
Techniques for evaluating explainer effectiveness
Feedback loops and continuous improvement
• Future of AI in Observability
Emerging trends and technologies
The future landscape and potential advancements
• Practicum and Case Study Analysis
Hands-on projects/assignments
Analyze case studies from eBay and other industry leaders
• Conclusion and Industry Insights
Key takeaways from the course
Discussions with industry experts and professionals

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Subjects

Computer Science