O11ycast
Ep. #79, AI and Otel: Look at your Data with Hamel Husain
Mar 18, 2025
Hamel Husain, a machine learning expert with over 25 years of experience at Airbnb and GitHub, dives deep into the world of AI and observability. He discusses the misleading nature of off-the-shelf metrics and highlights the critical role of error analysis in making AI models reliable. Listeners learn about the unique challenges in monitoring AI systems and the importance of insightful data analysis. Hamel also shares fascinating insights into Honeycomb's Query Assistant and how AI can evolve in coding practices.
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Quick takeaways
- Focusing on error analysis is essential for understanding AI system performance and identifying the most relevant metrics for improvement.
- A collaborative approach that combines human oversight with AI insights is crucial to avoid pitfalls associated with superficial metrics.
Deep dives
The Power of Error Analysis
Focusing on error analysis is crucial for understanding system performance. By systematically examining data and noting issues, users can identify which metrics are most relevant. This approach allows for the development of intuition around failure modes and performance bottlenecks, which is essential in complex systems. Notably, having an actionable understanding of what is going wrong enables prioritization and targeted improvements in observability.
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